Evidence map›Paper›PMID 41279814›Full record

ArticlebioRxiv : the preprint server for biology2025

Taxonomy-free fecal microbiome profiles enable robust prediction of immunotherapy response and toxicity in melanoma.

Anastasia Lucas, McKenna Reale, Yuri I Wolf, Bryant Duong, Yichi Zhang, Jayamanna Wickramasinghe, Lindsey Behlman, Steven M Jones, Stephanie Higgins, Ahmed M Moustafa and 5 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Anastasia LucasGraduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, Pennsylvania.
McKenna RealeThe Wistar Institute, Philadelphia, Pennsylvania.
Yuri I WolfComputational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland.
Bryant DuongThe Wistar Institute, Philadelphia, Pennsylvania.
Yichi ZhangDivision of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Jayamanna WickramasingheThe Wistar Institute, Philadelphia, Pennsylvania.
Lindsey BehlmanDivision of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Steven M JonesDivision of Gastroenterology, Hepatology, and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Stephanie HigginsDivision of Gastroenterology, Hepatology, and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Ahmed M MoustafaDivision of Gastroenterology, Hepatology, and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Abdurrahman ElbasirThe Wistar Institute, Philadelphia, Pennsylvania.
Ravi AmaravadiTara Miller Melanoma Center, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania.
Tara MitchellDivision of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Alexander HuangDivision of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Noam AuslanderThe Wistar Institute, Philadelphia, Pennsylvania.

Funding

Targeting exosomal PDL1 to improve immunotherapyP50CA261608 · NCI · WISTAR INSTITUTE · PI HERLYN, MEENHARD F · 2021 to 2025
$11.3M
Computational methods for discovery of disease-modulating microbial genesR01LM014503 · NLM · WISTAR INSTITUTE · PI Noam Auslander · 2024 to 2026
$1.2M
Modeling cancer evolution for prediction with neural networks: methods and applicationsR00CA252025 · NCI · WISTAR INSTITUTE · PI AUSLANDER, NOAM · 2021 to 2023
$747k
Computational methods for taxa-free microbial biomarker discovery and clinical risk stratificationF31LM014962 · NLM · UNIVERSITY OF PENNSYLVANIA · PI LUCAS, ANASTASIA · 2025 to 2025
$50k
NCI NIH HHS P50 CA261608NCI NIH HHS R00 CA252025NLM NIH HHS F31 LM014962NLM NIH HHS R01 LM014503
6 · The paper itself

Abstract

The gut microbiome has been causally linked to the efficacy of immune-checkpoint inhibitor therapy (ICI), prompting numerous clinical trials of microbiome-targeting strategies. Yet, mechanisms by which gut microbiota shape immune responses remain elusive as taxonomic biomarkers have failed to generalize across multiple cohorts. In this study, we develop a taxonomy-agnostic framework to identify microbial biomarkers of ICI response and immune-related adverse event (irAE) occurrence from metagenomic sequencing. Applying this approach to four independent melanoma cohorts from clinical centers across the United States, we uncover gut microbial proteins produced by diverse bacterial taxa that consistently predict ICI response. Notably, we uncover a previously uncharacterized operon involved in cellular redox homeostasis that is encoded by different bacteria and reliably predicts irAE occurrence. We further validated the predictive power of this operon in a prospectively sequenced melanoma cohort. Our results demonstrate that taxa-agnostic microbial protein biomarkers are robust, generalizable, and provide a path towards pretreatment risk stratification for melanoma patients initiating ICI therapy.

Identifiers

PMID41279814
PMCPMC12637426

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.