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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.

A distinct model of neutral evolution of rare cancer mutations is described and contrasted with models relying on the infinite sites approximation (that a specific mutation arises in only one cell at any instant). An explosion of genetic diversity is predicted at clinical cell numbers and may explain the progressive refractoriness of cancers during a clinical course. The widely used infinite sites assumption may not be applicable for clinical cancers., (© 2024. The Author(s).)

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.

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., (©2023 American Association for Cancer Research.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© 2023. The Author(s), under exclusive licence to The Drug Information Association, Inc.)

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., (© 2023 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

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., (© 2023. The Author(s).)

Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. We review selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker-defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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., Competing Interests: Author TS is/was employed by IBM. SM currently employed by Pfizer Worldwide R and D. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Stahlberg, Abdel-Rahman, Aguilar, Asadpoure, Beckman, Borkon, Bryan, Cebulla, Chang, Chatterjee, Deng, Dolatshahi, Gevaert, Greenspan, Hao, Hernandez-Boussard, Jackson, Kuijjer, Lee, Macklin, Madhavan, McCoy, Mohammed Mirzaei, Razzaghi, Rocha, Shahriyari, Shmulevich, Stover, Sun, Syeda-Mahmood, Wang, Wang and Zervantonakis.)

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., Competing Interests: Conflicts of Interest The authors do not report any conflicts of interest to disclose other than SMB and SM are current employees of AstraZeneca, Gaithersburg, MD. However, this study was done before they joined AstraZeneca.

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 k = 3 , 4 . 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.

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., Competing Interests: The authors have declared that no competing interests exist.

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., (Copyright © 2022 Elsevier Inc. All rights reserved.)

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."

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.

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-2D b 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., (Copyright © 2020 by The American Association of Immunologists, Inc.)

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.

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.

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.

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 (10 4 ) 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 ).

As biomarker information from early-phase trials can be unreliable due to high variability, it is logical to take a prospective two-stage approach when designing a late-phase confirmatory trial, ie, refining the target population at the first stage and performing the hypothesis testing at the second stage. The use of a reliable intermediate endpoint at the first stage can further improve the trial efficiency from both time and cost perspectives. Nevertheless, there are needs for expanding such two-stage confirmatory designs to more stages for monitoring efficacy on the refined population. There is limited literature on this matter, particularly for two popular designs with population selection midway, ie, the biomarker enrichment design and the basket design. In this manuscript, we focus on these two popular designs and discuss how to implement the interim efficacy analyses after population refinement while controlling type I error. Power and stopping probability are also explored for the two designs., (© 2019 John Wiley & Sons, Ltd.)

Despite recent advances in targeted drugs and immunotherapy, cancer remains "the emperor of all maladies" due to almost inevitable emergence of resistance. Drug resistance is thought to be driven by genetic alterations and/or dynamic plasticity that deregulate pathway activities and regulatory programs of a highly heterogeneous tumour. In this study, we propose a modelling framework to simulate population dynamics of heterogeneous tumour cells with reversible drug resistance. Drug sensitivity of a tumour cell is determined by its internal states, which are demarcated by coordinated activities of multiple interconnected oncogenic pathways. Transitions between cellular states depend on the effects of targeted drugs and regulatory relations between the pathways. Under this framework, we build a simple model to capture drug resistance characteristics of BRAF-mutant melanoma, where two cell states are determined by two mutually inhibitory - main and alternative - pathways. We assume that cells with an activated main pathway are proliferative yet sensitive to the BRAF inhibitor, and cells with an activated alternative pathway are quiescent but resistant to the drug. We describe a dynamical process of tumour growth under various drug regimens using the explicit solutions of mean-field equations. Based on these solutions, we compare efficacy of three treatment strategies from simulated data: static treatments with continuous and constant dosages, periodic treatments with regular intermittent active phases and drug holidays, and treatments derived from optimal control theory (OCT). Periodic treatments outperform static treatments with a considerable margin, while treatments based on OCT outperform the best periodic treatment. Our results provide insights regarding optimal cancer treatment modalities for heterogeneous tumours, and may guide the development of optimal therapeutic strategies to circumvent plastic drug resistance. They can also be used to evaluate the efficacy of suboptimal treatments that may account for side effects of the treatment and the cost of its application., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

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/m 2 loading dose; 250 mg/m 2 maintenance dose weekly) + cisplatin (100 mg/m 2 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., (©2018 American Association for Cancer Research.)

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.

It is well documented in this genomic era that an investigational new drug may have greater treatment effect in a biomarker positive population than in the biomarker negative population. However, limited by preclinical data and early phase clinical data, a lot of Phase 3 confirmatory trials are initiated without fully understanding the biomarker effect. In this article, we will investigate the impact of adaptive population expansion on the overall Type I error in two statistical designs. The endpoint for making the adaptive decision can be different from the primary endpoint of the study. The first design allows expansion of study population from biomarker positive patients to all-comers if the treatment effect in the biomarker positive population is more impressive than expected, suggesting broader activity of the study drug. We show that, under this design, the trial outcome can be tested at the desired alpha level without inflating the Type I error when the adaptive decision is based on the primary endpoint of the study or based on an endpoint non-negatively correlated with the primary endpoint, an assumption that generally holds in practice. The second design allows addition of biomarker positive patients in an all-comer study if the treatment effect in the biomarker negative population is less impressive than expected, suggesting lower probability of success in the all-comer population. We show that, under this design, the trial outcome can always be tested at the desired alpha level without inflating the Type I error., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: Competitive radiolabeled antibody imaging can determine the unlabeled intact antibody dose that fully blocks target binding but may be confounded by heterogeneous tumor penetration. We evaluated the hypothesis that smaller radiolabeled constructs can be used to more accurately evaluate tumor expressed receptors., Procedures: The Krogh cylinder distributed model, including bivalent binding and variable intervessel distances, simulated distribution of smaller constructs in the presence of increasing doses of labeled antibody forms., Results: Smaller constructs <25 kDa accessed binding sites more uniformly at large distances from blood vessels compared with larger constructs and intact antibody. These observations were consistent for different affinity and internalization characteristics of constructs. As predicted, a higher dose of unlabeled intact antibody was required to block binding to these distant receptor sites., Conclusions: Small radiolabeled constructs provide more accurate information on total receptor expression in tumors and reveal the need for higher antibody doses for target receptor blockade.

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., (Copyright © 2017 Elsevier B.V. All rights reserved.)

An experimental oncology immunotherapy may have the potential to be effective in a large number of tumor indications. We have considered a staggered approach for efficacy screening in subjects with an unmet medical need. A cohort of 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. The effectiveness of an experimental immunotherapy is unknown at the planning stage, and the assumptions at the planning stage are subject to revision later on. How many tumor indications should be investigated in the first wave for the development program to be cost-effective amid 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 (benefit) divided by the expected total sample size in the two waves and the subsequent trials for more definitive testing triggered by those with a positive outcome in the first wave (cost). It is found from the benefit-cost ratio analyses that, depending on resource availability, three to five tumor indications may be initiated in the first wave to properly balance the risk and benefit, and adequate investment is important to maintain the quality of statistical design., (Copyright © 2017 Elsevier Inc. All rights reserved.)

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.

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., (Copyright © 2017 John Wiley & Sons, Ltd.)

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., Competing Interests: DECLARATION OF CONFLICTING INTERESTS: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JK and KS are former employees of MolecularMD. XJ, RAB, and DF are former employees of Daiichi Sankyo Pharma Development. WF is an employee of Daiichi Sankyo Pharma Development. KN is an employee of Daiichi Sankyo Co., Ltd. MS was an employee of U3 Pharma GmbH. CS is an employee of MolecularMD.

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.

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: RAB is stockholder in Johnson & Johnson. Furthermore he is Founder and Chief Scientific Officer of Oncomind, LLC. CFB is employee and stake holder of AstraZeneca. MP is head of the Center for Medical Statistics, Informatics, and Intelligent Systems that receives grants from industry. SJ, FK and TO have declared that no competing interests exist. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background & Aims: Tigatuzumab is a humanized monoclonal antibody that acts as a death receptor-5 agonist and exerts tumour necrosis factor-related apoptosis-inducing ligand-like activity. In this phase II study, safety and tolerability of the combination of tigatuzumab and sorafenib was evaluated in patients with advanced hepatocellular carcinoma., Methods: Adults with advanced hepatocellular carcinoma, measurable disease, and an Eastern Cooperative Oncology Group performance score⩽1 were enrolled. Eligible subjects were randomly assigned 1:1:1 to tigatuzumab (6 mg/kg loading, 2 mg/kg/week maintenance) plus sorafenib 400 mg twice daily; tigatuzumab (6 mg/kg loading, 6 mg/kg/week maintenance) plus sorafenib 400 mg twice daily; or sorafenib 400 mg twice daily. The primary end point was time to progression. Secondary end points included overall survival and safety., Results: 163 subjects were randomized to treatment. Median time to progression was 3.0 months in the tigatuzumab 6/2 mg/kg combination group (p=0.988 vs. sorafenib), 3.9 months in the tigatuzumab 6/6 mg/kg combination group (p=0.586 vs. sorafenib), and 2.8 months in the sorafenib alone group. Median overall survival was 12.2 months in the tigatuzumab 6/6 mg/kg combination group (p=0.659 vs. sorafenib), vs. 8.2 months in both other treatment groups (p=0.303, tigatuzumab 6/2 mg/kg combination vs. sorafenib). The most common treatment-emergent adverse events were palmar-plantar erythrodysesthesia syndrome, diarrhea, and decreased appetite., Conclusions: Tigatuzumab combined with sorafenib vs. sorafenib alone in adults with advanced hepatocellular carcinoma did not meet its primary efficacy end point, although tigatuzumab plus sorafenib is well tolerated in hepatocellular carcinoma., (Copyright © 2015 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Purpose: CS-1008 (tigatuzumab) is a humanized, monoclonal immunoglobulin G1 (IgG1) agonistic antibody to human death receptor 5. The purpose of this study was to investigate the impact of CS-1008 dose on the biodistribution, quantitative tumor uptake, and antitumor response in patients with metastatic colorectal cancer (mCRC)., Patients and Methods: Patients with mCRC who had received at least one course of chemotherapy were assigned to one of five dosage cohorts and infused with a weekly dose of CS-1008. Day 1 and day 36 doses were trace-labeled with indium-111 ((111)In), followed by whole-body planar and regional single-photon emission computed tomography (SPECT) imaging at several time points over the course of 10 days., Results: Nineteen patients were enrolled. (111)In-CS-1008 uptake in tumor was observed in only 12 patients (63%). (111)In-CS-1008 uptake and pharmacokinetics were not affected by dose or repeated drug administration. (111)In-CS-1008 biodistribution showed gradual blood-pool clearance and no abnormal uptake in normal tissue. No anti-CS-1008 antibody development was detected. One patient achieved partial response (3.7 months duration), eight patients had stable disease, and 10 patients had progressive disease. Clinical benefit rate (stable disease + partial response) in patients with (111)In-CS-1008 uptake in tumor was 58% versus 28% in patients with no uptake. An analysis of individual lesions showed that lesions with antibody uptake were one third as likely to progress as those without antibody uptake (P = .07). Death-receptor-5 expression in archived tumor samples did not correlate with (111)In-CS-1008 uptake (P = .5) or tumor response (P = .6)., Conclusion: Death-receptor-5 imaging with (111)In-CS-1008 reveals interpatient and intrapatient heterogeneity of uptake in tumor, is not dose dependent, and is predictive of clinical benefit in the treatment of patients who have mCRC., (© 2015 by American Society of Clinical Oncology.)

Objective: Examine whether use of a grocery list is associated with healthier diet and weight among food desert residents., Methods: Cross-sectional analysis of in-person interview data from randomly selected household food shoppers in 2 low-income, primarily African American urban neighborhoods in Pittsburgh, PA with limited access to healthy foods., Results: Multivariate ordinary least-square regressions conducted among 1,372 participants and controlling for sociodemographic factors and other potential confounding variables indicated that although most of the sample (78%) was overweight or obese, consistently using a list was associated with lower body mass index (based on measured height and weight) (adjusted multivariant coefficient = 0.095) and higher dietary quality (based on the Healthy Eating Index-2005) (adjusted multivariant coefficient = 0.103) (P < .05)., Conclusions and Implications: Shopping with a list may be a useful tool for low-income individuals to improve diet or decrease body mass index., (Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.)