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• STimage: Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data

  Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. Deep learning models are used to predict gene expression or classify cell types directly from images, offering significant clinical potential but still requiring improvements in interpretability and robustness. We present STimage as a comprehensive suite of models to predict spatial gene expression and classify cell types directly from standard H\&E images. STimage enhances robustness by estimating gene expression distributions and quantifying both data-driven (aleatoric) and model-based (epistemic) uncertainty using ensemble approach with foundation models. Interpretability is achieved through attribution analysis at single-cell resolution integrated with histopathological annotations, functional genes, and latent representations. We validated STimage across diverse datasets, demonstrating its performance across various platforms. STimage-predicted gene expression can stratify patient survival and predict drug response. By enabling molecular and cellular prediction from routine histology, STimage offers a powerful tool to advance digital pathology.

  Study EGAS50000001503

• Geographical structure and differential natural selection among North European populations

  European populations and epidemiological cohorts are of special significance in the current era of genomic research aiming to characterize the background of common human diseases. The genome sequence, detailed information of genetic variations between individuals, high-throughput molecular technologies and novel statistical strategies create new possibilities to define genetic and life-style risk factors behind common health problems. Studies of large population cohorts are needed to transform the genetic information to detailed understanding of the predisposing factors in diseases affecting most human populations. European twin cohorts provide a unique competitive advantage for investigations of the role of genetics and environment or life style in the etiology of common diseases. This project will apply and develop new molecular and statistical strategies to analyze unique European twin and other population cohorts to define and characterise the genetic, environmental and life-style components in the background of health problems like obesity, migraine, coronary heart disease and stroke, representing major health care problems worldwide.

  Study EGAS00000000033

• Maturation of Human Intestinal Epithelial Cell Layers Fortifies the Apical Surface against Salmonella Attack

  Pathogen invasion of intestinal epithelial cells (IECs) is a key gut infection event. The dynamics of this process in intact epithelia and the factors governing IEC susceptibility remain underexplored. We present a resource for live-cell infection imaging in human enteroid-/colonoid-derived IEC layers, across maturation states and in the presence or absence of soluble mucus production. Utilization of this resource shows how human IEC maturation fortifies the apical surface against invasion by Salmonella Typhimurium (S.Tm). Immature IEC layers appear permissive to S.Tm invasion, but maturation towards an enterocyte/colonocyte phenotype reduces the susceptibility by up to tenfold. This shift couples to downregulated expression of actin regulatory proteins exploited by the pathogen, an increased dependence on the S.Tm effector SipA, and the build-up of the apical IEC glycocalyx linked to surface-attached mucins like MUC13 and nullifiable by StcE-enzyme treatment. This underscores how the maturation state of human IECs governs susceptibility to bacterial invasion.

  Study EGAS50000001241

• Transcriptome changes in circulating immune cells of critical COVID-19 patients predict a specific metabolic and epigenetic imprint

  The progression to severe COVID-19 arises predominantly from a dysregulated host immune response, although the underlying regulatory mechanisms still remain partially elusive. This limits a prompt prediction of disease progression and restrains our understanding of “long COVID”. Here, we analyzed the transcriptomes of peripheral blood mononuclear cells collected from COVID-19 patients experiencing different degrees of the disease and control patients. In critical COVID-19 patients, we found an altered regulatory network involving microRNAs, long non-coding RNAs, and coding genes that control mRNA translation-related genes, epigenetics, and metabolism. We also observed an upregulation of tRNA aminoacylation genes and increased expression of coding genes enriched for the cognate amino acids. Collectively, our findings indicate a broad perturbation of the gene expression landscape that characterizes the aberrant host immune response in critical COVID-19 patients and is potentially coordinated by alterations in microRNA expression and tRNA metabolism.

  Study EGAS50000000965

• MutWP6__CRUK_Grand_Challenge_Mutographs_of_Cancer__Botseq

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Within Mutographs, work lead by the Sanger Institute will investigate whether detection of somatic mutations and mutational signatures in circulating white blood cells can be developed into a practical, generic system for surveying and monitoring multiple different endogenous and exogenous exposures, providing an ‘observatory’ on somatic mutational processes in humans. Whole genome sequences are generated at the Wellcome Sanger Institute (Illumina HiSeqX). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001004330

• Initial leukemic epigenomic state determines hypomethylating agent response

  Hypomethylating agents (HMAs) are a mainstay of therapy for myeloid cancers, but genetic biomarkers do not predict who will respond to treatment. Using a variety of single-cell sequencing approaches to define the epigenomic state of responder and nonresponder leukemic cells, we aim to demonstrate that leukemic stem cells (LSC) exist in at least two different epigenomic states: a hematopoietic stem cell (HSC)-or multipotent progenitor (MPP)-like state that is sensitive to HMAs, independent of genetic mutations, or a lymphoid-primed MPP (LMPP)-like nonresponder state. Additionally, we aim to demonstrate that hypomethylation and chromatin accessibility at ZNF143- and CTCF-binding sites result in activation of HOXB4, which defines the HSC/MPP-like state and HMA-sensitivity. Our study aims to provide evidence that the epigenomic state of the LSC is a major determinant of response to HMAs, and demonstrates that a routine clinical assay can identify patients who will respond.

  Study EGAS50000000936

• Origination of Ovarian Cancer is Dependent on Specific Aneuploidy Landscape

  Ovarian high-grade serous carcinoma (HGSC), a highly chromosomally instable malignancy, develops through sequential stages of precursors in fallopian tubes including histologically unremarkable but TP53 mutated clones (p53 signature) and serous tubal intraepithelial carcinoma (STIC). Here, we applied RealSeqS to assess aneuploidy in 127 microdissected tubal regions. We found nearly all p53 signatures lost the entire Chr17 chromosome, offering a “two-hit” mechanism of both TP53 and BRCA1 in BRCA1 germline mutation carriers. Proliferatively active STICs harbor gains of 19q12 (CCNE1), 19q13.2, 8q24 (MYCyc), or 8q arm, while proliferatively dormant STICs show 22q loss. Based on aneuploidy patterns in a training set (n= 67), we developed an algorithm, REAL-FAST, to classify NFTE and STICs into 5 clusters. In an independent validation set (n= 72), REAL-FAST was able to detect STIC/HGSC with high sensitivity and specificity. Taken together, REAL-FAST promises a molecular test for diagnosing HGSC precursor lesions based on specific aneuploidy patterns.

  Study EGAS00001007220

• Immunomodulatory effects and improved outcomes with cisplatin- vs carboplatin-based chemotherapy plus atezolizumab in urothelial cancer

  In metastatic urothelial cancer (mUC), cisplatin versus carboplatin leads to durable disease control in a subset of patients. The IMvigor130 trial reveals more favorable effects with atezolizumab combined with gemcitabine and cisplatin (GemCis) versus gemcitabine and carboplatin (GemCarbo). This study investigates the immunomodulatory effects of cisplatin as a potential explanation for these observations. Our findings indicate that improved outcomes with GemCis versus GemCarbo are primarily observed in patients with pretreatment tumors exhibiting features of restrained adaptive immunity. Additionally, GemCis versus GemCarbo ± atezolizumab induces transcriptional changes in circulating immune cells, including upregulation of antigen presentation and T-cell activation programs. In vitro experiments demonstrate that cisplatin, compared with carboplatin, exerts direct immunomodulatory effects on cancer cells, promoting dendritic cell activation and antigen-specific T cell killing. These results underscore the key role of immune modulation in cisplatin's efficacy in mUC and highlight the importance of specific chemotherapy backbones in immunotherapy combination regimens.

  Study EGAS50000000104

• Karyotype Evolution in Response to Chemoradiotherapy and Upon Recurrence of Esophageal Adenocarcinomas

  The genome of esophageal adenocarcinoma (EAC) is highly unstable and might evolve over time. Here, we track karyotype evolution in EACs in response to treatment and upon recurrence through multi-region and longitudinal analysis. To this end, we introduce L-PAC, a bio-informatics technique that allows inference of absolute copy number aberrations (CNA) of low-purity samples by leveraging information of high-purity samples from the same cancer. Quantitative analysis of matched absolute CNAs reveals that the amount of karyotype evolution induced by chemoradiotherapy (CRT) is predictive for early recurrence and depends on the initial level of karyotype intra-tumor heterogeneity. We observe that CNAs acquired in response to CRT are partially reversed back to the initial state upon recurrence. CRT hence alters the fitness landscape to which tumors can adjust by adapting their karyotype. Together, our results indicate that karyotype plasticity contributes to therapy resistance of EACs.

  Study EGAS00001007711

• Single-cell RNA sequencing of human skin tumors (BCC, SCC and Melanoma)

  Cancer-associated fibroblasts (CAFs) play a key role in cancer progression and treatment outcome. Here, we elucidate the yet unresolved intra-tumoral CAF variety in three skin cancer types at molecular and spatial single-cell resolution in a large cohort. We show that two out of three CAF subtypes contribute to tumor immune surveillance with distinct mechanisms. Matrix CAFs (mCAFs) ensheath tumor nests and synthesize extracellular-matrix to prevent T cell invasion. Immunomodulatory CAFs (iCAFs), which express proinflammatory and immunomodulatory factors, are only detected in high abundance in aggressive tumors. Strikingly, iCAFs but not tumor cells are (apart from immune cells) the exclusive cell type producing chemokines and, thus, play a key role in immune cell recruitment and activation. Mechanistically, we show that cancer cells transform adjacent healthy fibroblasts into cytokine-expressing iCAFs, which subsequently recruit immune cells and modulate the immune response. In conclusion, targeting CAF variants holds promise for improved efficacy of immunotherapy.

  Study EGAS50000000365