Abstract 6127: MDM2 inhibition in combination with MEK inhibition in pre-clinical models of lung adenocarcinomas with <i>MDM2</i> amplification
Bibliographic record
Abstract
Abstract The eventual development of resistance to single-agent targeted therapies in lung adenocarcinomas (LUAD) is inevitable, and new strategies are needed. We hypothesize that combination therapies aimed at a known driver and a distinct targetable alteration could prolong time on oral targeted therapy. In an analysis of 7636 patients with LUAD who underwent MSK-IMPACT large panel NGS testing, 5.5% (416/7636) harbored MDM2 amplification (MDM2amp), a known mechanism of TP53 inactivation. MDM2amp was over-represented among tumors with alterations in METex14 (34.4%, p<0.001), EGFR (10%, p<0.001), RET (11%, p<0.05), and ALK (9.9%, p<0.002). The small molecule MDM2 inhibitor milademetan (mila) caused growth inhibition as a single-agent in MDM2amp patient-derived cell lines with concurrent kinase alterations including ECLC5-GLx (MDM2amp/TRIM33::RET/TP53 wildtype (WT)) and LUAD12c (MDM2amp/METex14/KRASG12S/TP53 WT). Mila also caused growth inhibition in a cell line with KRASG12C and WT TP53 without MDM2amp (SW1573 (KRASG12C/TP53WT)), but not in cell lines with TP53 mutations (LUAD-002AS1 (KIF5B::RET/TP53P128fs, H1792 (KRASG12C/TP53 splice site mut)). Treatment of ECLC5-GLx and LUAD12c with mila resulted in restoration of ERK phosphorylation, confirming a previous report of ERK activation upon MDM2 inhibition. At 48 hours, ERK phosphorylation was suppressed by concurrent mila and MEK inhibition using trametinib (tram). In contrast, ERK phosphorylation was not suppressed by concurrent mila and KIF5B::RET inhibition using selpercatinib (in ECLC5-GLx) or MET inhibition using capmatinib (in LUAD12c). The combination of mila+tram was synergistic in slowing growth of ECLC5-GLx, LUAD12c, and SW1573 cells, and increased expression of pro-apoptotic proteins PUMA and BIM, beyond that achieved by either agent alone. In ECLC5-GLx, mila+tram also caused increased apoptotic cells measured by Annexin-V compared to either agent alone (combination p<0.01 compared to mila, p<0.001 compared to tram). In vivo, combination mila+tram was more effective than mila or tram alone in ECLC5-GLx (p<0.0001 and p<0.0001, respectively), LX-285 (EGFRex19del/MDM2amp) (p<0.0001 and p<0.0001, respectively), and L-13BS1 (model resistant to capmatinib) (METex14/MDM2amp) (p<0.05 and p<0.0001, respectively). These results suggest that combined MDM2/MEK inhibition is effective in patient-derived LUAD models harboring MDM2amp. This combination, potentially applicable to LUADs with a wide variety of oncogenic driver mutations and kinase fusions will be investigated as part of a phase 1/2 clinical trial. Citation Format: Arielle Elkrief, Vladimir Markov, Álvaro Quintanal-Villalonga, Rebecca Caeser, Pawel Sobczuk, Emily Cheng, Alexander Drilon, Gregory J. Riely, William W. Lockwood, Elisa de Stanchina, Charles M. Rudin, Igor Odintsov, Romel Somwar. MDM2 inhibition in combination with MEK inhibition in pre-clinical models of lung adenocarcinomas with MDM2 amplification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6127.
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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) | 0.001 | 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) | 0.003 | 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".