Abstract B08: Beyond synthetic lethality: Multiple mechanisms can explain genetic interactions within childhood cancer
Bibliographic record
Abstract
Abstract Even though the survival rate of childhood cancer has increased in the last decades to around 80% today, it is still a leading cause of death for children in developed countries. Cancer develops through the acquisition of multiple mutations, and it is assumed that genetic interactions between mutated genes play an important role in cancer onset and progression. One approach to find genetic interactions in cancer is to search for pairs of mutated genes that occur more (or less) often than expected given the frequency of the individual mutated genes. Highly co-occurring mutated genes suggest a cooperative role of these altered genes in cancer development. Mutually exclusive gene pairs can be a signal of synthetic lethality, where the combination of the two mutations is lethal for the cancer cells, and could therefore point to possible cancer treatments. We developed a statistical pipeline, based on two genetic interaction tests, to detect significant cases of co-occurrence and mutual exclusivity in two pediatric cancer data sets, comprising over 2,500 tumors from 23 cancer types. In total we detect 15 co-occurring and 26 mutually exclusive candidate genetic interactions. Nearly all candidate gene pairs are uniquely found in one cancer type, supporting earlier findings that genetic interactions are often cell or cancer type specific. Interestingly, we find that synthetic lethality is not the main cause of the mutual exclusivity patterns in our data set. The majority of these patterns can be attributed to cancer subtypes and within pathway relationships, where the effect of a mutated gene is similar to mutating a (downstream) gene in the same pathway. This initial map of genetic interactions in childhood cancer provides a solid starting point to select suitable candidates for experimental validation as well as extending the analyses to investigate the contribution of genetic interactions towards structural variants, genetic predisposition, and cellular pathways. Citation Format: Josephine T. Daub, Saman Amini, Frank C.P. Holstege, Patrick Kemmeren. Beyond synthetic lethality: Multiple mechanisms can explain genetic interactions within childhood cancer [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B08.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".