Genetics of renal cancer: focus on MTOR

Ghosh, Arindam; Sudarshan, Sunil

doi:10.18632/aging.100937

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Cell lines in cluster C4 are predicted to be sensitive to multiple families of drugs, including RAF, KIT, PDGFR, and PI3K/mTOR inhibitors. This agrees with current clinical practice which uses mTOR and BRAF inhibitors as first-line therapy for kidney cancer and melanoma, respectively [ 63 , 64 ], since the c-Met/mTOR axis is frequently altered in kidney cancer [ 65 , 66 ] while BRAF mutations are the main genetic drivers of melanoma [ 64 ]. Finally, RTKs are frequently altered in glioblastoma [ 67 , 68 ].…”

Section: Resultssupporting

confidence: 85%

Predicting drug response from single-cell expression profiles of tumours

Pellecchia,

Viscido,

Franchini

et al. 2023

BMC Med

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Background Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention. Results Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. Conclusions DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP.

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Section: Resultssupporting

confidence: 85%

Predicting drug response from single-cell expression profiles of tumours

Pellecchia,

Viscido,

Franchini

et al. 2023

BMC Med

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show abstract

“…Cell lines in cluster C4 are predicted to be sensitive to multiple families of drugs, including RAF, KIT, PDGFR, and PI3K/mTOR inhibitors. This agrees with current clinical practice which uses mTOR and BRAF inhibitors as first-line therapy for kidney cancer and melanoma, respectively (58, 59), since the c-Met/mTOR axis is frequently altered in kidney cancer (60, 61) while BRAF mutations are the main genetic drivers of melanoma (59). Finally, RTKs are frequently altered in glioblastoma (62, 63).…”

Section: Resultssupporting

confidence: 88%

Predicting drug response from single-cell expression profiles of tumours

Pellecchia

Viscido

Gambardella

2023

Preprint

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Drug response prediction at the single cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens and functional enrichment analysis to predict single cell drug sensitivity from transcriptomic data. We extensively tested DREEP on several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. DREEP provides an in-silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerate drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP.

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“…In ccRCC, mTOR is one of the most frequently mutated genes [4], and the mTOR pathway is regularly activated [18,19]. mTOR exerts its effects as part of two protein complexes, mTOR complex 1 and 2 (mTORC1/2).…”

Section: Resultsmentioning

confidence: 99%

Features of increased malignancy in eosinophilic clear cell renal cell carcinoma

Nilsson

Lindgren

Axelson

et al. 2020

The Journal of Pathology

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Clear cell renal cell carcinoma (ccRCC) is the most common form of renal cancer. Due to inactivation of the von Hippel-Lindau tumour suppressor, the hypoxia-inducible transcription factors (HIFs) are constitutively activated in these tumours, resulting in a pseudo-hypoxic phenotype. The HIFs induce the expression of genes involved in angiogenesis and cell survival, but they also reset the cellular metabolism to protect cells from oxygen and nutrient deprivation. ccRCC tumours are highly vascularized and the cytoplasm of the cancer cells is filled with lipid droplets and glycogen, resulting in the histologically distinctive pale (clear) cytoplasm. Intratumoural heterogeneity may occur, and in some tumours, areas with granular, eosinophilic cytoplasm are found. Little is known regarding these traits and how they relate to the coexistent clear cell component, yet eosinophilic ccRCC is associated with higher grade and clinically more aggressive tumours. In this study, we have for the first time performed RNA sequencing comparing histologically verified clear cell and eosinophilic areas from ccRCC tissue, aiming to analyse the characteristics of these cell types. Findings from RNA sequencing were confirmed by immunohistochemical staining of biphasic ccRCC. We found that the eosinophilic phenotype displayed a higher proliferative drive and lower differentiation, and we confirmed a correlation to tumours of higher stage. We further identified mutations of the tumour suppressor p53 (TP53) exclusively in the eosinophilic ccRCC component, where mTORC1 activity was also elevated. Also, eosinophilic areas were less vascularized, yet harboured more abundant infiltrating immune cells. The cytoplasm of clear cell ccRCC cells was filled with lipids but had very low mitochondrial content, while the reverse was found in eosinophilic tissue. We herein suggest possible transcriptional mechanisms behind these phenomena.

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Genetics of renal cancer: focus on MTOR

Cited by 7 publications

References 7 publications

Predicting drug response from single-cell expression profiles of tumours

Predicting drug response from single-cell expression profiles of tumours

Predicting drug response from single-cell expression profiles of tumours

Features of increased malignancy in eosinophilic clear cell renal cell carcinoma

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