Combination chemotherapy with docetaxel and irinotecan in metastatic malignant melanoma

Bambu and its applications in the discovery of active molecules against melanoma

PURPOSE OR OBJECTIVE

Melanoma is one of the most dangerous forms of skin cancer and the discovery of novel drugs is an ongoing effort. Quantitative Structure Activity Relationship (QSAR) is a computational method that allows the estimation of the properties of a molecule, including its biological activity. QSAR models have been widely employed in the search for potential drug candidates, but also for agrochemicals and other molecules with applications in different branches of the industry. Here we present Bambu, a simple command line tool to generate QSAR models from high-throughput screening bioassays datasets.

METHODS

The tool was developed using the Python programming language and relies mainly on RDKit for molecule data manipulation, FLAML for automated machine learning and the PubChem REST API for data retrieval. As a proof-of-concept we have employed the tool to generate QSAR models for melanoma cell growth inhibition based on HTS data and used them to screen libraries of FDA-approved drugs and natural compounds. Additionally, Bambu was compared to QSAR-Co, another automated tool for QSAR model generation.

RESULTS

based on the developed tool we were able to produce QSAR models and identify a wide variety of molecules with potential melanoma cell growth inhibitors, many of which with anti-tumoral activity already described. The QSAR models are available through the URL http://caramel.ufpel.edu.br, and all data and code used to generate its models are available at Zenodo (https://doi.org/10.5281/zenodo.7495214). Bambu source code is available at GitHub (https://github.com/omixlab/bambu-v2). In the benchmark, Bambu was able to produce models with higher accuracy, recall, F1 and ROC AUC when compared to QSAR-Co for the selected datasets.

CONCLUSIONS

Bambu is an free and open source tool which facilitates the creation of QSAR models and can be futurely applied in a wide variety of drug discovery projects.

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

A highly efficient tumor-infiltrating MDSC differentiation system for discovery of anti-neoplastic targets, which circumvents the need for tumor establishment in mice

Myeloid-derived suppressor cells (MDSCs) exhibit potent immunosuppressive activities in cancer. MDSCs infiltrate tumors and strongly inhibit cancer-specific cytotoxic T cells. Their mechanism of differentiation and identification of MDSC-specific therapeutic targets are major areas of interest. We have devised a highly efficient and rapid method to produce very large numbers of melanoma-infiltrating MDSCs ex vivo without inducing tumors in mice. These MDSCs were used to study their differentiation, immunosuppressive activities and were compared to non-neoplastic counterparts and conventional dendritic cells using unbiased systems biology approaches. Differentially activated/deactivated pathways caused by cell type differences and by the melanoma tumor environment were identified. MDSCs increased the expression of trafficking receptors to sites of inflammation, endocytosis, changed lipid metabolism, and up-regulated detoxification pathways such as the expression of P450 reductase. These studies uncovered more than 60 potential novel therapeutic targets. As a proof of principle, we demonstrate that P450 reductase is the target of pro-drugs such as Paclitaxel, which depletes MDSCs following chemotherapy in animal models of melanoma and in human patients. Conversely, P450 reductase protects MDSCs against the cytotoxic actions of other chemotherapy drugs such as Irinotecan, which is ineffective for the treatment of melanoma.

Membrane transport proteins in human melanoma: associations with tumour aggressiveness and metastasis

BACKGROUND

Malignant melanoma, generally described as incurable, is notoriously refractory to chemotherapy. The mechanisms contributing to this have not yet been defined and the contributions of drug efflux pumps, implicated in chemo-resistance of many other cancer types, have not been extensively investigated in melanoma.

METHODS

In this study, expression of multi-drug resistant (MDR1/P-gp and MRP-1) proteins was examined, by immunohistochemistry, in archival specimens from 134 melanoma patients. This included 92 primary tumours and 42 metastases.

RESULTS

On assessing all specimens, MRP-1 and MDR1/P-gp expression was found to be common, with the majority (81%) of melanomas expressing at least one of these efflux pumps. Although there is significant association between expression of these pumps (P=0.007), MRP-1 was found to be the predominant (67% of cases) form detected. chi(2) analysis showed significant associations between expression of MRP-1 and/or MDR1/P-gp and the aggressive nature of this disease specifically increased Breslow's depth, Clark's level and spread to lymph nodes. This association with aggressiveness and spread is further supported by the observation that a significantly higher percentage of metastases, than primary tumours, express MRP-1 (91% vs 57%; P<0.0001) and MDR1/P-gp (74% vs 50%; P=0.010).

CONCLUSION

The predominant expression of these pumps and, in particular, MRP-1 suggests that they may be important contributors to the inherent aggressive and resistant nature of malignant melanoma.

The history and future of chemotherapy for melanoma

Melanoma is considered a chemotherapy-resistant cancer, but in reality there are several chemotherapy drugs with significant single-agent activity. Response rates to combination regimens are reproducibly higher than with standard dacarbazine, but of the randomized trials comparing combination regimens with dacarbazine, none were of sufficient size to detect a realistic effect on survival. Similarly, adjuvant chemotherapy has not had a realistic test in melanoma. Response to chemotherapy is associated reproducibly with better survival rates suggesting that regimens with higher response rates are needed. Recent observations suggest that combining antiangiogenic agents with either dacarbazine or temozolomide can double response rates. These combinations are worthy of further investigation and might serve as a foundation on which to build a combination regimen that improves overall survival in metastatic melanoma patients.