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Larotrectinib resistance in TRK fusion cancers: Analysis of a tumor-agnostic, global clinical trial dataset.

2025· article· en· W4410813735 on OpenAlexaff
Alexander Drilon, David S. Hong, Daniel Orbach, Daniel Shao-Weng Tan, Birgit Geoerger, Antoine Italiano, Shivaani Kummar, Ulrik Lassen, Rui‐Hua Xu, Changsong Qi, Domnita-Ileana Burcoveanu, Nicoletta Brega, Shalini Chaturvedi, Kui Shen, Hong Zheng, Natascha Neu, Cornelis M. van Tilburg, Theodore W. Laetsch

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsBayer (Canada)
Fundersnot available
KeywordsMedicineTrk receptorClinical trialOncologyInternal medicineCancer researchReceptor

Abstract

fetched live from OpenAlex

3066 Background: Larotrectinib (laro) is the first-in-class, highly selective, TRK inhibitor approved for tumor- and age-agnostic use in TRK fusion cancers. This is the seminal report of primary and secondary laro resistance based on an analysis of the regulatory dataset that supported drug approval across multiple countries. Methods: Genomic data from patients (pts) with non-primary CNS TRK fusion cancer enrolled in a global, prospective, multicenter database of three laro clinical trials including adult and pediatric pts were analyzed. Tumor DNA (Illumina TruSight Oncology [TSO] Comprehensive, TSO 500, or FoundationOne CDx) or circulating tumor DNA (Guardant360 or GuardantOMNI) NGS was performed pre-laro (baseline; BL) and post-laro initiation. On-target NTRK (solvent front [SF], gatekeeper [GK], xDFG) mutations and COSMIC-classified tier 1/2 off-target alterations were identified. Primary laro resistance analysis set included pts with no meaningful clinical benefit (PD/SD < 4 months). Secondary (acquired) laro resistance analysis set included pts who developed resistance after meaningful clinical benefit (CR/PR/SD ≥4 months). Data cutoff: July 20, 2024. Results: Of 304 adult and pediatric pts enrolled, 216 had BL genomic data. Primary laro resistance was observed in 24 pts. Only 1 pt had an on-target mutation ( NTRK3 G623R), likely attributable to prior crizotinib; 9 pts (38%) had off-target alterations involving AKT, BRAF, FGFR1, GNAS, KRAS, NRAS, and PIK3CA . Secondary laro resistance was observed in 55 pts with valid post-BL ctDNA (the most common of these TRK fusion cancers were infantile fibrosarcoma [22%], other soft tissue sarcoma [18%], thyroid [11%], lung and salivary gland [9% each]); acquired alterations were identified in 16 of these pts. On-target resistance alone was observed in 5 of 16 pts (31%) and were mainly SF or GK single or double mutation-mediated ( NTRK1 F589L, NTRK1 G595R, NTRK3 G623R [n = 2], NTRK3 G623R/G696A). One xDFG mutation was identified. Off-target resistance alone was observed in 7 of 16 pts (44%) and included hotspot KRAS G12D/A/S/V or G13D, PIK3CA E545K or E542A, BRAF V600E, and GNAS R844H/C mutations. Complex, combined on-target and off-target resistance was observed in 4 of 16 pts (25%): on-target SF or GK alterations ( NTRK1 G595R, NTRK1 F589L/G595R, NTRK3 G623R, NTRK3 G623R/F617L) co-occurred with KRAS G12D or G12D/G13D, and NRAS G12D or Q61H. An analysis of resistance profiles by cancer type and age will be presented. Conclusions: In this analysis, on-target resistance to laro, including potential double NTRK resistance mutations, was commonly observed. Off-target, largely MAPK or PI3K/AKT pathway reactivating resistance, also occurred. In select cases, complex and likely polyclonal resistance including both on-target and off-target alterations were identified. These observations impact novel therapy development for TRK fusion cancers. Clinical trial information: NCT02637687 , NCT02576431 , NCT02122913 .

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.152
GPT teacher head0.525
Teacher spread0.373 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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