Abstract P1-13-02: The Aberrant Activity of Retrotransposon Elements Mediates the Chemo-tolerant Persister Cells Relapse in TNBC
Bibliographic record
Abstract
Abstract The emergence of acquired drug resistance through therapeutic treatment remains a critical threat to efficient chemotherapy, target therapy, or immune therapy. These resistant cancer cells most often lead to relapse or metastasis. The development of drug resistance is a multi-step evolutionary adaptation for cancer cells. Tumor heterogeneity, cancer cells’ plasticity, and microenvironment contribute to the resistant clone’s formation. Therefore, a time-lapse adaptation model is critical to define the mechanism of drug resistance evolution. Recently, several studies have revealed that the initial acquired drug resistance might be conferred by transient events, such as drug-tolerant persisters (DTP) that might occur in a subpopulation of the cancer cells at the early stage of the treatment, which were then followed by the transcriptomic reprogramming and secondary-wave genetic mutations in the progression of resistance development. In the clinic, chemotherapy is still the mainstream treatment for TNBC, and one of the primary chemo agents is doxorubicin. Although the initial responsive rate of doxorubicin-based chemotherapy is up to 70%, it is well recognized that TNBC cells usually generate an evolutionary adaptive response that can result in the acquired drug-resistance and multi-drug resistant phenotypes. To date, numerous different mechanisms of acquired chemo-resistance have been reported, but the vast majority of these results have been derived from the continuous-high-dose-exposure acquired resistant cell line models. Since the chemo-treatment dosage in these artificial models is well above what is physiologically achievable in patients, few of them can mimic the actual situation of resistance development or improve the clinical trial outcomes. Moreover, most of these studies only characterized the terminal resistant cells, which are challenging to be resensitized because of their dominant genetic mutations. In this study, we hypothesize that the TNBC chemo-resistant cells may derive from the early-stage reversible chemo-tolerant “DTP-like” (CTP) cells, and early-stage epigenetic landscape perturbation might determine the progression of chemo-resistance development. To test the hypothesis and overcome the previous model limitations, based on the clinical drug exposure kinetics for doxorubicin, we developed an in vitro “pulsing-treatment CTPs regrowth” model (referred to as CTP model), which could mimic the clinical treatment and provide therapeutically relevant insights into the initial drug-induced stress response and resistance development. Leveraging this CTP model, we are able to define the early event for drug response, in which the doxorubicin-treated cells showed a senescence-like phenotype, and the interferon alpha (type I) pathway was activated. Furthermore, unexpectedly, we found that the expression of HERVs was significantly activated but LINE1s not. To further explore the TEs reactivation, we did the single cell RNA-seq for 0h, 2h, and 4 days samples. With a novel bioinformatic workflow, we integrated the TE expression information with coding genes mRNA profiling from the same single cell RNA-seq dataset and identified the IFN-enriched cluster had higher expression of HERVs. Herein, a subpopulation of HERVhigh cells with IFN activation was identified as a “hot-cluster” which might be the early determinant in the resistance evolution. Citation Format: Zijian Zhang, Yiyang Wang, Xinluo Luo, Xuwen Li, Xiaomei Zhan, Yumin Zheng, Jun Ding, Tao Wu. The Aberrant Activity of Retrotransposon Elements Mediates the Chemo-tolerant Persister Cells Relapse in TNBC [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-13-02.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".