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Biomedical subjects

Paolo Gandellini

Publications and source records attributed to Paolo Gandellini.

2 recordsLinked to original sources

Integrating molecular subtypes, genomics and functional dependencies to identify context-specific therapeutic vulnerabilities in small cell lung cancer.

Small cell lung cancer is one of the most aggressive malignancies, characterized by rapid tumor growth, early metastatic spread and extremely poor survival. Although most patients initially respond to platinum-based chemotherapy, relapse is almost inevitable and treatment options at recurrence remain limited. The recent introduction of immune checkpoint inhibitors has provided only modest clinical benefit, largely due to the fact that these tumors are immunologically cold. These limitations highlight the urgent need to better understand the molecular features of small cell lung cancer in order to identify more effective therapeutic strategies. In this review, we summarize current knowledge of the molecular landscape of small cell lung cancer, with particular emphasis on transcriptome-based classifications that have identified four major molecular subtypes defined by distinct transcriptional regulators and gene expression programs. We discuss how these classifications have improved the biological understanding of the disease and stimulated efforts to develop subtype-specific therapeutic strategies. At the same time, we highlight important limitations of this framework, including the remarkable transcriptional plasticity of tumor cells, which allows dynamic transitions between subtypes and may contribute to therapeutic resistance. To address these challenges, we examine additional molecular features that may represent more stable vulnerabilities, including recurrent genomic alterations, such as the widespread loss of tumor suppressor genes or oncogene amplifications through extrachromosomal DNA. We also discuss emerging approaches aimed at identifying novel context-specific cancer dependencies, including genome-scale functional screens in vitro and in vivo and genetic restraint analyses. Finally, we consider the growing potential of liquid biopsy strategies, which exploit the high level of circulating tumor DNA in patients with this disease to detect clinically relevant genomic alterations and monitor tumor evolution. Overall, this review highlights both the opportunities and challenges associated with molecular stratification in small cell lung cancer. The integration of transcriptional classifications with genomic and functional approaches may help identify more robust therapeutic vulnerabilities and guide the development of more effective treatments for this highly aggressive disease.

Cancer vulnerabilities

Post-transcriptional control drives Aurora kinase A expression in human cancers.

Aurora kinase A (AURKA) is a major regulator of the cell cycle. A prominent association exists between high expression of AURKA and cancer, and impairment of AURKA levels can trigger its oncogenic activity. In order to explore the contribution of post-transcriptional regulation to AURKA expression in different cancers, we carried out a meta-analysis of -omics data of 18 cancer types from The Cancer Genome Atlas (TCGA). Our study confirmed a general trend for increased AURKA mRNA in cancer compared to normal tissues and revealed that AURKA expression is highly dependent on post-transcriptional control in several cancers. Correlation and clustering analyses of AURKA mRNA and protein expression, and expression of AURKA-targeting hsa-let-7a miRNA, unveiled that hsa-let-7a is likely involved to varying extents in controlling AURKA expression in cancers. We then measured differences in the short/long ratio (SLR) of the two alternative cleavage and polyadenylation (APA) isoforms of AURKA mRNA across cancers compared to the respective healthy counterparts. We suggest that the interplay between APA and hsa-let-7a targeting of AURKA mRNA may influence AURKA expression in some cancers. hsa-let-7a and APA may also independently contribute to altered AURKA levels. Therefore, we argue that AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation.

Humans