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1475 Combination of LND101, a healthy donor microbiome transplantation product, to doublet immunotherapy or immunotherapy combined with targeted therapy in metastatic renal cell carcinoma patients

2024· article· en· W4404067494 on OpenAlexaffabout
Ricardo Fernandes, Adnan Rajeh, John Lenehan, Scott Ernst, Eric Winquist, Kelly J. Baines, René Figueredo, Leask Andrew, Devanand M. Pinto, Seema Nair Parvathy, Michael H. Silverman, Saman Maleki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsSt Joseph's Health CareLondon Health Sciences CentreNational Research Council CanadaWestern University
Fundersnot available
KeywordsImmunotherapyMedicineRenal cell carcinomaTransplantationOncologyAdoptive immunotherapyColon carcinomaInternal medicineCancer researchColorectal cancerCancer

Abstract

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Background Metastatic renal cell carcinoma (mRCC) is treated with dual immune checkpoint inhibitors (ICI) or ICI with a VEGF-TKI. Despite improved outcomes, some patients do not benefit from the treatment due to disease resistance or serious adverse events. We hypothesized that re-establishing a healthy microbiome in mRCC patients before and during treatment with ICI based combination therapy via healthy microbiome transplantation does not increase toxicity and improves clinical response. The PERFORM trial (NCT04163289) is a single-arm phase I study evaluating the safety of multiple microbiome transplantation procedures before and during ICI based therapy in the first-line setting (1L) for patients with mRCC. Methods In this single-centre study, 20 patients with untreated mRCC received one full dose of LND101(a full consortia healthy donor microbiome product) followed by two-half doses of LND101 before the first 3 cycles of doublet ICI or ICI combined with VEGF-TKI. The primary endpoint is the feasibility and safety of combining LND101 with standard of care. Secondary endpoints include the incidence of irAEs , objective response rate (ORR) , and finally changes in patient‘s microbiome, immune profile, and metabolome. We include a preliminary analysis of all 20 patients for the clinical outcomes. Results 16 patients received LND101 plus ipilimumab/nivolumab, 3 patients received LND101 plus pembrolizumab/axitinib, and one patient received pembrolizumab/lenvatinib. The median age was 60 years old, and 90% were male. All patients had intermediate or poor-risk disease. No dose-limiting toxicities due to LND101 were observed. Median follow-up was 22.87 months. Nine patients (45%) discontinued treatment due to irAEs to include colitis (n=3), arthritis (n=1), nephritis (n=1), diarrhea (n=1), pneumonitis (n=1), and bullous pemphigoid (n=1). Grade 3 AEs were experienced by 11 patients (55%). 18 patients had measurable disease according to RECIST 1.1 criteria and a response was confirmed in 8/18 patients (ORR 44%), including one complete response. Clinical benefit was confirmed in 13/18 patients (72%). Targeted plasma metabolomics revealed 67 metabolites elevated in patients with Grade 3 toxicity at an FDR of <0.05 relative to patients with no toxicity. Conclusions Our results suggest that microbiome modification with LND101 added to ICI-based combination therapy as 1L in patients with mRCC was safe. Furthermore, the addition of LND101 may prevent or reduce the occurrence of irAEs. For those who developed grade 3 irAEs, we observed important alterations in the gut microbiota. Finally, our results suggest that LND101 added to ICI-based combination therapy as 1L in mRCC may improve clinical outcomes. Acknowledgements Funding: Medical Oncology Research Funding, Academic Medical Organization of Southwestern Ontario Opportunities Grant, London Regional Cancer Centre Catalyst Keith Samitt Translational Cancer Research Grant and London Health Sciences Foundation. Trial Registration NCT04163289. Ethics Approval Ethics number: 114962.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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