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Record W2954486272 · doi:10.1158/1538-7445.am2019-3141

Abstract 3141: The relationship between BRAF/NRAS/KIT genomic driver mutations and anti-PD1 immunotherapy responses in patients with advanced melanoma

2019· article· en· W2954486272 on OpenAlexaff
April A. N. Rose, Susan Armstrong, David Hogg, Marcus O. Butler, Anthony M. Joshua, Danny Ghazarian, Suzanne Kamel‐Reid, Ayman Al Habeeb, Mary Anne Chappell, Kendra Ross, Eitan Amir, Phillippe L. Bedard, Lillian L. Siu, Anna Spreafico

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsNeuroblastoma RAS viral oncogene homologMedicineMelanomaImmunotherapyOncologyInternal medicineCancerIpilimumabHazard ratioCancer researchKRASConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: Anti-PD1 immunotherapy, when used alone or in combination (combo) with anti-CTLA4, prolongs overall (OS) and progression-free survival (PFS) of patients (pts) with advanced melanoma, compared to single-agent (SA) anti-CTLA4. Combo immunotherapy is more toxic than SA anti-PD1, therefore it is of interest to identify patients most likely benefit from SA anti-PD1 to spare these pts the toxicity of combo immunotherapy. We asked whether genomic driver mutations (mts) in BRAF, NRAS or KIT could predict anti-PD1 responses in melanoma pts. Methods: We preformed a single-center retrospective analysis of 292 melanoma pts who received SA or combo anti-PD1 immunotherapy at Princess Margaret Hospital between 2012-17. BRAF/NRAS/KIT mts and other covariates were abstracted from chart review. Multivariable Cox models were used to assess differences in OS/PFS. Results: We identified 209 and 83 melanoma patients who received SA-anti-PD1 or combo immunotherapy, respectively. Pt characteristics were: mean age 59 yrs; 56% male; 77% cutaneous/unknown primary 23% uveal or mucosal; 17% brain metastases; 34% stage M0/M1a/M1b, 66% stage M1c/M1d, 51% BRAF/NRAS/KIT mt. Among pts receiving SA anti-PD1, presence of a BRAF, NRAS, or KIT mt was an independent predictor of poor PFS (HR 1.84; 95% CI 1.21-2.81) and OS (HR 1.93; 95% CI 1.17-3.22) in multivariate models. Other negative predictors of poor survival were: elevated LDH, multiple previous lines of therapy, 3 or more sites of metastasis, and uveal or mucosal histology. Combo immunotherapy was independently associated with longer OS compared to SA anti-PD1 in BRAF/NRAS/KIT mt pts (HR 0.43; 95% CI 0.20-0.90), but not in BRAF/NRAS/KIT wild-type pts (HR 1.34; 95% CI 0.46-3.90). Conclusions: In this retrospective study, the presence of a BRAF NRAS or KIT mt was an independent predictor of poor PFS/OS in melanoma pts treated with SA anti-PD1. Combo immunotherapy prolonged survival vs SA anti-PD1 in BRAF/NRAS/KIT mt melanoma, but not in BRAF/NRAS/KIT wild-type melanoma. These data suggest that BRAF/NRAS/KIT wild-type melanoma pts may derive less added benefit from combo immunotherapy (vs single agent anti-PD1) compared to pts with BRAF/NRAS/KIT mutations. Validation analyses are on-going. Citation Format: April A.N. Rose, Susan M. Armstrong, David Hogg, Marcus Butler, Anthony M. Joshua, Danny Ghazarian, Suzanne Kamel-Reid, Ayman Al Habeeb, Mary Anne Chappell, Kendra Ross, Eitan Amir, Phillippe L. Bedard, Lillian Siu, Anna Spreafico. The relationship between BRAF/NRAS/KIT genomic driver mutations and anti-PD1 immunotherapy responses in patients with advanced melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3141.

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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.374
Teacher spread0.321 · 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
Published2019
Admission routes1
Has abstractyes

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