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Preventing adverse events in patients with renal cell carcinoma treated with doublet immunotherapy using fecal microbiota transplantation (FMT): Initial results from perform a phase I study.

2022· article· en· W4286297269 on OpenAlexaff
Ricardo Fernandes, Seema Nair Parvathy, D. Scott Ernst, Mansour Haeryfar, Jeremy P. Burton, Michael H. Silverman, Saman Maleki

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsLawson Health Research InstituteLondon Health Sciences CentreSt Joseph's Health CareWestern University
Fundersnot available
KeywordsMedicineRenal cell carcinomaClinical endpointAdverse effectImmunotherapyInternal medicineOncologyImmune systemTransplantationCancerGastroenterologyImmunologySurgeryClinical trial

Abstract

fetched live from OpenAlex

4553 Background: The treatment landscape of metastatic renal cell carcinoma (mRCC) has evolved with the advent of either dual immune checkpoint inhibition (ICI) or in combination with Vascular Endothelial Growth Factor Receptor Tyrosine Kinase Inhibitor. Gut microbiota plays a central role in developing local and systemic immunity, with potential influence in controlling anti-tumor immune response in cancer patients treated with ICI. We hypothesize that FMT from healthy donors given before immunotherapy will establish a more resilient gut microbiota, reducing the treatment toxicity and improving response to therapy. PERFORM is an ongoing phase I study evaluating the safety of FMT and immunotherapy combination in first-line (1L) mRCC, and assessing whether FMT will prevent or mitigate immune-related adverse events (irAE). Methods: Eligible patients with untreated mRCC received a full dose and 2 supportive FMT procedures prior to the first 3 cycles of doublet ICI or in combination with VEGF-TKI. Primary endpoint is the feasibility and safety of combining FMT using intestinal bacteria existing in the stool of healthy donors with immunotherapy. Secondary endpoints include incidence of irAEs, objective response rate (ORR; RECIST v1.1), and changes in pts microbiome and immune profile post-FMT. We included a preliminary analysis of the first 10 patients. Results: 10 patients received FMT and doublet ICI therapy (10 ongoing). 8/10 (80%) patients were male. Median Age: 59.5 (53-71) years-old. Most common histology was clear cell RCC (90%) and all patients had an intermediate or poor-risk disease. 93.3% of planned FMT were administered. No dose-limiting toxicities due to FMT were observed. Median (range) follow-up was 5.5 (1–22) months. 4 patients (40%) discontinued treatment due to irAEs: colitis (n = 3), arthritis (n = 1). IrAEs were reported in 8 (80%) patients, including diarrhea (n = 6; 60%) and skin rash (n = 2, 20%). Grade 3/4 AEs were experienced by 6 (60%) patients, including colitis (n = 4, 40%). ORR was confirmed in 4/9 patients (44%; 95% CI, 30–60); 1 (11%) partial response. Microbiota and immune analysis data to be presented. Conclusions: The role of microbiome modification in preventing immune-related toxicities by adding FMT to ICI therapy was associated with a safety profile in unselected 1L mRCC and promising clinical efficacy data. Further prospective studies to examine the changes in the immune and microbiota profiles to determine biomarkers related to healthy outcomes/less frequent toxicities in patients receiving immunotherapy are warranted. Clinical trial information: NCT04163289.

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)0.002
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.043
GPT teacher head0.385
Teacher spread0.342 · 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 designNon-randomized trial
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".

Quick stats

Citations16
Published2022
Admission routes1
Has abstractyes

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