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First-in-class microbial ecosystem therapeutics 4 (MET4) in metastatic solid cancer patients treated with immunotherapy: MET4-IO.

2020· article· en· W3030546134 on OpenAlexafffund
Daniel Vilarim Araújo, M. Oliva Bernal, Tira J. Tan, Alya Heirali, Pierre H. H. Schneeberger, Thiago Pimentel Muniz, Bo Chen, Lee-Anne Stayner, Kathy Xia, Aaron R. Hansen, Ben X. Wang, David Hogg, Kyla Cochrane, S. Langer, Nissim Mashiach, Wei Xu, Lillian L. Siu, Bryan Coburn, Anna Spreafico

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
FundersUniversity Health Network
KeywordsMedicineInternal medicineRandomized controlled trialCancerOncologyClinical trial

Abstract

fetched live from OpenAlex

3098 Background: Therapeutic augmentation of the intestinal microbiome to improve immunotherapy outcomes is an active area of investigation. Microbial Ecosystem Therapeutics (METs) are consortia of human-derived bacteria designed to be reproducible, scalable and safe alternatives to fecal transplant. MET4 is a first-in-class consortium of taxa associated with immune checkpoint inhibitor (ICI)-responsiveness. Here we describe preliminary results of MET4-IO, an interventional trial assessing the safety and ecological effects of MET4 in ICI recipients. Methods: MET4-IO is a randomized investigator-initiated trial, evaluating MET4 in solid cancer patients treated with ICI. MET4-IO involves 3 cohorts of 65 total patients: Group A, a safety cohort of 5 patients already on ICI; Group B, patients starting ICI, randomized 3:1 to receive MET4 or not; Group C, patients on ICI who experience radiological progression but not clinical deterioration, randomized 1:1 to receive MET4 or not. Stool and blood samples are collected at baseline and 4-5 additional time-points. For this interim analysis, 16S rRNA gene sequencing was performed on fecal specimens. Shannon diversity, relative abundance (RA), number and fold-change of MET4 taxa > RA 0.01 were assessed and compared to controls. Results: As of January 26, 2020, 21 patients were enrolled (A = 5,B = 12,C = 4), and 15 (71%) received MET4. The mean age was 65.9 years, 40% were females, 52% had head and neck cancer and 19% melanoma. Sixteen patients (76%) were treated with an anti-PD1 agent as monotherapy and 5 with a combination of anti-PD1 and anti-CTLA4 antibodies. G3-4 toxicities (CTCAEv5.0) attributed to ICI were observed in 13% vs. 17% of MET4 exposed and control patients, respectively. Three patients (20%) experienced toxicities attributed to MET4, all grade 1 except G2 dyspepsia in 1 patient. A greater number of MET4-associated taxa were detectable in MET4 recipients than controls (p < 0.01), with a trend towards higher cumulative RA (p = 0.10). No significant change in Shannon diversity after MET4 was observed, however controls were more likely to lose diversity overtime than MET4 recipients (p = 0.05). Colonization with MET4 varied by recipient and by taxon. Bifidobacterium, Collinsella and Enterococcus were significantly more common and abundant in MET4 recipients than controls. Conclusions: In this cohort, MET4 treatment was safe and associated with higher MET4-associated taxa in recipients than controls. Further analyses including peripheral blood immunophenotyping are ongoing. Clinical trial information: NCT03686202 .

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.145
GPT teacher head0.468
Teacher spread0.323 · 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

Citations28
Published2020
Admission routes2
Has abstractyes

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