Abstract 02: Pan-cancer analysis of hotspot mutations in genes encoding the members of mitogen activated protein kinase (MAPK) and phosphoinosidtide-3 kinase (PI3K) pathways among smokers and non-smokers
Bibliographic record
Abstract
Abstract Background: In lung cancer, the MAPK pathway is activated mainly by KRAS and EGFR mutations in smokers and non-smokers, respectively. It is relatively unknown how smoking affects MAPK and PI3K pathways across multiple cancers. Methods: Mutation data and smoking status were available from 854 solid tumor patients whose tumors were profiled by NGS with Illumina MiSeq TruSeq Amplicon Cancer Panel (48 genes, 212 amplicons) in the Princess Margaret Cancer Centre Integrated Molecular Profiling in Advanced Cancer Trial (IMPACT). The panel included hotspot exons of EGFR, ERBB2, KRAS, NRAS, BRAF, PIK3CA, PTEN and AKT1. Mutation frequencies between smokers and non-smokers (never smoker + former light smoker [<5pack year]) were compared using Chi-Square test or Fisher's exact test. Results: Lung cancers (N=101) from smokers contained more KRAS mutations (38% vs.12%, P=0.004) while those from non-smokers had more EGFR mutations (34% vs. 10%, P=0.003). In contrast, non-lung cancers (N=753) had no difference in KRAS mutation frequencies between smokers and non-smokers (19% vs.17%, P=0.47). Too few EGFR and NRAS mutations were found in this cohort for meaningful analysis. Across the cohorts, there was no difference in BRAF, PIK3CA, or PTEN mutation frequency between smokers and non-smokers (BRAF, 5% vs. 4.5%, P=0.6; PIK3CA, 14% vs.15%, P=0.8; PTEN, 4.5% vs. 3.6%, P=0.6). Five non-lung cancers (4 non-smokers, 1 smoker) had AKT1 mutations. All nine cases with ERBB2 mutations (2 lung and 7 non-lung cancers) were non-smokers. No difference in KRAS, BRAF, PIK3CA and PTEN mutation frequencies between smokers and non-smokers was observed within specific cancers; breast, cervix, colorectal, endometrium, ovarian, pancreatobiliary and upper aerodigestive (P values>0.05, N= 107, 36, 126, 55, 167, 70, 81 respectively). Conclusions: Our data suggest that, with the exception of lung cancer, there is no difference in frequencies of hotspot mutations in critical genes encoding MAPK and PI3K pathways members between smokers and non-smokers across multiple cancers analysed. ERBB2 hotspot mutations (N=9) were exclusively found in non-smokers. Citation Format: Kyaw L. Aung, Trevor J. Pugh, Tracy Stockley, Lisa Wang, Greg Korpanty, Stefano Serra, Patricia Shaw, Ming S. Tsao, Neesha Dhani, Helen Mackay, Frances A. Shepherd, Suzanne Kamel-Reid, Lillian L. Siu, Philippe L. Bedard. Pan-cancer analysis of hotspot mutations in genes encoding the members of mitogen activated protein kinase (MAPK) and phosphoinosidtide-3 kinase (PI3K) pathways among smokers and non-smokers. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".