Giuseppe Riccardo Diaferia

The advent of spatially resolved transcriptomics has revolutionized our understanding of tissue heterogeneity. While advanced spatial transcriptomics (ST) technologies offer unparalleled and unbiased, whole-slide molecular mapping, laser microdissection (LMD) remains a highly valuable and often superior approach for specific research questions, particularly when coupled with RNA sequencing (RNA-seq). LMD provides high spatial resolution through precise morphological annotation, allowing for the isolation of defined cell clusters or even rare populations from complex tissues. This enables deeper and more sensitive transcriptomic profiling than is often achievable with some high-throughput ST methods that depend on computational deconvolution. Crucially, LMD is well established for use with routine histological samples, including challenging formalin-fixed paraffin-embedded (FFPE) specimens, facilitating invaluable retrospective studies on large patient cohorts. Unlike emerging ST platforms that can present significant per-sample costs and substantial computational overhead, targeted LMD-RNA-seq offers a more cost-effective and scalable solution for analyzing numerous specific regions across large numbers of patients. Therefore, a robust protocol outlining the use of LMD for routine histological samples, coupled with RNA-seq, remains an invaluable tool for discovery, enabling highly specific molecular insights from clinically relevant biospecimens.

High-throughput screening of drug sensitivity of cancer cell lines (CCLs) holds the potential to unlock anti-tumor therapies. In this study, we leverage such datasets to predict drug response using cell line transcriptomics, focusing on models’ interpretability and deployment on patients’ data. We use large language models (LLMs) to match drug to mechanisms of action (MOA)-related pathways. Genes crucial for prediction are enriched in drug-MOAs, suggesting that our models learn the molecular determinants of response. Furthermore, by using only LLM-curated, MOA-genes, we enhance the predictive accuracy of our models. To enhance translatability, we align RNAseq data from CCLs, used for training, to those from patient samples, used for inference. We validated our approach on TCGA samples, where patients’ best scoring drugs match those prescribed for their cancer type. We further predict and experimentally validate effective drugs for the patients of two highly lethal solid tumors, i.e., pancreatic cancer and glioblastoma.

Background: Pancreatic ductal adenocarcinoma (PDAC), the most common and aggressive form of pancreatic cancer, exhibits profound intratumor morphological heterogeneity, complicating the elucidation of the underlying molecular mechanisms driving its progression. Results: We present and validate an optimized framework for RNA sequencing (RNA-seq) of multiple spatially resolved laser micro-dissected tumor areas (LMD-seq), along with methodological and analytical details to maximize reproducibility and data mining. This approach enhances sensitivity in detecting lowly expressed genes, outperforming single-cell RNA-seq methods, particularly in identifying rare tumor cell populations and transcriptional programs with low expression. We also present a detailed map of predicted regulatory networks underlying distinct PDAC morpho-biotypes, revealing novel mechanisms and key regulators associated with each subtype. Conclusions: This study provides fully reproducible workflows, including processed data objects, documented code, and computational predictions of the regulatory activities, enabling robust exploration of intratumor heterogeneity of PDAC. The proposed methodology, datasets, and catalog of the molecular and regulatory mechanisms offer a framework for future studies and applications in PDAC and other cancers.

While pancreatic ductal adenocarcinomas (PDACs) are addicted to KRAS-activating mutations, inhibitors of downstream KRAS effectors, such as the MEK1/2 kinase inhibitor trametinib, are devoid of therapeutic effects. However, the extensive rewiring of regulatory circuits driven by the attenuation of the KRAS pathway may induce vulnerabilities of therapeutic relevance. An in-depth molecular analysis of the transcriptional and epigenomic alterations occurring in PDAC cells in the initial hours after MEK1/2 inhibition by trametinib unveiled the induction of endogenous retroviruses (ERVs) escaping epigenetic silencing, leading to the production of double-stranded RNAs and the increased expression of interferon (IFN) genes. We tracked ERV activation to the early induction of the transcription factor ELF3, which extensively bound and activated nonsilenced retroelements and synergized with IRF1 (interferon regulatory factor 1) in the activation of IFNs and IFN-stimulated genes. Trametinib-induced viral mimicry in PDAC may be exploited in the rational design of combination therapies in immuno-oncology.

Native extracellular matrix probes for Atomic Force Microscopy can be successfully used for the quantitative investigation of the cellular mechanosensing of the microenvironment.