Abstract 2210: Microbiome profiling reveals that fecal microbiota transplantation (FMT) modulates response and toxicity when combined with immunotherapy in patients with lung cancer and melanoma (FMT-LUMINate NCT04951583)
Bibliographic record
Abstract
Background: Trials have shown that FMT plus immune checkpoint inhibitors (ICI) could improve ⍺PD-1 response in melanoma. The microbiome and immune shift, along with the clinical impact of FMT with ⍺PD-1 in non-small cell lung cancer (NSCLC) and in combination with ⍺PD-1+⍺CTLA-4 (dual ICI) in melanoma is unknown. Methods: 20 patients (pts) with advanced NSCLC treated with first-line (1L) ⍺PD-1 and 20 with advanced melanoma treated with 1L dual ICI were recruited. FMT from 1 healthy donor (HD) was administered orally to each pt post bowel prep. 1 week later ICI was initiated. A total of 11 different HD were used. The primary endpoint was objective response rate (ORR). The gut microbiome was profiled using shotgun metagenomics. Metabolomic profiling was performed using HPLC. Olink proteomics (96 markers) on plasma and flow cytometry (40 markers) on PBMCs were assessed. FMT was performed in germ-free (GF) mice to recapitulate engraftment. Results: In NSCLC, ORR was 80% (16 PR) with no grade >3 immune-related adverse events (irAE). In melanoma, ORR was 75% (5 CR), with 65% (13 pts) developing grade >3 irAE, including 3 (15%) myocarditis. The irAE occurred earlier than previously reported (median onset of 42 days). Pre-FMT patient microbiome composition was distinct from HD. Post-FMT, the responders (R) and non-responders (NR) clustered separately (p=0.03). Post-FMT, we observed increased polyamines and bile salts in R. Elevation of exhausted T central and T effector memory cells were noted in R. Conversely, proteomics analyses revealed elevated markers of immune exhaustion including CD28 and IL10 in NR. Addressing the link between microbiome and irAE, in melanoma, 6 out of the 13 pts (46%) that developed grade >3 irAE, including 2/3 of the myocarditis received FMT from the same donor (D5). D5 was not associated with irAE in NSCLC treated with ⍺PD-1 alone. Microbiome analysis revealed that D5 clustered separately (p<0.01) from other HD with a high relative abundance of Prevotella copri. We observed a signal for the development of irAE in pts that engrafted P. copri. Pts with grade>3 irAE exhibited higher levels of leaky gut markers such as sST2 post-FMT (p=0.002). In GF mice, oral gavage of baseline stool of R, only when followed by the stool of the respective donor, led to a significant increase in ⍺ diversity and number of species engrafted over time (p<0.01), while those that received baseline stool from NR with or without donor had no change in ⍺ diversity or species engrafted. Conclusions: This study validates the role of FMT to improve ICI activity in NSCLC and melanoma, while differentially modulating irAE according to ICI backbone. We demonstrated that the microbiome shift is distinct among R while engraftment of P. copri was associated with dual ICI irAE. Our results directly inform future microbiome-centered trials. Citation Format: Sreya Duttagupta, Meriem Messaoudene, Rahima Jamal, Catalin Mihalcioiu, Nicolas Marcoux, Sebastian Hunter, Gianmarco Piccinno, Michal Puncochár, Karl Bélanger, Mustapha Tehfe, Normand Blais, Scott Owen, Jacques Raphael, Jade Maillou, Yongjia Hu, Myriam Benlaifaoui, Somayeh Nilli, John Lenehan, Wiam Belkaid, Seema Nair Parvathy, Diogjena Katerina Prifti, Marine Fidelle, Seunghee Kim-schulze, Thomas Marron, Sylvere Durand, Guido Kroemer, Nicola Segata, Lisa Derosa, Laurence Zitvogel, Antoine Desilets, Michael Silverman, Saman MalekiVareki, Bertrand Routy, Arielle Elkrief. Microbiome profiling reveals that fecal microbiota transplantation (FMT) modulates response and toxicity when combined with immunotherapy in patients with lung cancer and melanoma (FMT-LUMINate NCT04951583) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2210.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".