Biology subjects

Maggio, V.

Publications and source records attributed to Maggio, V..

3 recordsLinked to original sources

bioRxiv2026

p21(CIP1/WAF1)-mediated partial senescence supports steroidogenesis and therapy resistance in primary prostate cancer cultures

BackgroundProgression of prostate cancer to castration-resistant disease is driven by early adaptive mechanisms that remain poorly defined. Increasing evidence suggests that senescence-associated programs and intratumoral steroidogenesis may contribute to therapeutic resistance, but their functional interplay in hormone-naive disease is unclear. MethodsHormone-naive primary prostate cancer cultures (hnPCs) were established from diagnostic biopsies and characterized for proliferation, therapeutic response, senescence markers, and steroidogenic gene expression. Functional relevance of CDKN1A was assessed by knockdown experiments. Public transcriptomic datasets from ETS fusion-negative, AR signaling inhibitor-naive metastatic castration-resistant prostate cancer (mCRPC) were interrogated for clinical validation. ResultshnPCs exhibited low proliferative capacity yet intrinsic resistance to androgen deprivation therapy and docetaxel. Most cultures displayed coordinated overexpression of CDKN1A/p21CIP1 together with steroidogenic enzymes, particularly AKR1C3, alongside p16INK4a expression and SA-{beta}-galactosidase activity, consistent with a partial senescence-like state. CDKN1A depletion selectively reduced AKR1C3 expression and increased proliferation in a subset of cultures, indicating that p21 maintains specific senescence-associated features while permitting adaptive plasticity. Analysis of ETS fusion-negative, ARSI-naive mCRPC tumors revealed positive correlations between CDKN1A and steroidogenic genes, supporting the translational relevance of this program. ConclusionsThese findings identify a previously underrecognized CDKN1A-associated partial senescence program that supports steroidogenic reprogramming, survival, and early therapeutic resistance in aggressive prostate cancer. This adaptive state may predispose tumors to progression toward castration resistance and represents a potential biomarker and therapeutic vulnerability in high-risk, ETS-negative disease.

cancer biology↗

bioRxiv2019

Integrating deep and radiomics features in cancer bioimaging

Almost every clinical specialty will use artificial intelligence in the future. The first area of practical impact is expected to be the rapid and accurate interpretation of image streams such as radiology scans, histo-pathology slides, ophthalmic imaging, and any other bioimaging diagnostic systems, enriched by clinical phenotypes used as outcome labels or additional descriptors. In this study, we introduce a machine learning framework for automatic image interpretation that combines the current pattern recognition approach (\"radiomics\") with Deep Learning (DL). As a first application in cancer bioimaging, we apply the framework for prognosis of locoregional recurrence in head and neck squamous cell carcinoma (N=298) from Computed Tomography (CT) and Positron Emission Tomography (PET) imaging. The DL architecture is composed of two parallel cascades of Convolutional Neural Network (CNN) layers merging in a softmax classification layer. The network is first pretrained on head and neck tumor stage diagnosis, then finetuned on the prognostic task by internal transfer learning. In parallel, radiomics features (e.g., shape of the tumor mass, texture and pixels intensity statistics) are derived by predefined feature extractors on the CT/PET pairs. We compare and mix deep learning and radiomics features into a unifying classification pipeline (RADLER), where model selection and evaluation are based on a data analysis plan developed in the MAQC initiative for reproducible biomarkers. On the multimodal CT/PET cancer dataset, the mixed deep learning/radiomics approach is more accurate than using only one feature type, or image mode. Further, RADLER significantly improves over published results on the same data.

bioinformatics↗

bioRxiv2018

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER

Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g. from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDAs MAQC project, designed to analyse causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test 3 different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with 4 datasets (5, 10, 20 or 30 classes), for a total 53000 tiles at 512 x 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for random features and random labels diagnostic tests to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning backbone pipeline and the HINT (Histological Imaging - Newsy Tiles) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for Digital Pathology.\n\nAuthor summaryIn this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. It is indeed a top priority that reproducibility-by-design gets adopted as standard practice in building and validating AI methods in the healthcare domain. Here we introduce DAPPER, a first framework to evaluate deep features and classifiers in digital pathology, based on a rigorous data analysis plan originally developed in the FDAs MAQC initiative for predictive biomarkers from massive omics data. We apply DAPPER on models trained to identify tissue of origin from the HINT benchmark dataset of 53000 tiles from 787 Whole Slide Images in the Genotype-Tissue Expression (GTEx) project. We analyze accuracy and feature stability of different deep learning architectures (VGG, ResNet and Inception) as feature extractors and classifiers (a fully connected multilayer, SVMs and Random Forests) on up to 20 classes. Further, we use the deep features from the VGG model (trained on HINT) on the 1300 annotated tiles of the KIMIA24 dataset for identification of slide of origin (24 classes). The DAPPER software is available together with the HINT benchmark dataset.

bioinformatics↗