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Record W4292265616 · doi:10.1200/po.22.00107

Clinical Activity of Mitogen-Activated Protein Kinase–Targeted Therapies in Patients With Non–V600 BRAF-Mutant Tumors

2022· review· en· W4292265616 on OpenAlexafffund
Matthew Dankner, Yifan Wang, Rouhi Fazelzad, Benny Johnson, Caroline A. Nebhan, Ibiayi Dagogo‐Jack, Nathaniel J. Myall, Georg Richtig, Jillian Wilhelmina Paulina Bracht, Marco Gerlinger, Eiji Shinozaki, Takayuki Yoshino, Daisuke Kotani, Jason Fangusaro, Oliver Gautschi, Julien Mazières, Jeffrey A. Sosman, Scott Kopetz, Vivek Subbiah, Michael A. Davies, Anna Groover, Ryan J. Sullivan, Keith T. Flaherty, Douglas B. Johnson, Andrea Benedetti, David W. Cescon, Anna Spreafico, George Zogopoulos, April A. N. Rose

Bibliographic record

VenueJCO Precision Oncology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsPrincess Margaret Cancer CentreMcGill University Health CentreUniversity of TorontoUniversity Health NetworkMcGill UniversityJewish General Hospital
FundersNational Cancer InstituteFonds de Recherche du Québec - SantéAssociation Neurofibromatoses et RecklinghauseAmerican Society of Clinical OncologyConquer Cancer Foundation
KeywordsMedicineMAPK/ERK pathwayLung cancerMelanomaTargeted therapyOncologyCancerCancer researchInternal medicineProtein kinase AMutantKinaseBiologyGeneGenetics

Abstract

fetched live from OpenAlex

PURPOSE: Non-V600 mutations comprise approximately 35% of all BRAF mutations in cancer. Many of these mutations have been identified as oncogenic drivers and can be classified into three classes according to molecular characteristics. Consensus treatment strategies for class 2 and 3 BRAF mutations have not yet been established. METHODS: We performed a systematic review and meta-analysis with published reports of individual patients with cancer harboring class 2 or 3 BRAF mutations from 2010 to 2021, to assess treatment outcomes with US Food and Drug Administration-approved mitogen-activated protein kinase (MAPK) pathway targeted therapy (MAPK TT) according to BRAF class, cancer type, and MAPK TT type. Coprimary outcomes were response rate and progression-free survival. RESULTS: A total of 18,167 studies were screened, identifying 80 studies with 238 patients who met inclusion criteria. This included 167 patients with class 2 and 71 patients with class 3 BRAF mutations. Overall, 77 patients achieved a treatment response. In both univariate and multivariable analyses, response rate and progression-free survival were higher among patients with class 2 compared with class 3 mutations, findings that remain when analyses are restricted to patients with melanoma or lung primary cancers. MEK ± BRAF inhibitors demonstrated greater clinical activity in class 2 compared with class 3 BRAF-mutant tumors than BRAF or EGFR inhibitors. CONCLUSION: This meta-analysis suggests that MAPK TTs have clinical activity in some class 2 and 3 BRAF-mutant cancers. BRAF class may dictate responsiveness to current and emerging treatment strategies, particularly in melanoma and lung cancers. Together, this analysis provides clinical validation of predictions made on the basis of a mutation classification system established in the preclinical literature. Further evaluation with prospective clinical trials is needed for this population.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.015
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.343
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations48
Published2022
Admission routes2
Has abstractyes

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