Biology subjects

Cavina, B.

Publications and source records attributed to Cavina, B..

3 recordsLinked to original sources

bioRxiv2026

Abolishing respiratory complex I decreases in vivo growth of high grade serous ovarian cancer cells and sensitizes to anti-angiogenic therapy

Targeting mitochondrial Complex I (CI) is a currently emerging anti-cancer strategy, with several enzyme inhibitors entering clinical trials. Among others, aggressive high-grade serous tubo-ovarian cancer (HGSOC) may particularly benefit from this therapeutic approach due to the scarce response to first- and second-line treatments, with consequent high mortality, such as the anti-angiogenic bevacizumab. We here show that CI represents a vulnerability in HGSOC, which can be exploited for therapeutic intervention. Indeed, ablating CI function in OV-90 HGSOC cells led to significant in vivo tumor growth decrease, smaller masses, and lower KI-67 proliferative index. This was confirmed in a switch-off system in which CI deprivation was induced during tumor progression to mimic pharmacologic treatment, suggesting this result can be achieved in growing neoplasms. We also show that abolishing CI in HGSOC cells leads to failure in stabilizing the hypoxia inducible factor-1a and to respond to hypoxia through the transcriptional activation of its target genes, ultimately lowering vascular endothelial growth factor (VEGF) and generating an immature intratumor vascular system accompanied by a decreased blood flow. Last, we demonstrate that targeting CI sets the biological basis for increased sensitivity to anti-angiogenics, as CI-deprived tumors displayed growth arrest when bevacizumab was administered, unlike their CI-competent counterpart. Our findings point to CI inhibition as a booster for anti-VEGF therapies and pave the way for combined protocols in treatment of HGSOC.

cancer biology↗

bioRxiv2026

Impact of Image Representation on Deep Learning-Based Single-Cell Classification by Holographic Imaging Flow Cytometry

Accurate cell type classification is essential for a wide range of biomedical applications, including disease diagnosis, drug discovery, and the study of cellular processes. Holographic imaging flow cytometry (HIFC) provides label-free quantitative phase imaging (QPI) of individual cells, enabling classification based on phase images. However, reconstructing holograms into phase images involves multi-step image processing, which introduces substantial computational overhead. The availability of diverse image representations across holographic reconstruction stages allows for flexible analytical strategies, enabling the optimization of trade-off between classification accuracy and computational efficiency. Moreover, deep learning offers an efficient alternative, accelerating the reconstruction process while performing accurate classification. However, despite its importance, this optimization challenge remains largely unexplored in the current literature. Here, we present the first systematic evaluation aimed at balancing classification accuracy with computational efficiency, highlighting how different image representations affect overall performance. We focus on a binary classification task discriminating natural killer cells from breast cancer cells. Six distinct classification pipelines are evaluated: direct processing of raw holograms, analysis of demodulated complex fields (CFs), refocused CFs, unwrapped phase images, and two deep learning-based methods that either replace the automatic refocusing stage or perform end-to-end hologram-to-phase reconstruction. For each strategy, we assess both computational cost and classification performance. Our results reveal a clear trade-off: reconstructed phase images provide the highest accuracy, whereas simpler representations or accelerated reconstruction methods significantly reduce processing time with minimal loss of accuracy. A Pareto analysis identifies the optimal set of strategies, offering practical guidelines for selecting image representations and processing pipelines based on available hardware and desired performance. Thus, this work offers a systematic framework for high-throughput deep learning classification in HIFC, serving as a potential reference for future biomedical applications.

biophysics↗

bioRxiv2025

Combining circulating tumor cell and circulating cell free DNA analyses enhances liquid biopsy sensitivity in detecting high grade serous tubo-ovarian carcinoma

BackgroundLiquid biopsy is a promising strategy for detecting and monitoring neoplastic diseases, with circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) being the most common objects of investigation. Most analyses have focused on these biomarkers separately, and simultaneous detection has not yet been attempted in high grade serous tubo-ovarian carcinoma (HGSOC). The aim of our study was to assess whether the tandem CTC/ctDNA approach increases HGSOC detection efficiency of peripheral blood liquid biopsy. MethodsFor CTC detection, by using healthy donor samples spiked with known cancer cell numbers, we tested gene expression assays and TP53 next-generation sequencing (NGS). The latter was also applied for ctDNA detection where analytical validity was ensured by calculating the optimal variant allele frequency (VAF) threshold for mutation calling. The clinical validity of the assays was then verified on two HGSOC cohorts and respective controls. Standard 2x2 contingency tables and Wilson method were used to evaluate clinical validity, by calculating specificity, sensitivity, and accuracy with 95% confidence intervals (CI). ResultsHigh analytical sensitivity and specificity were found for both gene expression and TP53 NGS based CTC detection, as these assays specifically detected as few as five cancer cells spiked in control sample. Regarding clinical validity, the gene expression-based CTC detection showed 0.48 accuracy, 13.3% sensitivity, and 100% specificity, whereas TP53 sequencing demonstrated better assay performance (0.77 accuracy, 46.7% sensitivity, 100% specificity). For circulating cell-free DNA (cfDNA) analysis, we first identified 0.31% VAF cut-off for accurate ctDNA TP53 mutation calling. Subsequent clinical validity assessment showed solid performance efficiency of the ctDNA based liquid biopsy (0.71 accuracy, 60% sensitivity, and 100% specificity), outperforming the CTC detection methods. Importantly, the tandem ctDNA/CTC analysis improved disease detection rate in both HGSOC cohorts, allowing to achieve, respectively, 73.3% and 93.3% sensitivity. Interestingly, TP53 NGS revealed CTC private variants, and shared ctDNA/CTC mutations undetected in the primary tissue, highlighting the ability of the dual-analyte approach to capture tumor heterogeneity and allow mutation cross-validation. ConclusionsOur study reveals the complementary value of simultaneous CTC and cfDNA analysis in HGSOC, advancing the translational potential of liquid biopsy integration for the management of this disease.

cancer biology↗