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.

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.

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.

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.