658 KAN-001, a defined live biotherapeutic product (LBP) for NSCLC, enhances ICI response in preclinical models and demonstrates manufacturing consistency and strain-resolved viability
Bibliographic record
Abstract
Background The gut microbiome modulates host immunity and responses to immune checkpoint inhibitors (ICIs) in cancers such as non-small cell lung cancer (NSCLC). Fecal microbiota transplantation (FMT) from ICI-responsive donors can improve outcomes in ICI-refractory patients, but FMT-based approaches suffer from donor availability, compositional variability, safety issues, and limited scalability. KAN-001 is a fully defined, Live Biotherapeutic Product (LBP) comprised of dozens of strains and designed to recapitulate the beneficial properties of responder FMT while offering a consistent and scalable manufacturing process supported by strain-level assays. Importantly, the strains comprising the product were isolated from a cancer patient who obtained a complete response to PD-1 therapy, and whose FMT was able to elicit response in cancer patients with tumors initially refractory to ICI. We report preclinical efficacy, strain engraftment data, and process development highlights supporting the planned clinical evaluation of KAN-001.Methods KAN-001 candidates were evaluated in the MCA205 murine tumor model in combination with anti-PD-1 therapy. Comparative arms included treatment with FMT from ICI-non-responder donors. Engraftment of KAN-001 strains was assessed in vivo by metagenomic sequencing and HiPR-FISH, a high-plex imaging approach that enables spatial mapping of strain colonization in host tissue. The use of the HiPR-FISH platform also enabled the optimization of KAN-001 candidates, including strain additions through HiPR-ID-based targeted isolation. Run-to-run manufacturing consistency was assessed using small-scale production runs analyzed by metagenomics and HiPR-Vie, a HiPR-FISH-based platform allowing strain-level viability profiling of complex microbial communities.Results KAN-001 combined with ICI treatment resulted in tumor volume regression superior to FMT from ICI-non-responder donors, with optimized candidates leveraging HiPR-FISH showing improved tumor control in the murine model. Metagenomic profiling confirmed reproducible in vivo engraftment of KAN-001 strains, with HiPR-FISH revealing spatially distinct colonization patterns across the GI tract. Manufacturing process development runs demonstrated consistent strain abundance across replicates, and HiPR-Vie analysis confirmed viable representation of all strains across runs.Conclusions KAN-001 is a rationally designed, defined LBP that enhances ICI response in multiple preclinical mouse models and exhibits robust manufacturing and analytical tractability. A Phase 1 clinical trial is planned in NSCLC patients with both ICI naïve and refractory cohorts. Primary endpoints will include safety and tolerability, with secondary assessments of engraftment and preliminary efficacy. KAN-001 will be administered orally in an encapsulated frozen liquid formulation with induction and maintenance dosing. Microbiome profiling will be performed throughout the trial to characterize host-microbiome interactions and identify potential response biomarkers.Acknowledgements We thank Nadim Ajami and Jennifer Wargo of the MD Anderson Cancer Center for their continued support of the KAN-001 program.Ethics Approval The in vivo mouse study was approved by the Institutional Animal Care Committee (CIPA) and carried out in compliance with the Canadian Council at the CRCHUM.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".