Deciphering the importance of biomarkers in colorectal and urothelial cancers in the era of precision oncology

1 ! ! ! TESE DE DOUTORAMENTO DECIPHERING THE IMPORTANCE OF BIOMARKERS IN COLORECTAL AND UROTHELIAL CANCERS IN THE ERA OF PRECISION ONCOLOGY Juan Ruiz Bañobre ESCOLA DE DOUTORAMENTO INTERNACIONAL PROGRAMA DE DOUTORAMENTO EN INVESTIGACIÓN CLÍNICA EN MEDICINA SANTIAGO DE COMPOSTELA 2021 ! 2 ! ! ! 3 ! ! ! D ECLARACIÓN DEL AUTOR DE LA TESIS DECIPHERING THE IMPORTANCE OF BIOMARKERS IN COLORECTAL AND UROTHELIAL CANCERS IN THE ERA OF PRECISION ONCOLOGY D. Juan Ruiz Bañobre Presento mi tesis, siguiendo el procedimiento adecuado al Reglamento, y declaro que: 1) La tesis abarca los resultados de la elaboración de mi trabajo. 2) En su caso, en la tesis se hace referencia a las colaboraciones que tuvo este trabajo. 3) La tesis es la versión definitiva presentada para su defensa y coincide con la versión enviada en formato electrónico. 4) Confirmo que la tesis no incurre en ningún tipo de plagio de otros autores ni de trabajos presentados por mí para la obtención de otros títulos. En Santiago de Compostela, 09 de marzo del 2021 Fdo. Juan Ruiz Bañobre ! 4 ! ! ! 5 ! ! ! ! DECLARACIÓN RESPONSABLE AUTOR TESIS COMPENDIO DE PUBLICACIONES DECIPHERING THE IMPORTANCE OF BIOMARKERS IN COLORECTAL AND UROTHELIAL CANCERS IN THE ERA OF PRECISION ONCOLOGY D. Juan Ruiz Bañobre MANIFIESTO QUE: • Presento mi tesis por compendio de publicaciones de las que soy coautor. • Ante la imposibilidad de recoger la firma de los coautores, que a continuación se señalan, de los artículos/publicaciones integrados en mi tesis, Da. Aurea Molina Díaz Da. Ana Medina Colmenero Da. Lucía Santomé Da. Natalia Fernández Núñez D. Ovidio Fernández Calvo Da. Noelia García Cid Da. Roshni Roy ! DECLARO RESPONSABLEMENTE: • Que la utilización de estas publicaciones /artículos cumplen con los requisitos establecidos en el artículo 37 c) del Reglamento de Estudios de Doctorado de la USC. En Santiago de Compostela, 09 de marzo del 2021 Fdo. Juan Ruiz Bañobre! D. Sergio Vázquez D. Martín Lázaro Quintela D. Raju Kandimalla Da. Miren Alustiza Fernández D. Óscar Murcia D. Rodrigo Jover D. Miguel Pera D. Francesc Balaguer ! ! 6 ! ! ! ! Resumo ! ! 7 ! ! ! AUTORIZACIÓN DEL DIRECTOR/TUTOR DE LA TESIS DECIPHERING THE IMPORTANCE OF BIOMARKERS IN COLORECTAL AND UROTHELIAL CANCERS IN THE ERA OF PRECISION ONCOLOGY D. Rafael López López Dª. María de la Fuente Freire D. Ángel Díaz Lagares INFORMAN: Que la presente tesis, se corresponde con el trabajo realizado por D. Juan Ruiz Bañobre, bajo nuestra dirección, y autorizamos su presentación, considerando que reúne los requisitos exigidos en el Reglamento de Estudios de Doctorado de la USC, y que como directores de esta no incurre en las causas de abstención establecidas en la Ley 40/2015. De acuerdo con lo indicado en el Reglamento de Estudios de Doctorado, declaramos también que la presente tesis doctoral es idónea para ser defendida en base a la modalidad de COMPENDIO DE PUBLICACIONES , en las que la participación del doctorando fue decisiva para su elaboración y las publicaciones se ajustan al Plan de Investigación. En Santiago de Compostela, 09 de marzo del 2021 Fdo. Rafael López López (Tutor y Director) Fdo. María de la Fuente Freire Fdo. Ángel Díaz Lagares (Directora) (Director) JUAN RUIZ BAÑOBRE ! 8 ! ! ! Resumo ! ! 9 ! ! ! CONFLICT OF INTEREST One of the studies of this thesis was partially supported by a 2015 Merck Serono Research Grant. Juan Ruiz Bañobre is supported by a Río Hortega fellowship from the Institute of Health Carlos III (CM19/00087). Juan Ruiz Bañobre´s conflict of interest outside of the present thesis: Travel, accommodations, and expenses: Bristol-Myers Squibb, Merck Sharp & Dohme, Ipsen, PharmaMar, and Roche. Honoraria for educational activities: Roche. Honoraria for consultancies: Boehringer Ingelheim. Institutional research funding: Roche. JUAN RUIZ BAÑOBRE ! 16 ! ! ! Resumo ! ! 17 ! ! ! Impossible is nothing – Adidas, 2004 JUAN RUIZ BAÑOBRE ! 18 ! ! ! Resumo ! ! 19 ! ! ! AGRADECIMIENTOS A mamá, ejemplo e inspiración constante. A papá, mi referente. Bonhomía y responsabilidad. A abuelo. Siempre en mi recuerdo. A Pablo y Martín. Presente y futuro. A María José (María, mi María). A Lau, Lauchi, Lauriña (y sus variantes). A Fiona, compañera fiel. A mi familia. A mi familia astur. A todos mis amigos. A todos mis compañeros del Servicio de Oncología Médica, grandes profesionales y mejores personas (si cabe). A todo el equipo del grupo ONCOMET. A todos los clínicos e investigadores con los que colaboro. A Rafael López, por introducirme y guiarme en la investigación traslacional en oncología. A los pacientes y a sus familias. ! ! JUAN RUIZ BAÑOBRE ! 20 ! ! ! Resumo ! ! 21 ! ! ! TABLE OF CONTENTS RESUMO ....................................................................................................................... 25 1. INTRODUCTION .................................................................................................... 33 1.1. HISTORY OF ONCOLOGY .................................................................................... 33 1.2. PRECISION ONCOLOGY: BIOMARKERS AND ENDPOINTS ................................. 34 1.2.1. PRECISION ONCOLOGY GLOSSARY ........................................................ 34 1.2.1.1. Precision Medicine ................................................................................ 34 1.2.1.2. Biomarker ........................................................................................... 35 1.2.1.2.1. Types of Biomarkers ..................................................................... 36 1.2.1.3. Endpoints in Precision Oncology ............................................................. 38 1.2.1.3.1. Types of Endpoints ....................................................................... 38 1.2.1.3.2. Principal Endpoints in Oncology ..................................................... 39 2. HYPOTHESES AND OBJECTIVES ..................................................................... 45 3. MATERIAL AND METHODS ............................................................................... 49 4. RESULTS .................................................................................................................. 53 4.1. COLORECTAL CANCER ....................................................................................... 53 4.2. UROTHELIAL CARCINOMA ............................................................................... 173 4.3. OTHER STUDIES CONDUCTED DURING THE DOCTORAL THESIS PERIOD ..... 207 5. GENERAL DISCUSSION ..................................................................................... 211 6. GENERAL CONCLUSIONS ................................................................................ 225 7. REFERENCES ....................................................................................................... 229 JUAN RUIZ BAÑOBRE ! 22 ! ! ! Resumo ! ! 23 ! ! ! RESUMO JUAN RUIZ BAÑOBRE ! 24 ! ! ! Resumo ! ! 25 ! ! ! RESUMO ¿Como un desastre ocorrido durante a Segunda Guerra Mundial provocou un avance no tratamento do cancro? Un derramo accidental de mostaza nitroxenada sobre as tropas dun barco bombardeado no porto de Bari (Italia) durante a Segunda Guerra Mundial foi determinante na historia da terapia contra o cancro. A observación de que tanto a medula ósea como os ganglios linfáticos destes homes expostos ao gas mostaza presentaban unha marcada aplasia, fomentou un interese crecente por examinar os potenciais efectos terapéuticos destes produtos químicos sobre os linfomas. Tras confirmar a capacidade da mostaza nitroxenada para inducir remisións tumorais, o seu uso como tratamento do linfoma estendeuse rapidamente. Ademais, a identificación de análogos do ácido fólico como resultado da investigación nutricional levada a cabo antes e durante a Segunda Guerra Mundial posibilitou o descubrimento do metotrexato como unha nova opción terapéutica para nenos con leucemia. Curiosamente, o metotrexato foi o primeiro medicamento capaz de curar un tumor sólido non hematolóxico, o coriocarcinoma de placenta. Ata ese momento, a cirurxía e a radioterapia dominaban o campo da terapia contra o cancro, logrando unha taxa de curación despois de tratamentos locais cada vez máis radicais cunha meseta ao redor do 33%, o que fixo pensar na existencia de micrometástases. Neste contexto nace o concepto de quimioterapia adxuvante, que en combinación coa cirurxía e/ou radioterapia podería abordar a presenza das devanditas micrometástases. O cancro de mama foi o primeiro tipo de tumor no que se investigou a terapia adxuvante. O éxito dos dous primeiros ensaios clínicos realizados provocaron un rebulir de estudos de adxuvancia en cancro de mama e outros tipos de tumores, incluído o cancro colorrectal. O uso de terapia adxuvante contribuíu a un descenso significativo da mortalidade por cancro, especialmente importante para os tumores de mama e colon. Non obstante, houbo outro feito determinante para cambiar o panorama do desenvolvemento de medicamentos, a chegada da bioloxía molecular. Como consecuencia dunha mellor comprensión das alteracións moleculares nas células cancerosas, as probas aleatorias de fármacos substituíronse gradualmente por unha selección máis racional de fármacos dirixidos contra dianas moleculares específicas. A era da quimioterapia daba paso á era da terapia dirixida. Retrospectivamente, na historia da oncoloxía hai varios precedentes que se poden considerar os primeiros exemplos de terapia dirixida. Este é o caso da terapia hormonal para o cancro de próstata ou incluso máis recentemente, a síntese de 5-fluorouracilo, un análogo do uracilo que exerce os seus efectos anticancerosos a través da inhibición da timidilato sintasa e a interrupción da síntese do ARN. Máis tarde, o descubrimento de novos oncoxenes, xenes supresores de tumores e vías de sinalización esenciais para a bioloxía do cancro levou á identificación de novas dianas farmacolóxicas que actualmente centran o foco do desenvolvemento de medicamentos antineoplásicos. Todos estes logros foron posibles debido aos extraordinarios avances tecnolóxicos e computacionais na secuenciación de ácidos nucleicos que, xunto coa bioloxía experimental, facilitaron unha comprensión máis detallada e completa da bioloxía molecular dos tumores. JUAN RUIZ BAÑOBRE ! 32 ! ! ! Introduction ! ! 33 ! ! ! 1. INTRODUCTION 1.1. HISTORY OF ONCOLOGY How a World War II (WWII) disaster led to a cancer treatment breakthrough? An accidental spill of sulfur mustards on troops from a bombed ship in Bari Harbor (Italy) during WWII was determinant in the history of cancer therapy1,2. Both, bone marrow and lymph nodes of those men exposed to the mustard gas were markedly depleted. This observation fostered a growing interest to examine the potential therapeutic effects of these chemicals on lymphomas, and after confirming the capacity of nitrogen mustard for induced marked remissions, its use as lymphoma treatment spread rapidly3. Moreover, the identification of folic acid analogues as a result of nutritional research before and during WWII led to the discovery of methotrexate, a new therapeutic option for children with leukemia4. Interestingly, methotrexate was the first drug able to cure a solid non-hematological tumor, the choriocarcinoma of the placenta5. Until that moment, surgery and radiotherapy dominated the field of cancer therapy, achieving a cure rate after ever more radical local treatments with a plateau at about 33%, which was blamed on the presence of heretofore-unappreciated micrometastases6,7. In this context borns the concept of adjuvant chemotherapy, which in combination with surgery and/or radiotherapy could deal with the presence of micrometastases. Breast cancer was the first tumor type where adjuvant therapy was investigated. The successful results of the first two clinical trials conducted set off a cascade of adjuvant studies in breast cancer8,9 and other tumor types, including colorectal cancer (CRC). The use of adjuvant therapy has contributed to a significant decline in cancer mortality, especially important for breast and colorectal cancers7. However, something else happened that contribute to change the landscape of drug development, the arrival of molecular biology. As a consequence of a better understanding of molecular aberrations in cancer cells, random drug testing was gradually replaced by screening against specific critical molecular targets. The chemotherapy era was transitioning to the age of targeted therapy7. In retrospect, in the history of oncology, there are various precedents that can be considered the very first examples of targeted therapy. This is the case of hormonal therapy for prostate cancer10 or even more recently, the synthesis of 5-fluorouracil11, an analogue of uracil which exerts its anticancer effects through the inhibition of thymidylate synthase and the disruption of RNA synthesis. Later, the discovery of new oncogenes, tumor suppressor genes, and signaling pathways essential for cancer biology led to the identification of new drug targets that are currently the focus of cancer drug development12. All these achievements were possible due to the extraordinary technological and computational advances in nucleic acid sequencing which together with the experimental biology, facilitated a more detailed and comprehensive understanding of molecular biology13. The concept of targeted therapy, also known as molecularly targeted therapy, encompass many different therapeutic strategies where drugs are designed to tackle tumor cells by interfering with specific molecules of cancer cells, or even more recently, to unleash the attack of the immune system against cancer. Targeted therapy includes mainly monoclonal antibodies, tyrosine kinase inhibitors, mTOR inhibitors, proteasome inhibitors, hormonal therapies, and more recently JUAN RUIZ BAÑOBRE ! 34 ! ! ! antibody-drug conjugates and KRAS inhibitors14–17. All these advances in the understanding of molecular biology and drug development have led to a new era where the primary goal is to fight cancer cells with more precision, in a more personalized way, and potentially with fewer side effects. This is the precision oncology era. 1.2. PRECISION ONCOLOGY: BIOMARKERS AND ENDPOINTS In the era of precision oncology, biomarkers have become increasingly important given their relevance in the decision to implement, or not, effective therapeutic strategies that may have substantial toxicities18. In this context, effective, and concise communication is essential for efficient translation of promising research discoveries into approved clinical realities. Unclear definitions and inconsistent use of key terms can hinder the evaluation and interpretation of scientific evidence and may pose significant hurdles to advance of medical product development19. Moreover, recognizing drug development as a timeand cost-consuming endeavor, any efficiency that can be realized during the development and regulatory processes will speed access of approved therapies and devices to patients20. The expected positive impact of biomarkers specifically on drug development is substantial, and coordinated efforts to identify biomarkers are a focus of intensive research and debate19–21. In this line, there have been several efforts to standardize the criteria for biomarker research22–24. In 2009, the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative proposed three semantics to determine if a genetic test should be used to manage care25: analytical validity, clinical validity, and clinical utility, which have been adopted by both the National Academy of Medicine (NAM)26 and ASCO for deliberations specifically regarding biomarkers in oncology26,27. In 2015, the Food and Drug Administration (FDA)-National Institutes of Health Joint Leadership Council identified the harmonization of terms used in translational science and medical product development as a priority need, with a focus on terms related to study endpoints and biomarkers. Working together with the goals of improving communication, aligning expectations, and improving scientific understanding, the two agencies developed the Biomarkers, EndpointS, and other Tools (BEST) Resource19. The first phase of BEST comprises a glossary that clarifies important definitions and describes some of the hierarchical relationships, connections, and dependencies among the terms it contains19. 1.2.1. PRECISION ONCOLOGY GLOSSARY To better understand the concept behind precision medicine in oncology, besides to set an understandable definition of the term itself, it is clear the necessity of describing concepts and terminologies specifically related to this area that are often poorly defined. For this purpose, this introduction presents some of the most important definitions collected in the European Society for Medical Oncology (ESMO) Precision Medicine and BEST glossaries19,28. 1.2.1.1. Precision Medicine Precision Medicine is defined as a healthcare approach with the primary aim of identifying which interventions are likely to be of most benefit to which patients based upon the features of the individual and their disease. In cancer, the term usually refers to the use of therapeutics that are expected to confer benefit to a subset of patients whose cancer displays specific molecular or cellular features (most commonly genomic changes and gene or protein expression patterns). Nevertheless, the term also includes the use of prognostic markers, Introduction ! ! 35 ! ! ! predictors of toxicities and any parameter such as environmental and lifestyle factors that leads to treatment tailoring. Characterization approaches in the future are expected to encompass a wider range of technologies such as functional imaging or global phosphoprotein analyses28. In short, it is an approach to patient care that is based on the idea that one person´s disease is not necessarily exactly the same in someone else who seemingly has the same disease. 1.2.1.2. Biomarker A biomarker is defined as a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention, including therapeutic interventions. Biomarkers may include molecular, histologic, radiographic, or physiologic characteristics19. A biomarker is not a measure of how an individual feels, functions, or survives. This succinct but comprehensive description intended to correctly identify the biomarker, its biologic plausibility, and its measurement method19. While not exhaustive, these key concepts included in the biomarker description bring important details to evaluate information from multiple sources and set a homogeneous framework: Biomarker Identity. The name of the biomarker includes the specific analyte, anatomic feature, or physiological characteristic that is measured. If applicable, the unique identifier for the biomarker and the commonly used acronym are useful information to ensure that two or more resources are referring to the same analyte. The specific source for the biomarker (for example plasma, serum, urine, tumor tissue, or computed tomography scan, among others) provides important context and determines not only measurement reference ranges but also the biomarker type itself19. Biologic Plausibility. A brief summary of the biological, physiological, or pathological pathway for the association of the biomarker with the disease or condition of interest provides a contextual linkage between a biomarker and its intended use. In addition, this information helps to delineate how multiple biomarkers may interplay as part of a common use19. Measurement Method. The measurement method that will be used to quantify the biomarker and the units of quantification is critical information when comparing information from independent platforms. This information is helpful throughout biomarker development, including early discovery. Sufficient detail should be included to facilitate the interpretation of the results across multiple resources19. On the other hand, in accordance with the EGAPP Initiative to determine if a test should be used to the decision-making process, there are three important biomarker features to consider in advance: Analytical validity Analytical validity of a biomarker test is defined by EGAPP as its ability to accurately and reliably measure the variable of interest in the clinical laboratory, and in specimens representative of the population of interest25,29. Analytic validity includes analytic sensitivity, analytic specificity, reliability, and assay robustness29. All these elements of analytic validity are themselves integral elements in the assessment of clinical validity29,30. Many evidence-based processes assume that evaluating clinical validity will address any analytic problems, and do not formally consider analytic validity31. As technologies are rapidly evolving, and validation data are limited in some circumstances, it is important to consider that review of analytic validity can to determine whether clinical validity can be improved by addressing test performance in new JUAN RUIZ BAÑOBRE ! 36 ! ! ! scenarios25. Clinical validity Clinical validity of a biomarker test is defined by EGAPP as its ability to accurately and reliably predict the clinically defined disorder or phenotype of interest. Clinical validity includes clinical sensitivity and specificity, and predictive values of positive and negative tests that take into account the disorder prevalence25. In short, the term clinical validity implies that the performance of a biomarker is acceptable for its intended purpose, and identifies different portions of one population, each of which has significant differences from the other18,19. Clinical utility Clinical utility defines the balance of benefits and harms associated with the use of the biomarker test in practice, including improvement in measureable clinical outcomes, and usefulness and added value in clinical management and decision-making compared with not using the biomarker25. Whereas it is unlikely that clinical utility would exist if the biomarker does not have clinical validity, clinical validity does not imply clinical utility18. Clinical utility includes effectiveness (utility in real clinical scenario), and the net benefit. Frequently, it also involves assessment of efficacy (evidence of utility in controlled settings like a clinical trial). A clear definition of the clinical scenario is of major importance, as the performance characteristics of a given test may vary depending on the intended use of the test. 1.2.1.2.1. Types of Biomarkers Although BEST glossary establishes several biomarker categories (susceptibility/risk biomarker, diagnostic biomarker, prognostic biomarker, predictive biomarker, monitoring biomarker, pharmacodynamic/response biomarker, safety biomarker)19, for the purpose of this thesis only predictive and prognostic biomarkers will be discussed in detail. Predictive Biomarker A predictive biomarker is that one used to identify individuals who are more likely than similar individuals without the biomarker to experience a favorable or unfavorable effect from exposure to a medical product or an environmental agent19. The effect could be a symptomatic benefit, improved survival, or a toxicity or adverse event19. A common example of use of a predictive biomarker in medical product development is predictive enrichment of the study population for a randomized controlled clinical trial of an investigational therapy, in which the biomarker is used either to select patients for participation or to stratify patients into biomarker positive and biomarker negative groups, with the primary endpoint being the effect in the biomarker positive group. If the biomarker is in fact predictive of a favorable outcome, then the effect of the investigational therapy compared to a control therapy (or placebo) will be greater (or present at all) in patients with the biomarker or some level of the biomarker. The notion of a predictive biomarker applies to a wide variety of interventions, including drugs, biologics, medical devices or procedures, and behavioral or dietary modifications for treatment or prevention of diseases or conditions19. The utility of predictive biomarkers is not limited to a clinical trial setting, as these biomarkers can also assist in informing patient care decisions, such as determining who might benefit from a particular treatment or selecting among multiple interventions. In the latter Introduction ! ! 37 ! ! ! situation, evidence that a biomarker predicts the comparative effectiveness of an intervention should be accompanied by specification of the alternative interventions involved in the comparison19. Predictive biomarkers for effects of interventions may be characteristics of the individual’s biological constitution (host characteristics) or features of the disease process or other medical condition. Biomarkers representing host characteristics are present irrespective of the individual’s disease or medical condition status, such as germline DNA, human leukocyte antigen (HLA) type or dihydropyrimidine dehydrogenase phenotype, renal or hepatic function, or metabolic characteristics. Predictive biomarkers for drugs are often chosen initially based on the mechanism of action of the drug and understanding of pathophysiology, but they could also be identified empirically based on previous studies. Understanding the impact on outcome of both host and disease or condition characteristics is important for efficient development and optimal application of interventions19. Establishing that a biomarker is predictive for an intervention’s effect generally requires a comparison of the intervention to a control treatment in individuals with and without the biomarker, usually in randomized trials. Although studying only biomarker positive patients would establish effectiveness of a particular intervention it does not specifically demonstrate the predictive role of the biomarker. It is therefore generally appropriate to stratify patients in the randomized trial by presence or absence of the biomarker (if dichotomous). Randomization to treatment and control groups is usually important because demonstrating that individuals who are positive for a biomarker and receive an investigational therapy experience a better outcome than those who receive the same therapy but are negative for the biomarker does not establish that the biomarker is predictive. Differences in outcome associated with the biomarker could be due to prognostic abilities of the biomarker and may be present irrespective of the therapy received. The greater differences between treatment and control in the biomarker positive compared to biomarker negative groups are what establish the biomarker as predictive19. Studies designed to evaluate a predictive biomarker should usually include patients with a range of biomarker values (or positive and negative for binary biomarkers). Sometimes there is sufficient prior evidence to strongly suggest that an investigational therapy will not be effective (or could even be harmful) in a certain subgroup of individuals defined by a biomarker; these circumstances may require excluding patients who are negative for the biomarker from trials of the investigational therapy. When a biomarker identifies a subgroup of patients who will benefit most from an investigational therapy, enrichment of a trial with individuals from that subgroup will provide increased statistical power for detection of the (larger) effect of that therapy; use of such an enrichment strategy will also affect the intended population to receive the therapy after its regulatory approval19. Prognostic Biomarker A prognostic biomarker is that one used to identify an increased (or decreased) likelihood of a future clinical event in an identified population19. Prognostic biomarkers are measured at a defined baseline, which may include a background treatment. Many familiar examples of prognostic biomarkers occur in clinical contexts where an individual is diagnosed with a disease or condition and there is interest in assessing the likelihood of a future clinical event. Examples of future events include death, disease progression, disease recurrence, or development of a new medical condition. In oncology, biomarkers such as tumor size, number of lymph nodes positive JUAN RUIZ BAÑOBRE ! 38 ! ! ! for tumor cells, and presence of metastasis have traditionally been used to indicate prognosis. Increasingly, molecular indicators or signatures measured on tumors are being used in lieu of, or in addition to, these clinicopathologic characteristics. The prognostic biomarker’s association with outcome is present without reference to different interventions. However, the presence or strength of a prognostic association may vary depending on the specific clinical setting and particular endpoint of interest, so it is important that prognostic biomarkers be described in the proper context19. Prognostic biomarkers are often used as eligibility criteria in clinical trials to identify patients who are more likely to have clinical events or disease progression. Thus, they are widely used as enrichment factors in drug development. Many clinical trials of medical interventions have as their endpoint either an event rate or time-to-event. The statistical power for a time-toevent endpoint to assess treatment effect in a controlled clinical trial is driven by the planned effect size and the planned number of events. Enrichment with patients who have a higher likelihood of experiencing an event will therefore increase statistical power. In a treatment setting, prognostic biomarkers can contribute to decisions about whether or how aggressively to intervene with the treatment19. Prognostic versus Predictive Biomarker Complexity A variety of factors influence a patient clinical outcome, including intrinsic characteristics of the patient, disease, or medical condition, and the effects of any treatments that the patient receives. Prognostic biomarkers and predictive biomarkers cannot generally be distinguished when only patients who have received a particular therapy are studied. Some biomarkers are both prognostic and predictive. Prognostic biomarkers are often identified from observational data and are regularly used to identify patients more likely to have a particular outcome19. To identify a predictive biomarker, as it was detailed in the previous specific section, there generally should be a comparison of a treatment to a control in patients with and without the biomarker. However, there are circumstances in which preclinical and early clinical data provide such compelling evidence that a new treatment will not work in patients without the biomarker that definitive clinical trials are performed only in populations enriched for the putative predictive biomarker19. Moreover, this prognostic-predictive complexity is also partly driven by the search for more effective therapies for patients who have a poor prognosis with standard treatments32. In this manner, genetic alterations classically associated with a poor prognosis in some cancer types, are now targets of some of the most promising targeted therapies and consequently, predictive biomarkers in their respective scenarios. 1.2.1.3. Endpoints in Precision Oncology To fully understand the nature of precision oncology in general, and biomarkers in particular, it seems essential to properly set the concept of endpoint and related terminology. 1.2.1.3.1. Types of Endpoints Endpoint An endpoint is a precisely defined variable intended to reflect an outcome of interest that is statistically analyzed to address a particular research question. A precise definition of an endpoint typically specifies the type of assessments made, the timing of those assessments, the Introduction ! ! 39 ! ! ! assessment tools used, and possibly other details, as applicable, such as how multiple assessments within an individual are to be combined19. Surrogate Endpoint A surrogate endpoint is that one used in clinical trials as a substitute for a direct measure of how a patient feels, functions, or survives. A surrogate endpoint does not measure the clinical benefit of primary interest in and of itself, but rather is expected to predict that clinical benefit or harm based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence19. From a regulatory standpoint, surrogate endpoints and potential surrogate endpoints can be characterized by the level of clinical validation: validated surrogate endpoint, reasonably likely surrogate endpoint, candidate surrogate endpoint19. 1.2.1.3.2. Principal Endpoints in Oncology In the next paragraphs, the more relevant endpoints used in oncology are described based on the information provided by the US FDA guideline Clinical trial endpoints for the approval of cancer drugs and biologics: guidance for industry33. Overall Survival Overall survival (OS) is defined as the time from randomization until death from any cause and is measured in the intent-to-treat population. Survival is considered the most reliable cancer endpoint, and when studies can be conducted to adequately assess survival, it is usually the preferred endpoint. This endpoint is precise and easy to measure without bias, documented by the date of death. Survival improvement should be analyzed as a risk-benefit analysis to assess clinical benefit. OS should be evaluated in randomized controlled studies. Data derived from externally controlled trials are seldom reliable for time-to-event endpoints, including OS. Apparent differences in outcome between external controls and current treatment groups can arise from differences other than drug treatment, including patient selection, improved imaging techniques, or improved supportive care. Randomized studies minimize the effect of these known and unknown differences by providing a direct outcome comparison. Demonstration of a statistically significant improvement in OS can be considered to be clinically significant if the toxicity profile is acceptable and has often supported new drug approval. Difficulties in performing and analyzing survival studies include long follow-up periods in large trials and subsequent cancer therapy potentially confounding survival analysis33. Disease-Free and Event-Free Survivals Disease-free survival (DFS) is defined as the time from randomization until disease recurrence or death from any cause. The most frequent use of this endpoint is in the adjuvant setting after definitive surgery or radiotherapy. DFS also can be an important endpoint when a large percentage of patients achieve complete responses (CRs) with chemotherapy. Although OS is a conventional endpoint for most adjuvant settings, DFS can be an important endpoint in situations where survival may be prolonged, making an OS endpoint impractical. An endpoint that is similar to DFS but is differentiated from it in that randomization takes place before definitive surgery or radiotherapy in the adjuvant setting is event-free survival (EFS). EFS is defined as time from randomization to any of the following events: progression of disease that precludes surgery, local or distant recurrence, or death due to any cause. Treatment effect JUAN RUIZ BAÑOBRE ! 40 ! ! ! measured by DFS or EFS can be a surrogate endpoint to support accelerated approval, a surrogate endpoint to support traditional approval, or it can represent direct clinical benefit based on the specific disease, context of use, magnitude of the effect, the disease setting, available therapy, and the risk-benefit relationship. Important considerations in evaluating DFS or EFS as a potential endpoint include the estimated size of the treatment effect and proven benefits of standard therapies. Moreover, the schedule for follow-up assessments and visits should be carefully delineate. Unscheduled assessments can occur for many reasons and differences between study arms in the frequency, timing, or reason for unscheduled assessments can introduce bias. Bias can be minimized by blinding patients and investigators to the treatment assignments, as appropriate. Application of the definition of DFS or EFS in a study can be complicated, particularly when deaths are noted without prior tumor progression documentation. These events can be scored either as disease recurrences or as censored events. Although all methods for statistical analysis of deaths have some limitations, considering deaths from all causes as recurrences can minimize bias. DFS or EFS can be overestimated using this definition, especially in patients who die after a long period without observation. Bias can be introduced if the frequency of long-term follow-up visits is dissimilar between the study arms or if dropouts are not random because of toxicity. Some analyses count cancer-related deaths as DFS or EFS events and censor non-cancer deaths. This method can introduce bias in the attribution of the cause of death. Furthermore, any method that censors observations on patients, whether at death or at the last visit, assumes that the patients with censored observations have the same risk of recurrence as patients with non-censored observations who have not yet experienced the event33. Objective Response Rate Objective response rate (ORR) is defined as the proportion of patients with tumor size reduction of a predefined amount and for a minimum time period. Response duration usually is measured from the time of initial response until documented tumor progression. Generally, the FDA has defined ORR as the sum of partial responses plus CRs. When defined in this manner, ORR is a direct measure of a drug antitumor activity, which can be evaluated in a single-arm study. Stable disease should not be a component of ORR. Stable disease can reflect the natural history of disease, whereas tumor reduction is a direct therapeutic effect. Also, stable disease can be more accurately assessed by time to progression (TTP) or progression-free survival (PFS) analysis. If available, standardized criteria should be used to ascertain response. A variety of response criteria have been considered appropriate, being the most widely used revised Response Evaluation Criteria In Solid Tumors (RECIST) guideline (version 1.1)34. The response criteria should be predefined in the protocol before the start of the study. The significance of ORR is assessed by its magnitude and duration, and the percentage of CRs. Treatment effect measured by ORR can be a surrogate endpoint to support accelerated approval, a surrogate endpoint to support traditional approval, or it can represent direct clinical benefit based on the specific disease, context of use, magnitude of the effect, the number of CRs, the durability of response, the disease setting, the location of the tumors, available therapy, and the risk-benefit relationship33. Complete Response CR is defined as no detectable evidence of tumor. CR is generally measured through imaging studies or through histopathologic assessment. Treatment effect measured by CR can be a surrogate endpoint to support accelerated approval, a surrogate endpoint to support traditional Introduction ! ! 41 ! ! ! approval, or it can represent direct clinical benefit based on the specific disease, context of use, magnitude of the effect, effect duration, disease setting, location of disease, available therapy, and the risk-benefit relationship33. Time to Progression and Progression-Free Survivals TTP and PFS have served as primary endpoints for drug approval. TTP is defined as the time from randomization until objective tumor progression; TTP does not include deaths. PFS is defined as the time from randomization until objective tumor progression or death, whichever occurs first. The precise definition of tumor progression is important and should be carefully detailed. Compared with TTP, PFS is the preferred regulatory endpoint. PFS includes deaths and thus can be a better correlate to OS. In TTP analysis, death events are censored, either at the time of death or at an earlier visit representing informative censoring (nonrandom pattern of loss from the study). PFS assumes that death events are randomly related to tumor progression. PFS can reflect tumor growth and be assessed before the determination of a survival benefit. Importantly, its determination is not confounded by subsequent therapy. For a given sample size, the magnitude of effect on PFS can be larger than the effect on OS. Data are usually insufficient to allow a robust evaluation of the correlation between effects on OS and PFS. Cancer trials are often small, and proven survival benefits of existing drugs are generally modest. Treatment effect measured by PFS can be a surrogate endpoint to support accelerated approval, a surrogate endpoint to support traditional approval, or it can represent direct clinical benefit based on the specific disease, context of use, magnitude of the effect, the disease setting, location of metastatic sites, available therapy, the risk-benefit relationship, and the clinical consequences of delaying or preventing progression in key disease sites such as the brain or spine, or delaying administration of more toxic therapies. It is important to carefully define tumor progression criteria in the protocol. Although there are no standard regulatory criteria for defining progression, RECIST criteria is currently the most frequently used33. Time to Treatment Failure Time to treatment failure (TTF) is defined as a composite endpoint measuring time from randomization to discontinuation of treatment for any reason, including disease progression, treatment toxicity, and death. TTF is generally not recommended as a regulatory endpoint for new molecular-targeted therapy approval33. Specific Symptom Endpoints Symptom improvement is a direct measure of clinical benefit rather than a surrogate endpoint. A decrease in the severity of cancer symptoms has been used to support traditional approval of anti-cancer agents where anti-tumor activity has also been demonstrated. The use of a symptom palliation endpoint requires that the population be symptomatic at baseline, which can be problematic in many cancer trials where patients can often be asymptomatic at baseline. This endpoint can also be subject to open label response bias, the magnitude of which is not well described33. Time to progression of cancer symptoms is a direct measure of clinical benefit rather than a potential surrogate endpoint. Because few cancer trials are blinded, symptom assessments can also be subject to response bias. A delay between tumor progression and the onset of cancer symptoms can occur. Often alternative treatments are initiated before achieving the symptom JUAN RUIZ BAÑOBRE ! ! 48 ! ! ! Material and Methods ! ! 49 ! ! ! 3. MATERIAL AND METHODS Material and methods are described in detail in the articles presented in the next sections. JUAN RUIZ BAÑOBRE ! ! 50 ! ! ! Results ! ! 51 ! ! ! 4. RESULTS JUAN RUIZ BAÑOBRE ! ! 52 ! ! ! Results ! ! 53 ! ! ! 4. RESULTS 4.1. COLORECTAL CANCER A. Predictive Biomarkers in Metastatic Colorectal Cancer Article 001 - Title: Predictive Biomarkers in Metastatic Colorectal Cancer: A Systematic Review. Article 002 - Title: DNA Mismatch Repair Deficiency and Immune Checkpoint Inhibitors in Gastrointestinal Cancers. ! 2 ! ! ! JUAN RUIZ BAÑOBRE ! ! 54 ! ! ! Article 001 - Title: Predictive Biomarkers in Metastatic Colorectal Cancer: A Systematic Review. Authors: Juan Ruiz-Bañobre, Raju Kandimalla, Ajay Goel. Specific contribution of the PhD candidate to the article: Conception and design of the study, analysis and interpretation of data, drafting of the manuscript and revision after peer-review. Journal: JCO Precision Oncology. ISSN: 2473-4284 (online). Publisher: Wolters Kluwer Health. Indexed in Web of Science – JCR 2019 impact factor: not available. Indexed in Scopus – SJR 2019 impact factor: 2.59 – Q1, Cancer Research. https://doi.org/10.1200/PO.18.00260 ! 2 ! ! ! Results ! ! 129 ! ! ! Article 002 - Title: DNA Mismatch Repair Deficiency and Immune Checkpoint Inhibitors in Gastrointestinal Cancers. Authors: Juan Ruiz-Bañobre, Ajay Goel. Specific contribution of the PhD candidate to the article: Conception and design of the study, analysis and interpretation of data, drafting of the manuscript and revision after peer-review. Journal: Gastroenterology. ISSN: 0016-5085. Publisher: Elsevier. Indexed in Web of Science – JCR 2019 impact factor: 17.373 – D1, Gastroenterology and Hepatology. Indexed in Scopus – SJR 2019 impact factor: 6.85 – Q1, Gastroenterology. https://doi.org/10.1053/j.gastro.2018.11.071 This article complements article 001, specifically in the section about immune checkpoint inhibitors development in mCRC, predictive biomarkers, and mechanisms of resistance. JUAN RUIZ BAÑOBRE ! ! 208 ! ! ! 8. Book Chapter: López-López R, Ruiz-Bañobre J, Muinelo-Romay L. 2018. Capítulo 3: Biopsia tisular versus biopsia líquida 50 Preguntas Clave en Oncología de Precisión. Permanyer. ISBN 9788417221416 9. Original Research Article: Vidal J, Muinelo L, Dalmases A, Jones F, Edelstein D, Iglesias M, Orrillo M, Abalo A, Rodríguez C, Brozos E, Vidal Y, Candamio S, Vázquez F, RuizBañobre J, et al. Plasma ctDNA RAS mutation analysis for the diagnosis and treatment monitoring of metastatic colorectal cancer patients. Ann Oncol. 2017;28(6):1325-1332 10. Letter to the Editor: Ruiz-Bañobre J#, Garcia-Gonzalez J. Anti-PD-1/PD-L1-induced psoriasis from an oncological perspective. J Eur Acad Dermatol Venereol. 2017;31(9):e407-e408. doi:10.1111/jdv.14217 11. Case Report: Ruiz-Bañobre J#, Abdulkader I, Anido U, Leon L, Lopez-Lopez R, GarciaGonzalez J. Development of de novo psoriasis during nivolumab therapy for metastatic renal cell carcinoma: immunohistochemical analyses and clinical outcome. APMIS. 2017;125(3):259-263. doi:10.1111/apm.12658 12. Original Research Article: Ruiz-Bañobre J#, Pérez-Pampín E, García-González J, et al. Development of psoriatic arthritis during nivolumab therapy for metastatic non-small cell lung cancer, clinical outcome analysis and review of the literature. Lung Cancer. 2017;0(0). doi:10.1016/j.lungcan.2017.04.007 13. Letter to the Editor: Ruiz-Bañobre J#, Anido U, García-González J. Re: Francesco Piva, Matteo Santoni, Marina Scarpelli, et al’s Letter to the Editor re: Daniel M. Geynisman. Anti-programmed Cell Death Protein 1 (PD-1) Antibody Nivolumab Leads to a Dramatic and Rapid Response in Papillary Renal Cell Carcinoma with Sarcom. Eur Urol. July 2016. doi:10.1016/j.eururo.2016.06.038 13 14. Case Report: Ruiz-Bañobre J#, Anido U, Abdulkader I, Antunez-Lopez J, Lopez-Lopez R, Garcia-Gonzalez J. Long-term Response to Nivolumab and Acute Renal Failure in a Patient with Metastatic Papillary Renal Cell Carcinoma and a PD-L1 Tumor Expression Increased with Sunitinib Therapy: A Case Report. Front Oncol. 2016;6:250. doi:10.3389/fonc.2016.00250 #Corresponding author. General Discussion ! ! 209 ! ! ! 5. GENERAL DISCUSSION JUAN RUIZ BAÑOBRE ! ! 210 ! ! ! General Discussion ! ! 211 ! ! ! 5. GENERAL DISCUSSION Precision oncology is a rapidly evolving field in many different aspects. Taking this in mind and considering the central role of biomarkers in precision oncology, this thesis affords various important aspects regarding predictive and prognostic biomarkers in two important clinical scenarios. First, this thesis presents a systematic and comprehensive review on the field of mCRC that summarizes the most relevant milestones achieved in the field of predictive biomarkers to various treatments; analyzes and discusses methodological aspects, current trends, and future directions in this exciting area. Second, because of the lack of biomarkers in the context of MC, this thesis affords the role of miRNAs from a biological and prognostic viewpoint in this specific CRC subtype. Lastly, this thesis presents a multicenter retrospective study in mUC patients under anti-PD-(L)1 monotherapy which investigates the safety and efficacy of anti-PD-(L)1 antibodies, and explores pretreatment factors influencing therapeutic outcomes in mUC in daily clinical practice, a context where prognostic biomarkers for risk stratification are an unmet medical need. 5.1. Predictive biomarkers in metastatic colorectal cancer Despite the tremendous body of effort devoted for the identification of predictive biomarkers for various treatments used in patients with mCRC, thus far only three of such markers have translated into routine clinical practice. The first one, the mutations in the RAS gene, serves as a negative predictive biomarker that is present in ~55% of mCRC patients42 and correlates with the lack of efficacy to anti-EGFR antibody treatments. The identification of RAS mutations as a negative predictive marker, which was initially based on retrospective studies, was subsequently retrospectively validated in cetuximab and panitumumab pivotal clinical trials. The second marker is the tumor MSI status, which has emerged as a predictive marker for anti-PD-1 drugs. In May and July of 2017, the US FDA approved the anti-PD-1 therapies pembrolizumab and nivolumab for the treatment of patients with MSI-H mCRC for whom the disease has progressed after treatment with fluoropyrimidine, oxaliplatin, and irinotecan. Almost a year later, in July 2018, a nivolumab plus ipilimumab combination regimen was approved, which opened three novel treatment options for patients with MSI-H or dMMR mCRC (patients with MSI-H or dMMR mCRC represent approximately 5% of all patients with mCRC)43. Although patients with MSI-positive mCRC have worse prognosis, it is thought that they derive clinical benefit from anti-PD-1 therapy because of a large proportion of lymphocytic infiltration and the presence of mutation-associated neoantigens44–48. This exciting discovery has led to universal MSI testing for the management of patients with mCRC. Not surprisingly, in May 2020 the US FDA approved pembrolizumab as first-line therapy for patients with MSI-H/dMMR mCRC. This approval was based on the results of the KEYNOTE-177 study (NCT02563002), a multicenter, international, open-label, activecontrolled, randomized trial that compared first-line therapy with pembrolizumab vs. chemotherapy in 307 patients with MSI-H/dMMR mCRC. This study demonstrated a statistically significant improvement in PFS, with a median PFS of 16.5 months vs. 8.2 months for pembrolizumab compared to chemotherapy standard-of-care. Longer-term analysis is needed to JUAN RUIZ BAÑOBRE ! ! 212 ! ! ! assess the effect on OS. Moreover, in June 2020 the US FDA granted accelerated approval to pembrolizumab for the treatment of patients with any unresectable or metastatic solid tumor with high mutational burden (as determined by the FDA-approved test, the FoundationOne CDx assay) whose cancer has progressed after previous treatment and has no satisfactory alternative treatment options 49. Several clinical trials evaluating the combination of anti-PD-1 therapy with chemotherapy are ongoing for previously untreated MSI-H/dMMR mCRC patients with the goal of improving on results from previous studies and further extending survival of these patients. Meanwhile, other different immunotherapeutic approaches are being evaluated for treatment of microsatellite stable (MSS) CRC, which is less responsive to immune checkpoint inhibition than MSI-H mCRC. Although all of these results represent substantial therapeutic advances in the treatment of mCRC, they also emphasize the growing need for more precise predictive biomarkers to support more rational development of immunotherapies. A more comprehensive understanding of the intersection between genomics, epigenomics, and immunology in mCRC seems essential for meeting this need for new strategies. Recently, Grasso et al.50 reported the results of a large-scale genomic analysis (TCGA, Nurses’ Health Study, and Health Professionals Follow-up Study cohorts) involving 1211 primary CRC tumor specimens. Mutations in genes involved in immune modulatory pathways, as well as in the neoantigen-presentation machinery (mainly B2M and HLA), significantly correlated with MSI-H. Along with JAK1/2 and IFN-gamma receptor 1 mutations, similar alterations have been observed in melanoma, non-small cell lung cancer, and CRC and deemed to be genetic drivers of primary or acquired resistance to immune checkpoint blockade, reflecting their role as a mechanism of adaptive resistance against T-cell tumor infiltration45,50–54. The interaction between somatic alterations and the immune system is complex, as indicated by a recent study in which 11 out of 13 B2M-mutant CRC patients achieved mCRC control with anti-PD-1 or antiPD-L1 agents, despite the presence of a mutation that, theoretically, conferred primary resistance to ICI55. On the other hand, for both MSS and MSI-H tumors, active WNT/β-catenin signaling was inversely associated with tumor T-cell infiltration, providing evidence of the existence of an anti-immune response mechanism beyond the MSI profile50. Lastly, the third marker is the BRAF V600E mutation as a predictive biomarker for BRAF inhibitor (BRAFi)-based regimens. BRAF mutations occur in 10–15% of all CRCs and in ~7% of all mCRC56,57. Although most BRAF mutations occur in codon 600 (mainly BRAF V600E), which leads to constitutive BRAF kinase activity and sustained MAPK pathway signaling, 2% of mCRCs have atypical BRAF mutations that are outside of codon 600, usually in codon 59458. Surprisingly, although monotherapy with BRAFi has proven effective in the treatment of BRAFmutant melanoma, it was ineffective in BRAF V600E-mutant CRCs. Preclinical evidence demonstrated that despite transient inhibition of pERK by BRAFi such as vemurafenib, rapid ERK reactivation occurs through EGFR-mediated activation of RAS and CRAF59. Furthermore, the fact that BRAF V600E-mutant CRCs express higher levels of pEGFR than do BRAF-mutant melanomas, positions them for EGFR-mediated resistance 59. Collectively, these findings provided rationale to test dual BRAF and EGFR blockade. Results from preclinical studies and early phase clinical trials, have demonstrated this strategy is feasible and safe, and can potentially improve therapeutic efficacy of BRAFi. Moreover, preclinical studies have suggested that combined inhibition of BRAF and MEK was more effective than dual BRAF and EGFR blockade. This strategy was tested in subsequent phase 1 and phase 2 clinical trials that combined BRAF inhibitors with both anti-EGFR monoclonal antibodies and MEK inhibitors59–61. Results of General Discussion ! ! 213 ! ! ! these trials led to US FDA approval (in April 2020) of encorafenib, a BRAF tyrosine kinase inhibitor, used in combination with cetuximab for the treatment of adult patients with BRAF V600E-mutated mCRC. The efficacy of this combination of drugs was evaluated in the BEACON CRC study62, a phase 3 randomized, active-controlled, open-label, multicenter trial (NCT02928224). In this trial, encorafenib plus cetuximab demonstrated a clinical and statistically significant OS and PFS benefit compared to the control arm of either irinotecan or FOLFIRI plus cetuximab in patients with BRAF V600E-mutated mCRC who had progressed on one or two prior regimens. This trial also evaluated the efficacy of triple-therapy with encorafenib, binimetinib (a MEK inhibitor [MEKi]), and cetuximab in a second experimental arm, but although this regimen showed an improved OS and PFS compared to the control arm, it was more toxic than the dual BRAF and EGFR blockade and had similar efficacy. Another BRAFi, vemurafenib, which has more modest clinical activity, was recently included in the NCCN guidelines as a treatment option for patients with BRAF V600E-mutated mCRC when used in combination with cetuximab/panitumumab plus irinotecan63,64. Inclusion in the guidelines was based on results of the randomized phase 2 Southwest Oncology Group (SWOG) 1406 trial, in which the tripletherapy (vemurafenib, cetuximab, and irinotecan) demonstrated improved PFS and ORR as compared with cetuximab plus irinotecan64. In addition to the previously described regimens, based on the results of a phase 1 study61, the NCCN Panel has recommended the combination of dabrafenib (BRAFi) plus trametinib (MEKi) plus either cetuximab or panitumumab as another treatment option beyond the first line setting for BRAF V600E-mutated mCRC63. Moreover, other well-described predictive biomarkers used in the management of several tumor types, have shown promising utility in selecting mCRC patients for various targeted therapy-based regimens: 1) HER-2 Blockade – Regarding the role of HER-2 amplification/overexpression as a predictive biomarker, a large body of evidence, accrued primarily from breast and gastric cancer patients, supports the role of HER-2 amplification or overexpression as a predictive biomarker for anti-HER-2-based therapies. Therefore, there is renewed interest in evaluating HER-2 as a clinically actionable target in mCRC. Although initial mCRC clinical trials interrogating the anti-HER-2 monoclonal antibody trastuzumab in combination with other chemotherapeutic agents (either FOLFOX or irinotecan) closed early due to lack of patient accrual, mechanistic insights gained from preclinical analyses of HER-2-amplified mCRC patient-derived xenografts have led to improved design of new clinical trials65–67. Three phase 2 clinical trials evaluated dual HER-2 blockade in a biomarker-selected subset of heavily pretreated mCRC patients. Study treatment included trastuzumab plus lapatinib (HERACLES trial, NCT03225937), pertuzumab and trastuzumab (MyPathway trial, NCT02091141), or the antibody-drug conjugate trastuzumab deruxtecan (DESTINY-CRC01, NCT03384940). Results of these studies demonstrated an impressive ORR of ~30–45%68–70. These data have paved the way for development of ongoing phase 2 clinical trials evaluating the efficacy of new anti-HER-2 agents, such as S1613 (NCT03365882), trastuzumab-emtansine (NCT03418558), or tucatinib (NCT03043313) in this clinical scenario comprising ~5% of RAS wild-type mCRC patients71. Furthermore, determining the utility of ctDNA analyses in monitoring therapeutic efficacy and in identifying mechanisms of resistance to dual HER-2 blockade is also an attractive area of study72. 2) Tyrosine Kinase Inhibitors – New drugs that target tyrosine kinase (TK) fusions in genes such as NRTK1/2/3, RET, ALK, and ROS1 are showing promising preliminary results in phase 1 and 2 clinical trials that include patients with CRC. One agent, LOXO 101 (larotrectinib), JUAN RUIZ BAÑOBRE ! ! 214 ! ! ! is a selective tropomyosin receptor kinase (TRK) inhibitor that demonstrated tumor-agnostic efficacy in patients with NTRK fusion-positive malignancies (including four patients with CRC who achieved a partial response)73. A second agent, entrectinib, an ALK, ROS1, TRKA, TRKB, and TRKC selective inhibitor, demonstrated clinical activity in patients who had fusions in the previously described TK genes74. Patients who responded to entrectinib included two patients whose mCRC harbored CAD-ALK or LMNA-NTRK1 gene fusions75,76. Anticipating potential resistance mechanisms to larotrectinib based on evidence from other pan-TK inhibitors, Drilon et al.77 developed LOXO-195 (selitrectinib), a potent and selective TRK kinase inhibitor designed to have a molecular structure that would overcome typical TRK resistance mutations. LOXO-195 was initially evaluated in a mCRC patient whose cancer had an LMNA-NTRK1 rearrangement with a G595R larotrectinib-resistance mutation. This patient successfully achieved a durable partial response77. Although the prevalence of rearrangements in TK genes in mCRC patients may be as low as 1.5%, the accelerated development of TK inhibitors offers new hope for some heavily pretreated mCRC patients who have no other therapeutic options78. Given these promising results, the US FDA granted accelerated approval to larotrectinib (November 2018) and entrectinib (August 2019) for patients with NTRK gene fusion-positive solid tumors without a known acquired resistance mutation. The Committee for Medicinal Products for Human Use of the European Medicines Agency has also recommended the granting of a conditional marketing authorization for larotrectinib (July 2019) and entrectinib (May 2020) for the same indication. 3) KRAS Inhibitors – KRAS is one of the most commonly altered oncogenes in human cancers, and was long considered an undruggable target because of the small size of abnormal KRAS proteins, the presence of few binding sites, and the rapid, tight binding of active KRAS to GTP. However, recent data have suggested that KRAS may be targetable. For example, preliminary data on the activity of AMG510 (sotorasib), a small covalent inhibitor, have shown that it rapidly and irreversibly occupies KRAS G12C and extinguishes its activity through a unique interaction with the P2 pocket79. The KRAS G12C mutation occurs in ~4% of CRC80. In a recent phase 1 trial, sotorasib showed encouraging anti-tumor activity in heavily pretreated patients who had advanced, KRAS G12C-mutated solid tumors17. A total of 129 patients were included in this study, 42 of whom had CRC. Within CRC patients, sotorasib treatment yielded an ORR and disease control rate (DCR) of 7.1% and 73.8%, respectively. The median duration of stable disease was 5.4 months and the median PFS was 4.0 months. Although sotorasib showed promising anticancer activity in patients with heavily pre-treated solid tumors bearing the KRAS G12C mutation, inconsistency was seen in tumor response between patients with non-small cell lung cancer and those with CRC, which the authors suggested indicated either that KRAS G12C is not the dominant oncogenic driver for CRC or that other pathways, such as the WNT or EGFR pathways, mediate oncogenic signaling beyond KRAS. These hypotheses are supported by solid preclinical evidence81–83, and therefore, clinical trials that combine sotorasib with other agents that block additional pathways have already been initiated (i.e., NCT04185883 and NCT04303780). Although many KRAS G12C inhibitors in addition to sotorasib are under development, to date only adagrasib, an irreversible covalent inhibitor, has shown promising antitumor activity in KRAS G12C-mutated CRC. Furthermore, inhibitors for mutations other than KRAS G12C are being developed. For example, initial preclinical data for MRTX1133, a new, first-in-class KRAS G12D inhibitor, have demonstrated significant tumor regression in preclinical animal models84. Thus, through development of a range of inhibitors, effective means of targeting KRAS are emerging. General Discussion ! ! 215 ! ! ! Nonetheless, the discovery and validation of novel predictive biomarkers that can assist in decision-making has been a challenging endeavor, resulting in a long list of failed predictive markers. As highlighted by the results of our study, this task seems particularly even more daunting in terms of conventional chemotherapy and antiangiogenic drugs. In CRC, since the use of single-agent chemotherapeutic regimens have shown limited efficacy, and the majority of current treatment options include various combinations of drugs, biomarker discovery for a specific drugs is not surprisingly more complicated due to the interactions between different cytotoxic agents63. Similar concerns remain for developing predictive biomarkers for therapeutic response to bevacizumab, since: a) it is also not used as a single agent in the clinic63, b) the poor understanding of its mechanism(s) of action85, and c) the very reason that angiogenesis is an intriguingly adaptive process which involves numerous factors86. Presumably, the inherent complexity of angiogenesis has been a significant hurdle in the attempts to develop response predictive biomarkers for other multi-targeted antiangiogenic drugs such as aflibercept or regorafenib. Additional insights into the tumor microenvironment, including the role of tumorassociated stromal cells, could possibly shed light on this tortuous process in the future. On the other hand, and based on the results of our study, the gap between the discovery phase and subsequent biomarker development steps looks evident, highlighting the necessity of the implementation of a robust worldwide platform to move forward predictive biomarker validation. These facts, together with the lack of effort to undertake external validation of initial findings, block the advancement of the majority of the presumed predictive biomarkers in the clinic. Another important question worthy of discussion in any biomarker discovery effort is the origin of tumor tissue samples ─ whether primary tumor tissue or metastatic lesions. An interesting example of this important concept is the TS expression as a predictive biomarker to 5FU based chemotherapy, since its efficacy has been discordant depending on the tumor tissue origin87,88. This concept is highly congruent with tumor heterogeneity, which is a possible source of discrepancy even when the molecular marker is analyzed in a different region of the same source89. Besides spatial heterogeneity, tumors are dynamic entities that continue to evolve over time, especially if they are under selective pressure90. For this reason, the time from sample acquisition to biomarker analysis is of significant clinical relevance ─ an issue that is often overlooked in most studies. Since only ~20% of CRC patients present with a metastatic disease at the time of diagnosis, it is often the practice or only option available to analyze archival tissues from the primary tumor to identify biomarkers ─ which is not always an optimal or preferred choice91.Patient selection is gaining importance, which is evidenced by the recent initiative, the US National Cancer Institute’s Exceptional Responder Program92,93. Consideration of extreme phenotypes such as long-term responders and extremely early progressors for biomarker discovery can facilitate successful identification of molecular alterations that better correlate with clinical phenotypes. For instance, in the majority of studies presented in this work, there was no consideration of PFS as a selection criterion, and many studies included patients with stable disease in the non-responders. In general, improved ORRs and longer PFS are superior indicators of the true efficacy of any drug intervention, while inclusion of gain in OS as a selection feature may inadvertently introduce bias. In addition, new biomarker-driven study designs such as basket or umbrella trials, which assign a treatment according to tumor molecular characteristics, not only are going to improve clinical drug development, but will also facilitate improved biomarker validation. While analysis of clinical specimens with robust follow-up data from retrospective series or randomized trials are of tremendous value, a well-designed biomarker discovery phase JUAN RUIZ BAÑOBRE ! ! 216 ! ! ! followed by technical validation in subsequent prospective clinical cohorts using longitudinally collected specimens is much needed to establish clinically translatable predictive biomarkers. Additionally, although many surgical specimens are of suitable quality, needle biopsy-derived metastatic lesions often yield lower amount of DNA/RNA required for robust sequencing experiments94,95; hence having access to liquid biopsy-based predictive markers would be transformative in overcoming this limitation in mCRC patients. Furthermore, in addition to helping the clinicians in quicker and easier decision-making, liquid biopsy biomarkers will improve patient compliance and eliminate the concerns surrounding intra-tumor heterogeneity associated with tumor/biopsy specimens, and may also help in disease monitoring as well as predicting secondary resistance. The international community has to consolidate initiatives to improve biomarker development studies, and more importantly undertake conscious efforts to validate the results gathered from retrospective studies in prospective randomized multicenter cohorts. Such efforts will guarantee improved success and will decrease the economic burden by allowing precision treatment of cancer patients. Furthermore, a significant majority of patients will be spared from unnecessary toxicity and side effects of treatments that will not benefit them clinically. Lastly, the implementation of novel high-throughput molecular analytical techniques and the integration of multi-omic approaches together with clinical and epidemiological data using machine-learning algorithms will definitely hasten the biomarker development in the coming years96. In spite of a large body of attempts over the last decades, there remain only three wellestablished predictive biomarkers ─ mutations in the RAS gene, the MSI status, and the BRAF V600E mutation (after the completion of our systematic review, as we previously discussed, the clinical utility of BRAF V600E was confirmed in a phase 3 randomized clinical trial) ─ that currently guide treatment decisions in patients with mCRC (Figure 1). Although the past efforts in this context may not have been as rewarding, we currently are a frontier, where the future looks quite promising. The integration of high-throughput deep techniques, together with the advent of machine-learning algorithms and novel clinical trial designs will definitely revolutionize predictive biomarkers for response to cancer therapeutics, as we usher into the new era of precision oncology. General Discussion ! ! 217 ! ! ! Figure 1. Predictive biomarkers for metastatic CRC treatment. Summary of currently known molecular tumor alterations that enable improved clinical decision-making regarding use of therapies that are tailored to the metastatic CRC (mCRC) patient. The therapies shown go beyond classical chemotherapeutic agents. Excluding RAS mutations for EGFR-targeted therapies, which are considered a negative predictive biomarker, the molecular alterations shown represent positive predictors of benefit with the indicated targeted therapies. Although bevacizumab is considered a useful therapeutic option in combination with chemotherapy in mCRC independent of any particular molecular alteration, currently there is no consistent predictive biomarker to guide bevacizumab use. Although many of these molecular alterations are applicable only to a minority of mCRC patients, collectively these low-prevalence actionable characteristics support a new-targeted therapeutic horizon for many patients. Color-coded boxes: yellow, US FDA-approved therapy; grey, not US FDA approved therapy. aHER2 amplification or overexpression. Abbreviations: BRAFi, BRAF inhibitors; CRC, colorectal cancer; ICB, immune checkpoint blockade; KRASi, KRAS inhibitors; MSI-H, microsatellite instability high; TKi, tyrosine kinase inhibitors; TMB-H, tumor mutation burden high. 5.2. Clinical significance of a microRNA signature for the identification and predicting prognosis in colorectal cancers with mucinous differentiation Accumulating evidence suggests that mucinous carcinoma represent a distinct entity, particularly in the context of CRC, and present a unique clinical challenge - both from a diagnostic and prognostic risk-stratification purposes. However, the current used definition to categorize a CRC as mucinous is arbitrary, and results in a considerable diagnostic inter-observer variability37–40. In addition, the prognostic significance of mucinous CRC subtype is not clear39,40. We addressed this important gap in knowledge based upon the growing recognition for the critical role of miRNAs and other non-coding RNAs in the regulation of various biological processes in carcinogenesis97–99. Apart from their significant role in the CRC pathogenesis, previous studies have also elegantly demonstrated the potential of miRNAs as biomarkers in multiple cancer types including CRC25. While quite a few studies have investigated the role of miRNAs in nonmucinous CRC, the biological and prognostic significance of miRNAs in mucinous CRCs JUAN RUIZ BAÑOBRE ! ! 224 ! ! ! General Conclusions ! ! 225 ! ! ! 6. GENERAL CONCLUSIONS 1. In spite of a large body of attempts over the last decades, there are only three wellestablished predictive biomarkers ─ mutations in the RAS gene, the MSI status, and the BRAF V600E mutation ─ that currently guide treatment decisions in patients with mCRC. 2. The integration of high-throughput deep techniques, together with the advent of machinelearning algorithms and novel clinical trial designs will revolutionize predictive biomarkers for response to cancer therapeutics in mCRC, as we usher into the new era of precision oncology. 3. There is a novel six-miRNA panel associated with mucinous differentiation in CRC patients; miR-31 is upregulated, and miR-196-b, miR-592, miR-1247, miR-1269, and miR-552 are downregulated in tumor tissue specimens of this CRC subtype. 4. The six-miRNA biomarker panel exhibits a robust diagnostic potential for the identification of CRC patients with mucinous differentiation. 5. The six-miRNA panel is an independent predictor for OS in CRC patients with mucinous differentiation. 6. In mucinous CRC, the integrative risk-assessment model comprising the combination of miRNA-based risk scores and TNM stage, improves the prognosis prediction in comparison to each component independently. 7. Monotherapy with anti-PD-(L)1 antibodies is a safe and effective treatment option in daily clinical-practice for mUC patients. 8. Peritoneal metastases represent an independent prognostic factor for OS in patients with mUC under anti-PD-(L)1 monotherapy. 9. 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