miR-193a-3p Enhanced the Chemosensitivity to Trametinib in Gallbladder Carcinoma by Targeting KRAS and Downregulating ERK Signaling

Molecular mechanisms and therapeutic strategies in overcoming chemotherapy resistance in cancer

Cancer remains a leading cause of mortality globally and a major health burden, with chemotherapy often serving as the primary therapeutic option for patients with advanced-stage disease, partially compensating for the limitations of non-curative treatments. However, the emergence of chemotherapy resistance significantly limits its efficacy, posing a major clinical challenge. Moreover, heterogeneity of resistance mechanisms across cancer types complicates the development of universally effective diagnostic and therapeutic approaches. Understanding the molecular mechanisms of chemoresistance and identifying strategies to overcome it are current research focal points. This review provides a comprehensive analysis of the key molecular mechanisms underlying chemotherapy resistance, including drug efflux, enhanced DNA damage repair (DDR), apoptosis evasion, epigenetic modifications, altered intracellular drug metabolism, and the role of cancer stem cells (CSCs). We also examine specific causes of resistance in major cancer types and highlight various molecular targets involved in resistance. Finally, we discuss current strategies aiming at overcoming chemotherapy resistance, such as combination therapies, targeted treatments, and novel drug delivery systems, while proposing future directions for research in this evolving field. By addressing these molecular barriers, this review lays a foundation for the development of more effective cancer therapies aimed at mitigating chemotherapy resistance.

Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update

BACKGROUND

The recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment.

METHODS

The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center.

RESULTS

Simulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 data instances. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes is associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation.

CONCLUSIONS

Our C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple log-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.