Anti-CD30 CAR T cells as consolidation after autologous haematopoietic stem-cell transplantation in patients with high-risk CD30+ lymphoma: a phase 1 study.

BACKGROUND: Chimeric antigen receptor (CAR) T cells targeting CD30 are safe and have promising activity when preceded by lymphodepleting chemotherapy. We aimed to determine the safety of anti-CD30 CAR T cells as consolidation after autologous haematopoietic stem-cell transplantation (HSCT) in patients with CD30+ lymphoma at high risk of relapse. METHODS: This phase 1 dose-escalation study was performed at two sites in the USA. Patients aged 3 years and older, with classical Hodgkin lymphoma or non-Hodgkin lymphoma with CD30+ disease documented by immunohistochemistry, and a Karnofsky performance score of more than 60% planned for autologous HSCT were eligible if they were considered high risk for relapse as defined by primary refractory disease or relapse within 12 months of initial therapy or extranodal involvement at the start of pre-transplantation salvage therapy. Patients received a single infusion of CAR T cells (2 × 107 CAR T cells per m2, 1 × 108 CAR T cells per m2, or 2 × 108 CAR T cells per m2) as consolidation after trilineage haematopoietic engraftment (defined as absolute neutrophil count ≥500 cells per µL for 3 days, platelet count ≥25 × 109 platelets per L without transfusion for 5 days, and haemoglobin ≥8 g/dL without transfusion for 5 days) following carmustine, etoposide, cytarabine, and melphalan (BEAM) and HSCT. The primary endpoint was the determination of the maximum tolerated dose, which was based on the rate of dose-limiting toxicity in patients who received CAR T-cell infusion. This study is registered with ClinicalTrials.gov (NCT02663297) and enrolment is complete. FINDINGS: Between June 7, 2016, and Nov 30, 2020, 21 patients were enrolled and 18 patients (11 with Hodgkin lymphoma, six with T-cell lymphoma, one with grey zone lymphoma) were infused with anti-CD30 CAR T cells at a median of 22 days (range 16-44) after autologous HSCT. There were no dose-limiting toxicities observed, so the highest dose tested, 2 × 108 CAR T cells per m2, was determined to be the maximum tolerated dose. One patient had grade 1 cytokine release syndrome. The most common grade 3-4 adverse events were lymphopenia (two [11%] of 18) and leukopenia (two [11%] of 18). There were no treatment-related deaths. Two patients developed secondary malignancies approximately 2 years and 2·5 years following treatment (one stage 4 non-small cell lung cancer and one testicular cancer), but these were judged unrelated to treatment. At a median follow-up of 48·2 months (IQR 27·5-60·7) post-infusion, the median progression-free survival for all treated patients (n=18) was 32·3 months (95% CI 4·6 months to not estimable) and the median progression-free survival for treated patients with Hodgkin lymphoma (n=11) has not been reached. The median overall survival for all treated patients has not been reached. INTERPRETATION: Anti-CD30 CAR T-cell infusion as consolidation after BEAM and autologous HSCT is safe, with low rates of toxicity and encouraging preliminary activity in patients with Hodgkin lymphoma at high risk of relapse, highlighting the need for larger studies to confirm these findings. FUNDING: National Heart Lung and Blood Institute, University Cancer Research Fund at the Lineberger Comprehensive Cancer Center.

Tumoral and stromal hMENA isoforms impact tertiary lymphoid structure localization in lung cancer and predict immune checkpoint blockade response in patients with cancer.

BACKGROUND: Tertiary Lymphoid Structures (TLS) correlate with positive outcomes in patients with NSCLC and the efficacy of immune checkpoint blockade (ICB) in cancer. The actin regulatory protein hMENA undergoes tissue-specific splicing, producing the epithelial hMENA11a linked to favorable prognosis in early NSCLC, and the mesenchymal hMENAΔv6 found in invasive cancer cells and pro-tumoral cancer-associated fibroblasts (CAFs). This study investigates how hMENA isoforms in tumor cells and CAFs relate to TLS presence, localization and impact on patient outcomes and ICB response. METHODS: Methods involved RNA-SEQ on NSCLC cells with depleted hMENA isoforms. A retrospective observational study assessed tissues from surgically treated N0 patients with NSCLC, using immunohistochemistry for tumoral and stromal hMENA isoforms, fibronectin, and TLS presence. ICB-treated patient tumors were analyzed using Nanostring nCounter and GeoMx spatial transcriptomics. Multiparametric flow cytometry characterized B cells and tissue-resident memory T cells (TRM). Survival and ICB response were estimated in the cohort and validated using bioinformatics pipelines in different datasets. FINDINGS: Findings indicate that hMENA11a in NSCLC cells upregulates the TLS regulator LTßR, decreases fibronectin, and favors CXCL13 production by TRM. Conversely, hMENAΔv6 in CAFs inhibits LTßR-related NF-kB pathway, reduces CXCL13 secretion, and promotes fibronectin production. These patterns are validated in N0 NSCLC tumors, where hMENA11ahigh expression, CAF hMENAΔv6low, and stromal fibronectinlow are associated with intratumoral TLS, linked to memory B cells and predictive of longer survival. The hMENA isoform pattern, fibronectin, and LTßR expression broadly predict ICB response in tumors where TLS indicates an anti-tumor immune response. INTERPRETATION: This study uncovers hMENA alternative splicing as an unexplored contributor to TLS-related Tumor Immune Microenvironment (TIME) and a promising biomarker for clinical outcomes and likely ICB responsiveness in N0 patients with NSCLC. FUNDING: This work is supported by AIRC (IG 19822), ACC (RCR-2019-23669120), CAL.HUB.RIA Ministero Salute PNRR-POS T4, "Ricerca Corrente" granted by the Italian Ministry of Health.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Spatial Relationships in the Tumor Microenvironment Demonstrate Association with Pathologic Response to Neoadjuvant Chemoimmunotherapy in Muscle-invasive Bladder Cancer.

BACKGROUND: Platinum-based neoadjuvant chemotherapy (NAC) is standard for patients with muscle-invasive bladder cancer (MIBC). Pathologic response (complete: ypT0N0 and partial:

Dominant CD4+ T cell receptors remain stable throughout antiretroviral therapy-mediated immune restoration in people with HIV.

In people with HIV (PWH), the post-antiretroviral therapy (ART) window is critical for immune restoration and HIV reservoir stabilization. We employ deep immune profiling and T cell receptor (TCR) sequencing and examine proliferation to assess how ART impacts T cell homeostasis. In PWH on long-term ART, lymphocyte frequencies and phenotypes are mostly stable. By contrast, broad phenotypic changes in natural killer (NK) cells, γδ T cells, B cells, and CD4+ and CD8+ T cells are observed in the post-ART window. Whereas CD8+ T cells mostly restore, memory CD4+ T subsets and cytolytic NK cells show incomplete restoration 1.4 years post ART. Surprisingly, the hierarchies and frequencies of dominant CD4 TCR clonotypes (0.1%-11% of all CD4+ T cells) remain stable post ART, suggesting that clonal homeostasis can be independent of homeostatic processes regulating CD4+ T cell absolute number, phenotypes, and function. The slow restoration of host immunity post ART also has implications for the design of ART interruption studies.

LENS: Landscape of Effective Neoantigens Software.

MOTIVATION: Elimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA-sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. RESULTS: We report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigens Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer-testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. AVAILABILITY AND IMPLEMENTATION: More information about LENS, including code, workflow documentation, and instructions, can be found at (https://gitlab.com/landscape-of-effective-neoantigens-software).

Efficacy of a Dual-Epitope Dendritic Cell Vaccine as Part of Combined Immunotherapy for HER2-Expressing Breast Tumors.

Previous work from our group and others has shown that patients with breast cancer can generate a T cell response against specific human epidermal growth factor 2 (HER2) epitopes. In addition, preclinical work has shown that this T cell response can be augmented by Ag-directed mAb therapy. This study evaluated the activity and safety of a combination of dendritic cell (DC) vaccination given with mAb and cytotoxic therapy. We performed a phase I/II study using autologous DCs pulsed with two different HER2 peptides given with trastuzumab and vinorelbine to a study cohort of patients with HER2-overexpressing and a second with HER2 nonoverexpressing metastatic breast cancer. Seventeen patients with HER2-overexpressing and seven with nonoverexpressing disease were treated. Treatment was well tolerated, with one patient removed from therapy because of toxicity and no deaths. Forty-six percent of patients had stable disease after therapy, with 4% achieving a partial response and no complete responses. Immune responses were generated in the majority of patients but did not correlate with clinical response. However, in one patient, who has survived >14 y since treatment in the trial, a robust immune response was demonstrated, with 25% of her T cells specific to one of the peptides in the vaccine at the peak of her response. These data suggest that autologous DC vaccination when given with anti-HER2-directed mAb therapy and vinorelbine is safe and can induce immune responses, including significant T cell clonal expansion, in a subset of patients.

A multispecies framework for modeling adaptive immunity and immunotherapy in cancer.

Predator-prey theory is commonly used to describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor "shows" the body) and the T-cell receptor (TCR) repertoire (how well the body "sees" cancer cells). The tumor immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poor response to immunotherapy in some tumor types, suggesting that the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Importantly, while tumor and T-cell populations are known to compete with each other for intratumoral resources, whether between-lineage competition among peripheral T cells influences the TCR repertoire is unknown and difficult to interrogate experimentally. Computational models may offer a way to investigate these phenomena and deepen our understanding of the tumor-immune axis. Here, we construct a predator-prey-like model and calibrate it to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of antigen-specific T-cell lineages and their consumption of both lineage-specific antigenic resources and lineage-agnostic, shared resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes; recapitulated response-associated effects of immunotherapy, including immunoediting; and allowed exploration of treatment of tumors with varying growth and mutation rates.

Evaluation of tumor antigen-specific antibody responses in patients with metastatic triple negative breast cancer treated with cyclophosphamide and pembrolizumab.

The role of B cells in antitumor immunity is becoming increasingly appreciated, as B cell populations have been associated with response to immune checkpoint blockade (ICB) in patients with breast cancer and murine models of breast cancer. Deeper understanding of antibody responses to tumor antigens is needed to clarify the function of B cells in determining response to immunotherapy. We evaluated tumor antigen-specific antibody responses in patients with metastatic triple negative breast cancer treated with pembrolizumab following low-dose cyclophosphamide therapy using computational linear epitope prediction and custom peptide microarrays. We found that a minority of predicted linear epitopes were associated with antibody signal, and signal was associated with both neoepitopes and self-peptides. No association was observed between signal presence and subcellular localization or RNA expression of parent proteins. Patient-specific patterns of antibody signal boostability were observed that were independent of clinical response. Intriguingly, measures of cumulative antibody signal intensity relative to immunotherapy treatment showed that the one complete responder in the trial had the greatest increase in total antibody signal, which supports a potential association between ICB-dependent antibody boosting and clinical response. The antibody boost in the complete responder was largely driven by increased levels of IgG specific to a sequence of N-terminal residues in native Epidermal Growth Factor Receptor Pathway Substrate 8 (EPS8) protein, a known oncogene in several cancer types including breast cancer. Structural protein prediction showed that the targeted epitope of EPS8 was in a region of the protein with mixed linear/helical structure, and that this region was solvent-exposed and not predicted to bind to interacting macromolecules. This study highlights the potential importance of the humoral immune response targeting neoepitopes as well as self epitopes in shaping clinical response to immunotherapy.

Shared graft-versus-leukemia minor histocompatibility antigens in DISCOVeRY-BMT.

T-cell responses to minor histocompatibility antigens (mHAs) mediate graft-versus-leukemia (GVL) effects and graft-versus-host disease (GVHD) in allogeneic hematopoietic cell transplantation. Therapies that boost T-cell responses improve allogeneic hematopoietic cell transplant (alloHCT) efficacy but are limited by concurrent increases in the incidence and severity of GVHD. mHAs with expression restricted to hematopoietic tissue (GVL mHAs) are attractive targets for driving GVL without causing GVHD. Prior work to identify mHAs has focused on a small set of mHAs or population-level single-nucleotide polymorphism-association studies. We report the discovery of a large set of novel GVL mHAs based on predicted immunogenicity, tissue expression, and degree of sharing among donor-recipient pairs (DRPs) in the DISCOVeRY-BMT data set of 3231 alloHCT DRPs. The total number of predicted mHAs varied by HLA allele, and the total number and number of each class of mHA significantly differed by recipient genomic ancestry group. From the pool of predicted mHAs, we identified the smallest sets of GVL mHAs needed to cover 100% of DRPs with a given HLA allele. We used mass spectrometry to search for high-population frequency mHAs for 3 common HLA alleles. We validated 24 predicted novel GVL mHAs that are found cumulatively within 98.8%, 60.7%, and 78.9% of DRPs within DISCOVeRY-BMT that express HLA-A∗02:01, HLA-B∗35:01, and HLA-C∗07:02, respectively. We confirmed the immunogenicity of an example novel mHA via T-cell coculture with peptide-pulsed dendritic cells. This work demonstrates that the identification of shared mHAs is a feasible and promising technique for expanding mHA-targeting immunotherapeutics.

Gene signatures derived from transcriptomic-causal networks stratified colorectal cancer patients for effective targeted therapy.

Predictive and prognostic gene signatures derived from interconnectivity among genes can tailor clinical care to patients in cancer treatment. We identified gene interconnectivity as the transcriptomic-causal network by integrating germline genotyping and tumor RNA-seq data from 1,165 patients with metastatic colorectal cancer (CRC). The patients were enrolled in a clinical trial with randomized treatment, either cetuximab or bevacizumab in combination with chemotherapy. We linked the network to overall survival (OS) and detected novel biomarkers by controlling for confounding genes. Our data-driven approach discerned sets of genes, each set collectively stratify patients based on OS. Two signatures under the cetuximab treatment were related to wound healing and macrophages. The signature under the bevacizumab treatment was related to cytotoxicity and we replicated its effect on OS using an external cohort. We also showed that the genes influencing OS within the signatures are downregulated in CRC tumor vs. normal tissue using another external cohort. Furthermore, the corresponding proteins encoded by the genes within the signatures interact each other and are functionally related. In conclusion, this study identified a group of genes that collectively stratified patients based on OS and uncovered promising novel prognostic biomarkers for personalized treatment of CRC using transcriptomic causal networks.

ACE configurator for ELISpot: optimizing combinatorial design of pooled ELISpot assays with an epitope similarity model.

The enzyme-linked immunosorbent spot (ELISpot) assay is a powerful in vitro immunoassay that enables cost-effective quantification of antigen-specific T-cell reactivity. It is used widely in the context of cancer and infectious diseases to validate the immunogenicity of predicted epitopes. While technological advances have kept pace with the demand for increased throughput, efforts to increase scale are bottlenecked by current assay design and deconvolution methods, which have remained largely unchanged. Current methods for designing pooled ELISpot experiments offer limited flexibility of assay parameters, lack support for high-throughput scenarios and do not consider peptide identity during pool assignment. We introduce the ACE Configurator for ELISpot (ACE) to address these gaps. ACE generates optimized peptide-pool assignments from highly customizable user inputs and handles the deconvolution of positive peptides using assay readouts. In this study, we present a novel sequence-aware pooling strategy, powered by a fine-tuned ESM-2 model that groups immunologically similar peptides, reducing the number of false positives and subsequent confirmatory assays compared to existing combinatorial approaches. To validate ACE's performance on real-world datasets, we conducted a comprehensive benchmark study, contextualizing design choices with their impact on prediction quality. Our results demonstrate ACE's capacity to further increase precision of identified immunogenic peptides, directly optimizing experimental efficiency. ACE is freely available as an executable with a graphical user interface and command-line interfaces at https://github.com/pirl-unc/ace.

Collaborative study from the Bladder Cancer Advocacy Network for the genomic analysis of metastatic urothelial cancer.

Urothelial Cancer - Genomic Analysis to Improve Patient Outcomes and Research (NCT02643043), UC-GENOME, is a genomic analysis and biospecimen repository study in 218 patients with metastatic urothelial carcinoma. Here we report on the primary outcome of the UC-GENOME-the proportion of subjects who received next generation sequencing (NGS) with treatment options-and present the initial genomic analyses and clinical correlates. 69.3% of subjects had potential treatment options, however only 5.0% received therapy based on NGS. We found an increased frequency of TP53E285K mutations as compared to non-metastatic cohorts and identified features associated with benefit to chemotherapy and immune checkpoint inhibition, including: Ba/Sq and Stroma-rich subtypes, APOBEC mutational signature (SBS13), and inflamed tumor immune phenotype. Finally, we derive a computational model incorporating both genomic and clinical features predictive of immune checkpoint inhibitor response. Future work will utilize the biospecimens alongside these foundational analyses toward a better understanding of urothelial carcinoma biology.

STING-induced regulatory B cells compromise NK function in cancer immunity.

An immunosuppressive tumour microenvironment is a major obstacle in the control of pancreatic and other solid cancers1-3. Agonists of the stimulator of interferon genes (STING) protein trigger inflammatory innate immune responses to potentially overcome tumour immunosuppression4. Although these agonists hold promise as potential cancer therapies5, tumour resistance to STING monotherapy has emerged in clinical trials and the mechanism(s) is unclear5-7. Here we show that the administration of five distinct STING agonists, including cGAMP, results in an expansion of human and mouse interleukin (IL)-35+ regulatory B cells in pancreatic cancer. Mechanistically, cGAMP drives expression of IL-35 by B cells in an IRF3-dependent but type I interferon-independent manner. In several preclinical cancer models, the loss of STING signalling in B cells increases tumour control. Furthermore, anti-IL-35 blockade or genetic ablation of IL-35 in B cells also reduces tumour growth. Unexpectedly, the STING-IL-35 axis in B cells reduces proliferation of natural killer (NK) cells and attenuates the NK-driven anti-tumour response. These findings reveal an intrinsic barrier to systemic STING agonist monotherapy and provide a combinatorial strategy to overcome immunosuppression in tumours.

Balance between immunoregulatory B cells and plasma cells drives pancreatic tumor immunity.

Plasma cell responses are associated with anti-tumor immunity and favorable response to immunotherapy. B cells can amplify anti-tumor immune responses through antibody production; yet B cells in patients and tumor-bearing mice often fail to support this effector function. We identify dysregulated transcriptional program in B cells that disrupts differentiation of naive B cells into anti-tumor plasma cells. The signaling network contributing to this dysfunction is driven by interleukin (IL) 35 stimulation of a STAT3-PAX5 complex that upregulates the transcriptional regulator BCL6 in naive B cells. Transient inhibition of BCL6 in tumor-educated naive B cells is sufficient to reverse the dysfunction in B cell differentiation, stimulating the intra-tumoral accumulation of plasma cells and effector T cells and rendering pancreatic tumors sensitive to anti-programmed cell death protein 1 (PD-1) blockade. Our findings argue that B cell effector dysfunction in cancer can be due to an active systemic suppression program that can be targeted to synergize with T cell-directed immunotherapy.

Comprehensive Analysis of the Immunogenomics of Triple-Negative Breast Cancer Brain Metastases From LCCC1419.

Background: Triple negative breast cancer (TNBC) is an aggressive variant of breast cancer that lacks the expression of estrogen and progesterone receptors (ER and PR) and HER2. Nearly 50% of patients with advanced TNBC will develop brain metastases (BrM), commonly with progressive extracranial disease. Immunotherapy has shown promise in the treatment of advanced TNBC; however, the immune contexture of BrM remains largely unknown. We conducted a comprehensive analysis of TNBC BrM and matched primary tumors to characterize the genomic and immune landscape of TNBC BrM to inform the development of immunotherapy strategies in this aggressive disease. Methods: Whole-exome sequencing (WES) and RNA sequencing were conducted on formalin-fixed, paraffin-embedded samples of BrM and primary tumors of patients with clinical TNBC (n = 25, n = 9 matched pairs) from the LCCC1419 biobank at UNC-Chapel Hill. Matched blood was analyzed by DNA sequencing as a comparison for tumor WES for the identification of somatic variants. A comprehensive genomics assessment, including mutational and copy number alteration analyses, neoantigen prediction, and transcriptomic analysis of the tumor immune microenvironment were performed. Results: Primary and BrM tissues were confirmed as TNBC (23/25 primaries, 16/17 BrM) by immunohistochemistry and of the basal intrinsic subtype (13/15 primaries and 16/19 BrM) by PAM50. Compared to primary tumors, BrM demonstrated a higher tumor mutational burden. TP53 was the most frequently mutated gene and was altered in 50% of the samples. Neoantigen prediction showed elevated cancer testis antigen- and endogenous retrovirus-derived MHC class I-binding peptides in both primary tumors and BrM and predicted that single-nucleotide variant (SNV)-derived peptides were significantly higher in BrM. BrM demonstrated a reduced immune gene signature expression, although a signature associated with fibroblast-associated wound healing was elevated in BrM. Metrics of T and B cell receptor diversity were also reduced in BrM. Conclusions: BrM harbored higher mutational burden and SNV-derived neoantigen expression along with reduced immune gene signature expression relative to primary TNBC. Immune signatures correlated with improved survival, including T cell signatures. Further research will expand these findings to other breast cancer subtypes in the same biobank. Exploration of immunomodulatory approaches including vaccine applications and immune checkpoint inhibition to enhance anti-tumor immunity in TNBC BrM is warranted.

NeoSplice: a bioinformatics method for prediction of splice variant neoantigens.

Motivation: Splice variant neoantigens are a potential source of tumor-specific antigen (TSA) that are shared between patients in a variety of cancers, including acute myeloid leukemia. Current tools for genomic prediction of splice variant neoantigens demonstrate promise. However, many tools have not been well validated with simulated and/or wet lab approaches, with no studies published that have presented a targeted immunopeptidome mass spectrometry approach designed specifically for identification of predicted splice variant neoantigens. Results: In this study, we describe NeoSplice, a novel computational method for splice variant neoantigen prediction based on (i) prediction of tumor-specific k-mers from RNA-seq data, (ii) alignment of differentially expressed k-mers to the splice graph and (iii) inference of the variant transcript with MHC binding prediction. NeoSplice demonstrates high sensitivity and precision (>80% on average across all splice variant classes) through in silico simulated RNA-seq data. Through mass spectrometry analysis of the immunopeptidome of the K562.A2 cell line compared against a synthetic peptide reference of predicted splice variant neoantigens, we validated 4 of 37 predicted antigens corresponding to 3 of 17 unique splice junctions. Lastly, we provide a comparison of NeoSplice against other splice variant prediction tools described in the literature. NeoSplice provides a well-validated platform for prediction of TSA vaccine targets for future cancer antigen vaccine studies to evaluate the clinical efficacy of splice variant neoantigens. Availability and implementation: https://github.com/Benjamin-Vincent-Lab/NeoSplice. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Hallmarks of Resistance to Immune-Checkpoint Inhibitors.

Immune-checkpoint inhibitors (ICI), although revolutionary in improving long-term survival outcomes, are mostly effective in patients with immune-responsive tumors. Most patients with cancer either do not respond to ICIs at all or experience disease progression after an initial period of response. Treatment resistance to ICIs remains a major challenge and defines the biggest unmet medical need in oncology worldwide. In a collaborative workshop, thought leaders from academic, biopharma, and nonprofit sectors convened to outline a resistance framework to support and guide future immune-resistance research. Here, we explore the initial part of our effort by collating seminal discoveries through the lens of known biological processes. We highlight eight biological processes and refer to them as immune resistance nodes. We examine the seminal discoveries that define each immune resistance node and pose critical questions, which, if answered, would greatly expand our notion of immune resistance. Ultimately, the expansion and application of this work calls for the integration of multiomic high-dimensional analyses from patient-level data to produce a map of resistance phenotypes that can be utilized to guide effective drug development and improved patient outcomes.