Related Experiment Video
Updated: Jul 1, 2025

Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors
Published on: December 16, 2022
A gut microbial signature for combination immune checkpoint blockade across cancer types
Ashray Gunjur1,2, Yan Shao3, Timothy Rozday3
1Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK. ag35@sanger.ac.uk.
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.
Insights
Strain-level gut microbiome analysis improves prediction of cancer immunotherapy response. Tailoring microbiome diagnostics to specific immune checkpoint blockade regimens, not cancer type, enhances biomarker validity across diverse patient cohorts.
Area of Science:
- Oncology
- Microbiome Research
- Immunotherapy
Background:
- Immune checkpoint blockade (ICB) shows variable patient responses in cancer treatment.
- Gut microbiota composition is linked to ICB response, but generalizable biomarkers are elusive.
- Previous studies focused on species-level microbial abundance, overlooking strain-specific functional variations.
Approach:
- Deep shotgun metagenomic sequencing of fecal samples from 106 patients with rare cancers treated with combination ICB.
- Developed machine learning models using strain-resolved microbial abundances to predict ICB response and progression-free survival.
- Conducted a meta-analysis of 364 gut metagenomes from six comparable studies for validation.
Key Points:
- Strain-resolved microbial abundances significantly improved machine learning predictions of ICB response and survival compared to species-level data or clinical factors.
- Strain-response signatures demonstrated cross-cancer and cross-country validity when ICB regimens were concordant (anti-PD-1 or combination anti-PD-1/anti-CTLA-4).
- Biomarker validity was dependent on the specific ICB treatment regimen, not the cancer type.
Conclusions:
- Microbiome-based biomarkers for ICB response are most effective when analyzed at the strain level.
- Future development of gut microbiome diagnostics and therapeutics for immunotherapy should be tailored to ICB treatment regimens.
- This approach offers a more precise strategy for personalizing cancer immunotherapy based on individual gut microbial profiles.
Related Concept Videos
Tumor Immunotherapy
Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

