Biology subjects

Sapino, A.

Publications and source records attributed to Sapino, A..

3 recordsLinked to original sources

bioRxiv2026

Pdk1-dependent metabolic heterogeneity promotes aggressive colorectal cancer through cell-intrinsic interferon signaling

Tumor heterogeneity is a major determinant of tumor progression and therapeutic resistance, yet the contribution of metabolism to intratumoral heterogeneity still remains poorly explored. Here, we find that pyruvate dehydrogenase kinase-1 (Pdk1) expression defines metabolically distinct cancer cell populations in colorectal tumors. Image-based spatial single-cell phenotyping shows that Pdk1-high cells are associated with tumor progression in orthotopic models of colorectal cancer (CRC). Genetic ablation experiments demonstrate the role of Pdk1-high cells in tumor growth, 5-fluorouracil chemoresistance and tumor relapse. Mechanistically, we identify a link between Pdk1-driven metabolic states and specific cell phenotypes, where Pdk1-high cancer cells exhibit an EMT, stem cell-like phenotype characterized by increased interferon (IFN) signaling. Mitochondrial remodeling in Pdk1-high cells leads to the release of mitochondrial DNA to the cytosol and activation of IFN pathways, promoting Pdk1-driven phenotypes. Inhibition of IFN signaling impairs the tumorigenic and aggressive features of Pdk1-high cancer cells, thus establishing a functional connection between metabolic heterogeneity, cell-intrinsic IFN signaling and aggressive CRC.

cancer biology↗

bioRxiv2023

High clonal diversity and spatial genetic admixture in early prostate cancer and surrounding normal tissue

Somatic copy number alterations (SCNAs) are pervasive in advanced human cancers, but their prevalence and spatial distribution in early-stage, localized tumors and their surrounding normal tissues are poorly characterized. Here, we performed multi-region, single-cell DNA sequencing to characterize the SCNA landscape across multiple tumor-rich and normal tissue regions ([~]125 mm3 tissue cubes) obtained from prostatectomy performed in two patients with localized prostate cancer. We identified two distinct populations of cells with abnormal karyotypes, one marked by sparse deletions or amplifications ( pseudo-diploid cells) and the second characterized by genome-wide copy number changes reminiscent of monster cells previously described in colorectal cancer. Pseudo-diploid cells formed numerous small-sized subclones ranging from highly spatially localized to broadly spread subclones mainly featuring (sub-)chromosomal arm deletions. In contrast, monster cells harbored whole-chromosome gains and losses and were mostly singular events detected throughout the prostate, including normal tissue regions. Targeted deep sequencing of cancer-associated genes revealed a more confined pattern of mutations overlapping with tumor-rich regions, although we also detected mutations in regions deemed normal based on morphological assessment and bulk RNA-seq. Highly localized pseudo-diploid subclones were confined within tumor-rich regions and typically carried deletions involving chromosome (chr) 6 and 13, resulting in simultaneous loss of multiple tumor-suppressor genes, including FOXO1 and FOXO3 encoding two transcription factors belonging to the Forkhead family previously implicated in prostate carcinogenesis. Tumor-rich regions also contained mutations in genes frequently mutated in prostate cancer, including FOXA1, LRP1B, SPOP, and SPTA1. Our study reveals that SCNAs are widespread in both normal and tumor regions across the prostate gland of patients with localized prostate cancer and suggests that a subset of pseudo-diploid cells harboring chromosomal deletions that result in the loss of specific tumor-suppressor genes drive tumorigenesis in the aging prostate.

cancer biology↗

bioRxiv2020

Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Although scRNAseq has some technical challenges, it would be of great interest being able to disclose biological information out of cell subpopulations, which can be defined by cluster analysis of scRNAseq data. In this manuscript, we evaluated the efficacy of sparsely-connected autoencoder (SCA) as tool for the functional mining of single cells clusters. We show that SCA can be uses as tool to uncover hidden features associated to scRNAseq data. Our approach is strengthened by two metrics, QCF and QCM, which respectively allow to evaluate the ability of SCA to reconstruct a cells cluster and to evaluate the overall quality of the neural network model. Our data indicate that SCA encoded spaces, derived by different experimentally validated data (TFs targets, miRNAs targets, Kinases targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

bioinformatics↗