Abstract 3121: Evaluation of novel combinations of PI3K-mTOR inhibitors with dacomitinib (dac) or chemotherapy in PTEN-deficient genomically characterized patient-derived tumor xenografts (gPTX)
Bibliographic record
Abstract
Abstract Background: Activation of the PI3K pathway has been associated with resistance to anti-human epidermal growth factor receptor (HER) therapy providing a rationale to combine PI3K-mTOR inhibitors and the pan-HER inhibitor dac. Loss of PTEN function may be associated with sensitivity to PI3K-mTOR inhibitors. The objectives of these experiments are to evaluate the combination of PI3K-mTOR inhibitors with chemotherapy or dac in PTEN-deficient gPTX models. Material and Methods: Three gPTX models [triple-negative breast cancer (TNBC), low-grade serous ovarian cancer (LGSOC), and non-small cell lung cancer (NSCLC)] were selected based on their deficient expression of PTEN by immunohistochemistry. Two dual PI3K-mTOR inhibitors were evaluated: PF-04691502 (5 mg/kg, daily, oral gavage) and PF-05212384 (10 mg/kg, twice weekly, intravenously). Three different combinations were evaluated: PI3K-mTOR inhibitor with 1) cisplatin (3 mg/kg, once weekly, intraperitoneally -IP); 2) with paclitaxel (10 mg/kg, twice weekly, IP); 3) with dac (3 mg/kg daily, oral gavage). This last experiment contained an acute dose component to evaluate target modulation by collecting tumor samples at 1 hr and 24 hr post dosing. Results: The percentages of tumor growth inhibition (TGI%) induced by the different treatment arms and daily tumor volume change (ΔV) for each arm compared to the control arm (p-value) are summarized in Table 1. Pharmacodynamic modulation of pS6 and pAKT was observed in the acute dose experiment in the arms of PI3K-mTOR inhibitor with or without dac. Conclusions: In gPTX with PTEN loss, the addition of a PI3K-mTOR inhibitor may improve the TGI% when compared to chemotherapy or dac alone. This benefit was largely offset in the NSCLC gPTX harboring KRAS G12C and/or TP53 mutations. The role of PTEN deficiency in the antitumor activity of these combinations should be further investigated in the clinic. Table 1gPTX tumor typeTNBCLGSOCNSCLCMolecular profilePTEN nullKRAS G12RPTEN lowKRAS G12CTP53 R181PPTEN lowTGI%ΔVp-valueTGI%ΔVp-valueTGI%ΔVp-valueExperiment 1PF-0521238432%0.0739%0.19-1%0.78Cis42%<0.05**43%0.0397%<0.05**PF-05212384 + Cis96%<0.05**68%<0.05**92%<0.05**Experiment 2PF-0521238422%0.6440%0.21PF-0469150233%<0.05**Pac84%<0.05**38%0.2079%<0.05**PF-05212384 + Pac110%^<0.05**56%<0.05**PF-04691502 + Pac45%<0.05**Experiment 3PF-0521238422%0.6140%0.26PF-0469150233%<0.05**Dac15%0.998%0.5323%0.35PF-05212384 + Dac55%0.0945%0.47PF-04691502 + Dac32%0.055Cis = cisplatin; Pac = paclitaxel; Dac = dacomitinib; TNBC = triple negative breast cancer; LGSOC = low grade serous ovarian cancer; NSCLC = non-small cell lung cancerTGI%: percentages of tumor growth inhibition; ΔV: differences between daily tumor volume change of each treatment arm and the control arm^ Tumor regression; ** statistically significant Citation Format: Irene Brana, Nhu-An Pham, Lucia Kim, Shingo Sakashita, Ming Li, Christine Ng, Yuhui Wang, Peter Loparco, Jose Rafael Sierra, Lisa Wang, Lillian L. Siu, Ming S. Tsao. Evaluation of novel combinations of PI3K-mTOR inhibitors with dacomitinib (dac) or chemotherapy in PTEN-deficient genomically characterized patient-derived tumor xenografts (gPTX). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3121. doi:10.1158/1538-7445.AM2014-3121
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".