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Predictive analytics is crucial in precision medicine for personalized patient care. To aid in precision medicine, this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types. To achieve this, experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical variables. In this study, the k‐means technique was used for clustering the diseased tissue types, and the multinomial logistic regression (MLR) technique was applied for classifying the diseased tissue types. Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables. The results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non‐landmark genes, and the difference is statistically significant. Furthermore, it was evident that both clinical and genetic variables were important in predicting the diseased tissue types. The top three clinical predictors for predicting diseased tissue types were identified as morphology, gender, and age of diagnosis. Additionally, this study explored the possibility of using the latent representations of the clusters of landmark and non‐landmark genes as predictors for an MLR classifier. The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables. This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine, enhancing diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Targeting multiple components of the MAPK pathway can prolong the survival of patients with BRAFV600E melanoma. This approach is not curative, as some BRAF-mutated melanoma cells are intrinsically resistant to MAPK inhibitors (MAPKi). At the systemic level, our knowledge of how signaling pathways underlie drug resistance needs to be further expanded. Here, we have shown that intrinsically resistant BRAF-mutated melanoma cells with a low basal level of mitochondrial biogenesis depend on this process to survive MAPKi. Intrinsically resistant cells exploited an integrated stress response, exhibited an increase in mitochondrial DNA content, and required oxidative phosphorylation to meet their bioenergetic needs. We determined that intrinsically resistant cells rely on the genes encoding TFAM, which controls mitochondrial genome replication and transcription, and TRAP1, which regulates mitochondrial protein folding. Therefore, we targeted mitochondrial biogenesis with a mitochondrium-targeted, small-molecule HSP90 inhibitor (Gamitrinib), which eradicated intrinsically resistant cells and augmented the efficacy of MAPKi by inducing mitochondrial dysfunction and inhibiting tumor bioenergetics. A subset of tumor biopsies from patients with disease progression despite MAPKi treatment showed increased mitochondrial biogenesis and tumor bioenergetics. A subset of acquired drug-resistant melanoma cell lines was sensitive to Gamitrinib. Our study establishes mitochondrial biogenesis, coupled with aberrant tumor bioenergetics, as a potential therapy escape mechanism and paves the way for a rationale-based combinatorial strategy to improve the efficacy of MAPKi., Introduction Cutaneous melanoma is a devastating disease, with a 10-year survival rate of less than 10% in patients diagnosed with stage IV melanoma (1). Approximately 50% of melanoma patients' tumors [...]