Fiber/probiotic intake, gut microbiome in melanoma ICB
309 fecal samples from melanoma patients in Houston, Texas and Bethesda, USA, studying dietary fiber intake and probiotic supplement use in relation to immune checkpoint blockade response, using a case-control design. machine-written summary
167 samples · 309 runs · US · Age unknown · 2021 · 2.62 Gbp / sample · cohort Dietary fiber and probiotics influence the gut mic
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 1,936 sample values (age, antibiotic exposure, bmi, collection date, country, latitude, location locality, location region) |
| ○ Abstract / description | read; no usable statement |
| ○ Full text | read; no usable statement or table · PMC11215408, PMC13274128, PMC8970537 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| ✓ External resources | 335 sample values (age, 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
Controlled / prescribed diet or feeding regimen Probiotic (live microorganisms; single strain or mixture) design observational_with_procedure · population other_cancer · Observed dietary fiber/probiotic use; preclinical low-fiber diet/probiotic feeding in mice
“mice receiving a low-fiber diet or probiotics” — abstract:34941392 (study level; per-sample arms are in the intervention column below)
Genera, mean relative abundance over 278 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 | 167 | 100% | ||||
| country | 167 | 100% | 167 | |||
| health_condition | 167 | 100% | 167 | |||
| subject_id | 0 | 0% | ||||
| age_at_collection_days | 167 | 100% | 166 | 1 | ||
| bmi | 167 | 100% | 167 | |||
| collection_date | 167 | 100% | 167 | |||
| detailed_location | 167 | 100% | ||||
| health_condition_detail | 167 | 100% | 167 | |||
| latitude | 167 | 100% | 167 | |||
| location_locality | 167 | 100% | 167 | |||
| location_region | 167 | 100% | 167 | |||
| longitude | 167 | 100% | 167 | |||
| sex | 167 | 100% | 167 | |||
| medication | 165 | 99% | 165 | |||
| medication_detail | 158 | 95% | 158 | |||
| antibiotic_exposure | 111 | 66% | 111 |
- Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. 2021 PMID 34941392 PMC8970537 own_data
- Consistent Stool Metagenomic Biomarkers Associated with the Response To Melanoma Immunotherapy. 2023 PMID 36809182 PMC10134792 unsure
- Gut microbial structural variation associates with immune checkpoint inhibitor response. 2023 PMID 37973916 PMC10654443 unsure
- Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma. 2024 PMID 38365950 PMC10957474 unsure
- Systemic metabolic depletion of gut microbiome undermines responsiveness to melanoma immunotherapy. 2024 PMID 38448159 PMC10917649 unsure
5 more
- Randomized Placebo-Controlled, Biomarker-Stratified Phase Ib Microbiome Modulation in Melanoma: Impact of Antibiotic Preconditioning on Microbiome and Immunity. 2024 PMID 38588588 PMC11215408
- 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
- Ecological and Functional Stratification of the Stool Microbiome Predicts Response to Immune Checkpoint Inhibitors across Cancer Types. 2026 PMID 42146906 PMC13173278
- Gut microbial markers of immunotherapy response in melanoma: a cross-cohort analysis including the first Russian dataset. 2026 PMID 42298353 PMC13274128
Archive description
Gut bacteria modulate response to immune checkpoint blockade (ICB) treatment in cancer, but the impact of diet and supplements on this interaction is not well-studied. We assessed fecal microbiota profiles, dietary habits, and commercially available probiotic supplement use in melanoma patients and performed parallel pre-clinical studies. Higher dietary fiber was associated with significantly improved progression-free survival in 128 patients on ICB, with the most pronounced benefit in patients with sufficient dietary fiber intake and no probiotic use. Findings were recapitulated in pre-clinical models, demonstrating impaired treatment response to anti-PD(L)-1 therapy in mice receiving a low fiber diet or probiotics, with a lower frequency of interferon-gamma-positive cytotoxic T-cells in the tumor microenvironment. Together, these data have clinical implications in patients receiving ICB for cancer. Additional resources related to software and analysis of this work is also publicly available on GitHub at https://github.com/mda-primetr/Spencer_et_al_2021.
How this project entered the registry
Classified as human and non-human (mixed) gut / stool shotgun DNA metagenome of elderly and adult by opus_adjudicated with confidence 0.80. Assay shotgun_dna.
- host run.scientific_name · “human metagenome”
- host run.scientific_name · “mouse gut metagenome”
- body site study.description · “We assessed fecal microbiota profiles”
- life stage sample.attr.age · “72 (n=93/309)”
- life stage sample.attr.age · “59 (n=72/309)”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN22443322 | age_at_collection_days | 28489.5 | R1 | 0.70 | sample.attr.age | 78 |
| SAMN22443749 | age_at_collection_days | 32872.5 | R2 | 0.75 | external.cmd source | age: 90; age_unit: (empty); age_group: Elderly |
| SAMN22443323 | antibiotic_exposure | no | R1 | 0.90 | biosample_attr | 0 |
| SAMN22443322 | bmi | 23.0 | R1 | 0.90 | sample.attr.bmi | 23 |
| SAMN22443322 | collection_date | 2015-04/2019-01 | R1 | 0.90 | biosample.attribute:collection_date | 2015-04/2019-01 |
| SAMN22443322 | country | US | R1 | 0.90 | sample.attr.geo_loc_name | USA:Houston,Texas |
| SAMN22443322 | health_condition | other_cancer | R2 | 0.75 | external.cmd source | disease: melanoma |
| SAMN22443322 | health_condition_detail | melanoma | R2 | 0.75 | external.cmd source | disease: melanoma |
| SAMN22443322 | latitude | 29.7604 | R1 | 0.90 | biosample.attribute:lat_lon | 29.7604 N 95.3698 W |
| SAMN22443322 | location_locality | Houston | R1 | 0.85 | biosample.attribute:geo_loc_name | USA:Houston,Texas |
| SAMN22443322 | location_region | Texas | R1 | 0.85 | biosample.attribute:geo_loc_name | USA:Houston,Texas |
| SAMN22443322 | longitude | -95.3698 | R1 | 0.90 | biosample.attribute:lat_lon | 29.7604 N 95.3698 W |
| SAMN22443322 | medication | immunosuppressant_or_biologic | R1 | 0.90 | biosample.attribute:treatment | anti-PD1 |
| SAMN22443427 | medication | statin | R1 | 0.90 | biosample.attribute:statins | 1 |
| SAMN22443665 | medication | ppi;statin | R1 | 0.90 | biosample.attribute:ppi | 1 |
| SAMN22443719 | medication | antihypertensive | R1 | 0.85 | biosample.attribute:other_hypertensive | 1 |
| SAMN22443322 | medication_detail | anti-PD1 | R1 | 0.90 | biosample.attribute:treatment | anti-PD1 |
| SAMN22443322 | sex | male | R1 | 0.90 | sample.attr.sex | male |
Sequencing
309 runs over 167 samples · mean 2.83 Gbp per run (median 2.62 Gbp, total 875.85 Gbp; 309 runs with a base count) · layout PAIRED 309 · platform ILLUMINA 309 · models Illumina HiSeq X (135), Illumina HiSeq 3000 (99), Illumina HiSeq 2000 (75) · 90% of runs with a Sandpiper profile · first public 2021-12-25 – 2022-02-10
Authors (125)
Spencer CN first · McQuade JL · Gopalakrishnan V · McCulloch JA · Vetizou M · Cogdill AP · Khan MAW · Zhang X · White MG · Peterson CB · Wong MC · Morad G · Rodgers T · Badger JH · Helmink BA · Andrews MC · Rodrigues RR · Morgun A · Kim YS · Roszik J · Hoffman KL · Zheng J · Zhou Y · Medik YB · Kahn LM · Johnson S · Hudgens CW · Wani K · Gaudreau PO · Harris AL · Jamal MA · Baruch EN · Perez-Guijarro E · Day CP · Merlino G · Pazdrak B · Lochmann BS · Szczepaniak-Sloane RA · Arora R · Anderson J · Zobniw CM · Posada E · Sirmans E · Simon J · Haydu LE · Burton EM · Wang L · Dang M · Clise-Dwyer K · Schneider S · Chapman T · Anang NAS · Duncan S · Toker J · Malke JC · Glitza IC · Amaria RN · Tawbi HA · Diab A · Wong MK · Patel SP · Woodman SE · Davies MA · Ross MI · Gershenwald JE · Lee JE · Hwu P · Jensen V · Samuels Y · Straussman R · Ajami NJ · Nelson KC · Nezi L · Petrosino JF · Futreal PA · Lazar AJ · Hu J · Jenq RR · Tetzlaff MT · Yan Y · Garrett WS · Huttenhower C · Sharma P · Watowich SS · Allison JP · Cohen L · Trinchieri G · Daniel CR · Wargo JA last · Zakharevich NV first · Morozov MD · Kanaeva VA · Filippov MS · Zyubko TI · Ivanov AB · Ulyantsev VI · Klimina KM · Olekhnovich EI 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 · Orletskaia VA first · 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
PRJNA770295.csv.gz (6.5 KB) · parquet (78.8 KB) — all 167 samples, every field with route and confidence · PRJNA770295_determinations.csv.gz (18.7 KB) — 2,271 evidence rows