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Tumor-agnostic genomic and clinical analysis of solid tumors with <i>BRAF </i>fusions.

2023· article· en· W4379282682 on OpenAlexaff
Monica F. Chen, Soo‐Ryum Yang, Jessica J. Tao, Antoine Desîlets, Ezra Y. Rosen, Yixuan Gong, Kerry Mullaney, Mark G. Kris, Maria E. Arcila, Mark T.A. Donoghue, Romel Somwar, Marc Ladanyi, Neal Rosen, Rona Yaeger, Alexander Drilon, Michael Offin, Yonina R. Murciano‐Goroff

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineFusion geneCancer researchExonDNA sequencingOncologyGeneInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

3146 Background: Approximately 15% of cancers harbor BRAF alterations, of which ~5% are BRAF fusions. BRAF fusions are class II alterations that have worse outcomes than class I V600E alterations. While prior investigational therapies for BRAF fusions had disappointing outcomes, novel therapies are in clinical trials, underscoring the need to better characterize these tumors. Methods: Data from adult and pediatric patients with BRAF fusion+ cancers identified between January 2014 and November 2022 utilizing a center-wide next generation sequencing (NGS) program of >99,000 sequenced samples were analyzed. DNA-based hybrid capture tumor NGS (MSK-IMPACT), ctDNA targeted NGS (MSK-ACCESS), and/or RNA anchored multiplex PCR tumor NGS (MSK-Fusion) were used. Fusions were manually reviewed and considered to have oncogenic potential if they had an in-frame protein fusion involving a non-BRAF partner gene and an intact BRAF kinase domain (exons 11-18). Fusions not previously reported in OncoKB, COSMIC, TCGA, NIH gene, Fusion GDB2, and PubMED were classified as novel. All cases underwent clinical data curation including baseline demographic, tumor characteristics, and treatment histories. Results: 212 patients (0.2%) with BRAF-fusion positive solid tumors were identified. 194 were identified by DNA-based tumor NGS (96 had sufficient tissue for confirmatory RNA-based NGS, all of which were positive). Six were identified by ctDNA NGS and 12 by RNA-based tumor NGS only (5 had insufficient tissue for DNA-based tumor NGS). 83 unique 5’ fusion partners were found, of which 42 were novel. The most frequent tumor types were pilocytic astrocytoma (n=30, 14%), prostate CA (n=28, 13%), melanoma (n=24, 11%), lung CA (n=21, 10%), and colon CA (n=15, 7%). 44% of patients with pilocytic astrocytoma had BRAF fusions, of which 90% were BRAF-KIAA1549. Concomitant alterations (≥10% frequency) included TP53 (24%), TERT (18%), CDKN2A deletions (13%), and CDKN2B deletions (12%). 20% (n=43) were treated with MAPK-pathway directed therapies spanning multiple histologies and lines of therapies. Of the 212 patients, 17 had acquired BRAF fusions after targeted therapy for another oncogene ( EGFR mutation (n=11), BRAF V600E (n=4), FGFR fusion (n=1), NTRK fusion (n=1)). The majority of patients with acquired BRAF fusions had EGFR-mutant lung adenocarcinoma (n=11, 76%); the median time from EGFR targeted therapy initiation to BRAF fusion detection was 25 months (range 16-38 months). Conclusions: A wide variety of adult and pediatric solid tumors harbored de novo BRAF fusions. Complementary RNA sequencing optimized fusion identification in many cases. Multiple novel fusion partners were found. Acquired BRAF fusions were identified after targeted therapy for a variety of distinct oncogenes, the majority of which were EGFR mutations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.047
GPT teacher head0.417
Teacher spread0.369 · 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
Published2023
Admission routes1
Has abstractyes

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