Transcriptome-based evaluation of the translatable potential of new treatments in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Full Abstract: Introduction: Triple-negative breast cancer (TNBC) is a highly metastatic type of breast cancer and one of the largest contributors to cancer mortality in women. Unlike other breast cancers, TNBC lacks any approved therapeutic targets. Scientists are rigorously attempting to decipher molecular pathways enriched in TNBC and to design clinically applicable therapeutics. Many TNBC drugs that successfully produce general antitumor effects in vitro fail to display significant long-lasting positive effects at the clinical level. This is in part because they do not effectively suppress the growth of cancer stem cells (CSCs), which have increased ability to evolve into metastatic tumors and are associated with enrichment of immunosuppressive pathways. Moreover, it has been shown that in TNBC, dormant CSCs are able to change their metabolic signature to escape the toxic effects of these drugs; these modified metabolic signatures are shown to be causally associated with increased metastasis. Therefore, a successful, clinically-applicable therapy must have the ability to selectively inhibit CSC growth, the metastatic metabolic signature, and pathways involved in immunosuppression. Objective: This study will evaluate the potential of four recently proposed TNBC treatments—which all successfully reduced tumor viability in vitro and/or in vivo—to inhibit genes involved in CSC survival, metastatic metabolic signature, and tumor immunosuppression. Methods: TNBC cell lines and/or patient-derived xenografts were treated with four different treatments: DCC-2036, 9Gy proton irradiation, miR302b+cisplatin combination, and DFX+doxorubicin combination. Genome-wide mRNA profiling (via either RNA-seq or microarray) was performed on control and treated groups. Data was obtained from publicly-deposited NCBI GEO datasets. We assessed the differential expression of over 40 genes associated with CSC growth, metastatic metabolic modifications, and immunosuppression in TNBC tumors. Limma statistical analysis was performed. GSEA was also used to complement results from individual gene expression analysis. Results: DCC-2036 treatment significantly induced the expression of CSC TNBC biomarkers—such as ALDH2, CD44, CCR5, and SNAI1—and genes associated with TNBC metastatic metabolomic signature—such as PPARGC1A. DCC-2036 showed inconsistent effects on the expression of immunosuppressive markers. 9Gy proton irradiation has mixed effects on the expression of our candidate genes, yet mostly induced the expression of stemness, metastatic, and immunosuppressive markers. miR302b+cisplatin and DFX+doxorubicin both failed to inhibit the candidate genes, yet without significantly inducing their expression. GSEA analysis confirmed the results obtained for all four treatments. Conclusions: Observing cancer rebound in TNBC patients after treatment with traditional cancer drugs is common and often happens when treatments fail to inhibit CSC growth, metabolic pathways associated with metastasis, and oncogenic immunosuppressive pathways. Our analysis shows that all four treatments failed to significantly impact the expression of protein pathways associated with increased metastasis and immunosuppression. It is worth noting that the researchers did report a decrease in tumor viability due to treatment of their experimental models with all four treatments. However, these findings correspond to the viability of the whole cell culture or tumor, not the viability of specifically the CSCs; in TNBC, CSCs make up only a small proportion of the total mass or the tumor, so the reported antiproliferative effects of the treatments do not necessarily suggest the treatment has effectively targeted the CSC population. Therefore, we hypothesize that these non-targeted therapies will likely not show positive effects in clinical studies. Furthermore, none of the researchers performed any assays evaluating CSC growth—such as CSC-labelled flow cytometry—or metastasis—such as secondary tumor transplantation. Therefore, we encourage the researchers to perform more rigorous assays to evaluate the translatable potential of their treatments. Finally, the outline of this study provides a useful rationale for future studies to evaluate emerging TNBC therapies and serves as a motivation for further in-silico research focus.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".