Bromocriptine monotherapy overcomes prostate cancer chemoresistance in preclinical models

Innovating cancer drug discovery with refined phenotypic screens

Before molecular pathways in cancer were known to a depth that could predict targets, drug development relied on phenotypic screening, where the effectiveness of candidate chemicals is judged from functional readouts without considering the mechanisms of action. The unraveling of tumor-specific pathways has brought targets for molecularly driven drug discovery, but precedents in the field have shown that awareness of pathways does not necessarily predict therapeutic efficacy, and many cancers still lack druggable targets. Phenotypic screening therefore retains a niche in drug development where a targeted approach is not informative. We analyze the unique advantages of phenotypic screens, and how technological advances have improved their discovery power. Notable advances include the use of larger biological panels and refined protocols that address the disease-relevance and increase data content with imaging and omic approaches.

Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer

Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.

Molecular panorama of therapy resistance in prostate cancer: a pre-clinical and bioinformatics analysis for clinical translation

Prostate cancer (PCa) is a malignant disorder of prostate gland being asymptomatic in early stages and high metastatic potential in advanced stages. The chemotherapy and surgical resection have provided favourable prognosis of PCa patients, but advanced and aggressive forms of PCa including CRPC and AVPC lack response to therapy properly, and therefore, prognosis of patients is deteriorated. At the advanced stages, PCa cells do not respond to chemotherapy and radiotherapy in a satisfactory level, and therefore, therapy resistance is emerged. Molecular profile analysis of PCa cells reveals the apoptosis suppression, pro-survival autophagy induction, and EMT induction as factors in escalating malignant of cancer cells and development of therapy resistance. The dysregulation in molecular profile of PCa including upregulation of STAT3 and PI3K/Akt, downregulation of STAT3, and aberrant expression of non-coding RNAs are determining factor for response of cancer cells to chemotherapy. Because of prevalence of drug resistance in PCa, combination therapy including co-utilization of anti-cancer drugs and nanotherapeutic approaches has been suggested in PCa therapy. As a result of increase in DNA damage repair, PCa cells induce radioresistance and RelB overexpression prevents irradiation-mediated cell death. Similar to chemotherapy, nanomaterials are promising for promoting radiosensitivity through delivery of cargo, improving accumulation in PCa cells, and targeting survival-related pathways. In respect to emergence of immunotherapy as a new tool in PCa suppression, tumour cells are able to increase PD-L1 expression and inactivate NK cells in mediating immune evasion. The bioinformatics analysis for evaluation of drug resistance-related genes has been performed.

[High expression of secretogranin II increases oxaliplatin resistance of colorectal cancer cells]

OBJECTIVE

To investigate the expression of secretogranin II (SCG2) in colorectal cancer (CRC) tissues and its impact on oxaliplatin resistance of CRC cells.

METHODS

We performed immunohistochemistry to detect the expression level of SCG2 on a tissue microarray containing 96 CRC and 84 adjacent tissues and analyzed the association of SCG2 expression with the clinical features of the CRC patients. SCG2 expression was also measured in DLD1 cells treated with oxaliplatin using immunoblotting and RT-qPCR analyses. The effects of SCG2 expression on oxaliplatin sensitivity and cell viability were evaluated in a DLD1 cell model of SCG2 knockout established using CRISPR-cas9 technique, and the expressions of apoptosis-related proteins were detected using Western blotting and RT-qPCR. We further examined SCG2 expression levels in an oxaliplatin-resistant DLD1 cell line and its parental DLD1 cells.

RESULTS

SCG2 expression was significantly increased in CRC tissues as compared with the adjacent tissues (1.932±0.816 vs 1), and the tumor tissues in advanced stages showed higher SCG2 expression levels. In DLD1 cells, treatment with oxaliplatin significantly increased SCG2 expression, and SCG2 knockout obviously increased oxaliplatin sensitivity of the cells and enhanced the expressions of apoptosis-related proteins. Compared with the parental cells, oxaliplatin-resistant DLD1 cells showed a significant increase of SCG2 expression by 3.901±0.471 folds.

CONCLUSION

SCG2 may serve as a risk gene in CRC, and its high expression increases oxaliplatin resistance of CRC cells.