Gut microbiota and anti-PD-1 response in melanoma (USA)
39 baseline stool samples from metastatic melanoma patients in the USA, profiled by 16S rRNA, shotgun metagenomics, and qPCR to study links between commensal microbiota and anti-PD-1 immunotherapy response, using a case-control design. machine-written summary
39 samples · 39 runs · US · Adult · 2018 · 10.79 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 117 sample values (collection date, country, subject id) |
| ○ Abstract / description | read; no usable statement |
| ✓ Full text | 39 sample values (location region) · PMC6707353, PMC9293081 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| ✓ External resources | 78 sample values (health condition, health condition detail) · from curatedMetagenomicData 3 (cMD 3) |
| – User contribution | nothing uploaded yet |
| – Human verified | not yet checked by a person · I checked this project — verify it |
intervention — arms not resolved
design observational_with_procedure · Human baseline stool samples; FMT into germ-free mice from responding vs non-responding patient
Genera, mean relative abundance over 35 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).
| field | samples | coverage | R1 | R2 | R3 | R4 |
|---|---|---|---|---|---|---|
| age_category | 39 | 100% | ||||
| country | 39 | 100% | 39 | |||
| health_condition | 39 | 100% | 39 | |||
| subject_id | 39 | 100% | 39 | |||
| collection_date | 39 | 100% | 39 | |||
| detailed_location | 39 | 100% | ||||
| health_condition_detail | 39 | 100% | 39 | |||
| location_region | 39 | 100% | 39 |
- The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. 2018 PMID 29302014 PMC6707353 own_data
- Early melanoma invasivity correlates with gut fungal and bacterial profiles. 2022 PMID 34227096 PMC9293081 own_data
- Gut microbiome stability and dynamics in healthy donors and patients with non-gastrointestinal cancers. 2021 PMID 33175106 PMC7664509 unsure
- Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma. 2020 PMID 33268597 PMC7714408 unsure
- A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors. 2021 PMID 33593881 PMC9053858 unsure
25 more
- geneshot: gene-level metagenomics identifies genome islands associated with immunotherapy response. 2021 PMID 33952321 PMC8097837
- Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. 2022 PMID 35228751 PMC8938272
- Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1. 2022 PMID 35228752 PMC10246505
- Predicting cancer immunotherapy response from gut microbiomes using machine learning models. 2022 PMID 35875611 PMC9295706
- The microbiome-derived metabolite TMAO drives immune activation and boosts responses to immune checkpoint blockade in pancreatic cancer. 2022 PMID 36083892 PMC9925043
- Machine learning for data integration in human gut microbiome. 2022 PMID 36419034 PMC9685977
- Consistent Stool Metagenomic Biomarkers Associated with the Response To Melanoma Immunotherapy. 2023 PMID 36809182 PMC10134792
- Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma. 2024 PMID 38365950 PMC10957474
- A gut microbial signature for combination immune checkpoint blockade across cancer types. 2024 PMID 38429524 PMC10957475
- Systemic metabolic depletion of gut microbiome undermines responsiveness to melanoma immunotherapy. 2024 PMID 38448159 PMC10917649
- Neoadjuvant vidutolimod and nivolumab in high-risk resectable melanoma: A prospective phase II trial. 2024 PMID 39486411 PMC11560503
- PreLect: Prevalence leveraged consistent feature selection decodes microbial signatures across cohorts. 2025 PMID 39753565 PMC11698977
- MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles. 2025 PMID 39885552 PMC11783787
- Gut microbiota-derived hexa-acylated lipopolysaccharides enhance cancer immunotherapy responses. 2025 PMID 39929976 PMC11879847
- Enriched pathways in gut microbiome predict response to immune checkpoint inhibitor treatment across demographic regions and various cancer types. 2025 PMID 40151642 PMC11937697
- Revealing gut microbiota biomarkers associated with melanoma immunotherapy response and key bacteria-fungi interaction relationships: evidence from metagenomics, machine learning, and SHAP methodology. 2025 PMID 40170844 PMC11959079
- Microbiome data integration via shared dictionary learning. 2025 PMID 40890119 PMC12402269
- Live biotherapeutic <i>enterococcus lactis</i> MNC-168 promotes the efficacy of immune checkpoint blockade in cancer therapy by activating STING pathway via bacterial membrane vesicles. 2025 PMID 40937720 PMC12439585
- Intestinal fungal signatures and their impact on immune checkpoint inhibitor efficacy: a multi-cohort meta-analysis. 2025 PMID 41006325 PMC12475161
- Multi-Kingdom Gut Microbiome Interaction Characteristics Predict Immune Checkpoint Inhibitor Efficacy Across Pan-Cancer Cohorts. 2025 PMID 41304278 PMC12654497
- Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types. 2026 PMID 42026803 PMC13114121
- Ecological and Functional Stratification of the Stool Microbiome Predicts Response to Immune Checkpoint Inhibitors across Cancer Types. 2026 PMID 42146906 PMC13173278
- Enterotype-specific microbial biomarkers of immune checkpoint inhibitor response revealed by large-scale integrated metagenomic analysis. 2026 PMID 42189287 PMC13388844
- Gut microbial markers of immunotherapy response in melanoma: a cross-cohort analysis including the first Russian dataset. 2026 PMID 42298353 PMC13274128
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities. 2026 PMID 42750046 PMC13584529
Archive description
Anti-PD-1-based immunotherapy is having a major impact on cancer outcomes but only benefits a subset of patients. Among the variables that could contribute to inter-patient heterogeneity is differential composition of the commensal microbiota, which preclinically has been shown to affect anti-tumor immunity and immunotherapy efficacy in mice. Baseline stool samples were analyzed from metastatic melanoma patients prior to immunotherapy treatment, through an integration of 16S rRNA sequencing, metagenomic shotgun sequencing, and quantitative PCR for selected bacteria. A significant association between commensal microbial composition and clinical response was observed. Reconstitution of germ-free mice with fecal material from a responding patient led to improved tumor control, augmented T cell responses, and a distinct microbiota compared to a non-responding patient. Our results indicate a mechanistic impact of the commensal microbiota on anti-tumor immunity in human cancer patients.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of adult by sonnet_x2 with confidence 0.72. Assay shotgun_dna.
- host study.title · “metastatic melanoma patients”
- body site study.description · “Baseline stool samples were analyzed”
- life stage study.description · “metastatic melanoma patients prior to immunotherapy”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN07599720 | collection_date | 2015 | R1 | 0.90 | biosample.attribute:collection_date | 2015 |
| SAMN07599720 | country | US | R1 | 0.90 | sample.attr.geo_loc_name | USA |
| SAMN07599720 | health_condition | other_cancer | R2 | 0.75 | external.cmd source | disease: Melanoma;Metastatic Malignant Neoplasm |
| SAMN07599720 | health_condition_detail | Melanoma;Metastatic Malignant Neoplasm | R2 | 0.75 | external.cmd source | disease: Melanoma;Metastatic Malignant Neoplasm |
| SAMN07599720 | location_region | Tuscany | R3 | 0.70 | paper.fulltext.methods | living in the province of Florence (Tuscany, Italy) |
| SAMN07599720 | subject_id | P01 | R1 | 0.85 | sample.attr.subject_id | P01 |
Sequencing
39 runs over 39 samples · mean 12.08 Gbp per run (median 10.79 Gbp, total 471.18 Gbp; 39 runs with a base count) · layout PAIRED 39 · platform ILLUMINA 39 · models NextSeq 500 (39) · 90% of runs with a Sandpiper profile · first public 2018-01-07
Authors (51)
Zhou Y first · Han W · Feng Y · Wang Y · Liu X · Sun T · Xu J last · Zhang L first · Zhou DD · Feng J · Liao ZJ · Shu XL · Yang RM · Gao YC · Zhou HH · Zhang W · Zou Y · Liu R last · Qiao T first · Zhu Z last · Candeliere F first · Busi E · Cerri S · Sola L · Lombardi M · Greco S · Pedroni S · Amaretti A · Raimondi S · Chiavelli C · Vitale MG · Bertolini F · Depenni R · Franchini G · Dominici M · Rossi M last · Strokach A first · Zakharevich N · Aginova V · Grigoryevskaya Z · Petukhova I · Bagirova N · Romanov M · Dyachkova M · Morozov M · Veselovsky V · Kanaeva V · Kalinin D · Larin A · Shitikov E · Klimina K last
Downloads
PRJNA399742.csv.gz (2.0 KB) · parquet (70.9 KB) — all 39 samples, every field with route and confidence · PRJNA399742_determinations.csv.gz (2.7 KB) — 234 evidence rows