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Purpose: Immune checkpoint inhibitors combined with antiangiogenic agents produce benefits in the treatment of advanced hepatocellular carcinoma (HCC). We investigated the efficacy and immunomodulatory activity of cabozantinib alone and combined with anti-PD1 in experimental models of HCC, and explored the potential target population that might benefit from this combination. Experimental Design: C57BL/6J mice bearing subcutaneous Hepa1-6 or Hep53.4 tumors received cabozantinib, anti-PD1, their combination, or placebo. Tumor and blood samples were analyzed by flow cytometry, IHC, transcriptome, and cytokine profiling. Cabozantinib-related effects were validated in a colorectal cancer patient-derived xenograft model. Transcriptomic data from three human HCC cohorts (cohort 1: n = 167, cohort 2: n = 57, The Cancer Genome Atlas: n = 319) were used to cluster patients according to neutrophil features, and assess their impact on survival. Results: The combination of cabozantinib and anti-PD1 showed increased antitumor efficacy compared with monotherapy and placebo (P < 0.05). Cabozantinib alone significantly increased neutrophil infiltration and reduced intratumor CD8+PD1+ T-cell proportions, while the combination with anti-PD1 further stimulated both effects and significantly decreased regulatory T cell (Treg) infiltration (all P < 0.05). In blood, cabozantinib and especially combination increased the proportions of overall T cells (P < 0.01) and memory/effector T cells (P < 0.05), while lowering the neutrophil-to-lymphocyte ratio (P < 0.001 for combination). Unsupervised clustering of human HCCs revealed that high tumor enrichment in neutrophil features observed with the treatment combination was linked to less aggressive tumors with more differentiated and less proliferative phenotypes. Conclusions: Cabozantinib in combination with anti-PD1 enhanced antitumor immunity by bringing together innate neutrophil-driven and adaptive immune responses, a mechanism of action which favors this approach for HCC treatment.

Motivation Genomic alterations can modulate the tumor immunophenotype depending on their nature and tissue of origin. Although this immune–genomic interaction may shape disease progression and response to immunotherapy, the factors governing such dynamics and the influence of each tissue-specific context remain poorly understood. Results Here, we have developed the PanCancer ImmunoGenomics (PCIG) tool, a web-based resource that provides researchers with the opportunity to mine immunome–genome relationships across several cancer types using data from the Pan-Cancer Analysis of Whole-Genomes (PCAWG) study, which comprises >2,600 samples spanning across 20 different cancer primary sites. PCIG yields an integrative analysis of the crosstalk between somatic genomic alterations and different immune features, thus helping to understand immune response-related processes. Availability and implementation PCIG is freely available at https://pcig.vhio.net and is supported by all major web browsers. PCIG was developed with Django, which is a Python-based free and open-source framework, and it uses SQL Server as a relational database management system. The code is freely available for download at GitHub https://github.com/AnnaPG/PCIG and in its online supplementary material. Supplementary information Supplementary data are available at Bioinformatics online.

Objective: Hepatocellular carcinoma (HCC) is increasingly associated with non-alcoholic steatohepatitis (NASH). HCC immunotherapy offers great promise; however, recent data suggests NASH-HCC may be less sensitive to conventional immune checkpoint inhibition (ICI). We hypothesised that targeting neutrophils using a CXCR2 small molecule inhibitor may sensitise NASH-HCC to ICI therapy. Design Neutrophil infiltration was characterised in human HCC and mouse models of HCC. Late-stage intervention with anti-PD1 and/or a CXCR2 inhibitor was performed in murine models of NASH-HCC. The tumour immune microenvironment was characterised by imaging mass cytometry, RNA-seq and flow cytometry. Results: neutrophils expressing CXCR2, a receptor crucial to neutrophil recruitment in acute-injury, are highly represented in human NASH-HCC. In models of NASH-HCC lacking response to ICI, the combination of a CXCR2 antagonist with anti-PD1 suppressed tumour burden and extended survival. Combination therapy increased intratumoural XCR1(+) dendritic cell activation and CD8(+) T cell numbers which are associated with anti-tumoural immunity, this was confirmed by loss of therapeutic effect on genetic impairment of myeloid cell recruitment, neutralisation of the XCR1-ligand XCL1 or depletion of CD8(+) T cells. Therapeutic benefit was accompanied by an unexpected increase in tumour-associated neutrophils (TANs) which switched from a protumour to anti-tumour progenitor-like neutrophil phenotype. Reprogrammed TANs were found in direct contact with CD8(+) T cells in clusters that were enriched for the cytotoxic anti-tumoural protease granzyme B. Neutrophil reprogramming was not observed in the circulation indicative of the combination therapy selectively influencing TANs. Conclusion: CXCR2-inhibition induces reprogramming of the tumour immune microenvironment that promotes ICI in NASH-HCC., Cancer Research UK (CRUK); Newcastle Experimental Cancer Medicine Centre; Accelerator Award; Fondazione AIRC; MRC program; AstraZeneca; Wellcome Trust; Newcatle University Faculty of Medical Sciences; WE Harker Foundation; Ministry of Economy and Competitiveness (MINECO) (MICINN); Fundació Universitària Agustí Pedro i Pons; Instituto de Salud Carlos III (ISCIII); European Union (EU); European Social Fund; German Research Foundation (DFG); Fulbright España; National Institutes of Health (NIH); Samuel Waxman Cancer Research Foundation; Spanish National Health Institute; Fundación Científica de la Asociación Española Contra el Cáncer; Generalitat de Catalunya; Sara Borrell Fellowship

Somatic copy number alterations (CNAs) are a hallmark of cancer, but their role in tumorigenesis and clinical relevance remain largely unclear. Here, we developed CNApp, a web-based tool that allows a comprehensive exploration of CNAs by using purity-corrected segmented data from multiple genomic platforms. CNApp generates genome-wide profiles, computes CNA scores for broad, focal and global CNA burdens, and uses machine learning-based predictions to classify samples. We applied CNApp to the TCGA pan-cancer dataset of 10,635 genomes showing that CNAs classify cancer types according to their tissue-of-origin, and that each cancer type shows specific ranges of broad and focal CNA scores. Moreover, CNApp reproduces recurrent CNAs in hepatocellular carcinoma and predicts colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and discrete genomic imbalances. In summary, CNApp facilitates CNA-driven research by providing a unique framework to identify relevant clinical implications. CNApp is hosted at https://tools.idibaps.org/CNApp/., eLife digest In most cases, human cells contain two copies of each of their genes, yet sometimes this can change, an effect called copy number alteration (CNA). Cancer is a genetic disease and thus, studying the DNA from tumor samples is crucial to improving diagnosis and choosing the right treatment. Most tumors contain cells with CNAs; however, the impact of CNAs in cancer progression is poorly understood. CNAs can be studied by examining the genome of tumor cells and finding which regions display an unusual number of copies. It may also be possible to gather information about different cancer types by analyzing the CNAs in a tumor, but this approach requires the analysis of large amounts of data. To aid the analysis of CNAs in cancer cells, Franch-Expósito, Bassaganyas et al. have created an online tool called CNApp, which is able to identify and count CNAs in genomic data and link them to features associated with different cancers. The hope is that a better understanding of the effect of CNAs in cancer could help better diagnose cancers, and improve outcomes for patients. Potentially, this could also predict what type of treatment would work better for a specific tumor. Besides, by using a machine-learning approach, the tool can also make predictions about specific cancer subtypes in order to facilitate clinical decisions. Franch-Expósito, Bassaganyas et al. tested CNApp using previously existing cancer data from 33 different cancer types to show how CNApp can help the interpretation of CNAs in cancer. Moreover, CNApp can also use CNAs to identify different types of bowel (colorectal) cancer in a way that could help doctors to make decisions about treatment. Together these findings show that CNApp provides an adaptable and accessible research tool for the study of cancer genomics, which could provide opportunities to inform medical procedures.

Somatic copy number alterations (CNAs) are a hallmark of cancer. Although CNA profiles have been established for most human tumor types, their precise role in tumorigenesis as well as their clinical and therapeutic relevance remain largely unclear. Thus, computational and statistical approaches are required to thoroughly define the interplay between CNAs and tumor phenotypes. Here we developed CNApp, a user-friendly web tool that offers sample- and cohort-level computational analyses, allowing a comprehensive and integrative exploration of CNAs with clinical and molecular variables. By using purity-corrected segmented data from multiple genomic platforms, CNApp generates genome-wide profiles, computes CNA scores for broad, focal and global CNA burdens, and uses machine learning-based predictions to classify samples. We applied CNApp to a pan-cancer dataset of 10,635 genomes from TCGA showing that CNA patterns classify cancer types according to their tissue-of-origin, and that broad and focal CNA scores positively correlate in samples with low amounts of whole-chromosome and chromosomal arm-level imbalances. Moreover, using the hepatocellular carcinoma cohort from the TCGA repository, we demonstrate the reliability of the tool in identifying recurrent CNAs, confirming previous results. Finally, we establish machine learning-based models to predict colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and specific genomic imbalances. In summary, CNApp facilitates data-driven research and provides a unique framework for the first time to comprehensively assess CNAs and perform integrative analyses that enable the identification of relevant clinical implications. CNApp is hosted at http://cnapp.bsc.es.