Oncology in the Precision Medicine Era: Value-Based Medicine [1st ed. 2020] 978-3-030-31470-5, 978-3-030-31471-2

Table of contents :
Front Matter ....Pages i-xiii
Healthcare Perspective (Joseph C. Alvarnas)....Pages 1-12
Lung Cancer (Joy Huang, Ashley Abing, Karen L. Reckamp)....Pages 13-30
Esophageal and Gastric Cancer (Michael J. Jang, Joseph Chao)....Pages 31-46
Sarcomas (An Ngoc Nhu Uche, Warren A. Chow)....Pages 47-59
Multiple Myeloma (Amrita Krishnan, Nitya Nathwani, Idoroenyi Amanam, Rohan Gupta)....Pages 61-95
Pancreatic Cancer (Addie Hill, Vincent Chung)....Pages 97-109
Colon Cancer (Blase Polite)....Pages 111-121
Renal Cell Carcinoma (Yash Dara, Nicholas Salgia, Sumanta K. Pal)....Pages 123-133
Prostate Cancer (Bertram Yuh, Zijie Sun)....Pages 135-149
Bladder Cancer (Tanya Dorff, Petros Grivas)....Pages 151-169
Molecular Testing in Ovarian Cancer: Recommendations and Treatment Considerations (Kathy Pan, Mihaela C. Cristea)....Pages 171-186
Genomic Cancer Risk Assessment (Jeffrey N. Weitzel, Thomas P. Slavin)....Pages 187-207
Triple-Negative Breast Cancer (Ritika Vankina, Yuan Yuan)....Pages 209-223
Melanoma (Kathryn Bollin, Kim Margolin)....Pages 225-246
Digital Patient Engagement and Social Media (Virginia Sun)....Pages 247-258
Hepatocellular Carcinoma (Rebecca Allen, Daneng Li)....Pages 259-271
Treatment Strategies in Head and Neck Cancers (Dan Zhao, Rebecca Pharaon, Erminia Massarelli)....Pages 273-294
Primary Central Nervous System Tumors (Rimas V. Lukas, Vinai Gondi, Orin Bloch, Maciej M. Mrugala)....Pages 295-325
Lymphoma (Leslie Popplewell)....Pages 327-340
Back Matter ....Pages 341-351

Citation preview

Oncology in the Precision Medicine Era Value-Based Medicine Ravi Salgia Editor

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Oncology in the Precision Medicine Era

Ravi Salgia Editor

Oncology in the Precision Medicine Era Value-Based Medicine

Editor Ravi Salgia Department of Medical Oncology & Therapeutics Research City of Hope National Medical Center Duarte, CA USA

ISBN 978-3-030-31470-5    ISBN 978-3-030-31471-2 (eBook) https://doi.org/10.1007/978-3-030-31471-2 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Just a few years ago, medicine, as we knew it, revolved around “standards of care,” the best practices of treatment or prevention for “the” patient. In other words, it was a “one-size-fits-all” approach for a given indication. Over the past decade, however, there has been a revolution, and a number of therapies, protocols, and diagnostic products that tend to “personalize” medicine signaling a new era have entered the clinic or come on the horizon. Thus, targeted therapies, tailored for patients with specific genetic alterations in actionable targets, such as oncogenes, have replaced conventional therapies. Such therapies have admittedly fared better with a relatively quick response rate but can be fairly ineffective or, even worse, have undesirable adverse effects if administered to a patient lacking the qualifying traits despite having the same disease/indication. Furthermore, the emergence of drug resistance in patients that respond well initially still remains a concern. Value-Based Medicine: Oncology in the Personalized/Precision Medicine Era covers the subject in depth across various types of cancers and addresses many of these concerns. It is organized and written in a format that is easy to follow for both clinicians and non-clinician scientists interested in personalized medicine. The chapters in the book range from defining the clinical problem and summary of recent findings, tumor biology and heterogeneity, genomics, examples of simple/complex cases, the biological pathways, future clinical trials, and financial considerations. Each chapter that is devoted to a cancer type is written by experts who are actively involved in translational research. With no comparable works to compete, this thoughtful treatise should serve as a useful resource for medical oncologists and healthcare providers looking toward a future where all stages of patient care, from prevention to diagnosis to treatment to follow-up, are truly personalized. It is hoped that the book will serve as a reference for those interested in tailoring medicine to the needs of the individual patient and keep themselves abreast of the latest developments in the field, especially with regard to current evidence, indications, and clinical trials for the treatment of cancer with targeted therapies, immunotherapies, and epigenetic modulators. The book is primarily meant for medical professionals and trainees including students, residents, and fellows interested in v

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treating lung cancer. However, we envisage that the book may also be well suited for scientists as well as advanced graduate students working on cancer both in academia and industry. Duarte, CA, USA  Ravi Salgia July 2019

Acknowledgments

I would like to thank my wife, Deborah, and children, Sabrina, Meghan, and Nicholas, for who they are and how supportive they have been and the patients and their families for the inspiration. This endeavor would not have been possible without the cooperation and enthusiasm of my esteemed colleagues who graciously agreed to contribute their respective chapters. I would like to thank my parents, Dr. Karanmal Salgia and Mrs. Kalavati Salgia, for always being supportive. I would especially like to thank Dr. Prakash Kulkarni for his endless energy, help, and friendship. Finally, I am pleased to acknowledge Ms. Margaret Burns, Developmental Editor, from Springer for her unstinted support and dedication. This book is dedicated to the memory of my father who was always very enthusiastic about medicine and caring for others.

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Contents

1 Healthcare Perspective����������������������������������������������������������������������������    1 Joseph C. Alvarnas 2 Lung Cancer ��������������������������������������������������������������������������������������������   13 Joy Huang, Ashley Abing, and Karen L. Reckamp 3 Esophageal and Gastric Cancer ������������������������������������������������������������   31 Michael J. Jang and Joseph Chao 4 Sarcomas ��������������������������������������������������������������������������������������������������   47 An Ngoc Nhu Uche and Warren A. Chow 5 Multiple Myeloma������������������������������������������������������������������������������������   61 Amrita Krishnan, Nitya Nathwani, Idoroenyi Amanam, and Rohan Gupta 6 Pancreatic Cancer������������������������������������������������������������������������������������   97 Addie Hill and Vincent Chung 7 Colon Cancer��������������������������������������������������������������������������������������������  111 Blase Polite 8 Renal Cell Carcinoma������������������������������������������������������������������������������  123 Yash Dara, Nicholas Salgia, and Sumanta K. Pal 9 Prostate Cancer����������������������������������������������������������������������������������������  135 Bertram Yuh and Zijie Sun 10 Bladder Cancer����������������������������������������������������������������������������������������  151 Tanya Dorff and Petros Grivas 11 Molecular Testing in Ovarian Cancer: Recommendations and Treatment Considerations ������������������������������  171 Kathy Pan and Mihaela C. Cristea

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12 Genomic Cancer Risk Assessment����������������������������������������������������������  187 Jeffrey N. Weitzel and Thomas P. Slavin 13 Triple-Negative Breast Cancer����������������������������������������������������������������  209 Ritika Vankina and Yuan Yuan 14 Melanoma ������������������������������������������������������������������������������������������������  225 Kathryn Bollin and Kim Margolin 15 Digital Patient Engagement and Social Media��������������������������������������  247 Virginia Sun 16 Hepatocellular Carcinoma����������������������������������������������������������������������  259 Rebecca Allen and Daneng Li 17 Treatment Strategies in Head and Neck Cancers ��������������������������������  273 Dan Zhao, Rebecca Pharaon, and Erminia Massarelli 18 Primary Central Nervous System Tumors��������������������������������������������  295 Rimas V. Lukas, Vinai Gondi, Orin Bloch, and Maciej M. Mrugala 19 Lymphoma������������������������������������������������������������������������������������������������  327 Leslie Popplewell Index������������������������������������������������������������������������������������������������������������������  341

Contributors

Ashley Abing  Johns Hopkins University, Baltimore, MD, USA Rebecca  Allen, BS  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Joseph  C.  Alvarnas, MD  Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA Idoroenyi  Amanam  Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA Orin Bloch, MD  Department of Neurological Surgery, University of California-­ Davis, Sacramento, CA, USA Kathryn  Bollin, MD  Department of Hematology and Oncology, Scripps, San Diego, CA, USA Joseph  Chao, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Warren  A.  Chow, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Vincent  Chung, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Mihaela  C.  Cristea, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Yash Dara  Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA, USA Tanya  Dorff, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Vinai  Gondi, MD  Northwestern Medicine Proton Center and Cancer Center Warrenville, Northwestern University, Warrenville, IL, USA xi

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Petros  Grivas, MD, PhD  Department of Medicine, Division of Oncology, University of Washington, Seattle Cancer Care Alliance, Seattle, WA, USA Rohan Gupta  Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA Addie Hill, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Joy  Huang  Department of Biology, Johns Hopkins University, Baltimore, MD, USA Michael  J.  Jang, MD  Department of Internal Medicine, Huntington Memorial Hospital, Pasadena, CA, USA Amrita  Krishnan, MD  Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA Daneng Li, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Rimas  V.  Lukas, MD  Department of Neurology, Northwestern University, Chicago, IL, USA Kim  Margolin, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Erminia Massarelli, MD, PhD  Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA Maciej M. Mrugala, MD, PhD  Department of Neurology, Mayo Clinic Scottsdale, Scottsdale, AZ, USA Nitya Nathwani  Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA Sumanta  K.  Pal, MD  Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA, USA Kathy  Pan, MD  Department of Hematology/Oncology, Kaiser Permanente Downey Medical Center, Bellflower, CA, USA Rebecca Pharaon  Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA Blase Polite, MD  Department of Medicine, Pritzker School of Medicine, University of Chicago Cancer Center, University of Chicago, Chicago, IL, USA Leslie  Popplewell, MD  Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA Karen L. Reckamp, MD, MS  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA

Contributors

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Nicholas  Salgia  Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA, USA Thomas  P.  Slavin, MD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Virginia  Sun, PhD, RN  Division of Nursing and Education, Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, USA Zijie  Sun, MD, PhD  Department of Cancer Biology, City of Hope National Medical Center, Duarte, CA, USA An  Ngoc  Nhu  Uche, MD  Department of Hematology and Oncology, Harbor-­ UCLA Medical Center, Torrance, CA, USA Ritika Vankina, MD, MPH  Department of Hematology and Oncology, Harbor-­ UCLA Medical Center, Torrance, CA, USA Jeffrey  N.  Weitzel, MD  Department of Medical Oncology and Experimental Therapeutics, and Department of Population Science, City of Hope National Medical Center, Duarte, CA, USA Yuan  Yuan, MD, PhD  Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA Bertram  Yuh, MD, MSHCPM, MISM  Department of Surgery, City of Hope National Medical Center, Duarte, CA, USA Dan Zhao  Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA

Chapter 1

Healthcare Perspective Joseph C. Alvarnas

An Era of Diagnostic and Therapeutic Innovation in Oncology We are in an unprecedented period of diagnostic and therapeutic innovation in oncology. The growing ability to characterize tumor heterogenetic through emerging genomic testing technologies, including next-generation sequencing, provides a near limitless series of opportunities to expand the therapeutic armamentarium through targeted agents that can eventually be delivered with individually tailored specificity [1]. This level of innovation is having a significant impact upon improving patient survival outcomes, even for those cancer types for which standard chemotherapy combinations have historically produced discouraging results [2]. These improvements in cancer diagnosis and therapeutics are having a measurable beneficial impact upon overall cancer survival rates in the United States. Data from the National Cancer Institution (NCI) indicate that between 2006 and 2015, cancer deaths improved by 1.8% and 1.4% per year for men and women, respectively [3]. Some of the diagnoses for which there have been the most striking improvements in survival reflect the impact of innovations in immunotherapy, targeted therapeutics, and an increasingly effective capacity to link therapeutics with rigorous molecular and biological segmentation of the population under the Precision Medicine care paradigm [4]. In the midst of this period of extraordinary hope and innovation, our ability to deliver these treatments to patients is challenged by the limitations of our healthcare delivery system. Advances in our scientific and therapeutic capacities have far outpaced our ability to deliver upon the promise of these advances in an equitable and economically sustainable way. Efforts by government and third-party payers to control overall healthcare costs, to expand healthcare access to large numbers of

J. C. Alvarnas (*) Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2020 R. Salgia (ed.), Oncology in the Precision Medicine Era, https://doi.org/10.1007/978-3-030-31471-2_1

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u­ ninsured Americans, to cope with an aging population, and to shift toward payment systems that reward the value of care rather than the quantity of care all present unique challenges for the delivery of Precision Medicine that must be managed carefully. To ensure that the extraordinary promise of these care advances is realized, we need to develop an excruciatingly clear understanding of the value proposition for Precision Medicine-based cancer care. We must also work to develop more transparent and sustainable payment models that can incentivize further innovation and the delivery of these care solutions at scale to those patients who may benefit from such care.

Unsustainable Healthcare Expenditures While there is extraordinary national enthusiasm for the improvements in therapeutic opportunities for patients affected by a cancer diagnosis, this is also accompanied by an increasingly sober sense that our current healthcare delivery system is not financially sustainable. As of 2016, United States national healthcare expenditures reached $3.3 trillion with an average per-capita expenditure of $10,348. This sum represents 17.9% of the gross domestic product (GDP). In 2016, the overall growth rate for healthcare expenditures outpaced the GDP growth rate by 1.5% [5]. Additionally, the growth rate in the cost of pharmaceuticals significantly exceeds that of overall healthcare expenditures and, unlike the growth rate of overall healthcare expenditures, there is no evidence that the passage of the Affordable Care Act (ACA) or other attempts at controlling healthcare costs have had any impact upon escalating drug costs. The overall and per-capita healthcare expenditures of the United States far exceed those of the leading 11 high-income countries. These nations expend between 9.6% and 12.4% of their respective GDPs on healthcare. Among these countries, per-capita spend ranges from $3377 (for the United Kingdom) to $6808 (for Switzerland) [6]. Despite a consistently higher per-capita expenditure on healthcare, the American healthcare system underperforms in comparison to the other high-income countries for key metrics related to primary and specialty care outcomes [6–8]. The one exception to this bleak picture of high-cost underperformance is the domain of cancer care where the American healthcare system appears to outperform the other leading high-income countries in survival outcomes and the rapidity with which new anticancer technologies are introduced [8]. While healthcare in the United States is costly, the domain of cancer care represents a significant outlier area of costs even within the American healthcare system. In 2011, the NCI projected that cancer healthcare expenditures in the United States would increase by 27% between 2010 and 2020 to $157.77 billion annually if the cost of care did not increase. Based upon a projected 2% annual increase in care

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costs, the NCI projected that costs would grow by 39% to $173 billion [9]. In retrospect, a projected 2% growth for cancer care costs likely represents a significant underestimation of the likely cancer-related healthcare cost inflation rate. Pharmaceutical pricing is a key factor in the growth in cancer-related costs. Following the initial approval of the checkpoint inhibitor class of anticancer therapeutics in 2011, growth in the costs for new anticancer agents has escalated at an unprecedented pace. As of 2014, the cost of the average new anticancer agent exceeded $135,000 annually, with transformational therapeutics like chimeric antigen receptor (CAR) T cells ranging up to $475,000 for the product acquisition alone (not including any of the cost of administering the agent or clinical support of the patient post-administration) [10]. Moreover, it is not only new agents that are getting more expensive, but increases in the cost of anticancer drugs following approval by the Food and Drug Administration (FDA) are significant. In one study, the estimated post-introduction growth rate in the cost of injectable anticancer drugs was approximately 18% [11]. As such, the original 2011 NCI cost projections seem likely to represent a profound underestimate of the likely 2020 cancer-related care costs. There is no evidence that efforts at slowing the growth rate for cancer care-­ related costs are having any impact whatsoever. Beyond the large systems-based impact of the high cost of healthcare, there is a much more personal impact to these rising costs. For patients, the rising patient-­ borne costs of cancer care (incurred through co-payments and co-insurance charges to the patient) have led to the concept of patient financial toxicity [12]. Patient financial toxicity is a concept in which the cost of cancer care becomes a threat to the patients’ well-being, their ability to comply with care, and their personal economic circumstances. There are data demonstrating that patient financial toxicity due to the high cost of cancer treatments lead to worse patient-reported measures of well-being and undermine the rate of therapeutic compliance [13]. The phenomena of medical bankruptcies due to the cost of cancer care are well-described and may result in an increased risk of patient mortality [14]. Inasmuch as therapeutic advances in immunotherapeutic anticancer technologies have improved patient outcomes, the resulting per-patient escalation in care costs has been exorbitant. For patients with chronic lymphocytic leukemia (CLL), the aggregate lifetime costs of care for a single patient have risen from $147,000 to $604,000 with a corresponding increase in the individual patient out-of-pocket costs (for beneficiaries of the Medicare program) from $9200 to $57,000 [15]. This level of cost escalation is unsustainable. Simple fixes have had little to no impact upon the aggregate cost of healthcare. It is naïve to presume that we can achieve any meaningful impact upon these rising care costs without the creation of more coordinated systems of delivering this care, more innovative care reimbursement models, greater cost and outcomes transparency, and increased accountability for physicians, healthcare systems, and pharmaceutical companies around the costs and effectiveness of care.

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The Value Conundrum in Cancer Care Given the growing financial challenges of managing escalating healthcare expenditures, federal policy leaders have proposed a shift in our payment system from one which pays based upon the volume and frequency of healthcare transactions toward one which aligns economic incentives more closely with the quality and value of healthcare services [16]. A nationally recognized conceptual model for the idealized state of efficient and effective healthcare delivery is the Institute for Healthcare Improvement (IHI) Triple Aim of Care (Fig. 1.1) [17]. The Triple Aim of Care provides an aspirational heuristic model for identifying opportunities to reduce per-­capita care costs, improve healthcare outcomes, and enhance the patient care experience. In the setting of primary and secondary care services, the assumptions of the IHI Triple Aim of Care provide a useful organizing framework for the organization of integrated, more effective, transparent, and efficient care delivery [18, 19]. Given the unprecedented complexity and individually tailored nature of Precision Medicine cancer diagnostics and therapeutic decision-making, the IHI Triple Aim of Care is a fundamentally defective model for which the inherent assumptions of the model are far too limited to describe value in oncology. As such it provides a poor fit as a tool for providing an organizing framework for realigning cancer care in a manner analogous to the ongoing reorganization of primary and secondary care services. Key challenges to the use of the IHI Triple Aim of Care in the oncology domain include the fact that per-capita care costs are an extremely poor measure for effective economic stewardship in care delivery for patients with cancer and there Improving the health of populations

Reducing the per capita cost of health care

Fig. 1.1  IHI Triple Aim of Care

Improving the patient experience of care

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are no minimal tier one available outcomes data on a per-practice/per-system basis so as to create a meaningful level of outcomes transparency [20, 21]. Without a meaningful linkage between clinical risk (which may require data obtained from expensive genomic testing technologies), care costs, and outcomes, this construct leads to false choices and a fundamental misalignment of economic incentives away from what represents most effective and efficient care for each patient. Given the importance of individual patient clinical risk that is based upon a rigorous assessment of tumor heterogeneity in determining patient prognosis, goals of care, therapeutic selection, and the resulting patient experience of care, a more robust conceptual model is required—one that includes the necessary complexity of delivering Precision Medicine care solutions to a complex population of patients with cancer. One such alternative approach to assessing care value is captured in the proposed Precision Medicine Triple Aim of Care heuristic model (Fig. 1.2). While the data complexities related to this model may pose a challenge to its easy generalizability, it nonetheless provides a more useful framing principle for illustrating the essential linkages between patient molecular clinical risk (reflecting tumor heterogeneity), the appropriate effective clinical matching of therapeutics to clinical risk, and an assessment of the patient and family experience of care, which must include information related to patient-reported outcomes and financial toxicity. Moving this conceptual construct for cancer care and reimbursement realignment from theory and toward reality may be a deeply quixotic goal, but it will be essential to achieve this in order to ensure that Precision Medicine can be delivered equitably and sustainably to patients across the United States. While our ability to evaluate and characterize value delivery in cancer care in the United States is deeply limited, many leaders in the cancer care domain embrace the ideal of a value-based cancer care paradigm as a framing principle for the sustainable reorganization of our cancer care delivery and reimbursement system [22]. This concept entails a Clinical Risk (Diagnostic, Molecular, Genomic, Proteomic)

Risk/Goal-Adapted Cost Per Case

Patient/Family Centered Experience of Care

Fig. 1.2  The proposed Precision Medicine Triple Aim of Care heuristic model

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realignment of care delivery and stewardship around the risk and goal-based care of patients across an extended continuum of care. Instead of focusing only upon the unit cost or cost of the individual transactions of care, the model focuses upon some key principles: a rigorous understanding of patients’ clinical risk (including risk related to genomic tumor mutations/tumor heterogeneity), a clear articulation of the goals of care (palliative versus curative treatment), pre-emptive management of treatment-related complications (including emergency department visits and inpatient admissions), prospective management of patient distress, careful stewardship over choosing the lowest cost care settings where clinically appropriate, careful stewardship over the use of imaging studies, and careful management of pharmaceutical selection and utilization.

 reating Transparent, Generalizable Value Models C for Oncology A key initial step in the move toward value-based cancer care delivery is the adoption of incremental clinical and financial models that increase the transparency around cancer care planning, care costs, and meaningful outcomes. These models represent important initial steps in the process of moving toward creating ecosystems for delivering more effective and efficient care. Such a cancer value ecosystem will need to have an adequately robust structure for the transparent delivery and reimbursement of Precision Medicine. The data and analytics needs for producing this value-center/value-based ecosystem will be enormous. At present there are several pilots and proposals that can provide important first steps toward realizing a data-driven cancer care ecosystem. While none of these is mature enough to provide a definitive paradigm for the economically sustainable future delivery of Precision Medicine, they do provide an initial foundation for the creation of an actuarially sound, clinical robust reimbursement and care delivery model.

 he Centers for Medicare and Medicaid Services Oncology Care T Model (OCM) In 2016 CMS established the OCM pilot through CMS Innovations Center (Center for Medicare and Medicaid Innovation). This innovation pilot includes 178 practices and 13 payers throughout the United States with a project goal of realigning economic incentives in pursuit of care transformation for Medicare beneficiaries with cancer with the objective of delivering more effective, cost-effective care [23, 24]. This pilot organizes care into “episodes of care” that last 6 months in duration or that may last fewer than 6 months but end upon progression of the patient’s can-

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cer or a failure to respond to treatment (in which case a new episode of care is triggered by initiation of the next outpatient treatment regimen). In addition to organizing care and patient data around analytic episodes, the model also provides a $160 per month reimbursement in the form of a monthly enhanced oncology service (MEOS) payments to help cover the costs of managing and coordinating care across an episode. Participants in the model are assessed based upon the totality of care costs for patients within episodes of care. These costs include pharmaceuticals, surgery, radiation therapy, admissions, readmissions, ER visits, and imaging studies. In addition, participants are required to report process and outcomes data related to 12 predetermined quality metrics. Under this model CMS calculates benchmark episode of care costs based upon historical data that are adjusted for clinical risk and geographic cost variation. Practices that deliver care below the CMS index pricing may receive performance-­ based payments [25]. This model allows practices to participate in the program with either one-sided or two-sided financial risk. The amount of downside financial risk is limited by CMS. As practices review data and learn from their participation in the current pilot model, some are beginning to consider taking on two-sided financial risk (Personal communication, K. Patel, 12/8/2019) [26]. Practices who participate in the two-sided risk model may qualify under the Quality Payment Program as a participating in an Advanced Alternative Payment Model. CMS continues to gather clinical demographics, care delivery, economics, and outcomes performance data under the model. These data sets may help to develop some of the key knowledge needed to help create a cancer value ecosystem. While the OCM is the most significant effort to date to create a patient- and value-centered ecosystem for the delivery of cancer care, a number of other profession societies and expert care organizations have made import forays into the creation of value tools and data sets to help improve the value of cancer care delivery, particularly related to the effective use of innovative, targeted, and immune-­ oncological anticancer therapeutics. The American Society of Clinical Oncology (ASCO) has proposed and revised a value construct to help clinicians navigate clinical decision-making related to regimen selection for patients with cancer [26]. This value construct takes into account key factors, such as the goals of care, effectiveness of the regimen, regimen costs, and potential toxicities, in selecting among different therapeutic alternatives for a patient. The National Comprehensive Cancer Networks (NCCN) has added “Evidence Blocks” to a number of its practice guidelines tools that provide a graphic representation of the effectiveness, cost, and potential toxicity of each possible therapeutic regimen so as to facilitate regimen selection [27]. Each of these tools is designed to help improve regimen selection and ensure that it reflects the goals of care, helps to reduce regimen cost where feasible and reflects a transparent process for making such decisions. An important adjunct to these regimen selection tools includes a rigorous cost-­ benefit analysis related to new and emerging therapeutic technologies. The Institute for Clinical and Economic Review (ICER) was formed in 2006 to provide an independent perspective on the comparative effectiveness of new therapeutics as they

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reach the market place. The ultimate goal of these analytic reports is identified by ICER at that of establishing “value-based price benchmark” for new pharmaceutical related to their impact upon survival and other patient-centered outcomes [28]. These reports painstakingly review the data related to new therapeutics, but also compare than to those related to existing therapeutics. This comparison includes the cost for each respective therapeutic. The final analysis includes incremental cost and outcome comparisons between innovative therapeutics and the best existing therapies for that particular indication. ICER reports are extensively researched and make use of expert oversight of the comparison process that is performed using transparent analytical and statistical methodologies [29]. In the following chapters of this book, the roadmap for current and future cancer care innovation is detailed at length. As emerging diagnostic and therapeutic data sets escalate in their pace and complexity, it will become increasingly difficult for cancer clinicians to fully absorb emerging these data sets and link them to financial and outcomes data in a rigorous and objective way. Inasmuch as the ecosystems for delivering cancer care embrace the requisite complexity of delivering cancer care in the Precision Medicine era, for individual clinicians to digest all of these data and create value hierarchies for care delivery is an impossible, Sisyphean task. As such, the decision support tools provided by ASCO Value Framework, the NCCN Evidence Blocks, and the ICER reports represent important first steps in creating the decision support tools and clinical-financial frameworks through which a robust value ecosystem may built.

Embracing Necessary Complexity Risk, outcomes, cost. There is no simple path toward value-based Precision Medicine. As clinical care evolves toward greater use of individually tailored therapeutic decision-making and patients with, ostensibly the same cancer type, become segmented into differentiated risk groups based upon genomic diagnostic testing, generic economic evaluations of the efficiency of care become increasingly meaningless. Billed and coded data lack sufficient data richness through which meaningful, robust, and sustainable value-based care can be delivered and reimbursed in a way that aligns economic incentives most appropriately with effectively delivering high-quality cancer care. As the extraordinary complexities of tumor diversity, potential anticancer therapeutic targets, and intra-patient variability in biology, comorbidities, and goals of care, it is increasingly apparent that any meaningful, sustainable cancer value-based care and payment model will require an unprecedented level of data and analytics support. The fatal flaw of existing cancer payment models is that they lack the ability to integrate the necessary level of information complexity into their clinical risk assessment and reimbursement models. Moving toward a “Big Data” analytics model will be essential to making progress toward more robust value-based care delivery and reimbursement mod-

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els [30]. The TechAmerica Foundation has defined Big Data as “a term that describes large volumes of high velocity, complex and variable data that require advanced t­echniques and technologies to enable the capture, storage, distribution, management, and analysis of the information” [31]. Beyond the billed and coded data sets that are commonly used for capturing healthcare encounters and making reimbursement decisions, other high value data sets that can be analyzed using a high-velocity analytic model include genomic testing data, unstructured data related to patient care, pharmaceutical utilization data from pharmacy and pharmacy benefits manager (PBM) records, and additional patient data from multiple non-linked databases (including CMS payment data, commercial payer data, healthcare systems data, employer data, data from patient-wearable devices). This level of data analytics can empower the integration of clinical complexity into care and reimbursement models. It can help physicians and healthcare systems more effectively manage both clinical and financial risk in cancer care delivery. It could, if leveraged appropriately, empower transformational advanced alternative payment models (AAPMs) in which financial risk is apportioned across multiple cancer care stakeholders in order to more effectively align financial incentives with more effective care delivery, including care based upon Precision Medicine assessments of patients’ therapeutic needs. It could also help to better articulate more patient-centric outcomes in the cancer domain, including restoration of functionality, return to work, and reductions in downstream healthcare costs and utilization following the index episode of care. These outcomes, coupled with survival outcomes data, are notably absent from inclusion in our current reimbursement models. We remain far from this idealized vision of how the “Big Data” revolution could help transform value-based care and payment in the cancer domain, but a number of organizations are helping to lead this transformation. These include ASCO’s Cancer Linq [32], Flatiron Health [33], and Cota [34], all of which are hoping to create comprehensive “Big Data” models which include clinical data (including data from unstructured clinical encounter notes), utilization and cost of care data, genomic testing data, treatment data (including chemo-immunotherapy, radiation therapy, and surgery), and payer data to empower more effective clinical decision-making and to eventually empower more effective reimbursement strategies [35]. While the “Big Data” revolution holds immense promise in bringing more complex and relevant patient information to cancer care decision support and reimbursement, this transformation does carry some risk. While these data systems mature, it is essential to take great care in relying upon these solutions prior to their complete validation in the context of clinical care delivery. Recently, concerns have been raised about decision support obtained from IBM Watson as directing “unsafe and incorrect treatment recommendations.” [36] As “Big Data” information systems evolve, it will be essential for physicians and healthcare systems to partner effectively with leaders in the data analytics field to validate these knowledge engines and ensure that the emerging information systems can power the safest and most effective cancer care possible.

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The Future of Cancer Care Inasmuch as the extraordinary advances in genomic diagnostics and the robust pipeline of targeted therapeutics promise unprecedented advancements in cancer care, these care solutions will not make a meaningful impact upon patients unless we help to create more robust, data- and value-centric systems for delivering this care. The escalating costs of innovative therapeutics have placed future innovations at risk. In a recent article, the authors noted that: Healthcare comes at a cost and not only will there be a limit, or ceiling, to the amount that people are willing to pay, but there will also be a limit to the national resources that are available. There may also be other factors within society that threaten sustainability, such as a lack of available human resources or budgetary priorities, whereby the funding of healthcare is weighed against infrastructure or education.

From an economic viewpoint, the ceiling of unsustainability is set at the point at which the cost of healthcare exceeds the benefit [37]. It is only by creating robust systems of care that include the functional integration (and, to some degree, financial risk to) of the breadth of stakeholders who partner in the care of cancer patients across the continuum of care [38]. These systems will require a level of data transparency that is currently lacking. They will also require the ability to align payment in accordance with patient genomic and clinical risk in ways that currently do not exist. This will require an extraordinary realignment of our care delivery and reimbursement systems so that clinical care, data and meta-­ data, and reimbursement are fully aligned around the needs of the individual patient. Until we achieve this level of realignment, the promise of Precision Medicine will remain unfulfilled. The other chapters of this book detail an extraordinary revolution in our understanding of genomic diagnostics and anticancer therapeutics. The next revolution will need to be one of systems creations so that we ensure that the potentially life-saving knowledge gleaned from Precision Medicine can be equitably and sustainably available to the patients whom we serve.

References 1. Zhang J, et al. Characterization of cancer genomic heterogeneity by next-generation sequencing advances precision medicine in cancer treatment. Prec Clin Med. 2018;1(1):29. https://doi. org/10.1093/pcmedi/pby007. 2. Gandhi L, et al. Pembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer. N Engl J Med. 2018;378:2078. https://doi.org/10.1056/NEJMoa1801005. 3. National Cancer Institute. Cancer statistics. https://www.cancer.gov/about-cancer/understanding/statistics. Retrieved 15 Oct 2018. 4. Shojaee S, Nana-Sinkam P. Recent advances in the management of non-small cell lung cancer. F1000Res. 2017;6:2011. https://doi.org/10.12688/f1000research.11471.1. 5. Hartman M, et al. National Health Care Spending in 2016: spending and enrollment growth slow after initial coverage expansions. Health Aff. 2018;37(1):150.

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6. Papanicolas I, et al. Health care spending in the United States and other high-income countries. JAMA. 2018;319(10):1024. https://doi.org/10.1001/jama.2018.1150. 7. Squires D. U.S. Health Care from a Global Perspective. The Commonwealth Fund. 8 October 2015. https://www.commonwealthfund.org/publications/issue-briefs/2015/oct/us-health-careglobal-perspective. Retrieved on 15 Oct 2015. 8. Allemani C, et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391(10125):1023. https://doi. org/10.1016/S0140-6736(17)33326-3. 9. Mariotto AB, et al. Projections of the cost of cancer care in the United States: 2010–2020. J Nat Cancer Inst. 2011;103(2):117. https://doi.org/10.1093/jnci/djq495. 10. Dolgin E. Bringing down the cost of cancer treatment. Nature. 2018;555:S26. 11. Gordon N, et al. Trajectories of injectable cancer drug costs after launch in the United States. J Clin Oncol. 2018;36(4):319. https://doi.org/10.1200/JCO.2016.72.2124. 12. Johnson CY. The burden of cancer isn’t just cancer. The Washington Post. 8 April 2016. https:// www.washingtonpost.com/news/wonk/wp/2016/04/08/cancers-sinister-side-effect-financialtoxicity/?utm_term=.feec5562c6b9. Retrieved 15 Oct 2018. 13. Tran G, Sy Z. Financial toxicity and implications for cancer care in the era of molecular and immune therapies. Ann Transl Med. 2018;6(9):66. https://doi.org/10.21037/atm.2018.03.28. 14. Ramsey D, et al. Financial insolvency as a risk factor for early mortality among patients with cancer. J Clin Oncol. 2016;34(9):980. https://doi.org/10.1200/JCO.2015.64.6620. 15. Chen Q, et al. Economic burden of chronic lymphocytic leukemia in the era of oral targeted therapies in the United States. J Clin Oncol. 2017;35(2):166. 16. Burwell SM. Setting value-based payment goals — HHS efforts to improve U.S. Health Care. N Engl J Med. 2015;372(10):897. https://doi.org/10.1056/NEJMp1500445. 17. Institute for Healthcare Improvement. IHI Triple Aim Initiative. http://www.ihi.org/Engage/ Initiatives/TripleAim/Pages/default.aspx. Retrieved on 15 Oct 2018. 18. Whittington JW, et al. Pursuing the triple aim: the first 7 years. Milbank Q. 2015;93(2):263. https://doi.org/10.1111/1468-0009.12122. 19. Berwick DM, et al. The triple aim: care, health, and cost. Health Aff. 2008;27(3):759. https:// doi.org/10.1377/hlthaff.27.3.759. 20. de Oliveira C, et al. Phase-specific and lifetime costs of cancer care in Ontario, Canada. BMC Cancer. 2016;16:809. https://doi.org/10.1186/s12885-016-2835-7. 21. Porter ME.  Outcomes measurement. 10 June 2014. https://www.hbs.edu/faculty/ Publication%20Files/Website_Outcomes%20Measurement_737ae94d-f1b3-48ed-a789025766d63670.pdf. Retrieved on 15 Oct 2018. 22. Young RC. Value-based cancer care. N Engl J Med. 2015;373:2593. https://doi.org/10.1056/ NEJMp1508387. 23. Centers for Medicare and Medicaid Innovation. Oncology Care Model. https://innovation.cms. gov/initiatives/Oncology-Care. Retrieved on 16 Oct 2018. 24. Polite B, et al. Pursuing value in cancer care: a model in progress. J Oncol Pract. 2017;13(7):407. https://doi.org/10.1200/JOP.2017.023648. 25. Centers for Medicare & Medicaid Services Innovation Center (CMMI). Oncology Care Model Overview. May 2018. https://innovation.cms.gov/Files/slides/ocm-overview-slides.pdf. Retrieved 16 October 2018. 26. Schnipper LE, et al. Updating the American Society of Clinical Oncology value framework: revisions and reflections in response to comments received. J Clin Oncol. 2016;34(24):2925. https://doi.org/10.1200/JCO.2016.68.2518. 27. Carlson RW, Jonasch E.  NCCN evidence blocks. JNCCN. 2016;14:161. https://doi. org/10.6004/jnccn.2016.0177. 28. Institute for Clinical and Economic Review (ICER). About. https://icer-review.org/about/. 29. Institute for Clinical and Economic Review (ICER). CAR T therapies. March 2018. https:// icer-review.org/wp-content/uploads/2018/03/ICER_CAR-T_RAAG_032318.pdf.

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30. Gandomi A.  Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag. 2015;35(2):137–44. 31. TechAmerica Foundation’s Federal Big Data Commission. Demystifying big data: a practical guide to transforming the business of government. 2012. http://www.techamerica.org/Docs/ fileManager.cfm?f=techamerica-bigdatareport-final.pdf. 32. ASCO Cancer Linq. https://cancerlinq.org. 33. Flatiron. https://flatiron.com. 34. Cota Healthcare. https://www.cotahealthcare.com. 35. https://aspe.hhs.gov/system/files/pdf/255906/OncologyBundledPaymentProgramCNACare. pdf. 36. Densford F.  Report: IBM Watson delivered ‘unsafe and inaccurate’ cancer recommendations. MassDevice. 25 July 2018. https://www.massdevice.com/ report-ibm-watson-delivered-unsafe-and-inaccurate-cancer-recommendations/. 37. Simoens S, et  al. What happens when the cost of cancer care becomes unsustainable? Eur Oncol Hematol. 2017;13(2):108–13. https://doi.org/10.17925/EOH.2017.13.02.108. 38. Porter ME, Lee TH.  The strategy that will fix health care. Harvard Business Review. 2013 October. https://hbr.org/2013/10/the-strategy-that-will-fix-health-care. Retrieved on 16 Oct 2018.

Chapter 2

Lung Cancer Joy Huang, Ashley Abing, and Karen L. Reckamp

Lung cancer is the leading cause of all cancer-related deaths affecting both men and women in the United States [1]. The American Cancer Society estimates 234,030 new cases in 2018 alone. Non-small cell lung cancer (NSCLC) is the most common form, accounting for 80–85% of lung cancer cases. While smoking remains the primary cause for lung cancer, 10–15% of cases are due to genetic susceptibility and environmental exposures, and unrelated to tobacco [2]. Research advancements continue to improve our knowledge of the genetic alterations that drive lung cancer which, in turn, heighten the potential that targeted therapeutics will positively impact outcomes. Furthermore, immunotherapy has become part of standard treatment for eligible patients, especially those without activating mutations. This chapter will provide insights into the current and emerging treatments for lung cancer with a focus on targeted and immune-based therapies. The use of targeted therapy and immunotherapy with biomarker-based treatment delivery has led to personalized options for patients with both NSCLC and small-cell lung cancer (SCLC). This impacts all aspects of care, including quality of life, financial issues, and, importantly, survival.

J. Huang Department of Biology, Johns Hopkins University, Baltimore, MD, USA A. Abing Johns Hopkins University, Baltimore, MD, USA K. L. Reckamp (*) Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2020 R. Salgia (ed.), Oncology in the Precision Medicine Era, https://doi.org/10.1007/978-3-030-31471-2_2

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Targeted Therapy in Non-small Cell Lung Cancer Epidermal Growth Factor Receptor (EGFR) Epidermal growth factor receptor (EGFR) is a glycoprotein with an extracellular receptor domain, transmembrane region, and an intracellular tyrosine kinase (TK) domain. Binding of its ligand plays a crucial role in regulating cell growth, differentiation, and migration [3]. Activating EGFR mutations are found in TK domain, and are frequently associated with adenocarcinoma histology, never-smokers, female gender, and Asian ethnicity [4]. In early clinical trials using EGFR TK inhibitors (TKI), patients with responses to treatment were found to have EGFR mutations, mainly in exon 19 or 21 [5, 6]. Subsequently, a prospective phase III trial that compared gefitinib to chemotherapy with carboplatin and paclitaxel in patients with a history of light or never smoking and advanced NSCLC with adenocarcinoma histology was performed in Asia [7]. Importantly, in the subgroup of patients with a confirmed EGFR mutation (n = 261), patients who received gefitinib experienced a significantly higher objective response rate (ORR) and progression-free survival (PFS). Patients who did not have an EGFR mutation (n = 176) assigned to the gefitinib had a statistically significant lower ORR and shorter PFS [7, 8]. The rate of EGFR mutations in this clinically enriched cohort was approximately 60%. This trial established EGFR mutation testing as required to predict clinical benefit from EGFR TKI therapy. Additional trials comparing EGFR TKI (gefitinib, erlotinib, afatinib, dacomitinib) to platinum doublets in patients with advanced EGFR mutant NSCLC have been performed, and have shown an improvement in ORR, PFS, and quality of life in the EGFR TKI arm [9–15]. Despite the significant efficacy of EGFR TKI for patients with EGFR mutant NSCLC, some do not respond to EGFR-targeted therapy, and for those who initially respond to therapy, secondary resistance eventually develops. A specific EGFR mutation, T790 M in exon 20, which generally develops after drug treatment with first- or second-generation TKI, accounts for approximately 50% of acquired resistance [16]. Osimertinib is an oral, third-generation EGFR TKI, which was first developed to inhibit T790  M and overcome resistance to EGFR TKI therapy. It has activity against both sensitizing mutations and the T790 M resistance mutation. In the initial phase I trial, 127 patients with confirmed EGFR T790  M were enrolled, and the ORR was 61% with PFS of 9.6 months, leading osimertinib to become the treatment of choice for patients with EGFR T790 M resistance following front-line therapy with a first- or second-generation EGFR TKI. The efficacy was confirmed in a randomized, phase III trial with 419 patients with T790  M-positive NSCLC who received osimertinib or chemotherapy with platinum and pemetrexed [17]. The median PFS was 10.1 months with osimertinib compared to 4.4 months with chemotherapy, and PFS was also improved in patients with central nervous system metastases.

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Subsequently, osimertinib demonstrated superior efficacy compared to either erlotinib or gefitinib (as standard of care) in a randomized, phase III trial of 556 patients. [18] The study demonstrated a superior PFS with osimertinib compared to standard EGFR TKI at 18.9 versus 10.2 months, respectively, with fewer adverse events with osimertinib. Overall survival (OS) showed a trend toward improvement with osimertinib but data were immature at the time of analysis. Understanding mechanisms of resistance to EGFR TKI is essential to develop appropriate therapies to improve outcomes for patients. As osimertinib shifts to front-line treatment, these mechanisms will be altered. Notably, patients who developed resistance to osimertinib can acquire the C797S mutation [19]. Alternatively, bypass mechanisms may lead to resistance, such as amplification of MET and activation of PI3K/AKT signaling [20]. Multiple classes of drugs are in development to overcome these mechanisms of resistance, including novel EGFR TKIs that irreversibly bind and inhibit multiple ERBB family members. Combination therapy utilizing EGFR inhibition with MET, HSP90, AKT, SRC, and mammalian target of rapamycin (mTOR) inhibitors is under investigation. EGFR mutation-positive NSCLC has been the model to understand other alterations and resistance that occur in lung cancer.

Anaplastic Lymphoma Kinase (ALK) ALK is located on chromosome 2p23 and consists of an extracellular domain with two ligands, a single-chain transmembrane segment, and an intracellular domain. Fusion of 2 genes, echinoderm microtubule-associated protein like-4 (EML4) with ALK results in the formation of the EML4-ALK fusion protein which leads to constitutive signaling triggering transforming properties [21]. Additional chimeric variants and other fusion partners have been reported. The alteration occurs in about 5% of NSCLC and is associated with younger (P = 0.049), male (P = 0.032), never/light smokers (P = 0.048), and adenocarcinomas [22]. Crizotinib, alectinib, brigatinib and ceritinib have demonstrated benefit as firstline treatment for patients with ALK-positive NSCLC. Crizotinib was the first treatment to demonstrate superior efficacy compared to chemotherapy in treating ALK-positive NSCLC in an open-label, phase III trial. The PFS was 10.9 months for crizotinib and 7 months for chemotherapy [23]. Subsequently, a randomized, phase III study comparing first-line ceritinib to chemotherapy demonstrated improved PFS in advanced ALK-positive NSCLC with 16.6 months for ceritinib and 8.1 months for chemotherapy [24]. Alectinib is a highly selective ALK inhibitor and was the first to achieve improved PFS compared to crizotinib as first-line therapy for patients with advanced ALK-positive NSCLC [25]. The 12-month event-free survival rate was 68.4% versus 48.7% in the crizotinib group, and notably, CNS progression occurred in only 12% on alectinib versus 45% on crizotinib. This has made alectinib the choice for first-line therapy for ALK-positive NSCLC. Brigatinib has also demonstrated improved PFS compared to crizotinib in the front-line setting [26].

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Similar to treatment of patients with EGFR mutation, resistance invariably occurs and several ALK TKI have shown benefit following crizotinib in patients with ALK-positive NSCLC.  If patients progress during first-line crizotinib treatment, alectinib, ceritinib, and brigatinib are used as second-line drugs. Ceritinib became available in 2014, based on a single-arm trial with an ORR of 44% [27]. A randomized, phase III trial confirmed the results with a PFS of 5.4 months for ceritinib compared to 1.6 months for chemotherapy [28]. Second-line alectinib was approved by the U.S. Food and Drug Administration (FDA) in 2015, based on two single-arm trials with ORR of 38% and 44% in addition to median duration of response (DOR) of 7.5 months and 11.2 months [29]. A randomized, multicenter, phase II trial demonstrated the efficacy of using a 180-mg brigatinib regimen with median PFS of 11.1 months [30]. Given the number of ALK TKI therapy available, the sequencing of drugs and uncovering resistance mechanisms will help to provide increased benefits to patients. Lorlatinib has demonstrated benefit in patients with ALK-positive NSCLC following multiple prior ALK TKI [31].

ROS Proto-oncogene 1 (ROS1) ROS1 is a receptor tyrosine kinase of the insulin receptor family that lies on chromosome 6q22 [32]. ROS1 gene rearrangements occur in 1–2% of NSCLCs and are predominantly found in females, non-smokers, those with Asian ethnicity, and advanced-stage clinical stage (III–IV). Crizotinib was the first treatment to show significant benefit for patients with ROS1 fusion NSCLC in a single-arm, phase II trial with an ORR of 72% and PFS of 19.2 months [33]. The results were confirmed in a second single-arm trial with 30 patients [34]. In addition, ceritinib was evaluated in 32 patients with ROS1 gene rearranged NSCLC, and ORR was 62% with PFS of 9.3 months in crizotinib-naïve patients [35]. Resistance also occurs with crizotinib and ceritinib in ROS1 fusion NSCLC, therefore, several drugs are being investigated for ROS1 fusions, including loratinib, entrectinib, and cabozantinib.

B-Raf Proto-oncogene (BRAF) BRAF encodes for the serine/threonine kinase that lies downstream of RAS in the RAS-RAF-MEK-ERK signaling pathway, a key molecular cascade that regulates cell growth [36]. This proto-oncogene is detected in 1–3% of NSCLC cases. The BRAF V600E mutation has been associated with never-smokers, while non-V600E patients have been associated with current and former smokers. No correlation has been found between BRAF mutation and sex, age, histology, or stage. Single-agent vemurafenib and dabrafenib revealed modest ORR in patients with V600E BRAF NSCLC [37, 38]. Combination therapy with dabrafenib andtrametinib

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resulted in increased ORR (63.2%) in a phase II clinical trial for V600E BRAF NSCLC [39, 40]. Dabrafenib andtrametinib combination treatment has been approved by the European Medicines Agency and FDA for patients with stage IV NSCLC with BRAF V600E mutation.

MET Proto-oncogene, Receptor Tyrosine Kinase (MET) MET is a proto-oncogene that encodes for the transmembrane MET tyrosine receptor kinase and binds to its ligand, hepatocyte growth factor (HGF). Binding to HGF activates signaling pathways such as phosphoinositide-3-kinase (PI3K)/AKT, mitogen-­activated protein kinase (MAPK), nuclear factor kappa B (NF-kB), and signal transducer and activator of transcription proteins (STATs), which induces increased cell proliferation and invasion [41, 42]. Protein overexpression and phosphorylation are the most common forms of MET-positive NSCLC, accounting for 35–72% and 67% of NSCLC cases, respectively. MET amplification has been found in 2–5% of newly diagnosed adenocarcinoma [43, 44]. MET exon 14 alterations are found in 4% of lung adenocarcinomas and are predominantly associated with older age (median age of 73 years) and significant smoking history [45]. Oral treatments targeting MET in lung cancer include multi-targeted TKIs (cabozantinib, crizotinib, merestinib, and others) and a variety of MET-specific TKIs with increased MET sensitivity (savolitinib, tepotinib, capmatinib, SAR125844, sitravatinib, AMG 337, tivantinib). Monoclonal antibodies are also under investigation for patients with MET-driven tumors. In patients with MET exon 14 mutations and MET amplification, treatment with crizotinib (a dual MET/ALK inhibitor) has led to antitumor responses [46, 47]. In addition, cabozantinib has demonstrated tumor response in patients with exon 14 mutations [48]. Camidge et al. conducted a phase I trial evaluating the safety and efficacy of crizotinib for patients with MET amplification. Patients with high levels of MET amplification (MET/CEP7 ≥ 4) demonstrated antitumor activity, with median PFS of 6.7 months [49].

Ret Proto-oncogene (RET) RET is a receptor tyrosine kinase that induces cellular proliferation, migration, and differentiation when activated [50]. RET fusions account for 1.4% of NSCLCs and are present predominantly in younger, never-smoking patients with adenocarcinoma. Early clinical data on the use of cabozantinib for RET fusion-positive patients in a phase II trial revealed 2 patients with partial response (PR) [51], and the final results in 25 RET-positive patients revealed an ORR of 28% [52]. The results from a global, multicenter registry of 165 RET-positive patients from Europe, Asia, and the United States were reported [53]. Of note, the ORR to cabozantinib, vandentaib, and sunitinib was detailed as 37%, 18%, and 22% respectively. The median

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progression-­ free survival was 2.3  months and median overall survival was 6.8 months in all patients. In addition, a novel RET inhibitor, known as LOXO-292, revealed ORR over 70% in RET-altered NSCLC and was well-tolerated [54].

Human Epidermal Growth Factor Receptor 2 (HER2) HER2 is a member of the erbB receptor tyrosine kinase family that activates signaling through PI3K-AKT and MEK-ERK pathways. HER2 has no known ligand and is activated by homo-dimerization or hetero-dimerization with other members of the erbB family [55]. HER2 is overexpressed in 13–20% of NSCLC cases, and is predominantly present in women, never-smokers, and adenocarcinomas [56]. Poziotinib has been identified as a novel, potent inhibitor of HER2 exon mutations with tumor responses in a phase II trial with 11 patients [57]. In a phase II basket trial, 18 patients with HER2-mutant lung adenocarcinomas were treated with ado-trastuzumab emtansine (T-DM1) in a phase II trial. This was the first positive trial evaluating T-DM1 in HER2 lung cancer patients, reporting a partial response rate of 44% and median progression-free survival of 5  months [58]. A retrospective study assessed patients treated with chemotherapy or HER2-targeted therapy, and 101 patients were assessed [59]. The median OS was 24 months for all patients regardless of therapy received. Sixty-five patients received HER2-targeted therapy (trastuzumab, neratinib, afatinib, lapatinib, T-DM1) and ORR was highest for those who received trastuzumab with or without chemotherapy or T-DM1 at 50.9% with PFS 4.8 months. A phase II trial investigated dacomitinib (a pan-HER inhibitor) in 30 patients with HER2-mutant or amplified NSCLC, and resulted in a 12% ORR [60].

Neurotrophic Tyrosine Kinase (NTRK) Tropomyosin-related kinase (TRK) encodes the tyrosine kinase receptors for neutrophins associated with the nerve growth factor (NGF) family [61]. Three members of this family include the NTRK1, NTRK2, and NTRK3 proto-oncogenes. Less than 1% of NSCLC cases have NTRK fusions [62]. NTRK fusions can be found in both men and women with wide ranges of age and smoking history [63]. A phase I study of entrectinib demonstrated antitumor activity in one patient positive with a NTRK1 fusion [64]. In a trial evaluating larotrectinib in TRK-positive patients, 55 patients were enrolled into either a phase I study for adults, a phase I–II study for children, or a phase II study for adolescents and adults [65]. The study demonstrated an ORR of 75% based on independent review. In addition, 71% of responses were ongoing and 55% patients remained progression-free at 1 year, demonstrating substantial activity in NTRK fusion-positive patients. Additional NTRK inhibitors are also in development.

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Immunotherapy in Lung Cancer Background Lung cancer has traditionally been treated as an immune-resistant disease [66], with limited response to immune-based therapies. However, clinical trials have shown that immune therapies can create durable responses with manageable toxicities, changing the treatment paradigm for lung cancer. The first successful treatments exploit the programmed death 1/programmed death ligand 1 (PD-1/PD-L1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) immune checkpoint pathway. Patients with NSCLC have improved survival with immunotherapy as firstand second-line treatments compared to chemotherapy, and combination chemotherapy and PD-1 or PD-L1 antibody therapy is beneficial for patients with NSCLC and SCLC. A large number of trials continue to explore combinations, timing and biomarkers for selection to improve the efficacy of immune-based therapy for both NSCLC and SCLC.

Biology of Cancer and Immune Therapy The immune system functions by differentiating between self, infiltrators, and harmful mutated cells: bacteria, viruses, and cancer cells, through innate and adaptive responses [67, 68]. Ordinarily, the immune system is capable of recognizing and eliminating malignant cells, but if cells go undetected, it can result in cancer growth and progression. The body’s innate response is quick and antigen nonspecific. Innate responses are mediated by natural killer cells (NK), antigen-presenting cells (APC), leukocytes, and mast cells [69]. Once activated, APC secrete interferon gamma (IFN-γ), perforin, and inflammatory cytokines to prompt the death, apoptosis, of tumor cells. On the other hand, adaptive responses are slower and antigen-specific but can potentially develop immune memory, which is preferred for antitumor responses [68]. Both innate and adaptive responses constitute the elimination step, also known as the first part of a three-step model, describing a proposed mechanism of immunoediting [70]. The second part is equilibrium, which occurs when a malignant cell goes undetected by the immune system, but remains dormant as it changes its immunogenicity according to the selective pressures placed by the adaptive immune response [69]. Once equilibrium is reached, the tumor cell progresses to the final step, escape. The tumor cell escapes detection through its reduced immune recognition, increased resistance and survival, or its immunosuppressive tumor microenvironment and develops into cancer. In lung cancer, cells can go undetected due to genetic changes and the alteration of the tumor microenvironment. Cells can evade recognition by downregulating antigen-presenting proteins: antigen peptide transporters 1 and 2, major

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h­ istocompatibility (MHC) molecules, and large multifunctional peptidases 2 and 7 [71]. Meanwhile, the overexpression of checkpoint ligands, PD-L1, PD-L2, B7-H3, and B7-H4, allows cells to evade detection. PD-1/PD-L1 and CTLA-4/B7 antibodies to the immune checkpoint pathway were the first successful immunotherapy treatments for NSCLC.

Immune Checkpoint Inhibitors CTLA-4 and PD-1 are immune checkpoint inhibitors that compromise antitumor immune responses by modulating and altering T-cell interactions with APC [72]. APC that go undetected by the immune system and proliferate can be detrimental if the APC is a cancer cell. By introducing agents that target CTLA-4, PD-1, and PD-L1, the goal is to disrupt the immune checkpoint inhibitor interactions, activate T cells, and elicit a long-term antitumor immune response. Therapies Targeting CTLA-4 In order to activate T cells, T-cell receptors must bind to the cell’s MHC and B7 ligands to CD28. However, when CTLA-4—a T cell surface protein—is expressed, its higher B7 ligand affinity causes CTLA-4 to bind to the B7 ligand and compete with CD28 [73]. Because CTLA-4 has a greater affinity for B7, its increased interaction results in a negative downregulation of T-cell function and activation, allowing APC to proliferate. The purpose of CTLA-4 targeted therapies is to introduce antibodies that target CTLA-4 to reduce and inhibit CTLA-4 and B7 ligand interactions and augment antitumor immune response. Ipilimumab is a monoclonal antibody that targets and prevents the binding of CTLA-4 to B7-1 and B7-2. Although it initially demonstrated efficacy in metastatic melanoma, its use has lung cancer and other malignancies. In a phase II clinical trial, chemotherapy-naïve advanced NSCLC patients were randomized and administered paclitaxel and carboplatin with placebo or ipilimumab as phased or concurrent doses [74]. The results revealed that immune-related progression-free survival (irPFS) was significantly higher in the phased dosing arm (5.7 months) when compared to placebo (4.6 months), but not concurrent dosage. Subsequently, a randomized phase III trial of paclitaxel and carboplatin plus ipilimumab or placebo on a phased induction schedule for patients with advanced squamous NSCLC reported no significant differences in OS, and an increase in the percent of patients who discontinued treatment due to treatment-related adverse events in the chemotherapy plus ipilimumab arm [75]. This trial revealed that the addition of ipilimumab to first-line chemotherapy did not prolong OS in patients with advanced squamous NSCLC. In lung cancer, ipilimumab is being studied in combination with nivolumab or other immune-modulating therapies in most trials.

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PD-1 and PD-L1/PD-L2 Targeted Therapies PD-1 is an inhibitory receptor expressed by activated T cells, B cells, natural killer T cells, activated monocytes, and dendritic cells [76]. Its expression regulates T-cell functions during various immune-related responses: infection, cancer, and immune homeostasis [77]. PD-1 receptor has two ligands, PD-L1 and PD-L2. Both ligands are generally overexpressed in cancer cells and seen in 20–65% of NSCLC [78]. PD-L1 interactions allow cancer cells to escape from the immune system by obstructing the activation of cytotoxic T cells [79]. Therapies that interrupt the PD-1 and PD-L1 and PD-L2 pathways liberate deactivated T cells and allow cells to resume a normal immune response. In the front-line setting for patients with metastatic NSCLC, pembrolizumab has shown benefit in survival as monotherapy and in combination with chemotherapy. An early phase I study of pembrolizumab, Keynote 001 assessed the safety and efficacy of pembrolizumab in patients with advanced NSCLC and a PD-L1 tumor proportion score (TPS) of ≥1% [80]. PD-L1 expression was assessed with a prototype immunohistochemistry (IHC) assay that categorized patients based on PD-L1 expression. Using this assay, the study reported a positive correlation between PD-L1 expression and improved effects. Patients with a TPS ≥ 50% expression had longer PFS and OS compared to patients with TPS 1–49% expression. The duration of response was similar across all patients regardless of expression. Patients with a TPS  ≥  50% expression had longer PFS and OS compared to patients with TPS 1–49% expression. In addition to PD-L1 IHC, there was a positive correlation between smoking and treatment response (ORR of 22.5% in current or former smokers vs. 10.3% nonsmokers). Keynote 024, an open-label phase III study, compared the effects of pembrolizumab versus platinum-based chemotherapy in treatment-­naïve advanced NSCLC patients with no EGFR mutation and PD-L1 expression over 50% [81]. The results revealed that the pembrolizumab arm had a significantly longer median PFS of 10.3  months than the chemotherapy arm at 6 months. When compared to the chemotherapy group, patients who received pembrolizumab had a higher estimated rate of OS at 6 months (80.2% vs. 72.4% respectively), a higher response rate (44.8% vs. 27.8%), longer responses to therapy, and fewer treatment-related adverse events. The study changed the treatment paradigm for patients with advanced NSCLC with no EGFR mutation and over 50% PD-L1 expression who could receive single-agent immunotherapy rather than chemotherapy as front-line treatment. Moreover, Keynote 042, a phase III study, evaluated and compared pembrolizumab to platinum-based chemotherapy as first-line therapy monotherapy for advanced NSCLC with a PD-L1 TPS ≥ 1% [82]. OS was improved over chemotherapy for patients with PD-L1 TPS ≥ 1%, ≥ 20%, and ≥ 50%, although the survival benefit was driven by the TPS ≥50% group. CheckMate 026, a randomized phase III trial, compared nivolumab to platinum-based chemotherapy in patients with stage IV or recurrent advanced NSCLC and a PD-L1 TPS of ≥5% [83]. The results revealed that nivolumab had a preferable safety profile over chemotherapy but did not significantly prolong PFS or OS. A subset analysis based on tumor mutational burden (TMB) was performed. This was based on earlier work

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suggesting that the level of tumor antigen expression and mutation burden could be associated with response to immunotherapy [84]. The level of tumor antigen expression may also provide valuable information regarding tumor response to PD-1 inhibition. Rizvi and colleagues performed whole-exome sequencing in patients with NSCLC who received pembrolizumab, and found mutation rate was associated with clinical benefit [85]. They found that a higher somatic nonsynonymous mutation burden was associated with clinical benefit defined as partial or stable response lasting greater than 6 months, in addition to better ORR and PFS. This data is consistent with the theory that somatic mutations and associated neoantigens are integral for PD-1 efficacy. Thus, in the subset analysis for CheckMate 026, patients with a high TMB had a longer PFS and higher ORR than patients with low TMB. Moreover, patients with both high TMB and high PD-L1 expression had the highest ORR than patients with only one of the two factors. Although patients with a higher TMB had a longer PFS, the OS was similar regardless of TMB. Anti-PD-1 and PD-L1 drugs also improved survival when used as second-line treatment in advanced NSCLC.  Keynote 010, a phase II/III trial, compared the effects of pembrolizumab to docetaxel for previously treated, PD-L1-positive NSCLC patients. Patients received either 2 mg/kg or 10 mg/kg of pembrolizumab or docetaxel every 3 weeks [86]. The study showed that patients in both dose levels of pembrolizumab had a significantly longer OS than the docetaxel arm (10.4 months vs. 12.7 months vs. 8.5 months respectively). Overall, there was no significant difference in PFS across all three treatments. CheckMate 017 and CheckMate 057 compared the effects of nivolumab to docetaxel in patients with advanced squamous (CheckMate 017) and non-squamous (CheckMate 057) NSCLC [87, 88]. CheckMate 017 revealed that the nivolumab arm resulted in a longer OS of 9.2 months over docetaxel (6.0 months) [87]. PD-L1 expression was evaluated and showed no predictive association with expression and efficacy. CheckMate 057 also demonstrated and improved OS with nivolumab compared to docetaxel (12.2 months vs. 9.4 months) [88]. In this nonsquamous NSCLC population, CheckMate 057 revealed a correlation between higher PD-L1 expression and better response. Meanwhile OAK, a randomized phase III study, compared atezolizumab (a human IgG1 monoclonal antibody to PD-L1) to docetaxel in previously treated, advanced NSCLC [89]. The study revealed improved OS in the atezolizumab arm (13.6 months vs. 9.6 months) and more favorable safety profile in favor of atezolizumab. Anti-PD-1 and PD-L1 agents have also been used in early-stage lung cancer as neoadjuvant and adjuvant therapies. In a pilot study, nivolumab was used as a neoadjuvant treatment for patients with surgically resectable NSCLC [90]. In this study, patients received nivolumab every 2 weeks for 4 weeks before surgery. Regardless of PD-L1 expression, treatment responses include increased T-cell clones in the tumor and peripheral blood vessels. Meanwhile, in the PACIFIC trial, patients were randomly assigned to durvalumab or placebo post chemoradiation for unresectable stage III NSCLC [91]. Patients who received durvalumab had significantly longer PFS (16.8 months vs. 5.6 months), a higher response rate (28.4% vs. 16.0%), and a similar safety profile compared to placebo. Furthermore, OS was shown to be sig-

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nificantly improved for patients who received durvalumab consolidation therapy [92]. This is an example of another immune checkpoint inhibitor changing the standard of care for patients with unresectable stage III lung cancer.

Combination Therapies Immunotherapy and chemotherapy combination treatments have become the first-­ line standard of care in advanced NSCLC. In Keynote 189, patients with treatment-­ naïve, metastatic nonsquamous NSCLC were randomly assigned to chemotherapy with carboplatin or cisplatin and pemetrexed plus either pembrolizumab or placebo [93]. The results indicated that an estimated 69.2% of patients that received pembrolizumab were alive at 12 months vs. 49.4% in the placebo-combination group. Additionally, median OS was not reached in the pembrolizumab arm, while patients who were administered a placebo had a median OS of 11.3 months. In patients with advanced squamous NSCLC, Keynote 407 observed the effects of pembrolizumab vs. placebo in combination with chemotherapy with carboplatin and paclitaxel or nab-paclitaxel as front-line therapy [94]. In this study, the addition of pembrolizumab improved OS at 15.9 months compared to 11.3 months with chemotherapy alone for metastatic squamous cell NSCLC patients. Furthermore, IMPower 150 compared the effects of atezolizumab and chemotherapy with carboplatin and paclitaxel ± bevacizumab vs. chemotherapy and bevacizumab vs. chemotherapy and atezolizumab in nonsquamous NSCLC [95]. The results of IMpower150 showed a significant OS benefit with atezolizumab in combination with chemotherapy and bevacizumab vs. chemotherapy and bevacizumab. Importantly, this was the first study to include patients with EGFR and ALK gene alterations and show a similar benefit in these patients to the intent to treat group. The advantages that anti-PD-1/PD-L1 therapies bring over traditional platinum-­ based therapy have directed the focus of multiple trials to study the effects of anti-­ PD-­1/PD-L1 agents in combination with CTLA-4 agents. Because the PD-1/PD-L1 and CTLA-4 pathways are independent of each other, the theory behind combining therapies is to have the two treatments complement each other therapeutically and increase the number of patients who derive benefit [79]. CheckMate 227, a randomized phase III trial, compared the effects of ipilimumab in combination with nivolumab to nivolumab plus chemotherapy, or chemotherapy in patients with metastatic or recurrent NSCLC. PD-L1 TPS and TMB were used to assess outcomes in patient subgroups [96]. Similarly to CheckMate 26, the results indicated a direct relationship between PFS and TMB in the ipilimumab plus nivolumab therapy arm. The 1-year PFS was 42.6% with nivolumab plus ipilimumab versus 13.2% with chemotherapy and an ORR of 45.3% and 26.9% respectively in patients with high TMB.  TMB can potentially be used to distinguish patients who might benefit from immune checkpoint inhibitor therapy regardless of PD-L1 expression level. Multiple trials are assessing the combination of nivolumab with ipilimumab in lung cancer.

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Immunotherapy in Small-Cell Lung Cancer Standard first-line treatment for patients with SCLC includes platinum-based chemotherapy with or without radiation, which has not changed for decades. Furthermore, most patients relapse, and second-line treatment provides limited benefit [97]. Given the effectiveness of immunotherapy in NSCLC, trials have been conducted to study the effects of immunotherapy for patients with SCLC. In addition, patients with SCLC typically have high levels of tumor mutation, increasing interest to incorporate immune-based modalities into treatment. A randomized phase III trial evaluating the addition of phased ipilimumab to etoposide/platinum for SCLC patients revealed no overall survival benefit with the addition of ipilimumab to chemotherapy, but a statistical difference in PFS that could be measured in days and was not clinically significant [98]. Meanwhile, Keynote 028, a phase I trial that assessed the efficacy and safety of pembrolizumab administered to patients with incurable advanced biomarker-positive solid tumors, demonstrated that a subgroup of PD-L1-positive SCLC patients had an ORR of 35% and durable responses to therapy [99]. Moreover, CheckMate 032, a phase I/II trial comparing nivolumab monotherapy and nivolumab plus ipilimumab in patients who have relapsed, saw durable responses to treatment. Improved PFS was also seen in patients with high TMB in this study [100]. Immunotherapy in SCLC is shifting toward becoming a standard of care. The IMPower 133 trial was a randomized phase III trial comparing carboplatin and etoposide to chemotherapy with atezolizumab in patients with untreated, extensive-­ stage SCLC [101]. This was the first trial to demonstrate an improvement in OS and PFS with an immunotherapy-based regimen for SCLC. A multitude of clinical trials are ongoing using immunotherapy for SCLC, including combination with chemotherapy, ipilimumab and nivolumab, and other combination therapies.

Immune-Related Toxicities Immune-related toxicities are autoimmune in nature and mainly involve the gut, skin (grade I–II IRAE), and digestive tract (grade III–IV IRAE) but can potentially affect any tissue [102]. Although IRAE are frequent and have shown to increase in severity with dosage, IRAE can be relieved with steroids or discontinuation of therapy.

Conclusions Genomic sequencing has begun to unravel the vast complexities of lung cancer. While this advancement fuels progress in patient care, it also prompts the provision of unique, individualized courses of treatment. As we expand our understanding of

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the molecular pathways that regulate lung cancer, current knowledge of primary and secondary forms of resistance is limited which necessitates further research into targeted therapeutics. This precision medicine offers benefits to patients who maintain quality of life for longer periods of time, which leads to more productivity within society. Immunotherapy’s manageable toxicity profile in addition to its lasting responses has generated a vast amount of research in the field. Many trials are focused on determining accurate biomarkers for therapies, determining the most beneficial duration of therapy, the best sequence of therapy (neoadjuvant, first line, maintenance), and various combination therapies. However, the greatest hurdle to these therapies is economic access. The average cost per month of nivolumab and pembrolizumab in 2017 was over $13,000 [38]. With the average American household earning $56,000 a year, it is crucial to reduce treatment cost in order for treatment to be available to all patients. These issues must be addressed by collaborative teams to provide optimal treatment for all patients with lung cancer.

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75. Govindan R, Szczesna A, Ahn MJ, et al. Phase III trial of Ipilimumab combined with paclitaxel and carboplatin in advanced squamous non-small-cell lung cancer. J Clin Oncol. 2017;35:3449–57. 76. Brahmer JR. Immune checkpoint blockade: the hope for immunotherapy as a treatment of lung cancer? Semin Oncol. 2014;41:126–32. 77. Sharpe AH, Pauken KE.  The diverse functions of the PD1 inhibitory pathway. Nat Rev Immunol. 2018;18:153–67. 78. Sundar R, Cho BC, Brahmer JR, Soo RA. Nivolumab in NSCLC: latest evidence and clinical potential. Therap Adv Med Oncol. 2015;7:85–96. 79. Robainas M, Otano R, Bueno S, Ait-Oudhia S. Understanding the role of PD-L1/PD1 pathway blockade and autophagy in cancer therapy. Onco Targets Ther. 2017;10:1803–7. 80. Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372:2018–28. 81. Reck M, Rodriguez-Abreu D, Robinson AG, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med. 2016;375:1823–33. 82. Lopes G, Wu YL, Kudaba I, et al. Pembrolizumab (pembro) versus platinum-based chemotherapy (chemo) as first-line therapy for advanced/metastatic NSCLC with a PD-L1 tumor proportion score (TPS) ≥ 1%: Open-label, phase 3 KEYNOTE-042 study. J Clin Oncol. 2018;36:abstr LBA4. 83. Carbone DP, Reck M, Paz-Ares L, et al. First-line nivolumab in stage IV or recurrent non-­ small-­cell lung cancer. N Engl J Med. 2017;376:2415–26. 84. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–21. 85. Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124–8. 86. Herbst RS, Baas P, Kim DW, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet. 2016;387:1540–50. 87. Brahmer J, Reckamp KL, Baas P, et al. Nivolumab versus docetaxel in advanced squamous-­ cell non-small-cell lung cancer. N Engl J Med. 2015;373:123–35. 88. Borghaei H, Paz-Ares L, Horn L, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med. 2015;373:1627–39. 89. Fehrenbacher L, Spira A, Ballinger M, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet. 2016;387:1837–46. 90. Forde PM, Chaft JE, Smith KN, et al. Neoadjuvant PD-1 blockade in resectable lung cancer. N Engl J Med. 2018;378:1976. 91. Antonia SJ, Villegas A, Daniel D, et al. Durvalumab after chemoradiotherapy in stage III non-­ small-­cell lung cancer. N Engl J Med. 2017;377:1919–29. 92. Antonia SJ, Villegas A, Daniel D, et al. Overall survival with durvalumab versus placebo after chemoradiotherapy in stage III NSCLC: updated results from PACIFIC. J Thorac Oncol: Off Publ Int Assoc Study Lung Cancer. 2018;suppl:abstract PL02.1. 93. Gandhi L, Rodriguez-Abreu D, Gadgeel S, et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N Engl J Med. 2018;378:2078. 94. Paz-Ares LG, Luft A, Tafreshi A, et al. Phase 3 study of carboplatin-paclitaxel/nab-paclitaxel (Chemo) with or without pembrolizumab (Pembro) for patients (Pts) with metastatic squamous (Sq) non-small cell lung cancer (NSCLC). J Clin Oncol. 2018;36:abstr 105. 95. Socinski MA, Jotte RM, Cappuzzo F, et al. Overall survival (OS) analysis of IMpower150, a randomized Ph 3 study of atezolizumab (atezo) + chemotherapy (chemo) ± bevacizumab (bev) vs chemo + bev in 1L nonsquamous (NSQ) NSCLC. J Clin Oncol. 2018;36:abstr 9002. 96. Hellmann MD, Ciuleanu TE, Pluzanski A, et al. Nivolumab plus Ipilimumab in lung cancer with a high tumor mutational burden. N Engl J Med. 2018;378:2093.

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97. Li Q, Yuan D, Ma C, et  al. A new hope: the immunotherapy in small cell lung cancer. Neoplasma. 2016;63:342–50. 98. Reck M, Luft A, Szczesna A, et al. Phase III randomized trial of Ipilimumab plus etoposide and platinum versus placebo plus etoposide and platinum in extensive-stage small-cell lung cancer. J Clin Oncol. 2016;34:3740–8. 99. Ott PA, Elez E, Hiret S, et al. Pembrolizumab in patients with extensive-stage small-cell lung cancer: results from the phase Ib KEYNOTE-028 study. J Clin Oncol. 2017;35:3823–9. 100. Antonia SJ, Lopez-Martin JA, Bendell J, et al. Nivolumab alone and nivolumab plus ipilimumab in recurrent small-cell lung cancer (CheckMate 032): a multicentre, open-label, phase 1/2 trial. Lancet Oncol. 2016;17:883–95. 101. Liu SV, Mansfield AS, Szczesna A, et  al. IMpower 133: primary PFS, OS and safety in a PH1/3 study of 1L atezolizumab + carboplatin + etoposide in extensive-stage SCLC.  J Thorac Oncol: Off Publ Int Assoc Study Lung Cancer 2018;suppl:abstract. 02:7. 102. Michot JM, Bigenwald C, Champiat S, et al. Immune-related adverse events with immune checkpoint blockade: a comprehensive review. Eur J Cancer. 2016;54:139–48.

Chapter 3

Esophageal and Gastric Cancer Michael J. Jang and Joseph Chao

In 2012, gastric cancer was the fourth most common malignancy worldwide, and the third and fifth leading cause of cancer death in men and women, respectively [1]. Unfortunately, two-thirds of newly diagnosed gastric cancer is stage III or IV; only one-tenths is stage I. Thus, although the incidence of gastric cancer has decreased over the last few decades, mortality still remains very high. Most localized disease (up to stage III) is treated with multimodality therapy, which in approximately 40% of patients, can increase 5-year survival. First-line therapy for the majority of patients with gastric or gastroesophageal junction (GEJ) adenocarcinoma is chemotherapy with cisplatin and fluorouracil-based therapy. However, in advanced disease (which includes unresectable, recurrent, or metastatic disease), therapies are limited and cure is extremely rare. Studies have shown that the median overall survival (OS) is approximately 10 months in advanced gastric cancer. Our knowledge in treatment continues to expand every year, particularly in the field of targeted therapies. In this chapter, we will explore the progress and setbacks of the research in a variety of biomolecular targets.

HER2 Human epidermal growth factor receptor 2 (HER2, also known as ErbB-2) is a transmembrane tyrosine kinase (TK) receptor that is part of a four-member family known as the epidermal growth factor receptors (EGFRs). These receptors influence M. J. Jang Department of Internal Medicine, Huntington Memorial Hospital, Pasadena, CA, USA J. Chao (*) Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2020 R. Salgia (ed.), Oncology in the Precision Medicine Era, https://doi.org/10.1007/978-3-030-31471-2_3

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cell proliferation, apoptosis, adhesion, migration, and differentiation [2]. It is now known that HER2 can be a key driver of tumorigenesis in a subset of gastric cancers. Literature has estimated that HER2 is overexpressed in 7–34% of tumors [3]. Currently, HER2 is the only biomarker to guide addition of a biologic agent to first-­ line chemotherapy for advanced gastroesophageal adenocarcinoma [4]. While some studies, such as the ToGA trial, have established the efficacy of HER2-targeted therapy strategies, other follow-up trials to expand HER2 targeting options have been negative.

ToGA Trial The ToGA Trial (Trastuzumab for Gastric Cancer) was an international, multicenter, open-label, randomized controlled trial with 24 sites across Asia, Central and South America, and Europe [5]. It evaluated whether adding trastuzumab to standard chemotherapy improved outcomes. Trastuzumab is a monoclonal antibody that inhibits the HER2 receptor. Patients enrolled had advanced gastric or GEJ cancer with HER2 overexpression. HER2 overexpression status was confirmed by immunohistochemistry (IHC) or gene amplification by fluorescence in-situ hybridization (FISH). Results demonstrated that trastuzumab with chemotherapy prolonged median overall survival (OS) compared to chemotherapy alone (13.8  months vs 11.1 months, hazard ratio 0.74; 95% CI 0.60–0.91; p = 0.0046), with no increase in overall grade 3 or 4 adverse events. Due to the improved clinical outcomes seen in this trial, trastuzumab in combination with chemotherapy is now the standard first-­ line treatment for advanced, HER2-positive, gastric and gastroesophageal cancer.

 ER2 Testing and Clinical Decision-Making H in Gastroesophageal Adenocarcinoma A vital component of successful targeted therapy is establishing a specific predictive biomarker that will identify the subset of patients in which the therapy will be most efficacious. Part of the difficulty of defining positive HER2 status is that gastroesophageal adenocarcinoma demonstrates far greater intratumoral heterogeneity of HER2 expression compared to breast cancer [6]. A multidisciplinary panel of experts from the College of American Pathologists, American Society for Clinical Pathology, and American Society of Clinical Oncology developed 11 recommendations that have become the standardized guidelines for determining positive HER2 status in gastric and gastroesophageal adenocarcinoma. The panel’s recommendations included an algorithm for HER2 status testing in patients with advanced gastroesophageal adenocarcinoma. Patients should initially be tested for HER2 status with IHC. IHC is performed on a surgical specimen or a biopsy specimen. IHC results can range from 0 to 3+. The patient is considered to

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be IHC 0 if the surgical specimen either has no reactivity or has membranous reactivity in 25-fold amplification of MET, tissue from the distal esophagus had no evidence of MET amplification. In addition, molecular analysis of the ascitic tumor cells also demonstrated no evidence of MET amplification. Instead, these ascitic tumor cells demonstrated a > 25-fold EGFR amplification, even though there was no evidence of EGFR amplification in any of the primary tumors. Furthermore, all tissue samples, pre- and posttreatment, were found to share TP53 and SMAD4 mutations, which again likely suggested heterogeneity in gene amplification arising from a common clonal origin. Thus, Patient #5 demonstrated how intratumoral heterogeneity can lead to treatment failure [24]. The authors also discussed the rapidly emerging arena of “liquid biopsies” or assessing cell-free, circulating tumor DNA (ctDNA). CtDNA is shed into the blood-

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stream by tumor cells, and can possibly be used to assess clonal heterogeneity without biopsying and analyzing multiple lesions. In Patient #5, peripheral blood was collected prior to treatment and at timed intervals during treatment. Droplet digital polymerase chain reaction (ddPCR) was used to monitor the levels of specific molecular alterations in the ctDNA. Patient #5’s serial ctDNA analysis showed that the levels of TP53 and SMAD4 (the shared “truncal” mutations) initially decreased after starting treatment, which correlated with clinical improvement. The ctDNA analysis also showed an increase in the levels of TP53 and SMAD4 after disease progression. The serial ctDNA analysis also showed that MET levels were initially elevated, but decreased to near-normal levels after the first 2  months of therapy. Interestingly, the ctDNA analysis also found EGFR mutations in the first peripheral blood sample, which suggests that EGFR mutation clones were present from the beginning. The levels of the EGFR increased markedly throughout the serial ctDNA analysis. Seeing how ctDNA analysis was able to detect these mutations, the authors propose using liquid biopsies in patient care, as a biopsy of a single tumor lesion for molecular analysis may be inadequate to identify the full scope of molecular heterogeneity in a patient’s cancer. Maron et al. studied the possible mechanisms of resistance to anti-EGFR therapy in gastroesophageal adenocarcinoma. The authors identified eight patients who were prospectively screened with intention-to-treat using anti-EGFR therapy. Out of these eight patients, seven received at least one dose of treatment: three received first-line FOLFOX plus ABT-806 (a novel anti-EGFR monoclonal antibody), one patient received second-line FOLFIRI plus cetuximab, and three patients received third- or fourth-line cetuximab monotherapy. The last patient, who had concurrent MET and HER2 amplification, functionally declined significantly after FOLFOX therapy and subsequently enrolled into hospice. Their methods included pre- and posttreatment tumor next-generation sequencing (NGS), serial plasma circulating tumor DNA (ctDNA) NGS, and tumor IHC/Fluorescence in-situ hybridization (FISH)_ for EGFR. Multiple suspected mechanisms of resistance were identified with the pretreatment analysis. Five of the seven patients (Patients 1, 2, 5, 7, and 8) had intratumoral and/or intertumoral heterogeneity in EGFR amplification, demonstrated through tumor NGS and IHC/FISH. Other proposed mechanisms of resistance included coamplification of other oncogenic biomarkers, including HER2 in three patients (Patients 2, 4, and 8), NRAS in Patient 4, KRAS in Patient 6, MYC in four patients (Patient 1, 2, 4, and 6), and CCNE1 in two patients (Patients 4 and 6). The authors also identified a KRAS mutation in Patient 5 and GNAS mutation (another stimulatory G-protein alpha subunit) in Patient 6 [25]. Patient disease progression was divided into two groups: patients who had retained EGFR amplification and the patients who did not. Serial ctDNA had demonstrated that all seven patients initially had steep decrease in EGFR levels after starting anti-EGFR therapy. However, the serial ctDNA of Patients 1 and 3 saw a return and then an eventual increase in their EGFR amplification. Patient 1, who had retained EGFR amplification in tissue, demonstrated an acquired PTEN deletion and a de novo PIK3CA mutation (identified in ctDNA), which likely contributed to the mechanism of resistance. Interestingly, Patient 3 demonstrated persistent EGFR

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amplification by ctDNA, but his posttreatment biopsies (which included biopsies of new lung metastases and the residual primary tumor) were not EGFR amplified. In addition, his serial ctDNA analyses demonstrated that he had acquired BRAF, MET, and MYC coamplification. In contrast, patients who had not retained EGFR amplification (Patients 2, 4, and 5) were thought to have disease progression from non-­ EGFR amplifying tumor cells. Patient 2 pre-treatment had 50–50% coamplification of EGFR and HER2 but was subsequently found to have only residual HER2 amplification posttreatment. Likewise, Patient 4’s EGFR levels declined sharply after starting cetuximab, but had a concomitant rise in HER2 levels that correlated with disease progression. Patient 5’s pre-treatment primary tumor and initial ctDNA demonstrated MET and EGFR coamplification, but his liver metastases showed KRAS mutation and no EGFR amplification. After disease progression, posttreatment, the patient had increased KRAS-mutation levels in the ctDNA and absent EGFR amplification in the tissue and ctDNA. Patient 7 was different in that he never had systemic EGFR amplification detected in his metastatic biopsies or ctDNA. In addition, Patient 7 demonstrated significant EGFR-amplification heterogeneity, with only 10% of his primary tumor overexpressing EGFR.  Other than the intra-tumoral heterogeneity, no other baseline or acquired mechanisms of resistance have been identified. In conclusion, not only is there evidence that genomic heterogeneity is present from the beginning of diagnosis, but also there is increasing evidence that tumor genomic alterations change significantly during targeted agent therapy. This was further evaluated by Kim et al., who studied changes in genomics in lapatinib-treated patients. This was an open-label, single-arm, phase II study of a Korean cohort of advanced gastric cancer patients treated at an academic medical center. Thirty-two patients were enrolled. Patients had metastatic and/or recurrent gastric adenocarcinoma. Patients were HER2-positive in either their primary or metastatic tumors, which was confirmed with IHC/FISH. All patients had potentially resectable tumors even in the metastatic setting: e.g., those with liver metastases were limited to two to five liver metastases amenable to resection. Patients were treated with capecitabine, oxaliplatin, and lapatinib for eight 21-day  cycles. The primary endpoint was the proportion of complete responses. Biomarkers were studied serially through IHC and NGS of tumor and blood samples. Seven out of 32 patients had complete response. Fifteen out of thirty-two had partial responses. ORR was 68.6%. This study also used serial ctDNA sequencing to demonstrate that the gastric cancer had evolved and changed its genomic profile throughout the lapatinib treatment. The ctDNA analysis of the tumors that had progressed demonstrated emergences of other genomic aberrations such as MYC, EGFR, FGFR2, and MET amplifications [26]. In conclusion, there is increasing evidence that intra-patient and intertumoral heterogeneity in gastric and gastroesophageal cancers are drivers for resistance. Because it is not practical and likely not possible to detect heterogeneity adequately through biopsies, more research is needed to evaluate the role of IHC/ FISH, tumor NGS, and serial ctDNA in shaping therapeutic options.

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Concluding Remarks In the past 10 years, advanced gastroesophageal cancers have finally garnered precision medicine approaches with currently recommended testing of HER2, MSI, and PD-L1 tumor biomarkers. The latter two only recently emerged in the past 2 years, and PD-L1 determination still remains imperfect with questions remaining on optimal cutoff levels and if timing of tumor sampling matters with the dynamic nature of activation of immune pathways. Ongoing clinical trial efforts will provide some answers to these questions as immuno-oncology approaches are being increasingly integrated into earlier lines of treatment. To develop further biomarkers for novel molecularly targeted therapies, spatial and temporal intra-patient tumoral heterogeneity will need to be better understood in order to inhibit shifting oncogenic signaling pathways that occurs with clonal evolution and development of therapeutic resistance. Such composite testing strategies can hopefully be brought to fruition as newer technologies enabling single cell and serial ctDNA analyses are realized in the clinic.

References 1. American_Cancer_Society. Global cancer facts & figures. 3rd ed. Atlanta: American Cancer Society; 2015. 2. Gravalos C, Jimeno A. HER2 in gastric cancer: a new prognostic factor and a novel therapeutic target. Ann Oncol. 2008;19(9):1523–9. 3. Bartley AN, et al. HER2 testing and clinical decision making in gastroesophageal adenocarcinoma: guideline from the College of American Pathologists, American Society for Clinical Pathology, and the American Society of Clinical Oncology. J Clin Oncol. 2017;35(4):446–64. 4. Dong H, et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med. 2002;8(8):793–800. 5. Bang YJ, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet. 2010;376(9742):687–97. 6. Bartley AN, et  al. HER2 testing and clinical decision making in gastroesophageal adenocarcinoma: guideline summary from the College of American Pathologists, American Society for Clinical Pathology, and American Society of Clinical Oncology. J Oncol Pract. 2017;13(1):53–7. 7. Peddi PF, Hurvitz SA. Trastuzumab emtansine: the first targeted chemotherapy for treatment of breast cancer. Future Oncol. 2013;9(3):319–26. 8. Thuss-Patience PC, et  al. Trastuzumab emtansine versus taxane use for previously treated HER2-positive locally advanced or metastatic gastric or gastro-oesophageal junction ­adenocarcinoma (GATSBY): an international randomised, open-label, adaptive, phase 2/3 study. Lancet Oncol. 2017;18(5):640–53. 9. Kang YK, et al. Nivolumab in patients with advanced gastric or gastro-oesophageal junction cancer refractory to, or intolerant of, at least two previous chemotherapy regimens (ONO-­ 4538-­12, ATTRACTION-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017;390(10111):2461–71.

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10. Wainberg ZA, et al. LBA28_PRKEYNOTE-059 update: efficacy and safety of pembrolizumab alone or in combination with chemotherapy in patients with advanced gastric or gastroesophageal (G/GEJ) cancer. Ann Oncol. 2017;28(suppl_5):mdx440.020-mdx440.020. 11. Fuchs CS, et  al. Safety and efficacy of pembrolizumab monotherapy in patients with previously treated advanced gastric and gastroesophageal junction cancer: phase 2 clinical KEYNOTE-059 trial. JAMA Oncol. 2018;4(5):e180013. 12. Janjigian YY, et al. CheckMate-032 study: efficacy and safety of nivolumab and nivolumab plus ipilimumab in patients with metastatic esophagogastric cancer. J Clin Oncol. 2018;36(28):2836–44. 13. Shitara K, et al. Pembrolizumab versus paclitaxel for previously treated, advanced gastric or gastro-oesophageal junction cancer (KEYNOTE-061): a randomised, open-label, controlled, phase 3 trial. Lancet. 2018;392(10142):123–33. 14. Llosa NJ, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015;5(1):43. 15. Le DT, et  al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372(26):2509–20. 16. Comoglio PM, et al. Drug development of MET inhibitors: targeting oncogene addiction and expedience. Nat Rev Drug Discov. 2008;7(6):504–16. 17. Catenacci DVT, et  al. Rilotumumab plus epirubicin, cisplatin, and capecitabine as first-­ line therapy in advanced MET-positive gastric or gastro-oesophageal junction cancer (RILOMET-1): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2017;18(11):1467–82. 18. Shah MA, et  al. Effect of fluorouracil, leucovorin, and oxaliplatin with or without onartuzumab in HER2-negative, MET-positive gastroesophageal adenocarcinoma: the METGastric randomized clinical trial. JAMA Oncol. 2017;3(5):620–7. 19. Kang YK, et al. A phase II trial of a selective c-Met inhibitor tivantinib (ARQ 197) monotherapy as a second- or third-line therapy in the patients with metastatic gastric cancer. Investig New Drugs. 2014;32(2):355–61. 20. Kim M, et al. EGFR in gastric carcinomas: prognostic significance of protein overexpression and high gene copy number. Histopathology. 2008;52(6):738–46. 21. Lordick F, et al. Capecitabine and cisplatin with or without cetuximab for patients with previously untreated advanced gastric cancer (EXPAND): a randomised, open-label phase 3 trial. Lancet Oncol. 2013;14(6):490–9. 22. Waddell T, et al. Epirubicin, oxaliplatin, and capecitabine with or without panitumumab for patients with previously untreated advanced oesophagogastric cancer (REAL3): a randomised, open-label phase 3 trial. Lancet Oncol. 2013;14(6):481–9. 23. Pectasides E, et al. Genomic heterogeneity as a barrier to precision medicine in gastroesophageal adenocarcinoma. Cancer Discov. 2018;8(1):37–48. 24. Kwak EL, et al. Molecular heterogeneity and receptor coamplification drive resistance to targeted therapy in MET-amplified esophagogastric cancer. Cancer Discov. 2015;5(12):1271–81. 25. Maron SB, et al. Targeted therapies for targeted populations: anti-EGFR treatment for EGFR-­ amplified gastroesophageal adenocarcinoma. Cancer Discov. 2018;8(6):696–713. 26. Kim ST, et  al. Impact of genomic alterations on lapatinib treatment outcome and cell-free genomic landscape during HER2 therapy in HER2+ gastric cancer patients. Ann Oncol. 2018;29(4):1037–48.

Chapter 4

Sarcomas An Ngoc Nhu Uche and Warren A. Chow

Cancer treatment is rapidly evolving toward personalized medicine, which accounts for a person’s genes, proteins, and/or environment to treat their disease. Significant advancements have been made in the treatment of non-small cell lung cancer and breast cancer, where therapy is now often based on an individualized biomarker-­ driven approach. However, this targeted approach has not yet become a reality for soft-tissue sarcomas (STS) due to its low incidence and high level of histopathological heterogeneity. STS accounts for only about 1% of all adult malignancies and comprises more than 50 different histological subtypes. Except for gastrointestinal stromal tumor (GIST), and other rarer STS, the use of targeted therapies in STS is still limited. Immunotherapy remains investigational in STS. In this review, we aim to describe the current treatments of STS based on subtype and advancements in molecular diagnostics.

Targeted Therapies in the Treatment of Advanced STS For the past several decades, cytotoxic chemotherapy remained the mainstay for patients with advanced STS. The anthracycline doxorubicin is the most commonly used agent with a response rate (RR) of 12–24% and an associated median overall survival (OS) of only 12 to 18 months [1]. Attempts to improve OS by combining

A. N. N. Uche Department of Hematology and Oncology, Harbor-UCLA Medical Center, Torrance, CA, USA W. A. Chow (*) Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2020 R. Salgia (ed.), Oncology in the Precision Medicine Era, https://doi.org/10.1007/978-3-030-31471-2_4

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doxorubicin with ifosfamide, another cytotoxic agent with activity in advanced or metastatic STS, failed to show an improvement in OS [2]. Platelet-derived growth factor receptor alpha (PDGFR-A) and its ligand are co-­ expressed in many types of cancer, including STS. They are involved in stimulating growth and regulating stromal-derived fibroblasts and angiogenesis, which are important pathways in sarcomagenesis. Olaratumab, a recombinant human immunoglobulin G subclass 1 (IgG1) monoclonal antibody (MoAb) that specifically binds PDGFR-A, has demonstrated antitumor activity in human sarcoma xenograft models in preclinical studies (Table 4.1). This led to a phase 1b/randomized phase 2 Table 4.1  Selected agents and their targets in STS Agents Targets Targeted agents Olaratumab PDGFR-α Pazopanib Imatinib

Sunitinib

Regorafenib

Ponatinib Crenolanib Cediranib Sorafenib

Bevacizumab Chemotherapy Trabectedin

Eribulin

VEGFR 1–3, PDGFR-­A/B, and c-kit BCR-ABL1, KIT, PDGFRA/B

KIT, PDGFRA/B, VEGFR 1–3, FLT3, RET, CSF-1 cKIT, PDGFRA/B, VEGFR 2–3, BRAF, RET BCR-ABL, KIT PDGFRA/B VEGFR 1–3 BRAF, VEGFR 1–3, PDGFRB, FLT3, and KIT VEGF Alkylating agent that binds the minor groove of DNA Microtubules inhibitor

Antihormonal therapy Letrozole Aromatase inhibitor Immunotherapy Adoptive T cell Against NY-NEO-1 therapy antigen

Sarcoma subtypes Approved for use in combination with doxorubicin for metastatic and surgically incurable STS Approved for nonliposarcomatous STS refractory to anthracycline Approved for use as first-line treatment for advanced and metastatic GIST and adjuvant setting for GIST with high-risk recurrence Approved for use in DFSP Approved for use as second-line treatment in GIST Investigational with potential benefit in ASPS Approved for use as second-line treatment GIST

Investigational in cKIT exon 17 and PDGFRA D842V-mutated GIST Investigational in PDGFRA D842V-mutated GIST Investigational in ASPS Investigational in AS

Investigational in AS Approved for use in previously treated advanced or metastatic LPS and LMS Approved for use in previously treated advanced or metastatic LPS Investigational in uterine LMS with ER/PR positivity and low burden disease Investigational in SS

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study, in which 133 patients with metastatic STS were randomly assigned to receive olaratumab in combination with doxorubicin versus doxorubicin alone [3]. The trial showed no statistically significant increase in progression-free survival (PFS). However, OS was significantly higher in the combination group compared to the standard therapy group with a striking, almost doubling of the median OS (26.5 vs. 14.7 months, HR 0.46, p = 0.0003). In October 2016, olaratumab received an accelerated approval by the US Food and Drug Administration (FDA) for use in combination with doxorubicin in the treatment of metastatic or surgically incurable STS, making it the first new therapy for use in first-line treatment of STS in the past 40 years (Figs. 4.1, 4.2, and 4.3). However, the confirmatory phase 3 trial of doxorubicin with, or without olaratumab, in the first-line setting for patients with

Fig. 4.1 Treatment response to olaratumab. Coronal computed tomography scans of a patient with recurrent metastatic myxoid liposarcoma showing large mediastinal mass before treatment with olaratumab

Fig. 4.2 Coronal computed tomography scan of the same patient showing marked reduction in size of mediastinal mass after treatment with six cycles of doxorubicin and olaratumab

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Fig. 4.3 Coronal computed tomography of the same patient showing ongoing response to olaratumab with continued reduction in size of mediastinal mass after 22 cycles of olaratumab

advanced or metastatic soft tissue sarcoma did not confirm the clinical benefits of adding olaratumab to standard of care doxorubicin reported in the phase 1b/2 trial [3]. There was no difference in the median OS in the overall population (20.4 vs 19.7 months for doxorubicin plus olaratumab vs. doxorubicin plus placebo, respectively, HR = 1.05). Median PFS and overall response rate (ORR) were reduced in patients who received doxorubin plus olaratumab, compared to doxorubicin plus placebo (PFS 5.4 vs. 6.8 months, respectively; ORR 14% vs. 18.3%, respectively) [4]. Although full data from the trial are not yet published, the FDA has recommended against starting olaratumab plus doxorubicin for new patients, unless it is in the context of a clinical trial. Patients who are currently receiving olaratumab, in consultation with their treating physician, may continue therapy if they are receiving clinical benefit. Vascular endothelial growth factor (VEGF)/VEGFR (receptor) signaling is necessary for neoangiogenesis during tumor development. VEGF is expressed in many types of STS, with increased expression being associated with higher malignancy grade and higher metastatic rate [5, 6]. This pathway can be targeted using either monoclonal antibody (mAb) targeting VEGF (bevacizumab), or small molecules that inhibit the tyrosine kinase activity of the receptor. The randomized, phase 3 PALETTE trial investigated the use of pazopanib, a multi-targeted tyrosine kinase inhibitor (TKI) (VEGFR 1–3, PDGFR-A/B, and KIT) versus placebo in patients with metastatic, nonlipomatous STS after failure of standard chemotherapy [7]. This trial demonstrated that pazopanib significantly improved median PFS from 1.6 to 4.6  months (HR 0.31, 95% CI 0.24–0.40; p