Abstract 1151: Pharmacogenomics genotyping from clinical somatic whole exome sequencing: Aldy, a computational tool
Bibliographic record
Abstract
Abstract Background Pharmacogenomics (PGx) testing can reduce toxicities and improve efficacy of several drugs used to treat cancer and associated symptoms. PGx results can be determined from germline whole-exome sequencing (WES), but somatic mutations may cause discordance between tumor and germline DNA. Since clinical diagnostic sequencing in oncology frequently only includes tumor DNA, there would be clinical value in calling germline PGx genotypes from tumor DNA. Thus, the purpose of this study was to assess the feasibility of using somatic WES data to call germline PGx genotypes. Methods Germline and somatic WES data were obtained as part of the clinical workflow for 64 patients treated at the solid molecular tumor board clinic at Indiana University. Aldy v3.3 was implemented in LifeOmic’s Precision Health Cloud™ to call PGx genotypes from somatic WES. Somatic Aldy calls were compared with previously validated Aldy germline calls for 8 genes: CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, and TPMT. Somatic read depth was >100x, except for the intronic CYP3A4*22 variant, which was >30x. Results Somatic and germline Aldy calls were compared for a total of 512 genotypes and 56 (11%) calls were discordant. Discordant calls were most common for CYP2B6 (23.4%), followed by CYP2D6 (14.1%), CYP2C19 (10.9%), CYP2C8 (6.3%), and DPYD (6.3%). In contrast, all Aldy calls were concordant for CYP3A5 and TPMT. 38 out of 64 subjects (59%) had discordant calls for at least one gene. The most common first cancer diagnoses in our cohort were colorectal (9.3%), breast (7.8%), and pancreatic (7.8%), and the rates of discordant Aldy calls did not differ by cancer type (p>0.05 for all cancer types). Based on our analyses of discordant calls, we anticipate that adjusting Aldy’s thresholds for variant calling may allow Aldy to determine genotypes from somatic WES data. Conclusion In most cases, genotype calls of drug metabolism genes from tumor DNA reflected the germline genotypes; however, additional work needs to be done to determine if the remaining discordant calls can be corrected by modifying the informatics tools or if they are due to somatic mutations. Citation Format: Wilberforce A. Osei, Tyler Shugg, Reynold C. Ly, Steven M. Bray, Benjamin A. Salisbury, Ryan R. Ratcliff, Victoria M. Pratt, Ibrahim Numanagić, Todd Skaar. Pharmacogenomics genotyping from clinical somatic whole exome sequencing: Aldy, a computational tool [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1151.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".