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Intestinal microbiome characterization in immune checkpoint inhibition (ICI) resistant disease.

2023· article· en· W4379283245 on OpenAlexaff
Pavlina Spiliopoulou, Ashley M. Rooney, Sofia Genta, Maria Kulikova, Ben X. Wang, Ming‐Sound Tsao, Sam Felicen, Vanessa Speers, Theresa Patrick, Alisa Nguyen, Marcus O. Butler, Lawson Eng, Aaron R. Hansen, Sam Saibil, Philippe L. Bédard, Trevor J. Pugh, Lillian L. Siu, Bryan Coburn, Anna Spreafico

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsToronto General HospitalPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineMicrobiomeGastroenterologyMelanomaProgressive diseaseAdjuvantColorectal cancerImmune systemOncologyDiseaseCancerImmunologyCancer researchBioinformaticsBiology

Abstract

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2515 Background: The gut microbiome modifies response to ICI treatment. Despite durable response rates seen with ICI, most pts do not benefit from treatment (primary resistance) or have only a period of disease control (acquired resistance). The Immune Resistance Interrogation Study (IRIS, NCT03702309) is a prospective pan-cancer study that aims at characterizing ICI-resistant disease through multiomic and gut microbial composition profiling. Herein we compare the gut microbial diversity of pts with PR vs AR to PD-1/PD-L1 based treatment as part of the wider IRIS framework. Methods: Patients who progressed after anti-PD1/PD-L1-based ICI were classified into 2 groups. Acquired resistance (AR): complete response (CR), partial response (PR) or stable disease (SD) for ≥6 months (m) with subsequent (PD) or PD after ≥3m from last dose of adjuvant ICI; or primary resistance (PR): PD at first imaging, SD but progressed ≤6m or progression ≤3m from last dose of adjuvant ICI. Stool samples were collected at time of PD and underwent DNA extraction and metagenomic sequencing. Species-level Shannon diversity indices were calculated and compared using unpaired Wilcoxon test. Principal component analysis (PCA) of Bray-Curtis dissimilarity indices was performed to assess differences in taxonomic composition. Results: Stool samples from 62 pts (PR n=38; AR n=24) were evaluated. Tumor types were melanoma n=37 (60%), head and neck n=17/62 (27%), gastrointestinal n=3/62 (5%), gynaecological n=2/62 (3%) and other n=3/61 (5%). Most pts n=59/62 (95%) were treated with palliative intent, and n= 3/62 (5%) with adjuvant therapy. Anti-PD-1/PD-L1 monotherapy was received by 32/62 (52%) pts and n=30/62 (48%) received combination anti-PD-1/PD-L1 with either anti-CTLA-4 inhibition, experimental immunotherapy, chemotherapy, or targeted treatment. The median Shannon diversity index was similar between pts with PR (median: 3.5; range: 2.5 - 4.3) vs AR (median: 3.4; range: 0.9 – 3.9; p = 0.7). There were no differences in composition by group by PCA of Bray-Curtis dissimilarity indices. Abundance testing did not reveal any differentially abundant species between pts with AR vs PR while controlling for antibiotic use within 1m of sampling and type of treatment. No taxa were significantly different between AR and PR; top taxa per group are shown. Conclusions: Primary analysis showed no significant differences in gut microbial composition between primary vs acquired resistance to ICI. Comparative analysis between ICI-resistance samples and ICI-naïve samples from historical patient cohorts is underway. These results will be collated with other analyses from IRIS to create a multi-modal characterisation of ICI resistance. Clinical trial information: NCT03702309 . [Table: see text]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.426
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2023
Admission routes1
Has abstractyes

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