nextNEOpi: a comprehensive pipeline for computational neoantigen prediction.

SUMMARY: Somatic mutations and gene fusions can produce immunogenic neoantigens mediating anticancer immune responses. However, their computational prediction from sequencing data requires complex computational workflows to identify tumor-specific aberrations, derive the resulting peptides, infer patients' Human Leukocyte Antigen types and predict neoepitopes binding to them, together with a set of features underlying their immunogenicity. Here, we present nextNEOpi (nextflow NEOantigen prediction pipeline) a comprehensive and fully automated bioinformatic pipeline to predict tumor neoantigens from raw DNA and RNA sequencing data. In addition, nextNEOpi quantifies neoepitope- and patient-specific features associated with tumor immunogenicity and response to immunotherapy. AVAILABILITY AND IMPLEMENTATION: nextNEOpi source code and documentation are available at https://github.com/icbi-lab/nextNEOpi. CONTACT: dietmar.rieder@i-med.ac.at or francesca.finotello@uibk.ac.at. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma.

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

Tumor-targeted therapy with BRAF-inhibitor recruits activated dendritic cells to promote tumor immunity in melanoma.

BACKGROUND: Tumor-targeted therapy causes impressive tumor regression, but the emergence of resistance limits long-term survival benefits in patients. Little information is available on the role of the myeloid cell network, especially dendritic cells (DC) during tumor-targeted therapy. METHODS: Here, we investigated therapy-mediated immunological alterations in the tumor microenvironment (TME) and tumor-draining lymph nodes (LN) in the D4M.3A preclinical melanoma mouse model (harboring the V-Raf murine sarcoma viral oncogene homolog B (BRAF)V600E mutation) by using high-dimensional multicolor flow cytometry in combination with multiplex immunohistochemistry. This was complemented with RNA sequencing and cytokine quantification to characterize the immune status of the tumors. The importance of T cells during tumor-targeted therapy was investigated by depleting CD4+ or CD8+ T cells in tumor-bearing mice. Tumor antigen-specific T-cell responses were characterized by performing in vivo T-cell proliferation assays and the contribution of conventional type 1 DC (cDC1) to T-cell immunity during tumor-targeted therapy was assessed using Batf3-/- mice lacking cDC1. RESULTS: Our findings reveal that BRAF-inhibitor therapy increased tumor immunogenicity, reflected by an upregulation of genes associated with immune activation. The T cell-inflamed TME contained higher numbers of activated cDC1 and cDC2 but also inflammatory CCR2-expressing monocytes. At the same time, tumor-targeted therapy enhanced the frequency of migratory, activated DC subsets in tumor-draining LN. Even more, we identified a cDC2 population expressing the Fc gamma receptor I (FcγRI)/CD64 in tumors and LN that displayed high levels of CD40 and CCR7 indicating involvement in T cell-mediated tumor immunity. The importance of cDC2 is underlined by just a partial loss of therapy response in a cDC1-deficient mouse model. Both CD4+ and CD8+ T cells were essential for therapy response as their respective depletion impaired therapy success. On resistance development, the tumors reverted to an immunologically inert state with a loss of DC and inflammatory monocytes together with the accumulation of regulatory T cells. Moreover, tumor antigen-specific CD8+ T cells were compromised in proliferation and interferon-γ-production. CONCLUSION: Our results give novel insights into the remodeling of the myeloid landscape by tumor-targeted therapy. We demonstrate that the transient immunogenic tumor milieu contains more activated DC. This knowledge has important implications for the development of future combinatorial therapies.

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer.

CD8+ T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8+ T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8+ T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8+ T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific 'self-like' features. MSI CRC CD8+ T cells showed tumor-specific activation reminiscent of canonical 'T cell hot' tumors, whereas the MSS CRC CD8+ T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of 'pseudo-T cell hot' tumors. Consequently, MSI and MSS CRC CD8+ T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8+ T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8+ T cells to build a bulk tumor transcriptome classification for CRC patients. This "Immune Subtype Classification" (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8+ T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making.