Rarity: discovering rare cell populations from single-cell imaging data.

MOTIVATION: Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a small number of genes. RESULTS: Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent, and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC datasets. AVAILABILITY AND IMPLEMENTATION: Implementation of Rarity together with examples is available from the Github repository (https://github.com/kasparmartens/rarity).

Apoptosis in mesenchymal stromal cells activates an immunosuppressive secretome predicting clinical response in Crohn's disease.

In vivo apoptosis of human mesenchymal stromal cells (MSCs) plays a critical role in delivering immunomodulation. Yet, caspase activity not only mediates the dying process but also death-independent functions that may shape the immunogenicity of apoptotic cells. Therefore, a better characterization of the immunological profile of apoptotic MSCs (ApoMSCs) could shed light on their mechanistic action and therapeutic applications. We analyzed the transcriptomes of MSCs undergoing apoptosis and identified several immunomodulatory factors and chemokines dependent on caspase activation following Fas stimulation. The ApoMSC secretome inhibited human T cell proliferation and activation, and chemoattracted monocytes in vitro. Both immunomodulatory activities were dependent on the cyclooxygenase2 (COX2)/prostaglandin E2 (PGE2) axis. To assess the clinical relevance of ApoMSC signature, we used the peripheral blood mononuclear cells (PBMCs) from a cohort of fistulizing Crohn's disease (CD) patients who had undergone MSC treatment (ADMIRE-CD). Compared with healthy donors, MSCs exposed to patients' PBMCs underwent apoptosis and released PGE2 in a caspase-dependent manner. Both PGE2 and apoptosis were significantly associated with clinical responses to MSCs. Our findings identify a new mechanism whereby caspase activation delivers ApoMSC immunosuppression. Remarkably, such molecular signatures could implicate translational tools for predicting patients' clinical responses to MSC therapy in CD.

The transcriptional landscape of endogenous retroelements delineates esophageal adenocarcinoma subtypes.

Most cancer types exhibit aberrant transcriptional activity, including derepression of retrotransposable elements (RTEs). However, the degree, specificity and potential consequences of RTE transcriptional activation may differ substantially among cancer types and subtypes. Representing one extreme of the spectrum, we characterize the transcriptional activity of RTEs in cohorts of esophageal adenocarcinoma (EAC) and its precursor Barrett's esophagus (BE) from the OCCAMS (Oesophageal Cancer Clinical and Molecular Stratification) consortium, and from TCGA (The Cancer Genome Atlas). We found exceptionally high RTE inclusion in the EAC transcriptome, driven primarily by transcription of genes incorporating intronic or adjacent RTEs, rather than by autonomous RTE transcription. Nevertheless, numerous chimeric transcripts straddling RTEs and genes, and transcripts from stand-alone RTEs, particularly KLF5- and SOX9-controlled HERVH proviruses, were overexpressed specifically in EAC. Notably, incomplete mRNA splicing and EAC-characteristic intronic RTE inclusion was mirrored by relative loss of the respective fully-spliced, functional mRNA isoforms, consistent with compromised cellular fitness. Defective RNA splicing was linked with strong transcriptional activation of a HERVH provirus on Chr Xp22.32 and defined EAC subtypes with distinct molecular features and prognosis. Our study defines distinguishable RTE transcriptional profiles of EAC, reflecting distinct underlying processes and prognosis, thus providing a framework for targeted studies.

Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment.

BACKGROUND: The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood. METHODS: Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with. RESULTS: The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. CONCLUSIONS: Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .

USP7 inactivation suppresses APC-mutant intestinal hyperproliferation and tumor development.

Adenomatous polyposis coli (APC) mutation is the hallmark of colorectal cancer (CRC), resulting in constitutive WNT activation. Despite decades of research, targeting WNT signaling in cancer remains challenging due to its on-target toxicity. We have previously shown that the deubiquitinating enzyme USP7 is a tumor-specific WNT activator in APC-truncated cells by deubiquitinating and stabilizing ß-catenin, but its role in gut tumorigenesis is unknown. Here, we show in vivo that deletion of Usp7 in Apc-truncated mice inhibits crypt hyperproliferation and intestinal tumor development. Loss of Usp7 prolongs the survival of the sporadic intestinal tumor model. Genetic deletion, but not pharmacological inhibition, of Usp7 in Apc+/- intestine induces colitis and enteritis. USP7 inhibitor treatment suppresses growth of patient-derived cancer organoids carrying APC truncations in vitro and in xenografts. Our findings provide direct evidence that USP7 inhibition may offer a safe and efficacious tumor-specific therapy for both sporadic and germline APC-mutated CRC.

Topoisomerase I poison-triggered immune gene activation is markedly reduced in human small-cell lung cancers by impairment of the cGAS/STING pathway.

BACKGROUND: Current immunotherapy strategies have contrasting clinical results in human lung cancer patients as small-cell lung cancers (SCLC) often show features of immunological cold tumours. Topoisomerase 1 (TOP1) poisons are effective antitumor drugs with good efficacy against lung cancers. METHODS: We used molecular, genetic and bioinformatic approaches to determine the mechanism of micronuclei formation induced by two TOP1 poisons in different human cancer cells, including SCLC cell lines. RESULTS: TOP1 poisons stimulate similar levels of micronuclei in all tested cell lines but downstream effects can vary markedly. TOP1 poisons increase micronuclei levels with a mechanism involving R-loops as overexpression of RNaseH1 markedly reduces or abolishes both H2AX phosphorylation and micronuclei formation. TOP1 poison-induced micronuclei activate the cGAS/STING pathway leading to increased expression of immune genes in HeLa cells, but not in human SCLC cell lines, mainly due to lack of STING and/or cGAS expression. Moreover, the expression of STING and antigen-presenting machinery genes is generally downregulated in patient tumours of human lung cancer datasets. CONCLUSIONS: Altogether, our data reveal an immune signalling mechanism activated by TOP1 poisons, which is often impaired in human SCLC tumours.

Exome sequencing of hepatocellular carcinoma in lemurs identifies potential cancer drivers: A pilot study.

Background and objectives: Hepatocellular carcinoma occurs frequently in prosimians, but the cause of these liver cancers in this group is unknown. Characterizing the genetic changes associated with hepatocellular carcinoma in prosimians may point to possible causes, treatments and methods of prevention, aiding conservation efforts that are particularly crucial to the survival of endangered lemurs. Although genomic studies of cancer in non-human primates have been hampered by a lack of tools, recent studies have demonstrated the efficacy of using human exome capture reagents across primates. Methodology: In this proof-of-principle study, we applied human exome capture reagents to tumor-normal pairs from five lemurs with hepatocellular carcinoma to characterize the mutational landscape of this disease in lemurs. Results: Several genes implicated in human hepatocellular carcinoma, including ARID1A, TP53 and CTNNB1, were mutated in multiple lemurs, and analysis of cancer driver genes mutated in these samples identified enrichment of genes involved with TP53 degradation and regulation. In addition to these similarities with human hepatocellular carcinoma, we also noted unique features, including six genes that contain mutations in all five lemurs. Interestingly, these genes are infrequently mutated in human hepatocellular carcinoma, suggesting potential differences in the etiology and/or progression of this cancer in lemurs and humans. Conclusions and implications: Collectively, this pilot study suggests that human exome capture reagents are a promising tool for genomic studies of cancer in lemurs and other non-human primates. Lay Summary: Hepatocellular carcinoma occurs frequently in prosimians, but the cause of these liver cancers is unknown. In this proof-of-principle study, we applied human DNA sequencing tools to tumor-normal pairs from five lemurs with hepatocellular carcinoma and compared the lemur mutation profiles to those of human hepatocellular carcinomas.

Reflected stemness as a potential driver of the tumour microenvironment.

A fundamental requirement for cancer initiation is the activation of developmental programmes by mutant cells. Oncogenic signals often confer an undifferentiated, stem cell-like phenotype that supports the long-term proliferative potential of cancer cells. Although cancer is a genetically driven disease, mutations in cancer-driver genes alone are insufficient for tumour formation, and the proliferation of cells harbouring oncogenic mutations depends on their microenvironment. In this Opinion article we discuss how the reprogrammed status of cancer cells not only represents the essence of their tumorigenicity but triggers 'reflected stemness' in their surrounding normal counterparts. We propose that this reciprocal interaction underpins the establishment of the tumour microenvironment (TME).

Genome-Protective Topoisomerase 2a-Dependent G2 Arrest Requires p53 in hTERT-Positive Cancer Cells.

Topoisomerase 2a (Topo2a)-dependent G2 arrest engenders faithful segregation of sister chromatids, yet in certain tumor cell lines where this arrest is dysfunctional, a PKCε-dependent failsafe pathway can be triggered. Here we elaborate on recent advances in understanding the underlying mechanisms associated with this G2 arrest by determining that p53-p21 signaling is essential for efficient arrest in cell lines, in patient-derived cells, and in colorectal cancer organoids. Regulation of this p53 axis required the SMC5/6 complex, which is distinct from the p53 pathways observed in the DNA damage response. Topo2a inhibition specifically during S phase did not trigger G2 arrest despite affecting completion of DNA replication. Moreover, in cancer cells reliant upon the alternative lengthening of telomeres (ALT) mechanism, a distinct form of Topo2a-dependent, p53-independent G2 arrest was found to be mediated by BLM and Chk1. Importantly, the previously described PKCε-dependent mitotic failsafe was engaged in hTERT-positive cells when Topo2a-dependent G2 arrest was dysfunctional and where p53 was absent, but not in cells dependent on the ALT mechanism. In PKCε knockout mice, p53 deletion elicited tumors were less aggressive than in PKCε-replete animals and exhibited a distinct pattern of chromosomal rearrangements. This evidence suggests the potential of exploiting synthetic lethality in arrest-defective hTERT-positive tumors through PKCε-directed therapeutic intervention. SIGNIFICANCE: The identification of a requirement for p53 in stringent Topo2a-dependent G2 arrest and engagement of PKCε failsafe pathways in arrest-defective hTERT-positive cells provides a therapeutic opportunity to induce selective synthetic lethality.

A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution.

Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information. Currently, high-dimension imaging data analysis is technology-specific and requires multiple tools, restricting analytical scalability and result reproducibility. Here we present SIMPLI (Single-cell Identification from MultiPLexed Images), a flexible and technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After raw image processing, SIMPLI performs a spatially resolved, single-cell analysis of the tissue slide as well as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for large datasets. SIMPLI produces multiple tabular and graphical outputs at each step of the analysis. Its containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. Software is available at "SIMPLI [ https://github.com/ciccalab/SIMPLI ]".

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource.

BACKGROUND: Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. RESULTS: Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. CONCLUSIONS: Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/ .

Clone competition as a mechanism to reduce tumor formation.

With age, clones carrying somatic mutations in well-known cancer driver genes progressively populate adult tissues, yet cancer transformation is rare. In a recent issue of Nature, Colom et al. showed that competition between mutated clones with different fitness could act as a tumor-protective mechanism.

Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts.

BACKGROUND & AIMS: Colorectal cancer (CRC) shows variable response to immune checkpoint blockade, which can only partially be explained by high tumor mutational burden (TMB). We conducted an integrated study of the cancer tissue and associated tumor microenvironment (TME) from patients treated with pembrolizumab (KEYNOTE 177 clinical trial) or nivolumab to dissect the cellular and molecular determinants of response to anti- programmed cell death 1 (PD1) immunotherapy. METHODS: We selected multiple regions per tumor showing variable T-cell infiltration for a total of 738 regions from 29 patients, divided into discovery and validation cohorts. We performed multiregional whole-exome and RNA sequencing of the tumor cells and integrated these with T-cell receptor sequencing, high-dimensional imaging mass cytometry, detection of programmed death-ligand 1 (PDL1) interaction in situ, multiplexed immunofluorescence, and computational spatial analysis of the TME. RESULTS: In hypermutated CRCs, response to anti-PD1 immunotherapy was not associated with TMB but with high clonality of immunogenic mutations, clonally expanded T cells, low activation of Wnt signaling, deregulation of the interferon gamma pathway, and active immune escape mechanisms. Responsive hypermutated CRCs were also rich in cytotoxic and proliferating PD1+CD8 T cells interacting with PDL1+ antigen-presenting macrophages. CONCLUSIONS: Our study clarified the limits of TMB as a predictor of response of CRC to anti-PD1 immunotherapy. It identified a population of antigen-presenting macrophages interacting with CD8 T cells that consistently segregate with response. We therefore concluded that anti-PD1 agents release the PD1-PDL1 interaction between CD8 T cells and macrophages to promote cytotoxic antitumor activity.

ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.

High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in high-dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

Pan-cancer detection of driver genes at the single-patient resolution.

BACKGROUND: Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. RESULTS: We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. CONCLUSIONS: sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).

Normal Somatic Mutations in Cancer Transformation.

Gene alterations play a prominent role in driving cancer initiation and progression. However, the genetic events that occur in normal cells prior to tumorigenesis are still unknown. Recent studies have started to map somatic mutations in normal human tissues, and here we discuss their implications for our understanding of tumorigenesis.

O-linked mucin-type glycosylation regulates the transcriptional programme downstream of EGFR.

Aberrant mucin-type O-linked glycosylation is a common occurrence in cancer where the upregulation of sialyltransferases is often seen leading to the early termination of O-glycan chains. Mucin-type O-linked glycosylation is not limited to mucins and occurs on many cell surface glycoproteins including EGFR, where the number of sites can be limited. Upon EGF ligation, EGFR induces a signaling cascade and may also translocate to the nucleus where it directly regulates gene transcription, a process modulated by Galectin-3 and MUC1 in some cancers. Here, we show that upon EGF binding, breast cancer cells carrying different O-glycans respond by transcribing different gene expression signatures. MMP10, the principal gene upregulated when cells carrying sialylated core 1 glycans were stimulated with EGF, is also upregulated in ER-positive breast carcinoma reported to express high levels of ST3Gal1 and hence mainly core 1 sialylated O-glycans. In contrast, isogenic cells engineered to carry core 2 glycans upregulate CX3CL1 and FGFBP1 and these genes are upregulated in ER-negative breast carcinomas, also known to express longer core 2 O-glycans. Changes in O-glycosylation did not significantly alter signal transduction downstream of EGFR in core 1 or core 2 O-glycan expressing cells. However, striking changes were observed in the formation of an EGFR/galectin-3/MUC1/ß-catenin complex at the cell surface that is present in cells carrying short core 1-based O-glycans but absent in core 2 carrying cells.

Approaching Cancer Evolution from Different Angles.

Dr Francesca Ciccarelli (The Francis Crick Institute, UK) and Dr James De Gregori (University of Colorado, USA) interview 3 top scientists in clinical (Dr Charles Swanton, The Francis Crick Institute, UK), molecular (Dr Kornelia Polyak, Dana-Farber Cancer Institute, USA), and evolutionary cancer research (Dr Carlo Maley, Arizona State University, USA) to discuss the current status of knowledge, the challenges, and the opportunities to move the field forward.

Identification of non-cancer cells from cancer transcriptomic data.

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Molecular Biology and Evolution of Cancer: From Discovery to Action.

Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.