medRxiv (2024)

Kieran O’Neill, Erin Pleasance, Jeremy Fan, Vahid Akbari, Glenn Chang, Katherine Dixon, Veronika Csizmok, Signe MacLennan, Vanessa Porter, Andrew Galbraith, Cameron J. Grisdale, Luka Culibrk, John H. Dupuis, Richard Corbett, James Hopkins, Reanne Bowlby, Pawan Pandoh, Duane E. Smailus, Dean Cheng, Tina Wong, Connor Frey, Yaoqing Shen, Luis F. Paulin, Fritz J. Sedlazeck, Jessica M.T. Nelson, Eric Chuah, Karen L. Mungall, Richard A. Moore, Robin Coope, Andrew J. Mungall, Melissa K. McConechy, Laura M. Williamson, Kasmintan A. Schrader, Stephen Yip, Marco A. Marra, Janessa Laskin, Steven J.M. Jones

Abstract

note: this article is a preprint and has not been peer-reviewed. [what does this mean?]

doi: 10.1101/2024.02.20.24302959

Journal of Clinical Oncology (2023)

Laura K. Hilton, Henry S. Ngu, Brett Collinge, Kostiantyn Dreval, Susana Ben-Neriah, Christopher K. Rushton, Jasper C H Wong, Manuela Cruz, Andrew Roth, Merrill Boyle, Barbara Meissner, Graham W. Slack, Pedro Farinha, Jeffrey W. Craig, Alina S. Gerrie, Ciara L. Freeman, Diego Villa, Judith A. Rodrigo, Kevin Song, Michael Crump, Lois Shepherd, Annette E Hay, John Kuruvilla, Kerry J. Savage, Robert Kridel, Aly Karsan, Marco A. Marra, Laurie H. Sehn, Christian Steidl, Ryan D. Morin, David W. Scott

Abstract

Purpose: Diffuse large B-cell lymphoma (DLBCL) is cured in more than 60% of patients, but outcomes remain poor for patients experiencing disease progression or relapse (refractory or relapsed DLBCL [rrDLBCL]), particularly if these events occur early. Although previous studies examining cohorts of rrDLBCL have identified features that are enriched at relapse, few have directly compared serial biopsies to uncover biological and evolutionary dynamics driving rrDLBCL. Here, we sought to confirm the relationship between relapse timing and outcomes after second-line (immuno)chemotherapy and determine the evolutionary dynamics that underpin that relationship.

Patients and methods: Outcomes were examined in a population-based cohort of 221 patients with DLBCL who experienced progression/relapse after frontline treatment and were treated with second-line (immuno)chemotherapy with an intention-to-treat with autologous stem-cell transplantation (ASCT). Serial DLBCL biopsies from a partially overlapping cohort of 129 patients underwent molecular characterization, including whole-genome or whole-exome sequencing in 73 patients.

Results: Outcomes to second-line therapy and ASCT are superior for late relapse (>2 years postdiagnosis) versus primary refractory (<9 months) or early relapse (9-24 months). Diagnostic and relapse biopsies were mostly concordant for cell-of-origin classification and genetics-based subgroup. Despite this concordance, the number of mutations exclusive to each biopsy increased with time since diagnosis, and late relapses shared few mutations with their diagnostic counterpart, demonstrating a branching evolution pattern. In patients with highly divergent tumors, many of the same genes acquired new mutations independently in each tumor, suggesting that the earliest mutations in a shared precursor cell constrain tumor evolution toward the same genetics-based subgroups at both diagnosis and relapse.

Conclusion: These results suggest that late relapses commonly represent genetically distinct and chemotherapy-naïve disease and have implications for optimal patient management.

PubMed ID: 37319384

doi: 10.1200/JCO.23.00570

Nature Medicine (2023) 

Bertrand Routy, John G. Lenehan, Wilson H. Miller Jr, Rahima Jamal, Meriem Messaoudene, Brendan A. Daisley, Cecilia Hes, Kait F. Al, Laura Martinez-Gili, Michal Punčochář, Scott Ernst, Diane Logan, Karl Belanger, Khashayar Esfahani, Corentin Richard, Marina Ninkov, Gianmarco Piccinno, Federica Armanini, Federica Pinto, Mithunah Krishnamoorthy, Rene Figueredo, Pamela Thebault, Panteleimon Takis, Jamie Magrill, LeeAnn Ramsay, Lisa Derosa, Julian R. Marchesi, Seema Nair Parvathy, Arielle Elkrief, Ian R. Watson, Rejean Lapointe, Nicola Segata, S. M. Mansour Haeryfar, Benjamin H. Mullish, Michael S. Silverman, Jeremy P. Burton, Saman Maleki Vareki

Abstract

Fecal microbiota transplantation (FMT) represents a potential strategy to overcome resistance to immune checkpoint inhibitors in patients with refractory melanoma; however, the role of FMT in first-line treatment settings has not been evaluated. We conducted a multicenter phase I trial combining healthy donor FMT with the PD-1 inhibitors nivolumab or pembrolizumab in 20 previously untreated patients with advanced melanoma. The primary end point was safety. No grade 3 adverse events were reported from FMT alone. Five patients (25%) experienced grade 3 immune-related adverse events from combination therapy. Key secondary end points were objective response rate, changes in gut microbiome composition and systemic immune and metabolomics analyses. The objective response rate was 65% (13 of 20), including four (20%) complete responses. Longitudinal microbiome profiling revealed that all patients engrafted strains from their respective donors; however, the acquired similarity between donor and patient microbiomes only increased over time in responders. Responders experienced an enrichment of immunogenic and a loss of deleterious bacteria following FMT. Avatar mouse models confirmed the role of healthy donor feces in increasing anti-PD-1 efficacy. Our results show that FMT from healthy donors is safe in the first-line setting and warrants further investigation in combination with immune checkpoint inhibitors. ClinicalTrials.gov identifier NCT03772899.

PubMed ID: 37414899

doi: 10.1038/s41591-023-02453-x

bioRxiv (2023)

Theodore B. Verhey, Heewon Seo, Aaron Gillmor, Varsha Thoppey-Manoharan, David Schriemer, Sorana Morrissy

Abstract

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

note: this article is a preprint and has not been peer-reviewed. [what does this mean?]

doi: 10.1101/2023.08.18.553919

Value Health (2022)

Samantha Pollard, Deirdre Weymann, Brandon Chan, Morgan Ehman, Sarah Wordsworth, James Buchanan, Timothy P. Hanna, Cheryl Ho, Howard J. Lim, Paula K. Lorgelly, Adam J. N. Raymakers, Christopher McCabe, Dean A. Regier

Abstract

Objectives: Precision oncology is generating vast amounts of multiomic data to improve human health and accelerate research. Existing clinical study designs and attendant data are unable to provide comparative evidence for economic evaluations. This lack of evidence can cause inconsistent and inappropriate reimbursement. Our study defines a core data set to facilitate economic evaluations of precision oncology.

Methods: We conducted a literature review of economic evaluations of next-generation sequencing technologies, a common application of precision oncology, published between 2005 and 2018 and indexed in PubMed (MEDLINE). Based on this review, we developed a preliminary core data set for informal expert feedback. We then used a modified-Delphi approach with individuals involved in implementation and evaluation of precision medicine, including 2 survey rounds followed by a final voting conference to refine the data set.

Results: Two authors determined that variation in published data elements was reached after abstraction of 20 economic evaluations. Expert consultation refined the data set to 83 unique data elements, and a multidisciplinary sample of 46 experts participated in the modified-Delphi process. A total of 68 elements (81%) were selected as required, spanning demographics and clinical characteristics, genomic data, cancer treatment, health and quality of life outcomes, and resource use.

Conclusions: Cost-effectiveness analyses will fail to reflect the real-world impacts of precision oncology without data to accurately characterize patient care trajectories and outcomes. Data collection in accordance with the proposed core data set will promote standardization and enable the generation of decision-grade evidence to inform reimbursement.

PubMed ID: 35216902

doi: 10.1016/j.jval.2022.01.005