The Reversed Multi-Arm Two-Stage (RevMATS) Design in Oncology Dose Optimization

Speaker: Yue Yang, PhD, Sanofi

Yue Yang is an associated statistical project leader at Sanofi. She obtained her Ph.D. in statistics from North Carolina State University and currently works in the oncology statistics team at Sanofi. She has led several early oncology projects for both solid tumors and hematologic malignancies. Her current research focuses on clinical trial design and decision-making in oncology, including but not limited to basket trial design, seamless phase 2/3 design, study design with an external control arm, and dose optimization design.

Abstract

In response to the FDA’s Project Optimus, which redefined the dose optimization paradigm in oncology drug development, various novel trial designs have emerged. Jiang et al. (2023) proposed the Multi-Arm Two-Stage (MATS) design, integrating proof-of-concept (PoC) evaluation and dose optimization across multiple indications under a Bayesian hierarchical model (BHM) framework. For all considered indications/cohorts, the MATS design enrolls participants at the most promising dose initially, followed by dose randomization only in selected cohorts. We propose the Reversed MATS (RevMATS) design, which reverses these two stages—randomizing participants to multiple doses across indications/cohorts first before selecting the optimal dose for continued enrollment (or stopping due to early futility). This approach is suited for cases where multiple doses show similar early activities or when evidence is insufficient to favor one dose over the other a priori. The RevMATS design further improves the BHM by incorporating broader dose-response profiles (as seen in recent novel therapies of different modalities) and explicitly leveraging early-phase dose-escalation data to enhance decision-making.

DTE-BOP2: A Bayesian Optimal Phase II Clinical Trial Design Accounting for Delayed Treatment Effects in Immunotherapy

Speaker: Haitao Pang, Associate Professor of Biostatistics, St. Jude Children’s Research Hospital

Dr. Haitao Pan is an Associate Professor of Biostatistics at St. Jude Children’s Research Hospital. His research focuses on innovative clinical trial designs, particularly in oncology, across all phases of development. He has authored over 60 peer-reviewed publications and a book published by Springer Nature, and has developed 18 R software packages to support adaptive trial design and sample size calculation—many of which are used in clinical trial protocols at St. Jude. Dr. Pan is the statistician for the New Approaches to Neuroblastoma Therapy (NANT) consortium and a member of the PCDC INRG Committee. He also holds adjunct faculty appointments at Florida State University and The University of Memphis. Dr. Pan earned Ph.D. degrees in Preventive Medicine (China) and Biostatistics (MD Anderson Cancer Center).

Abstract

DTE-BOP2 is a novel Bayesian optimal phase II clinical trial design tailored for immunotherapy trials that exhibit delayed treatment effects. Unlike conventional designs that often ignore such delays and risk reduced statistical power, DTE-BOP2 explicitly models the uncertainty in delay duration using a truncated gamma prior, informed either by expert elicitation or default specifications. Integrated into a Bayesian decision-making framework, this approach adaptively adjusts trial decision rules while rigorously controlling type I and type II error rates. Built upon the established BOP2 framework, DTE-BOP2 achieves substantial sample size reductions and notable power gains in the presence of treatment delays, as demonstrated by extensive simulation studies. To support implementation, we provide an open-source R package, DTEBOP2, enabling researchers and clinicians to design and evaluate immunotherapy phase II trials with improved efficiency and statistical rigor.

Defining ‘optimal’ dose in early phase trials

Speaker: Dr. Shivaani Kummar, Oregon Health & Science University

Dr. Shivaani Kummar is Margaret and Lester DeArmond Chair of Molecular Oncology, interim Chief Executive of the Knight Cancer Institute, Division Chief of Hematology and Medical Oncology, co-Director of the Center of Experimental Therapeutics, Oregon Health & Science University, Portland, Oregon, USA. She specializes in conducting pharmacokinetic and pharmacodynamic driven first-in-human trials tailored to make early, informed decisions regarding the suitability of novel molecular agents for further clinical investigation. She is a member of scientific planning committees of national and international professional organizations and has authored over 180 peer reviewed publications, 10 book chapters, and co-edited a book on ‘Novel Designs of Early Phase Trials for Cancer Therapies’. She is currently a member of the American Association for Cancer Research Exploratory IND/Phase 0 Clinical Trials Task Force, and served as co-Chair of the Clinical Trials Committee, AACR Annual Meeting 2023-2024.

Abstract

Objectives of Phase I trials have traditionally been to determine the safety and tolerability of a novel agent and to determine the recommended phase 2 dose (RP2D). However, clinical, pharmacokinetic and pharmacodynamics considerations in defined patient populations have led to the need to define a dose range (taking into account dose and schedule) based on data from a dose confirmation component of the phase I trial. Presentation will cover trial designs and considerations around defining the dose in phase I trials.

Maintaining Flexibility in Dose Optimization in Early Phase Oncology Trials

Speaker: Dr. Leena Gandhi, NextPoint Therapeutics

Dr. Leena Gandhi is a thoracic oncologist with a focus on immunotherapy and early drug development. She received her PhD from the UC Berkeley and her MD from New York University. She was a faculty member at Dana-Farber Cancer Institute where she developed multiple investigator-initiated trials with associated correlative studies to help define biomarkers of response. She also led pivotal studies demonstrating the utility of PDL1 as a biomarker for efficacy of anti-PD1 agents in lung cancer and the value of combining immunotherapy and chemotherapy in the treatment of non-small cell lung cancer. Dr. Gandhi has served as the Director of Thoracic Medical Oncology at New York University and as Vice President of Immuno-Oncology Development at Eli Lilly leading the early development of novel immuno-oncology agents across cancer types before returning to Dana-Farber in 2020, where she was the founding director of the Center for Cancer Therapeutic Innovation, a cross-malignancy novel therapeutics hub, prior to joining NextPoint Therapeutics in 2023 where she oversees translational and clinical development.

Discussant: Yue Yang, PhD, Sanofi

Naitee Ting is a Fellow of American Statistical Association (ASA). He is currently Vice President of StatsVita. Naitee is also an Adjunct Professor of Department of Statistics at University of Connecticut, Adjunct Professor of Department of Biostatistics at Columbia University. He joined StatsVita in October, 2024. Before StatsVita, Naitee has been with Boehringer Ingelheim Pharmaceuticals, Inc. (BI) for 15 years, and he was working at Pfizer Inc. for 22 years (1987-2009). Naitee received his Ph.D. in 1987 from Colorado State University (major in Statistics). He has an M.S. degree from Mississippi State University (1979, Statistics) and a B.S. degree from College of Chinese Culture (1976, Forestry) at Taipei, Taiwan.

Naitee published articles in Technometrics, Statistics in Medicine, Drug Information Journal, Journal of Statistical Planning and Inference, Journal of Biopharmaceutical Statistics, Biometrical Journal, Statistics and Probability Letters, and Journal of Statistical Computation and Simulation. His book “Dose Finding in Drug Development” was published in 2006 by Springer, and is considered as the leading reference in the field of dose response clinical trials. The book “Fundamental Concepts for New Clinical Trialists”, co-authored with Scott Evans, was published by CRC in 2015. Another book “Phase II Clinical Development of New Drugs”, co-authored with Chen, Ho, and Cappelleri was published in 2017 (Springer). Naitee is an adjunct professor of Columbia University, University of Connecticut, and Colorado State University. Naitee has been an active member of both the ASA and the International Chinese Statistical Association (ICSA).

AI-Driven Insights from Clinico-Genomics Data for Precision Oncology

Speaker: Ruishan Liu, PhD, USC

Ruishan Liu is an Assistant Professor of Computer Science at USC. She received her PhD in Electrical Engineering at Stanford University in 2022 and was a Postdoctoral Fellow in Biomedical Data Science at Stanford University from 2022 to 2023. Her research lies in AI for healthcare and biomedicine. She was selected as the Rising Star in Data Science, the Next Generation in Biomedicine, and the Rising Star in Engineering in Health. She led the project Trial Pathfinder - AI for clinical trials design, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021.

Abstract

Understanding how genetic alterations influence treatment response is key to advancing precision oncology. In this talk, I will discuss our large-scale analysis of 78,287 cancer patients, integrating somatic mutation data with real-world treatment outcomes (Liu et al. Nature Communications 2024). First, I will highlight 776 identified mutation-treatment interactions across 20 cancer types, uncovering key genomic biomarkers that predict responses to immunotherapies, chemotherapies, and targeted therapies. Second, I will present our pathway-treatment interaction analysis, showing how broader signaling pathways influence treatment efficacy. Finally, I will introduce our machine learning-based risk score for predicting immunotherapy response in advanced non-small cell lung cancer. This work demonstrates the power of large-scale clinico-genomics data and AI in guiding personalized cancer therapy.

Network Analysis and Deep Learning of Adaptive Immune Sequences in Cancer Subjects Receiving COVID Vaccination

Speaker: Dr. Li Zhang, Professor, UCSF

Dr. Li Zhang is a Professor of Department Medicine and Department of Epidemiology and Biostatistics at University of California San Francisco (UCSF) and a member and principal statistician of UCSF Helen Diller Family Comprehensive Cancer Center (HDFCCC). She obtained her Ph.D. in Statistics from University of Florida in 2006. Dr. Zhang has almost twenty years of experience in applying statistical method in biomedical research. Her statistical methodological research interests are cancer epidemiology and immunoinformatics. She received multiple research rewards as a principal investigator (PI) and co-Investigator (Co-I) from NIH, DoD and other foundation grants. She has about 200 peer-reviewed publications. She has served on American Society of Clinical Oncology (ASCO) Career Development Award and Young Investigator Review Committee as a statistical reviewer and NIH/NCI Study Sections. She is also the Council of representative of ASA Statistical Consulting Section.

Abstract

Characterization of adaptive immune sequences is critical for understanding the adaptive immune system and developing treatments for cancer, infectious diseases, and autoimmune conditions. The COVID-19 pandemic presented the opportunity to capture longitudinal adaptive immune sequence data in the context of controlled (vaccination) and variable (cancer) immune responses to field test the value of adaptive immune sequences as a biomarker for general and specific immune responses. This study aims to identify TCR-based immune signatures linked to cancer, SARS-CoV-2 infection and vaccination, and demographic factors by utilizing the TCR-sequence data from the cancer cohort of the SeroNet study, which were treated at Cedars-Sinai Medical Center. We constructed TCR networks, identified the clonal expansion of public TCR clusters over time, and found candidate vaccine-associated TCR clusters by using the NAIR R package. Furthermore, we explored TCR-antigen peptide recognition using our PepTCR-Net framework. We found that cancer patients exhibit different T-cell phenotypes and specificity to COVID-19 vaccination, and cancer treatments influence this T-cell response and TCR repertoire. These findings underscore the clinical potential of TCR repertoire analysis in monitoring immune responses to vaccines and stratifying subjects based on immune profiles. Identifying TCR-peptide interactions relevant to disease and therapy paves the way for personalized medicine and improved treatment strategies for cancer and viral infections.