Abstract P3-06-10: Temozolomide & vorinostat as a targeted therapy strategy for triple negative breast cancer
Bibliographic record
Abstract
Abstract Approximately 15% of newly diagnosed breast cancers (BC) are classified as triple negative (TNBC), which have a worse prognosis than hormonal receptor-positive BCs. Standard treatment for TNBC includes cytotoxic chemotherapies that are associated with significant toxicities. Currently the only targeted therapy (PARP inhibition) exists for the 10% of TNBC patients who have germline BRCA mutations. There is an ongoing unmet need for TNBC that may include targeted strategies to improve outcomes. The alkylating agent temozolomide (TMZ) is an oral chemotherapy drug used in the treatment of glioblastomas. The therapeutic benefit of TMZ largely depends on cells not being able to fully repair the DNA damage it causes. O6-methylguanine-DNA methyltransferase (MGMT) is normally expressed and able to repair damage caused by TMZ; however, MGMT is often silenced by promoter methylation in many tumours (mMGMT), and such tumours show a better response to TMZ than those with unmethylated promoters (uMGMT). MGMT has been shown to be methylated in up to 50% of TNBC, suggesting TMZ may be a targeted treatment option. Additionally, studies in other tumour sites suggest that adding an HDAC inhibitor (HDACi) to TMZ can increase response and early phase trials have shown this combination is well tolerated. We have previously shown cytotoxicity with TMZ in mMGMT TNBC cell lines and now hypothesize that the addition of HDAC inhibition to TMZ will have synergistic effects. Using in vitro models, we explored the efficacy of Vorinostat (HDACi) alone and in combination with TMZ and/or cisplatin utilizing TNBC cell lines: HTB132 (uMGMT) and HTB26 (mMGMT). Cytotoxicity assays were utilized to determine median effective dose (EC50) for each treatment and then we performed EC50 synergy testing. Percent viability was calculated as the absorbance ratio of treated/untreated cells X 100. The EC50 was then determined by calculating the intercept of the log effect vs log RV dose. We found that Cisplatin and HDAC inhibition showed similar cytotoxicities in both cell lines. The addition of cisplatin to either TMZ or HDACi increased cytotoxicity compared to either treatment alone in the mMGMT cell line HTB26; however, the addition of cisplatin to TMZ in the uMGMT cell line showed the greatest cytotoxicity. Interestingly, we also saw an increase in cytotoxicity when cells were treated with the combination TMZ and HDACi, regardless of methylation status of the MGMT promotor. Finally, the triple combination therapy did not increase cytotoxicity. Although HDAC inhibition showed cytotoxicity regardless of methylation status of the MGMT promotor; it was increased by the combination with TMZ in both cell lines evaluated. Further, the addition of cisplatin to TMZ did not improve cytotoxicity in the mMGMT cell line but did increase cytotoxicity in the uMGMT cell line. There appears to be no benefit to triple therapy in either cell line. We are currently evaluating these combinations in a xenograft murine model to provide pre-clinical support for a Phase II funded clinical trial in TNBC patients. Citation Format: Elizabeth N Kornaga, Ailian Yang, Kathy Gratton, Zhong Qiao Shi, Nancy A Nixon, Gloria Roldan Urgoiti, Don G Morris. Temozolomide & vorinostat as a targeted therapy strategy for triple negative breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-06-10.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".