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The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients, and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing published data sets of >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system. To enable in situ mapping of immune populations for digital pathology, we applied SPOTlight, combining single-cell and spatial transcriptomics data and identifying colocalization patterns of immune, stromal, and cancer cells in tumor sections. We expect the tumor immune cell atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immunotherapy. This publication is part of a project (BCLLATLAS) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 810287). This work has received funding from the Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P; AEI/FEDER, UE) and the Fundació La Marató de TV3 (201903-30-31-32).We further acknowledge funding from the St. Vincent’s Clinic Foundation (V.T.C.) and the National Health and Medical Research Council Investigator Grant (APP1175781, J.E.P.), the Fundación Asociación Española contra el Cáncer (AECC), FERO (EDM), Ramón Areces Foundation, Cellex Foundation, BBVA (CAIMI), the ISCIII, FIS (PI16/01278), Juan de la Cierva formación fellowship (C.R.-P.) and Sara Borrell fellowship (E.P.-R.). Core funding is from the ISCIII and the Generalitat de Catalunya. We acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, the CERCA Programme / Generalitat de Catalunya, the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) through the nstituto de Salud Carlos III and the Generalitat de Catalunya through Departament de Salut and Departament d’Empresa i Coneixement. We also acknowledge the cofinancing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014–2020 Smart Growth Operating Program

Introduction: Clonal evolution drives cancer development due to the emergence and/or selection of proliferatively advantageous subclones. Its understanding may facilitate the design of anticipation-based management strategies. Richter transformation (RT) is a paradigmatic tumor evolution in which chronic lymphocytic leukemia (CLL), an indolent neoplasia of mature B-cells, transforms into a high-grade lymphoma, usually diffuse large B-cell lymphoma (DLBCL), conferring a dismal prognosis. The evolutionary trajectories of RT and its driving (epi)genomic mechanisms remain largely unknown. Aims: To reconstruct the evolutionary history of RT and to reveal the molecular processes underlying this transformation. Methods: We characterized the whole genome (WGS), epigenome (DNA methylation, H3K27ac, ATAC-seq), and transcriptome (RNA-seq), combined with single-cell DNA and RNA sequencing analyses, of 19 CLL patients developing RT before (n=3) or after treatment with chemoimmunotherapy (n=6) and targeted therapies (BCR or BCL2 inhibitors, n=10). We analyzed 54 longitudinal samples covering up to 19 years of disease course. Results: Our WGS analyses uncovered that RT is characterized by a remarkable structural complexity. We also identified a novel treatment-independent RT-specific mutational process, which we named SBS-RT. The genetic driver landscape of RT is a compendium of alterations in genes involved in cell cycle, MYC, and NF-κB pathways, frequently targeted in single catastrophic events including chromothripsis and chromoplexy. The WGS-based phylogenic reconstruction and single-cell DNA/RNA-seq analyses identified a very early diversification of CLL leading to emergence of RT-cells carrying specific genetic drivers and transcriptomic profiles of RT already at CLL diagnosis. These small subclones were dormant for 6-19 years until rapid expansion associated with the clinical transformation. While the DNA methylome kept track of the cell of origin and proliferative history of RT cells, their chromatin configuration and transcriptional program converged into the overexpression of cell cycle regulators, Toll-like receptors, MYC, MTORC1, and OXPHOS related transcripts, as well as downregulation of BCR pathway. This phenotypic shift was related to de novo activation of key transcription factors. In vitro experiments confirmed that RT cells have a 4-fold higher oxygen consumption at routine respiration and electron transfer system capacity compared to CLL. The resistance of RT to BCR inhibition is consistent with its high OXPHOS and low BCR signaling, which mimics de novo DLBCL-OXPHOS insensitive to BCR inhibition. This OXPHOShigh-BCRlow transcriptional axis of RT can be exploited therapeutically. Conclusions: These findings demonstrate the early seeding of subclones driving advanced stages of cancer evolution and uncover therapeutic targets for the, once expanded, lethal Richter transformation. Citation Format: Ferran Nadeu, Romina Royo, Ramon Massoni-Badosa, Beatriz Garcia-Torre, Martí Duran-Ferrer, Kevin J. Dawson, Marta Kulis, Ander Diaz-Navarro, Neus Villamor, Juan L. Melero, Vicente Chapaprieta, Ana Dueso-Barroso, Julio Delgado, Riccardo Moia, Sara Ruiz-Gil, Domenica Marchese, Núria Verdaguer-Dot, Mónica Romo, Maria Rozman, Gerard Frigola, Alfredo Rivas-Delgado, Tycho Baumann, Miguel Alcoceba, Marcos González, Fina Climent, Pau Abrisqueta, Josep Castellví, Francesc Bosch, Marta Aymerich, Anna Enjuanes, Sílvia Ruiz-Gaspà, Armando López-Guillermo, Pedro Jares, Sílvia Beà, Dolors Colomer, Núria López-Bigas, Josep LlGelpí, David Torrents, Peter J. Campbell, Ivo Gut, Pablo M. Garcia-Roves, Davide Rossi, Gianluca Gaidano, Xose S. Puente, Holger Heyn, Francesco Maura, José I. Martín-Subero, Elías Campo. Early seeding of Richter transformation in chronic lymphocytic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3795.

The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system for a harmonized interpretation. To enable in situ mapping of immune populations for digital pathology, we applied SPOTlight, combining single-cell and spatial transcriptomics data and identifying striking spatial immune cell patterns in tumor sections. We expect the tumor immune cell atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immuno-therapy.

Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

Background: Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies. Results: We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer’s disease. Using tools from graph theory, we compute an unbiased quantification of a gene’s biological relevance and accurately pinpoint key players in organ function and drivers of diseases. Conclusions: Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology. This work has received funding from the Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P; AEI/FEDER, UE). Core funding is from the ISCIII and the Generalitat de Catalunya. We acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness(MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, the CERCA Programme /Generalitat de Catalunya, the Spanish Ministry of Economy, Industry and Competitiveness (MEIC)through the Instituto de Salud Carlos III and the Generalitat de Catalunya through Departament deSalut and Departament d’Empresa i Coneixement. We also acknowledge the Co-financing by theSpanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the EuropeanRegional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth OperatingProgram.