Massimo F. Loda

This study shows that inhibiting de novo lipogenesis and supplementing with PUFAs suppresses prostate cancer progression by inducing oxidative stress and cell death.

This study identifies CRY1 as a stage-specific regulator of DNA damage repair in prostate cancer, showing it promotes NHEJ/BER in hormone-sensitive disease and HR in castration-resistant disease.

This study identifies TMPO as a driver of CTC heterogeneity and metastasis in prostate cancer by enhancing survival under metabolic stress.

This paper introduces MIMA, a deep learning framework for multi-omics integration and batch correction, which effectively removes batch effects and preserves biological information across modalities.

The paper investigates DNA methylation differences in prostate cancer across African, European, and Asian ancestries, finding ancestry-driven methylation patterns linked to disease aggressiveness in African tumors.

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

KODAMA improves spatial transcriptomics analysis by reducing dimensionality while preserving spatial information, showing high accuracy across multiple datasets.

This study identifies canonical AREs as tumor suppressive regulatory elements in prostate cancer, showing their reactivation can induce death in AR+ cancer cells and is linked to better prognosis.

This study identifies cis-vaccenic acid as a critical oncogenic factor in prostate cancer, showing it rescues cell viability loss from SCD1 inhibition and promotes growth.

This paper integrates multiple spatial-omics modalities in prostate cancer samples to reveal molecular heterogeneity and correlations between lipids and gene expression.

The paper uses spatial transcriptomic data to show that bulk genomic signature scoring in prostate cancer biopsies is confounded by tumour heterogeneity, leading to inaccurate risk assessments.

This study identifies multi-omics prognostic signatures for prostate cancer using transcriptomics and metabolomics data, showing their ability to stratify patients into distinct risk groups.

This study shows MYC overexpression disrupts AR transcriptional pause release, promoting aggressive prostate cancer progression.

This study uses rapid diagnostics and metatranscriptomics to analyze SARS-CoV-2 infection, revealing viral evolution, host responses, and drug interactions in NYC samples.

This study creates a single-cell atlas of mouse and human prostate epithelium, identifying conserved progenitor populations and anatomical relationships between species.

DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.