Transcriptome-based evaluation of the translatable potential of new treatments in Triple-Negative Breast Cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».