Abstract 3088: Farnesyl/geranylgeranyl transferase dual inhibitor thwarts mutant KRas-driven patient-derived pancreatic tumors
Bibliographic record
Abstract
Abstract Although mutant KRas is a significant driver of pancreatic oncogenesis and resistance to therapy, there are no KRas inhibitors available for these patients. Farnesyltransferase inhibitors (FTIs) were developed as potential anticancer drugs because KRas requires farnesylation for its membrane localization and cancer-causing activity. However, KRas becomes geranylgeranylated and active when cancer cells are treated with FTIs. In this study, we designed a Ras C-terminal mimetic dual Farnesyl/geranylgeranyltransferase-1 (GGT-1) inhibitor, FGTI-2734, to overcome the geranylgeranylation-dependent resistance to FTIs. Immunofluorescence, cellular fractionation, and gel shift assays showed that FGTI-2734, but not the selective FTI-2148 and GGTI-2418, inhibited membrane localization of KRas in mt KRas pancreatic, lung, and colon human cancer cells. FGTI-2734 inhibited the growth in mice of mt KRas-dependent but not -independent human tumors, indicating its selectivity for mt KRas-driven cancers. Importantly, FGTI-2734 inhibited the in vivo growth of xenografts derived from four pancreatic cancer patients with mt KRas (two G12D, and two G12V) tumors. In addition, FGTI-2734 was highly effective at inhibiting, in three-dimensional co-cultures with chemotherapy resistance-promoting pancreatic stellate cells, the viability of primary and metastatic mutant KRas (G12D, G13D, and G12V) tumor cells derived from 8 pancreatic cancer patients. Finally, FGTI-2734 suppressed oncogenic pathways mediated by Akt, mTOR, and cMyc while upregulating p53 and inducing apoptosis in patient-derived xenografts in vivo. Thus, the development of this novel dual FT and GGT-1 inhibitor overcomes a major hurdle in KRas resistance, thwarting the growth of patient-derived mutant KRas-driven xenografts from pancreatic cancer patients, and as such it warrants further advanced preclinical and clinical studies. Citation Format: Aslamuzzaman Kazi, Shengyan Xiang, Hua Yang, Liwei Chen, Perry Kennedy, Muhammad Ayaz, Steven Fletcher, Christopher Cummings, Harshani Lawrence, Francisca Beato, Ya'an Yang, Michael P. Kim, Andrea Delitto, Patrick Underwood, Jason B. Fleming, Jose Trevino, Andrew D. Hamilton, Said M. Sebti. Farnesyl/geranylgeranyl transferase dual inhibitor thwarts mutant KRas-driven patient-derived pancreatic tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3088.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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 teacher head, 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".