A confirmatory basket design considering non-inferiority and superiority testing

For multiple rare diseases as defined by a common biomarker signature, or a disease with multiple disease subtypes of low frequency, it is often possible to provide confirmatory evidence for these disease or subtypes (baskets) as a combined group. A novel drug, as a second generation, may have marginal improvement in efficacy overall but superior efficacy in some baskets. In this situation, it is appealing to test hypotheses of both non-inferiority overall and superiority on certain baskets. The challenge is designing a confirmatory study efficient to address multiple questions in one trial. A two-stage adaptive design is proposed to test the non-inferiority hypothesis at the interim stage, followed by pruning and pooling before testing a superiority hypothesis at the final stage. Such a design enables an efficient and novel registration pathway, including an early claim of non-inferiority followed by a potential label extension with superiority on certain baskets and an improved benefit-risk profile demonstrated by longer term efficacy and safety data. Operating characteristics of this design are examined by simulation studies, and its appealing features make it ready for use in a confirmatory setting, especially in emerging markets, where both the need and the possibility for efficient use of resources may be the greatest.

Tumor-Agnostic Approvals: Insights and Practical Considerations

Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/estrogen receptor-positive (HER2+/ER+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of patients with HER2+/ER+ receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized 2 in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities. To mimic ETR to aromatase inhibitors (AIs), we developed 2 long-term estrogen deprivation (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 subtyping, and genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells. Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of aggressive MM361 LTEDs identified mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and ferroptosis-associated antioxidant genes, including GPX4. Combining a GPX4 inhibitor with anti-HER2 agents induced significant cell death in both MM361 and BT474 LTEDs. The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

Aiding the Adoption of Master Protocols by Optimizing Patient Engagement

Master protocols (MPs) are an important addition to the clinical trial repertoire. As defined by the U.S. Food and Drug Administration (FDA), this term means "a protocol designed with multiple sub-studies, which may have different objectives (goals) and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure." This means we now have a unique, scientifically based MP that describes how a clinical trial will be conducted using one or more potential candidate therapies to treat patients in one or more diseases. Patient engagement (PE) is also a critical factor that has been recognized by FDA through its Patient-Focused Drug Development (PFDD) initiative, and by the European Medicines Agency (EMA), which states on its website that it has been actively interacting with patients since the creation of the Agency in 1995. We propose that utilizing these PE principles in MPs can make them more successful for sponsors, providers, and patients. Potential benefits of MPs for patients awaiting treatment can include treatments that better fit a patient's needs; availability of more treatments; and faster access to treatments. These make it possible to develop innovative therapies (especially for rare diseases and/or unique subpopulations, e.g., pediatrics), to minimize untoward side effects through careful dose escalation practices and, by sharing a control arm, to lower the probability of being assigned to a placebo arm for clinical trial participants. This paper is authored by select members of the American Statistical Association (ASA)/DahShu Master Protocol Working Group (MPWG) People and Patient Engagement (PE) Subteam. DahShu is a 501(c)(3) non-profit organization, founded to promote research and education in data science. This manuscript does not include direct feedback from US or non-US regulators, though multiple regulatory-related references are cited to confirm our observation that improving patient engagement is supported by regulators. This manuscript represents the authors' independent perspective on the Master Protocol; it does not represent the official policy or viewpoint of FDA or any other regulatory organization or the views of the authors' employers. The objective of this manuscript is to provide drug developers, contract research organizations (CROs), third party capital investors, patient advocacy groups (PAGs), and biopharmaceutical executives with a better understanding of how including the patient voice throughout MP development and conduct creates more efficient clinical trials. The PE Subteam also plans to publish a Plain Language Summary (PLS) of this publication for clinical trial participants, patients, caregivers, and the public as they seek to understand the risks and benefits of MP clinical trial participation.

Design and Execution of Sustainable Decentralized Clinical Trials

The decentralized clinical trial (DCT) approach is increasingly recognized as a means to accelerate the development of potential therapeutic interventions. DCTs have a crucial advantage over traditional clinical trials: patients are monitored in their environment using technology (e.g., wearables), that capture data as they continue in daily life. This narrative review outlines a gap analysis focused on the frameworks and guidance from expert working groups and regulatory agencies for the design and execution of DCTs. Eight DCT elements guided the analysis and summarized the frameworks and guidance: (1) suitability, (2) protocol, (3) investigational medicinal product (IMP) supply, (4) investigators and health care providers, (5) safety, (6) regulatory and ethics, (7) data and technology, and (8) engagement, communication, and advocacy. Based on the gap analysis, two key takeaways were identified: (1) a need for a comprehensive sustainability assessment of each DCT element; and (2) current frameworks and guidance provide recommendations on social sustainability and some on economic sustainability. DCTs are an essential evolution in healthcare research; however, more guidance related to a comprehensive assessment of designing and executing sustainable DCTs is needed. This is especially the case for environmental sustainability, including, for example, carbon footprint and disposal of IMPs and sensors.

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

Background

Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/ estrogen receptor-positive (HER2+/HR+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of HER2+/ER+ patients receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized two distinct in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities.

Methods

To mimic ETR to aromatase inhibitors (AI), we developed two long-term estrogen-deprived (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 molecular subtyping, genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells.

Results

Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of the more aggressive MM361 LTED model system identified exonic mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and antioxidant genes associated with ferroptosis, including GPX4. Combining the GPX4 inhibitor RSL3 with anti-HER2 agents induced significant cell death in both the MM361 and BT474 LTEDs.

Conclusions

The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective

BACKGROUND

Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials.

MAIN BODY

Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home.

CONCLUSION

This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.

Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.

Real-World Studies Link Nonsteroidal Anti-inflammatory Drug Use to Improved Overall Lung Cancer Survival

Inflammation is a cancer hallmark. Nonsteroidal anti-inflammatory drugs (NSAIDs) improve overall survival (OS) in certain cancers. Real-world studies explored here if NSAIDs improve non-small cell lung cancer (NSCLC) OS. Analyses independently interrogated clinical databases from The University of Texas MD Anderson Cancer Center (MDACC cohort, 1987 to 2015; 33,162 NSCLCs and 3,033 NSAID users) and Georgetown-MedStar health system (Georgetown cohort, 2000 to 2019; 4,497 NSCLCs and 1,993 NSAID users). Structured and unstructured clinical data were extracted from electronic health records (EHRs) using natural language processing (NLP). Associations were made between NSAID use and NSCLC prognostic features (tobacco use, gender, race, and body mass index, BMI). NSAIDs were statistically-significantly (P < 0.0001) associated with increased NSCLC survival (5-year OS 29.7% for NSAID users versus 13.1% for non-users) in the MDACC cohort. NSAID users gained 11.6 months over nonusers in 5-year restricted mean survival time. Stratified analysis by stage, histopathology and multicovariable assessment substantiated benefits. NSAID users were pooled independent of NSAID type and by NSAID type. Landmark analysis excluded immortal time bias. Survival improvements (P < 0.0001) were confirmed in the Georgetown cohort. Thus, real-world NSAID usage was independently associated with increased NSCLC survival in the MDACC and Georgetown cohorts. Findings were confirmed by landmark analyses and NSAID type. The OS benefits persisted despite tobacco use and did not depend on gender, race, or BMI (MDACC cohort, P < 0.0001). These real-world findings could guide future NSAID lung cancer randomized trials.

A framework for assessing the impact of accelerated approval

The FDA's Accelerated Approval program (AA) is a regulatory program to expedite availability of products to treat serious or life-threatening illnesses that lack effective treatment alternatives. Ideally, all of the many stakeholders such as patients, physicians, regulators, and health technology assessment [HTA] agencies that are affected by AA should benefit from it. In practice, however, there is intense debate over whether evidence supporting AA is sufficient to meet the needs of the stakeholders who collectively bring an approved product into routine clinical care. As AAs have become more common, it becomes essential to be able to determine their impact objectively and reproducibly in a way that provides for consistent evaluation of therapeutic decision alternatives. We describe the basic features of an approach for evaluating AA impact that accommodates stakeholder-specific views about potential benefits, risks, and costs. The approach is based on a formal decision-analytic framework combining predictive distributions for therapeutic outcomes (efficacy and safety) based on statistical models that incorporate findings from AA trials with stakeholder assessments of various actions that might be taken. The framework described here provides a starting point for communicating the value of a treatment granted AA in the context of what is important to various stakeholders.

Abstract 1776: Loss of estrogen receptor alters drug responsiveness and supports a basal-like phenotype in endocrine-resistant HER2+/ER+ breast cancer

At least 50% of human epidermal growth factor 2 (HER2) enriched breast cancer (BC) is estrogen receptor positive (ER+). In clinical trials, patients with HER2+ BC expressing ER respond poorly to neoadjuvant anti-HER2 therapy versus those lacking ER expression. Additionally, combining anti-HER2 with endocrine therapy (ET), like aromatase inhibitor (AI), does not significantly improve pathological complete response in HER2+/ER+ patients. In advanced or de novo HER2+/ER+ metastatic BC (MBC), treatment with ET plus anti-HER2 yields the highest 5-year overall survival. Unfortunately, in the real-world setting outside of clinical trials, just 23% of patients receive this combination, and nearly 40% receive ET alone. The clinical challenge associated with treating advanced HER2+/ER+ BC demands a better understanding of ER signaling and downstream pathways in HER2-driven BC. Our goal is to identify alternative targeted therapies and/or tailor therapeutic combinatorial strategies to treat and/or delay progression of HER2+/ER+ BC. In this study, we established two long-term estrogen (E2) deprived (LTED) HER2+/ER+ cell line models from BT-474 (BT) and MDA-MB-361 (MM) to mimic endocrine therapy resistance (ETR) to AI, and characterized the response of these cell lines to anti-HER2 and other ETs. We also performed whole-genome sequencing (WGS) and single-cell RNA sequencing (scRNAseq) to identify differences between parental and derived ETR-LTEDs. The ER expression was retained in BT-LTEDs but lost in MM-LTEDs as compared to parental cells. Additionally, ER transcriptional activity was confirmed by increased expression of ER-target genes upon E2 stimulation only in BT-LTEDs. However, both LTED variants were less responsive to fulvestrant and showed upregulation of pro-survival AKT signaling. Focusing on the now ER- MM-LTED model, WGS and targeted duplex sequencing identified a significant increase in exonic missense mutations, notably C&gt;T and C&gt;A. Several of the mutated genes encode for transcription and chromatin regulatory factors. scRNAseq analysis showed that MM-LTEDs shift from a luminal- to more basal-like phenotype, with enrichment of genes that regulate immune response and cell motility. Thus, loss of ER expression in MM-LTEDs on the mRNA and protein levels may explain observed intrinsic resistance to fulvestrant and gain of the basal-like phenotype. In summary, we report that our ETR BT- and MM-LTED models capture distinct ER-associated phenotypes of HER2+/ER+ BC. Additional studies are necessary to understand the functional significance of these missense mutations on the development of drug resistance in HER2+/ER+ BC. Ongoing studies are testing pharmacological inhibition of key upregulated genes associated with the basal phenotype to potentially delay disease progression and provide better treatments for ETR HER2+/ER+ BC.

Citation Format: Shaymaa Bahnassy, Hillary Stires, Lu Jin, Stanley Tam, Yasmin J. Dunn, Brendan F. Kohrn, Lawrence A. Loeb, Manasi Balachandran, Mircea Podar, Matthew D. McCoy, Robert A. Beckman, Rebecca B. Riggins. Loss of estrogen receptor alters drug responsiveness and supports a basal-like phenotype in endocrine-resistant HER2+/ER+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1776.

Impact of COVID-19 pandemic on oncology clinical trial design, data collection and analysis

BACKGROUND

To identify and assess via simulation the impact of COVID-19 pandemic on oncology trials and discuss potential mitigation strategies for study design, data collection, endpoints and analyses.

METHODS

We simulated clinical trials to evaluate the COVID-19 impact on overall survival and progression-free survival. We evaluated survival in single-region trials with different proportions of impacted patients across treatment arms, and in multi-region randomized trials with different proportions of impacted patients across regions. We also assessed the impact on PFS when the missingness of disease assessment and censoring rules vary. Impact on the trial success and robustness of statistical inference was summarized.

RESULTS

Without regional impact, the impact on OS analysis is minimal if proportions of impacted patients are similar across arms, however, if a larger proportion of treatment arm patients are impacted, trials may suffer substantial power loss and underestimate treatment effect size. For multi-region trials, if more treatment arm patients are enrolled from more severely impacted regions, trials also have poorer performance. For PFS analysis, the intent-to-treat rule performs well even when the treatment arm patients are more likely to miss disease assessments, while the consecutive-missing censoring rule may lead to poorer performance.

CONCLUSION

COVID-19 affects oncology trials. Simulations would be highly informative to Data Monitoring Committee in understanding the impact and making appropriate recommendations, upon which the sponsor could start planning potential remedies. We also recommend a decision tree for choosing the appropriate methods for PFS evaluation in the presence of missing disease assessments due to COVID-19.

Efficiency of a randomized confirmatory basket trial design constrained to control the family wise error rate by indication

Basket trials pool histologic indications sharing molecular pathophysiology, improving development efficiency. Currently, basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power even with modest effect sizes, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since family wise error rate by indications may sometimes be required, we now simulate a variant of this basket design controlling family wise error rate at 0.025 k, the total family wise error rate of k separate randomized trials. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling family wise error rate and minimizing estimation bias were allowable. Optimal performance results when [Formula: see text]. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power (“uncorrected”) or at the power achieved in the basket trial (“corrected,” because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with a higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible. Even under family wise error rate control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical.

How Should Cancer Models Be Constructed?

Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.

Planning for the Next Pandemic: Ethics and Innovation Today for Improved Clinical Trials Tomorrow

The coronavirus pandemic has brought public attention to the steps required to produce valid scientific clinical research in drug development. Traditional ethical principles that guide clinical research remain the guiding compass for physicians, patients, public health officials, investigators, drug developers and the public. Accelerating the process of delivering safe and effective treatments and vaccines against COVID-19 is a moral imperative. The apparent clash between the regulated system of phased randomized clinical trials and urgent public health need requires leveraging innovation with ethical scientific rigor. We reflect on the Belmont principles of autonomy, beneficence and justice as the pandemic unfolds, and illustrate the role of innovative clinical trial designs in alleviating pandemic challenges. Our discussion highlights selected types of innovative trial design and correlates them with ethical parameters and public health benefits. Details are provided for platform trials and other innovative designs such as basket and umbrella trials, designs leveraging external data sources, multi-stage seamless trials, preplanned control arm data sharing between larger trials, and higher order systems of linked trials coordinated more broadly between individual trials and phases of development, recently introduced conceptually as "PIPELINEs."

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell-based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2Db are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., K d < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell-based vaccines.

Efficient two-stage sequential arrays of proof of concept studies for pharmaceutical portfolios

Previous work has shown that individual randomized "proof-of-concept" (PoC) studies may be designed to maximize cost-effectiveness, subject to an overall PoC budget constraint. Maximizing cost-effectiveness has also been considered for arrays of simultaneously executed PoC studies. Defining Type III error as the opportunity cost of not performing a PoC study, we evaluate the common pharmaceutical practice of allocating PoC study funds in two stages. Stage 1, or the first wave of PoC studies, screens drugs to identify those to be permitted additional PoC studies in Stage 2. We investigate if this strategy significantly improves efficiency, despite slowing development. We quantify the benefit, cost, benefit-cost ratio, and Type III error given the number of Stage 1 PoC studies. Relative to a single stage PoC strategy, significant cost-effective gains are seen when at least one of the drugs has a low probability of success (10%) and especially when there are either few drugs (2) with a large number of indications allowed per drug (10) or a large portfolio of drugs (4). In these cases, the recommended number of Stage 1 PoC studies ranges from 2 to 4, tracking approximately with an inflection point in the minimization curve of Type III error.

Prioritizing targeted therapies in an evidence-based manner, integrating biological context and functional precision medicine

e14065

Background: It is becoming increasingly common for cancer patients to undergo molecular profiling of their tumors in order to see whether there are any actionable DNA, gene expression, or protein expression signatures. For example, individuals with ER+ or HER2+ breast cancer or KRAS wild type (non-mutated) colorectal cancer are prescribed specific targeted therapies. When an individual’s molecular alterations do not match any currently-approved recommendations for their tumor type, their clinician may consider prescribing a therapy approved in a different tumor type. Unfortunately, tumors often eventually become resistant to the therapies they are exposed to, leading to a narrowing of options after each therapy line. Methods: We previously developed CDGnet, an evidence-based approach and web-based tool for prioritizing targeted therapies based on tumor molecular profiles based on known pathways which provide biological context. CDGnet considers approved therapies with biomarkers among the alterations for the individual’s tumor type and other tumor types as the first and second evidence level categories respectively. These are followed by therapies that target or have as biomarkers genes or proteins downstream of altered oncogenes, considering curated pathways for the individual’s tumor type and other tumor types as the third and fourth evidence level categories respectively. We are currently expanding CDGnet in order to include data from high-throughput screening (HTS) experiments of NCI oncologic drugs performed on patient-derived organoids. The concept of “functional precision medicine” consists of using functional drug efficacy determination directly on individual patients, in this case by considering drugs with low half maximal effective concentrations (EC50) which are tested on tissues derived from the actual patients. Results: We will present extensions to CDGnet that allow users to upload both the molecular profiles and the HTS data to see whether any drugs are predicted by both approaches or whether specific combinations appear promising for further testing. Preliminary results on a set of glioblastoma samples will be presented. Conclusions: We hope that extending CDGnet to also include HTS data will eventually allow a truly multi-factorial personalized oncology approach, whereby both molecular alterations at the DNA, RNA, and protein levels and patient-derived organoids will be considered in deciding on treatment plans for individuals.

A roadmap to using historical controls in clinical trials - by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG)

Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs.

Evidence-Based Network Approach to Recommending Targeted Cancer Therapies

PURPOSE

In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information.

METHODS

CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration-approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem.

RESULTS

We present a scenario for a patient who has estrogen receptor (ER)-positive breast cancer with FGFR1 amplification. Although many therapies exist for patients with ER-positive breast cancer, FGFR1 amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1.

CONCLUSION

CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.

Extensive subclonal mutational diversity in human colorectal cancer and its significance

Human colorectal cancers (CRCs) contain both clonal and subclonal mutations. Clonal driver mutations are positively selected, present in most cells, and drive malignant progression. Subclonal mutations are randomly dispersed throughout the genome, providing a vast reservoir of mutant cells that can expand, repopulate the tumor, and result in the rapid emergence of resistance, as well as being a major contributor to tumor heterogeneity. Here, we apply duplex sequencing (DS) methodology to quantify subclonal mutations in CRC tumor with unprecedented depth (104) and accuracy (<10-7). We measured mutation frequencies in genes encoding replicative DNA polymerases and in genes frequently mutated in CRC, and found an unexpectedly high effective mutation rate, 7.1 × 10-7. The curve of subclonal mutation accumulation as a function of sequencing depth, using DNA obtained from 5 different tumors, is in accord with a neutral model of tumor evolution. We present a theoretical approach to model neutral evolution independent of the infinite-sites assumption (which states that a particular mutation arises only in one tumor cell at any given time). Our analysis indicates that the infinite-sites assumption is not applicable once the number of tumor cells exceeds the reciprocal of the mutation rate, a circumstance relevant to even the smallest clinically diagnosable tumor. Our methods allow accurate estimation of the total mutation burden in clinical cancers. Our results indicate that no DNA locus is wild type in every malignant cell within a tumor at the time of diagnosis (probability of all cells being wild type, 10-308).

Abstract 3770: Unexpectedly high subclonal mutational diversity in human colorectal cancer and its significance

Human colorectal cancers (CRC) contain numerous positively selected clonal somatic mutations in 10% or more of the cells. In addition, subclonal mutations in a fraction of cells contribute to phenotypic heterogeneity. Several groups have shown either by analysis of the number of unique subclones as a function of sequencing depth, or by the evaluation of synonymous to nonsynonymous mutation ratios, that most subclonal mutations are not selected and evolve neutrally. Because of the branching nature of tumor evolution, the clonal mutations arise in the founder cell, or very early thereafter. Subclonal mutations appear next, the rare ones on progressively smaller branches of the evolutionary tree. Here, we apply Duplex Sequencing (DS) methodology to quantify subclonal mutations in 5 fresh human MSI-low CRC diagnostic samples with unprecedented depth (104) and accuracy (10-7), and confirm neutral evolution further forward in time than previously known. We find that CRCs without known DNA repair deficits harbor unexpectedly many subclonal mutations; indicating that the mutational diversity of CRCs has been greatly underestimated. The “effective mutation frequency”, or mutation frequency per new cell added to the tumor (taking cell death into account) is also unexpectedly high: 6 X 10-7per base. Given a genome length of 3 X 109, a new daughter cell (taking turnover into account) would have ca 2000 new private mutations compared to its parent. Further, the smallest clinically diagnosable tumor has ca 109 cells, leading to violation of the common modeling assumption that a given mutation arises uniquely in only one cell (“infinite sites assumption”), since at an effective mutation frequency of 6 X 10-7, a cell generation leading to the formation of 109 new cells would produce the same mutation in 600 of those cells. We have developed a new theoretical approach to model neutral evolution independent of the infinite sites assumption, and find that intratumoral heterogeneity in clinical tumors is also underestimated by previous theoretical approaches. Our novel experimental and theoretical methods show that every possible somatic point mutation is present when a tumor is clinically detectable, leading to pre-existing resistant cells to any therapy. We can more accurately predict emergence of cells simultaneously mutationally resistant to multiple non-cross resistant therapies with increasing cell number; targeting genetic instability itself may prevent this.

Citation Format: Robert A. Beckman, Brendan Kohrn, Kaitlyn Loubet-Senear, Jasmin Dunn, Jacintha OSullivan, Mary Bronner, Lawrence A. Loeb. Unexpectedly high subclonal mutational diversity in human colorectal cancer and its significance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3770.

Eye-Tracking Study to Enhance Usability of Molecular Diagnostics Reports in Cancer Precision Medicine

PURPOSE

We conducted usability studies on commercially available molecular diagnostic (MDX) test reports to identify strengths and weaknesses in content and form that drive clinical decision making. Given routine genomic testing in cancer medicine, oncologists must interpret MDX reports as well as evidence concerning clinical utility of biomarkers accurately for treatment or trial selection. This work aims to evaluate effectiveness of MDX reports in facilitating cancer treatment planning.

METHODS

Fourteen clinicians at an academic tertiary care medical facility, with a wide range of experience in oncology and in the use of molecular testing, participated in this study. Three commercially available, widely used, Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists (CAP)-accredited test reports (labeled Laboratories A, B, and C) were used. Eye tracking, surveys, and think-aloud protocols were used to collect usability data for these MDX reports focusing on ease of comprehension and actionability.

RESULTS

Clinicians found two primary areas in molecular diagnostic reports most useful for patient care: therapy options with benefit or lack of benefit to patients, including enrolling clinical trials; and pathogenic tumor molecular anomalies detected. Therapeutic implications and therapy classes such as US Food and Drug Administration-approved off-label, on-label, clinical trials were critical for decision making. However, all reports had usability and comprehension issues in these areas and could be improved.

CONCLUSION

Focused usability studies can help drive our understanding of the clinical workflow for use of molecular diagnostic tests in cancer care. This in turn can have major effects on quality of care, outcomes, costs, and patient satisfaction. This study demonstrates the use of specific usability techniques (eye tracking and think-aloud protocols) to help clinical laboratories improve MDX report design in a precision oncology treatment setting.

Patritumab with Cetuximab plus Platinum-Containing Therapy in Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck: An Open-Label, Phase Ib Study

PURPOSE

Patritumab plus cetuximab with platinum as first-line therapy for patients with recurrent and/or metastatic (R/M) squamous cell carcinoma of the head and neck (SCCHN) was evaluated for safety and to determine the recommended phase II combination dose.

PATIENTS AND METHODS

Patients aged ≥18 years with confirmed R/M SCCHN received intravenous patritumab (18 mg/kg loading dose; 9 mg/kg maintenance dose every 3 weeks) + cetuximab (400 mg/m² loading dose; 250 mg/m² maintenance dose weekly) + cisplatin (100 mg/m² every 3 weeks) or carboplatin (AUC of 5) for six cycles or until toxicity, disease progression, or withdrawal. Primary endpoints were dose-limiting toxicities [DLT; grade ≥3 (21-day observation period)] and treatment-emergent adverse events (TEAE). Pharmacokinetics, human antihuman antibodies (HAHA), tumor response, progression-free survival (PFS), and overall survival (OS) were assessed.

RESULTS

Fifteen patients completed a median (range) of 8.7 (2.0-20.7) patritumab cycles. No DLTs were reported. Serious adverse events were reported in 9 patients (patritumab-related n = 4). TEAEs (N = 15 patients) led to patritumab interruption in 7 patients. Patritumab-related dose reductions were reported in 1 patient. Patritumab (18 mg/kg) pharmacokinetics (N = 15) showed mean (SD) AUC_0-21d of 2,619 (560) μg/day/mL and maximum concentration of 499.9 (90.4) μg/mL. All patients were HAHA-negative at study end (single, transient low titer in 1 patient). Tumor response rate (complete plus partial response; N = 15) was 47%. Median (95% confidence interval) PFS and OS (N = 15) were 7.9 (3.7-9.7) and 13.5 (6.6-17.5) months, respectively.

CONCLUSIONS

Patritumab (18 mg/kg loading dose, 9 mg/kg maintenance dose) plus cetuximab/platinum was tolerable, active in SCCHN, and selected as the phase II dose regimen.

Abstract 3377: Extensive subclonal mutations in human colorectal cancers detected by duplex sequencing

The accumulation of somatic mutations is a defining hallmark of cancer. Human colorectal cancers (CRC) contain thousands of mutations that are detected at frequencies greater than 1%. Some of these are “driver mutations” that have been positively selected during tumor progression. In addition, subclonal mutations—those occurring only in a fraction of malignant cells—are randomly distributed and contribute to phenotypic and morphologic heterogeneity of cancer cells within a tumor. Cells containing specific subclonal mutations may be present prior to therapy and can be positively selected and account for the rapid emergence of therapeutic resistance. The extent of subclonal mutations in cancer, however, has been difficult to quantify, as the high error rate of next-generation sequencing precludes reliable detection of mutations present in fewer than 1-5% of cells. Here, we apply the highly accurate duplex sequencing methodology to quantify the extent of subclonal mutations in CRC. We analyzed somatic mutations in genes encoding replicative DNA polymerases (POLδ and POLε) as well as in genes reported to be frequently mutated in CRC. Duplex sequencing was carried out at depths varying from 500X-30,000X and an accuracy of 10-8. We find that CRCs with DNA repair deficits harbor an unexpectedly large complement of unique subclonal mutations; indicating that the mutational diversity of CRCs has been greatly underestimated. The frequency of subclonal mutations in normal colonic mucosa and CRC is greater than reported for clonal mutations. The burden of tumor-associated subclonal mutations does not correlate with age and the spectrum of single-nucleotide substitutions is different from that in nonmalignant cells, indicating that different mechanisms of mutation accumulation are operative in normal and CRC. Our data are consistent with a mutation rate of 1.1 x 10 per base pair, indicating that each malignant cell and its daughter encoded genomic DNA differs by more 3,000 nucleotides. The linearity of subclonal mutation accumulation as a function of sequencing depth, using DNA obtained from five different tumors, is in accord with a neutral model of tumor evolution. We calculate that the probability of mutations at any type is so high that it is likely that every possible somatic point mutation is present by the time a tumor is clinically detectable, and this could account for the high frequency by which tumors become resistant to therapeutic agents.

Citation Format: Lawrence A. Loeb, Kaitlyn J. Loubet-Senear, Brendan F. Kohrn, Michael W. Schmitt, Mary P. Bronner, Robert A. Beckman. Extensive subclonal mutations in human colorectal cancers detected by duplex sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3377.

Quantification and expert evaluation of evidence for chemopredictive biomarkers to personalize cancer treatment

Predictive biomarkers have the potential to facilitate cancer precision medicine by guiding the optimal choice of therapies for patients. However, clinicians are faced with an enormous volume of often-contradictory evidence regarding the therapeutic context of chemopredictive biomarkers.

We extensively surveyed public literature to systematically review the predictive effect of 7 biomarkers claimed to predict response to various chemotherapy drugs: ERCC1-platinums, RRM1-gemcitabine, TYMS-5-fluorouracil/Capecitabine, TUBB3-taxanes, MGMT-temozolomide, TOP1-irinotecan/topotecan, and TOP2A-anthracyclines. We focused on studies that investigated changes in gene or protein expression as predictors of drug sensitivity or resistance. We considered an evidence framework that ranked studies from high level I evidence for randomized controlled trials to low level IV evidence for pre-clinical studies and patient case studies.

We found that further in-depth analysis will be required to explore methodological issues, inconsistencies between studies, and tumor specific effects present even within high evidence level studies. Some of these nuances will lend themselves to automation, others will require manual curation. However, the comprehensive cataloging and analysis of dispersed public data utilizing an evidence framework provides a high level perspective on clinical actionability of these protein biomarkers. This framework and perspective will ultimately facilitate clinical trial design as well as therapeutic decision-making for individual patients.

Optimized adaptive enrichment designs

Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.

Abstract 3596: How many tumor indications should be initially studied in clinical development of next-generation immunotherapies

An experimental oncology immunotherapy may have the potential to be effective in a large number of tumor indications. Once a recommended Phase II dose (RP2D) is determined, under resource constraint, a natural strategy is to conduct Phase II proof-of-concept (POC) trials in two waves. A cohort of potential tumor indications is selected for the first wave investigation and the second wave investigation in a different cohort of tumor indications is initiated only after the drug has been demonstrated to be effective in the first wave. Immunotherapy development is a dynamic environment with rapidly evolving mechanistic understanding, constant flow of new data and frequent changes in the competitive landscape. How many tumor indications should be investigated in the first wave given the uncertainties? We attempt to answer this question by maximizing a benefit-cost ratio, defined to be the expected number of effective tumor indications correctly identified in the two waves divided by the expected total sample size for the POC trials in the two waves and the total sample size for the Phase III trials triggered by those with a positive outcome in the first wave. It is found that the optimal number of the first wave POC trials is in a range of approximately three to six, which may vary with the resource constraint but is otherwise robust to the key factors we have considered. A recommendation is made on how much resource should be invested in the first wave.

Citation Format: Cong Chen, Qiqi Deng, Linchen He, Devan Mehrotra, Eric H. Rubin, Robert A. Beckman. How many tumor indications should be initially studied in clinical development of next-generation immunotherapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3596. doi:10.1158/1538-7445.AM2017-3596

Evolutionary dynamics and significance of multiple subclonal mutations in cancer

For the last 40 years the authors have collaborated on trying to understand the complexities of human cancer by formulating testable mathematical models that are based on mutation accumulation in human malignancies. We summarize the concepts encompassed by multiple mutations in human cancers in the context of source, accumulation during carcinogenesis and tumor progression, and therapeutic consequences. We conclude that the efficacious treatment of human cancer by targeted therapy will involve individualized, uniquely directed specific agents singly and in simultaneous combinations, and take into account the importance of targeting resistant subclonal mutations, particularly those subclones with alterations in DNA repair genes, DNA polymerase, and other genes required to maintain genetic stability.

Real-Time Reverse-Transcription Quantitative Polymerase Chain Reaction Assay Is a Feasible Method for the Relative Quantification of Heregulin Expression in Non-Small Cell Lung Cancer Tissue

In preclinical studies, heregulin (HRG) expression was shown to be the most relevant predictive biomarker for response to patritumab, a fully human anti–epidermal growth factor receptor 3 monoclonal antibody. In support of a phase 2 study of erlotinib ± patritumab in non–small cell lung cancer (NSCLC), a reverse-transcription quantitative polymerase chain reaction (RT-qPCR) assay for relative quantification of HRG expression from formalin-fixed paraffin-embedded (FFPE) NSCLC tissue samples was developed and validated and described herein. Test specimens included matched FFPE normal lung and NSCLC and frozen NSCLC tissue, and HRG-positive and HRG-negative cell lines. Formalin-fixed paraffin-embedded tissue was examined for functional performance. Heregulin distribution was also analyzed across 200 NSCLC commercial samples. Applied Biosystems TaqMan Gene Expression Assays were run on the Bio-Rad CFX96 real-time PCR platform. Heregulin RT-qPCR assay specificity, PCR efficiency, PCR linearity, and reproducibility were demonstrated. The final assay parameters included the Qiagen FFPE RNA Extraction Kit for RNA extraction from FFPE NSCLC tissue, 50 ng of RNA input, and 3 reference (housekeeping) genes (HMBS, IPO8, and EIF2B1), which had expression levels similar to HRG expression levels and were stable among FFPE NSCLC samples. Using the validated assay, unimodal HRG distribution was confirmed across 185 evaluable FFPE NSCLC commercial samples. Feasibility of an RT-qPCR assay for the quantification of HRG expression in FFPE NSCLC specimens was demonstrated.

Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines

A personalized medicine may benefit a subpopulation with certain predictive biomarker signatures or certain disease types. However, there is great uncertainty about drug activity in a subpopulation when designing a confirmatory trial in practice, and it is logical to take a two-stage approach with the study unless credible external information is available for decision-making purpose. The first stage deselects (or prunes) non-performing subpopulations at an interim analysis, and the second stage pools the remaining subpopulations in the final analysis. The endpoints used at the two stages can be different in general. A key issue of interest is the statistical property of the test statistics and point estimate at the final analysis. Previous research has focused on type I error control and power calculation for such two-stage designs. This manuscript will investigate estimation bias of the treatment effect, which is implicit in the adjustment of nominal type I error for multiplicity control in such two-stage designs. Previous work handles the treatment effect of an intermediate endpoint as a nuisance parameter to provide the most conservative type I error control. This manuscript takes the same approach to explore the bias. The methodology is applied to the two previously studied designs. In the first design, patients with different biomarker levels are enrolled in a study, and the treatment effect is assumed to be in an order. The goal of the interim analysis is to identify a biomarker cut-off point for the subpopulations. In the second design, patients with different tumour types but the same biomarker signature are included in a trial applying a basket design. The goal of the interim analysis is to identify a subset of tumour types in the absence of treatment effect ordering. Closed-form equations are provided for the estimation bias as well as the variance under the two designs. Simulations are conducted under various scenarios to validate the analytic results that demonstrated that the bias can be properly estimated in practice. Worked examples are presented. Extensions to general adaptive designs and operational considerations are discussed. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract A08: Extensive subclonal mutations in human colorectal cancers detected by Duplex Sequencing

The accumulation of somatic mutations is a defining hallmark of cancer. The genomes of solid tumors contain thousands of mutations that are present in most or all of the malignant cells in that tumor. In addition to these clonal mutations, subclonal mutations, those occurring only in a fraction of malignant cells, likely contribute to the phenotypic and morphologic heterogeneity of cancer cells within a tumor. The extent of subclonal mutations in cancer, however, has been difficult to quantify, as the high error rate of next-generation sequencing precludes reliable detection of mutations present in fewer than 5% of cells. Here, we apply the highly accurate Duplex Sequencing methodology to quantify the extent of subclonal mutations in colorectal cancer (CRC) and paired normal mucosa. By sequencing known CRC driver and non-driver genes, we find that colorectal cancers without known DNA repair deficits harbor an extensive complement of subclonal mutations, in addition to the large number of clonal mutations previously identified. We show that normal colonic mucosa also accumulates substantial numbers of subclonal mutations in an age-dependent manner. The frequency of tumor-associated subclonal mutations, however, does not correlate with age, and has a spectrum distinct from that seen in normal tissue, indicating different mechanisms of mutation accumulation are operative in normal and tumor tissue. The frequency of tumor cells harboring unique subclonal mutations is so high that it is likely that every possible neutral somatic point mutation is present by the time a tumor is clinically detectable. We propose that these subclonal mutations likely accumulate in a series of punctuated bursts, making it highly likely that resistant subclones will emerge during chemotherapy.

Citation Format: Edward John Paul Fox, Michael W. Schmitt, Kate S. Reid-Bayliss, Robert A. Beckman, Lawrence A. Loeb. Extensive subclonal mutations in human colorectal cancers detected by Duplex Sequencing. [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer: From Initiation to Outcomes; 2016 Sep 17-20; Tampa, FL. Philadelphia (PA): AACR; Cancer Res 2017;77(3 Suppl):Abstract nr A08.

Long range personalized cancer treatment strategies incorporating evolutionary dynamics

Background

Current cancer precision medicine strategies match therapies to static consensus molecular properties of an individual’s cancer, thus determining the next therapeutic maneuver. These strategies typically maintain a constant treatment while the cancer is not worsening. However, cancers feature complicated sub-clonal structure and dynamic evolution. We have recently shown, in a comprehensive simulation of two non-cross resistant therapies across a broad parameter space representing realistic tumors, that substantial improvement in cure rates and median survival can be obtained utilizing dynamic precision medicine strategies. These dynamic strategies explicitly consider intratumoral heterogeneity and evolutionary dynamics, including predicted future drug resistance states, and reevaluate optimal therapy every 45 days. However, the optimization is performed in single 45 day steps (“single-step optimization”).

Results

Herein we evaluate analogous strategies that think multiple therapeutic maneuvers ahead, considering potential outcomes at 5 steps ahead (“multi-step optimization”) or 40 steps ahead (“adaptive long term optimization (ALTO)”) when recommending the optimal therapy in each 45 day block, in simulations involving both 2 and 3 non-cross resistant therapies. We also evaluate an ALTO approach for situations where simultaneous combination therapy is not feasible (“Adaptive long term optimization: serial monotherapy only (ALTO-SMO)”). Simulations utilize populations of 764,000 and 1,700,000 virtual patients for 2 and 3 drug cases, respectively. Each virtual patient represents a unique clinical presentation including sizes of major and minor tumor subclones, growth rates, evolution rates, and drug sensitivities.

While multi-step optimization and ALTO provide no significant average survival benefit, cure rates are significantly increased by ALTO. Furthermore, in the subset of individual virtual patients demonstrating clinically significant difference in outcome between approaches, by far the majority show an advantage of multi-step or ALTO over single-step optimization. ALTO-SMO delivers cure rates superior or equal to those of single- or multi-step optimization, in 2 and 3 drug cases respectively.

Conclusion

In selected virtual patients incurable by dynamic precision medicine using single-step optimization, analogous strategies that “think ahead” can deliver long-term survival and cure without any disadvantage for non-responders. When therapies require dose reduction in combination (due to toxicity), optimal strategies feature complex patterns involving rapidly interleaved pulses of combinations and high dose monotherapy.

Reviewers

This article was reviewed by Wendy Cornell, Marek Kimmel, and Andrzej Swierniak. Wendy Cornell and Andrzej Swierniak are external reviewers (not members of the Biology Direct editorial board). Andrzej Swierniak was nominated by Marek Kimmel.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0153-2) contains supplementary material, which is available to authorized users.

PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms

Adaptive, seamless, multisponsor, multitherapy clinical trial designs executed as large scale platforms, could create superior evidence more efficiently than single-sponsor, single-drug trials. These trial PIPELINEs also could diminish barriers to trial participation, increase the representation of real-world populations, and create systematic evidence development for learning throughout a therapeutic life cycle, to continually refine its use. Comparable evidence could arise from multiarm design, shared comparator arms, and standardized endpoints-aiding sponsors in demonstrating the distinct value of their innovative medicines; facilitating providers and patients in selecting the most appropriate treatments; assisting regulators in efficacy and safety determinations; helping payers make coverage and reimbursement decisions; and spurring scientists with translational insights. Reduced trial times and costs could enable more indications, reduced development cycle times, and improved system financial sustainability. Challenges to overcome range from statistical to operational to collaborative governance and data exchange.

Optimizing Trial Designs for Targeted Therapies

An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor's as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.

Adaptive Design for a Confirmatory Basket Trial in Multiple Tumor Types Based on a Putative Predictive Biomarker

Increasingly, tumors are defined on a molecular basis rather than only on histology, and targeted agents, which address these molecular subtypes, are being approved. This profusion of molecular subtypes creates "rare" diseases as subsets of common cancers, leading to difficulties in enrolling sufficiently large cohorts for confirmatory trials. However, if the molecular subtype is shared across various histologies, these may be pooled into a basket trial. To date, basket trials have been primarily for exploratory early development. In this perspective, we consider qualitative designs for confirmatory basket trials. These confirmatory basket designs will provide patients in niche indications with enhanced access to novel therapies, facilitate development and full approval for niche indications, allow accelerated approval for indications within a basket based on a surrogate endpoint, reduce development cost by combining trials, and enhance the ability of regulatory authorities to evaluate risk and benefit in niche indications.

Abstract LB-338: Extensive subclonal mutations in human colorectal cancers detected by duplex sequencing

The accumulation of somatic mutations is a defining hallmark of cancer. The genomes of solid tumors contain thousands of mutations that are present in most or all of the malignant cells in that tumor. In addition to these clonal mutations, subclonal mutations - those occurring only in a fraction of malignant cells - likely contribute to the phenotypic and morphologic heterogeneity of cancer cells within a tumor. These sub-populations may be the source for the rapid emergence of therapeutic resistance. The extent of subclonal mutations in cancer, however, has been difficult to quantify, as the high error rate of next-generation sequencing precludes reliable detection of mutations present in fewer than 5% of cells. Here, we apply the highly accurate Duplex Sequencing methodology to quantify the extent of subclonal mutations in 15 colorectal cancers (CRC) and paired normal mucosa. We also quantify the clonal somatic mutation load by exome sequencing. By sequencing known CRC driver and non-driver genes (depth &gt;5,000X and accuracy &gt;10-7), we find that colorectal cancers without known DNA repair deficits harbor an extensive complement of subclonal mutations, suggesting that mutational diversity of CRCs has been greatly underestimated. We show that normal colonic mucosa also accumulates substantial numbers of subclonal mutations, predominantly CG&gt;TA transitions at CpG dinucleotides, in an age-dependent manner. However, the burden of tumor-associated subclonal mutations does not correlate with age and has a distinct spectrum (relative increase in TA&gt;CG transitions) indicating different mechanisms of mutation accumulation are operative in normal and tumor tissue. The frequency of tumor cells harboring unique subclonal mutations is so high that it is likely that every possible neutral somatic point mutation is present by the time a tumor is clinically detectable This could account for the high frequency by which tumors become resistant to chemotherapeutic agents.

Citation Format: Edward J.P. Fox, Michael W. Schmitt, Kate S. Reid-Bayliss, Robert A. Beckman, Lawrence A. Loeb. Extensive subclonal mutations in human colorectal cancers detected by duplex sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-338.