Abstract 3099: <i>KRAS</i> and clinical context: Differential dynamic signaling output of <i>KRAS</i> mutant lung, colorectal and pancreatic cancer cell lines when exposed to targeted anticancer drugs
Bibliographic record
Abstract
Abstract Background Clinical trials have shown that cancers originating from different tissues driven by the same oncogene respond differently to targeted anticancer drugs. We aimed to understand different signaling patterns in KRAS mutant cells derived from non-small cell lung cancer (NSCLC), colorectal cancer (CRC) and pancreatic cancer. Materials and methods We optimized a 50 phosphoprotein antibody-based assay on the Luminex 200 platform. We then exposed a panel of 15 KRAS mutant cell lines (5 cell lines each originating in the lung, pancreas and colon) to a DMSO control (n = 3) and clinically significant concentrations (Cmax achieved in humans adjusted for protein binding in culture medium) of a PI3K (GDC-0941), AKT (AZD5363), m-TOR (everolimus), BRAF (vemurafenib), EGFR (gefitinib), MEK (trametinib) and an HSP90 inhibitor (luminespib) for 1 hr. We quantified the change in phosphorylation of proteins for each drug compared to control. Logistic regression analysis was used to analyse differences between KRAS-driven cell lines originating from different anatomical sites. Results There were changes in phosphorylation related to the pharmacodynamic effects of the drug independent of cell line of origin; however, there were interesting differences between KRAS mutant cells originating from different anatomical sites. In NSCLC cell lines, p-EGFR levels changed significantly less when exposed to PI3K, AKT and m-TOR inhibitors (p = 0.047, 0.022 and 0.047, respectively) when compared to cells originating from CRC and pancreatic cancer. CRC cell lines, when compared to NSCLC and pancreatic cancer cell lines, showed significantly less changes in phosphorylation of key cell cycle regulators such as CHK1 when exposed to PI3K, AKT and m-TOR inhibitors, (p = 0.001, 0.047 and 0.047, respectively) and RB when exposed to an AKT and m-TOR inhibitor (p = 0.047 and 0.047, respectively). Interestingly, pancreatic cell lines showed significantly more changes in p-m-TOR compared to CRC and NSCLC cell lines following exposure to PI3K and AKT inhibitors (p = 0.0095 and 0.022, respectively). Of note, drugs not directly targeting the PI3K pathway differentially regulated different nodes in the PI3K pathway, for example, BRAF inhibitors significantly differentially changed levels of phosphorylation at different nodes in the PI3K pathway such as AKT in NSCLC cell lines, p = 0.047, p70S6K in CRC cell lines, p = 0.0472 and PRAS40 in the pancreatic cancer cell lines, p = 0.022. Conclusion These results suggest that there are significant differences in signaling patterns caused by PI3K pathway inhibitors in KRAS mutant NSCLC, CRC and pancreatic cancer cell lines. Our findings shed light on the putative use of PI3K pathway inhibitors in KRAS mutant cancers. They also question the universal application of solely using genetic mutations to stratify patients in ‘basket’ clinical studies. Citation Format: Adam Stewart, Elizabeth Coker, Anna Minchom, Parames Thavasu, Alexandros Georgiou, Anguraj Sadanandam, Timothy A. Yap, Johann S. de Bono, Bissan Al-Lazikani, Udai Banerji. KRAS and clinical context: Differential dynamic signaling output of KRAS mutant lung, colorectal and pancreatic cancer cell lines when exposed to targeted anticancer drugs. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3099.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".