Gunjur et al. performed deep shotgun metagenomic sequencing of baseline fecal samples from 106 patients with diverse rare cancers treated with anti-PD-1 alone or together with anti-CTLA-4, and highlighted the added value of strain-level resolution in developing gut microbial ICB biomarkers. Strain-resolved microbial classification improved machine learning predictions of ICB response and 12-month PFS relative to models built using species-rank quantifications. A baseline strain-level gut microbial abundance signature was generalizable across cancer histology types when the training and test cohorts used concordant ICB regimens.
Contributed by Shishir Pant
ABSTRACT: Immune checkpoint blockade (ICB) targeting programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte protein 4 (CTLA-4) can induce remarkable, yet unpredictable, responses across a variety of cancers. Studies suggest that there is a relationship between a cancer patient's gut microbiota composition and clinical response to ICB; however, defining microbiome-based biomarkers that generalize across cohorts has been challenging. This may relate to previous efforts quantifying microbiota to species (or higher taxonomic rank) abundances, whereas microbial functions are often strain specific. Here, we performed deep shotgun metagenomic sequencing of baseline fecal samples from a unique, richly annotated phase 2 trial cohort of patients with diverse rare cancers treated with combination ICB (n = 106 discovery cohort). We demonstrate that strain-resolved microbial abundances improve machine learning predictions of ICB response and 12-month progression-free survival relative to models built using species-rank quantifications or comprehensive pretreatment clinical factors. Through a meta-analysis of gut metagenomes from a further six comparable studies (n = 364 validation cohort), we found cross-cancer (and cross-country) validity of strain-response signatures, but only when the training and test cohorts used concordant ICB regimens (anti-PD-1 monotherapy or combination anti-PD-1 plus anti-CTLA-4). This suggests that future development of gut microbiome diagnostics or therapeutics should be tailored according to ICB treatment regimen rather than according to cancer type.
Author Info: (1) Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. ag35@sanger.ac.uk. Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK. ag35@s
Author Info: (1) Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. ag35@sanger.ac.uk. Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK. ag35@sanger.ac.uk. (2) Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. (3) Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. (4) Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia. Department of Medical Oncology, Austin Health, Melbourne, Victoria, Australia. Central Clinical School, Monash University, Melbourne, Victoria, Australia. (5) Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK. (6) Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. Center for Experimental and Molecular Medicine, Amsterdam UMC, Amsterdam, Netherlands. (7) Department of Medical Oncology, Monash Health, Melbourne, Victoria, Australia. Department of Medical Oncology, Alfred Health, Melbourne, Victoria, Australia. School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia. (8) Department of Medical Oncology, Austin Health, Melbourne, Victoria, Australia. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. Rare Cancer Laboratory, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia. (9) Department of Medical Oncology, Blacktown and Westmead Hospitals, Sydney, New South Wales, Australia. Melanoma Institute of Australia, University of Sydney, Sydney, New South Wales, Australia. (10) Border Medical Oncology and Haematology Research Unit, Albury-Wodonga Regional Cancer Centre, Albury-Wodonga, New South Wales, Australia. Rural Medical School, University of New South Wales, Albury, New South Wales, Australia. (11) Department of Medical Oncology, Monash Health, Melbourne, Victoria, Australia. (12) Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia. (13) Department of Medical Oncology, Blacktown and Westmead Hospitals, Sydney, New South Wales, Australia. (14) Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia. (15) Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia. Department of Medical Oncology, Austin Health, Melbourne, Victoria, Australia. (16) Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia. (17) Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK. (18) Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. tl2@sanger.ac.uk.
