Biology subjects

Kovanen, P.

Publications and source records attributed to Kovanen, P..

2 recordsLinked to original sources

bioRxiv2025

Complex I Drives Glutamine-Dependent TCA Cycle to Support Viability of MYChigh Breast Cancer Cells

In many cancers, stably elevated MYC levels drive persistent and concerted activation of cell growth promoting anabolic programs and the cell cycle in ways that are distinct from normal cells. Therefore, synthetic-lethal strategies to target MYC reprograming of these pathways may identify new selective anticancer therapies for the treatment of MYChigh tumors. Here, we identify enhanced mitochondrial respiration as a hallmark of MYC overexpressing cancer cells. Mitochondrial respiration sustains the TCA cycle by regenerating NAD+ through complex I-mediated oxidation of NADH. Metabolic carbon tracing analysis revealed that MYC shifts TCA cycles carbon source from glucose to glutamine. Inhibition of the glutamine-fueled TCA cycle using NAD+-depleting complex I inhibitors resulted in MYC-dependent synthetic lethality in breast cancer cells. In mouse models of MYChigh tumors, persistent inhibition of tumor growth was achieved through combined inhibition of complex I and glutaminolysis. Our results suggest that the high respiration rate observed in MYChigh cells supports glutamine carbon-enriched TCA cycle, rendering MYChigh tumors selectively vulnerable to inhibitors of mitochondrial respiration and glutaminolysis.

cancer biology↗

bioRxiv2020

Histopathological Landscape of Molecular Genetics and Clinical Determinants in MDS Patients

In myelodysplastic syndrome (MDS), bone marrow (BM) histopathology is visually assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, many morphological findings elude the human eye. Here, we extracted visual features of 236 MDS, 87 MDS/MPN, and 10 control BM biopsies with convolutional neural networks. Unsupervised analysis distinguished underlying correlations between tissue composition, leukocyte metrics, and clinical characteristics. We applied morphological features in elastic net-regularized regression models to predict genetic and cytogenetic aberrations, prognosis, and clinical variables. By parallelizing tile, pixel, and leukocyte-level image analysis, we deconvoluted each model to texture and cellular composition to dissect their pathobiological context. Model-based mutation predictions correlated with variant allele frequency and number of affected genes per pathway, demonstrating the models ability to identify relevant visual patterns. In summary, this study highlights the potential of deep histopathology in hematology by unveiling the fundamental association of BM morphology with genetic and clinical determinants.

bioinformatics↗