Pierluigi Di Chiaro

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.

Atomic Force Microscopy (AFM) is successfully used for the quantitative investigation of the cellular mechanosensing of the microenvironment. To this purpose, several force spectroscopy approaches aim at measuring the adhesive forces between two living cells and also between a cell and a suitable reproduction of the extracellular matrix (ECM), typically exploiting tips suitably functionalised with single components (e.g. collagen, fibronectin) of the ECM. However, these probes only poorly reproduce the complexity of the native cellular microenvironment and consequently of the biological interactions. We developed a novel approach to produce AFM probes that faithfully retain the structural and biochemical complexity of the ECM; this was achieved by attaching to an AFM cantilever a micrometric slice of native decellularised ECM, which was cut by laser microdissection. We demonstrate that these probes preserve the morphological, mechanical, and chemical heterogeneity of the ECM. Native ECM probes can be used in force spectroscopy experiments aimed at targeting cell-microenvironment interactions. Here, we demonstrate the feasibility of dissecting mechanotransductive cell-ECM interactions in the 10 pN range. As proof-of-principle, we tested a rat bladder ECM probe against the AY-27 rat bladder cancer cell line. On the one hand, we obtained reproducible results using different probes derived from the same ECM regions; on the other hand, we detected differences in the adhesion patterns of distinct bladder ECM regions, such as submucosa and detrusor, in line with the disparities in composition and biophysical properties of these ECM regions. Our results demonstrate that native ECM probes, produced from patient-specific regions of organs and tissues, can be used to investigate cell-microenvironment interactions and early mechanotransductive processes by force spectroscopy. This opens new possibilities in the field of personalised medicine.

Objective Pancreatic ductal adenocarcinomas (PDACs) include heterogeneous mixtures of low-grade cells forming pseudoglandular structures and compact nests of high-grade cells organised in non-glandular patterns. We previously reported that low-grade PDAC cells display high expression of interferon regulatory factor 1 (IRF1), a pivotal transcription factor of the interferon (IFN) system, suggesting grade-specific, cell-intrinsic activation of IFN responses. Here, we set out to determine the molecular bases and the functional impact of the activation of IFN-regulated responses in human PDACs. Design We first confirmed the correlation between glandular differentiation and molecular subtypes of PDAC on the one hand, and the expression of IRF1 and IFN-stimulated genes on the other. We next used unbiased omics approaches to systematically analyse basal and IFN-regulated responses in low-grade and high-grade PDAC cells, as well as the impact of IRF1 on gene expression programmes and metabolic profiles of PDAC cells. Results High-level expression of IRF1 in low-grade PDAC cells was controlled by endodermal lineage-determining transcription factors. IRF1-regulated gene expression equipped low-grade PDAC cells with distinctive properties related to antigen presentation and processing as well as responsiveness to IFN stimulation. Notably, IRF1 also controlled the characteristic metabolic profile of low-grade PDAC cells, suppressing both mitochondrial respiration and fatty acid synthesis, which may in part explain its growth-inhibiting activity. Conclusion IRF1 links endodermal differentiation to the expression of genes controlling antigen presentation and processing as well as to the specification of the metabolic profile characteristic of classical PDAC cells.