Abstract 5853: Identification of <i>TSC1</i> and <i>TSC2</i> as potential determinants of sensitivity to trastuzumab emtansine
Bibliographic record
Abstract
Abstract Trastuzumab emtansine (T-DM1, Kadcyla) is an antibody-drug conjugate used in the treatment of HER2-positive breast cancer. However, its use is limited by acquired and intrinsic resistance, the mechanisms of which are not well understood. Further knowledge of T-DM1 resistance may provide new combination strategies or therapeutic targets to overcome resistance or new predictive biomarkers to identify the patients most likely to benefit from T-DM1 therapy. To discover genes responsible for T-DM1 sensitivity and resistance in an unbiased manner, we have conducted CRISPR/Cas9 functional genomics screens by a two-stage process. Firstly, we performed whole genome screens in MDA-MB-361 and MDA-MB-453 cells transduced with Cas9 and the GeCKOv2 lentiviral library that were exposed to T-DM1 and its effector DM1 for 8-13 weeks. Gene knockouts enriched or depleted in response to T-DM1 or DM1 treatment in either cell line were identified by sequencing of genomic DNA and differentially expressed genes by RNA sequencing, revealing 599 candidate genes of T-DM1 sensitivity. For high-throughput validation of the 599 genes, we developed a custom library of 2539 guide RNAs (gRNAs) to target these 599 genes, plus non-targeting controls. Cas9-expressing MDA-MB-361 cells were transduced with the custom library and exposed to T-DM1 for 28 days. MAGeCK analysis of gRNA sequencing revealed 11 genes that were significantly enriched and one gene that was significantly depleted at a false discovery rate (FDR) of <0.1. Two of the top hits in the secondary screen were two genes whose loss is known to promote T-DM1 resistance: ERBB2 (HER2) and SLC46A3 (P<8 × 10−5; FDR <0.007). Other top hits were TSC1 and TSC2 (P<3 × 10−6; FDR= 0.0004); which are both tumor suppressor genes and negative regulators of mTOR complex 1 (mTORC1). For subsequent validation, we have generated TSC2 knockout cell pools, which were more resistant to T-DM1 than wildtype cells in a competition growth assay. Knockout clones have been isolated and are being tested for T-DM1 resistance. Since mTOR inhibitors can phenocopy TSC1 and TSC2 by inhibiting mTORC1 activity, we have also evaluated T-DM1 in combination with the mTOR inhibitor KU-0063794 in a sulforhodamine B assay in MDA-MB-361 and MDA-MB-453 cells. Each agent potently inhibited cell proliferation and demonstrated synergistic anti-proliferative activity in combination. Together, our results suggest that TSC1 and TSC2 knockout may promote T-DM1 resistance and that targeting mTOR may be an effective strategy to overcome T-DM1 resistance. Citation Format: Francis W. Hunter, Barbara A. Lipert, Kyla N. Siemens, Aziza Khan, Hilary R. Barker, Troy W. Ketela, William R. Wilson, Tet-Woo Lee, Stephen M. Jamieson. Identification of TSC1 and TSC2 as potential determinants of sensitivity to trastuzumab emtansine [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5853.
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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) | 0.002 | 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".