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Record W4393092985 · doi:10.1158/1538-7445.am2024-7363

Abstract 7363: Clinical, genomic, transcriptomic and immunologic profile of BRAF mutant colorectal tumors

2024· article· en· W4393092985 on OpenAlexaff
Emmanuelle Rousselle, Suzanne Kazandjian, April A. N. Rose

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsColorectal cancerTranscriptomeMutantCancer researchMedicineBiologyInternal medicineCancerGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Abstract Background: Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. BRAF, a protein kinase of the Mitogen-Activated Protein Kinase (MAPK) pathway, is mutated in 12% of colorectal cancers and is a poor prognostic factor for patients. BRAF mutations at the V600 codon are most studied but non-V600 BRAF mutations also exist and behave differently. BRAF V600 CRC treated with BRAF+MEK inhibitors show 12% response rates while we observe 65% response rates in BRAF V600 Melanoma. Additionally, no approved targeted therapies exist for non-V600 BRAF mutant cancers and retrospective studies show poorer PFS with BRAF+MEK inhibitors vs BRAF V600 cancers. We interrogated publicly available data of BRAF mutant CRC to uncover differences in the tumor microenvironment and signalling pathways of BRAF V600 and non-V600 tumors in hopes of uncovering better treatments. Methods: We analyzed the clinical and genomic profile of BRAF mutant CRC tumors from the GENIE v12 data. Gene Set Enrichment Analysis was performed on RNAseq from the TCGA data (discovery cohort) and the combined data from CPTAC and Sidra-LUMC data (validation cohort). We investigated the composition of tumor infiltrating immune cells using CIBERSORTx (deconvolution algorithm for bulk RNAseq). Results: Non-V600 mutations (n=219/1061) were most represented in patients of Asian or Black race compared to White patients (p<0.0001) and importantly, were found in younger patients (p<0.0001). We identified n=67 and n=74 BRAF mutant RNAseq samples in the discovery and validation cohorts, respectively. In the discovery cohort: BRAF V600 CRC were enriched for Interferon Gamma Signaling, Complement pathway, and IL6 JAK STAT3 Signaling (p<0.05) while the non-V600 BRAF mutant CRC showed an enrichment for Wnt-beta Catenin, Notch, and Hedgehog Signaling pathways (p<0.05). In the validation cohorts these same pathways were enriched in V600 and non-V600 BRAF mutant tumors. Interestingly, enrichment of genes co-mutated (GENIE data) in non-V600 BRAF mutant CRC also revealed the Wnt pathway. Non-V600 CRC tumors have a higher proportion of M0 macrophages (p=0.0035) and CD4 memory resting T cells (p<0.0001) while patients with V600 CRC have higher proportions of CD8 T cells (p=0.006) and activated mast cells (p=0.0341). Conclusion: No optimal therapeutic strategies have been defined for these potentially actionable non-V600 BRAF mutant CRCs. By exploring the multi-omic landscape of these tumors we have identified unique characteristics of non-V600 BRAF mutant CRC that could be therapeutically exploited. Citation Format: Emmanuelle Rousselle, Suzanne Kazandjian, April Rose. Clinical, genomic, transcriptomic and immunologic profile of BRAF mutant colorectal tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7363.

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)0.000
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.113
GPT teacher head0.450
Teacher spread0.336 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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