Autophagy and cancer drug resistance in dialogue: Pre-clinical and clinical evidence

Qu, Yi; Ashrafizadeh, Milad; Mongiardini, V; Grimaldi, Benedetto; Crea, Francesco; Rietdorf, Katja; Győrffy, Balázs; Klionsky, Daniel J.; Ren, Jun; Zhang, Wei; Zhang, Xianbin

doi:10.1016/j.canlet.2023.216307

Cited by 127 publications

(22 citation statements)

References 210 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use STRING 17 to perform network analysis on the isolated genes and observe significant enrichment of the PI3K-Akt (19 genes), MAPK (11 genes), and RAS (12 genes) signaling pathways. This aligns with documented evidence of transcriptomic changes in Akt pathways inducing docetaxel resistance 18 – 21 . Moreover, this suggests SiamCDR RF has learned to leverage signals associated with transcriptomic modulation of CDR to tailor its predictions, making it a valuable tool for precision oncology.…”

Section: Resultssupporting

confidence: 88%

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

Lawrence,

Burns,

Ning

2024

npj Precis. Onc.

View full text Add to dashboard Cite

Due to cancer’s complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patient makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods’ abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanisms of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balanced reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.

show abstract

Section: Resultssupporting

confidence: 88%

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

Lawrence,

Burns,

Ning

2024

npj Precis. Onc.

View full text Add to dashboard Cite

show abstract

“…In addition, because both the circadian cycle and autophagy have been related to cancer drug resistance, ,− 24 constitutes a valuable experimental candidate in dedicated preclinical studies for identifying improved drug combination therapies.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Dual Autophagy and REV-ERB Inhibitor with in Vivo Anticancer Efficacy

Palomba,

Vecchio,

Allavena

et al. 2023

J. Med. Chem.

Self Cite

View full text Add to dashboard Cite

The autophagy process appears as a promising target for anticancer interventions. Chloroquine (CQ) and its derivative hydroxychloroquine (HCQ) are the only FDA-approved autophagy flux inhibitors. Although diverse anticancer clinical trials are providing encouraging results, several limitations associated with the need of high dosage and long-term administration of these autophagy inhibitors are also emerging. We showed that the inhibition of REV-ERB, a nuclear receptor regulating circadian rhythm and metabolism, enhances CQ-mediated cancer cell death and identified a class of dual inhibitors of autophagy and REV-ERB displaying an in vitro anticancer activity against diverse tumor cells greatly higher than CQ. Herein, we describe our lead optimization strategy that led to the identification of compound 24 as a dual autophagy and REV-ERB inhibitor, showing improved potency in blocking autophagy, enhanced toxicity against cancer cells, optimal drug-like properties, and efficacy in a mouse xenograft model of melanoma as a single anticancer agent.

show abstract

“…It is therefore conceivable that this miRNA could play different roles at different stages of disease progression (e.g. onco-suppressive for early stage, oncogenic for advanced stage), as demonstrated for other cancer-related genes (26). Further studies are therefore warranted to dissect the role of this miRNA at different stages of HCC progression, and to confirm its prognostic role in other clinical datasets.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of differentially expressed miRNAs in Hepatocellular carcinoma: prognostic significance and pathway insights

Smith,

Beach,

Silva

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Robust prognostic and predictive factors for hepatocellular carcinoma, a leading cause of cancer-related deaths worldwide, have not yet been identified. Previous studies have identified potential HCC determinants such as genetic mutations, epigenetic alterations, and pathway dysregulation. However the clinical significance of these molecular alterations remains elusive. MicroRNAs are major regulators of protein expression. MiRNA functions are frequently altered in cancer. In this study, we aimed to explore the prognostic value of differentially expressed miRNAs in HCC and elucidate their associated pathways. To this aim, bioinformatics techniques and clinical dataset analyses were employed to identify differentially expressed miRNAs in HCC compared to normal hepatic tissue. We validated known associations and identified novel miRNAs with potential prognostic significance and proposed new targeting pathways based on our comprehensive analysis.

show abstract

Autophagy and cancer drug resistance in dialogue: Pre-clinical and clinical evidence

Cited by 127 publications

References 210 publications

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

Identification of a Dual Autophagy and REV-ERB Inhibitor with in Vivo Anticancer Efficacy

Comprehensive analysis of differentially expressed miRNAs in Hepatocellular carcinoma: prognostic significance and pathway insights

Contact Info

Product

Resources

About