Abstract B009: Exploring the putative Kras-p53 mutational interface for vulnerability
Bibliographic record
Abstract
Abstract Introduction: Kras Gain of Function mutations are frequently detected in lung, colorectal and pancreatic cancers, in addition to others. Activating Kras mutations results in a constantly active protein, which we reasoned is quite unphysiological, yet it is tolerated by cells since this mutation is seen in hyperplasia. Activating this oncogene is insufficient alone for malignant transformation. Mutations in the tumor suppressor gene (TSG) p53 (the most frequently mutated gene in human cancer), cooperate with mutant Kras and is sufficient to permit full display of the actions of oncogenic Ras (as confirmed in Genetically Engineered Mouse Models and in clinical cases). The phenomenon of oncogene addiction may in fact be the result of obligatory requirements (cellular metabolism or otherwise) brought on by the constant oncogene signaling. This required adjustment in cellular circuitry can be afforded by specific cooperating TSG and since it too is a DNA constant change, becomes an new rigid reality for a cell with a given set of oncogene-TSG pair. p53 mutational spectrum was described as ‘enigmatic’ presumably because they almost never completely abrogate the function of this major regulator suggesting to us a possible essential mechanistic role for the retained transcriptional targets within the (well annotated) p53 transcriptional network. This could represent a putative synthetic lethality opportunity against activated Kras, an oncogene that has proven difficult to drug. We hypothesized that specific mutations in p53 with their respective transcriptional lesions cooperate with unique mutant Kras in tissue specific manner identifying a short list of gene targets for synthetic lethality experiments. Methods: To examine this hypothesis, we analyzed the TCGA database to determine a conserved pair cooperation between common Kras 12C; D or V and the top 6 reported p53 (hot spot) mutations [on residues 175; 245;248;249;273 and 282] in a stage-agnostic manner. To examine whether those putative interactions are cell-type specific, we performed this analysis in 3 different histologies (lung, colon, and pancreas). Results: The results suggested a non-random distribution of p53 mutants among the Kras driven cancers. KRAS (12 C, 12 V, 12 D were reported in 80% of Colon, Pancreas, and Lung Cancer. p53 (175 - 30%) (245 - 30%) (248 - 15%) (249 - 12%) (273 - 6.5%) (282 - 6.5%). More detailed analysis is planned for the poster session. Conclusion: Despite having a single activated oncogene, the distribution of the cooperating p53 mutations is nonrandom so examining the transcriptionally retained gene list represents a novel approach to explore for gene editing experiments. This provides and approach to drug the addiction of cancers to their oncogenes in a cancer specific, and occasionally to target essential proto-oncogene such as Myc where direct inhibition is highly undesirable due to its physiologic roles. Citation Format: Nishanth Thalambedu, Shallya Anand, Haya Safar, Farah Mazahreh, Ahmad Mazin M. Safar. Exploring the putative Kras-p53 mutational interface for vulnerability [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B009.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".