Abstract 996: A translational phosphoproteomic approach to study differences in <i>KRAS</i> signaling in pancreatic, colorectal and lung cancers
Bibliographic record
Abstract
Abstract Aims To understand any context-dependent differences in signaling pathways between pancreatic (PAN), colorectal (CR) and lung (LU) cancers with KRAS mutations using a targeted phosphoproteomic approach in cell lines and patient-derived cancer cells exposed to targeted anticancer drugs ex-vivo. Materials and Methods We studied a panel of 30 KRAS mutant cell lines: 10 PAN, 10 CR and 10 LU cell lines. Cancer cells were also immuno-magnetically isolated from pleural effusions and ascites of patients with KRAS mutant CR and LU cancer and exposed to a DMSO control and clinically relevant concentrations of PI3K (pictilisib), AKT (AZD5363), mTOR (everolimus), EGFR (gefitinib), BRAF (vemurafenib), MEK (trametinib) and HSP90 (luminespib) inhibitors for 1 hr. Dynamic changes in a panel of 52 relevant phosphoproteins were studied using the Luminex 200 platform. Hierarchical clustering and logistic regression were used to find differences in dynamic changes in phosphoproteins between KRAS mutant, PAN, CR and LU cancer cells. Results Supervised clustering studying exposure to different drugs revealed that when exposed to the PI3K inhibitor, pictilisib, KRAS mutant LU cancers did not significantly cluster together; p=0.008, p=0.104 following Benjamini-Hochberg correction. Independently, logistic regression showed significant differences in signaling of KRAS mutant cells when exposed to the PI3K inhibitor, pictilisib. PAN and CR cancers showed an increase in p-MEK while LU cancer cells did not; p=0.0195. LU cancer cell lines showed significantly more reduction of p-AKT compared to PAN and CR cell lines when exposed to the PI3K inhibitor; p=0.0423. As expected, exposure to vemurafenib increased p-MEK levels across the majority of the KRAS mutant cell lines, however compensatory reductions in p-mTOR levels were seen significantly more in PAN and CR cell lines and not in LU cell lines; p=0.0084. The dynamic phosphoprotein changes caused by pictilisib were validated in cancer cells isolated from serous effusions of 3 KRAS mutant LU and 4 KRAS mutant CR cancer patients. Validation of these findings using multiple other inhibitors and time-points is ongoing. Interpretation/conclusions We hypothesise that the significantly greater reduction in p-AKT and less increase of compensatory p-MEK caused by PI3K inhibition in KRAS mutant LU cells compared to KRAS mutant PAN and CR cell lines represents preferential signaling of these cells through the PI3K pathway. Increase in p-MEK driven by BRAF inhibitors caused a reduction in p-mTOR in PAN and CR but not in LU cell lines also indicating preferential dependence of signaling in KRAS mutant lung cancer cells through the PI3K pathway. These findings are important while designing clinical trials of KRAS mutant cancers and more broadly to precision medicine where mutation status independent of tissue context is often used. Citation Format: Adam Stewart, Elizabeth A. Coker, Anna Minchom, Sebastian Pölsterl, Alexandros Georgiou, Paul Huang, Bissan Al-Lazikani, Udai Banerji. A translational phosphoproteomic approach to study differences in KRAS signaling in pancreatic, colorectal and lung cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 996. doi:10.1158/1538-7445.AM2017-996
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".