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Cancer genomics has enabled the exhaustive molecular characterization of tumors and exposed hepatocellular carcinoma (HCC) as among the most complex cancers. This complexity is paralleled by dozens of mouse models that generate histologically similar tumors but have not been systematically validated at the molecular level. Accurate models of the molecular pathogenesis of HCC are essential for biomedical progress; therefore we compared genomic and transcriptomic profiles of four separate mouse models [MUP transgenic, TAK1-knockout, carcinogen-driven diethylnitrosamine (DEN), and Stelic Animal Model (STAM)] with those of 987 HCC patients with distinct etiologies. These four models differed substantially in their mutational load, mutational signatures, affected genes and pathways, and transcriptomes. STAM tumors were most molecularly similar to human HCC, with frequent mutations in Ctnnb1, similar pathway alterations, and high transcriptomic similarity to high-grade, proliferative human tumors with poor prognosis. In contrast, TAK1 tumors better reflected the mutational signature of human HCC and were transcriptionally similar to low-grade human tumors. DEN tumors were least similar to human disease and almost universally carried the Braf V637E mutation, which is rarely found in human HCC. Immune analysis revealed that strain-specific MHC-I genotype can influence the molecular makeup of murine tumors. Thus, different mouse models of HCC recapitulate distinct aspects of HCC biology, and their use should be adapted to specific questions based on the molecular features provided here.

Individual MHC genotype constrains the mutational landscape during tumorigenesis. Immune checkpoint inhibition reactivates immunity against tumors that escaped immune surveillance in approximately 30% of cases. Recent studies demonstrated poorer response rates in female and younger patients. Although immune responses differ with sex and age, the role of MHC-based immune selection in this context is unknown. We find that tumors in younger and female individuals accumulate more poorly presented driver mutations than those in older and male patients, despite no differences in MHC genotype. Younger patients show the strongest effects of MHC-based driver mutation selection, with younger females showing compounded effects and nearly twice as much MHC-II based selection. This study presents evidence that strength of immune selection during tumor development varies with sex and age, and may influence the availability of mutant peptides capable of driving effective response to immune checkpoint inhibitor therapy. Here the authors show that stronger immune selection and immune editing in females and younger patients lead to the accumulation of poorly presented driver mutations in tumors. These results may explain why young and female patients are characterized by lower response rates to immune checkpoint blockade therapies.

Background The major histocompatibility complex class I (MHC-I) molecule is a protein complex that displays intracellular peptides to T cells, allowing the immune system to recognize and destroy infected or cancerous cells. MHC-I is composed of a highly polymorphic HLA-encoded alpha chain that binds the peptide and a Beta-2-microglobulin (B2M) protein that acts as a stabilizing scaffold. HLA mutations have been implicated as a mechanism of immune evasion during tumorigenesis, and B2M is considered a tumor suppressor gene. However, the implications of somatic HLA and B2M mutations have not been fully explored in the context of antigen presentation via the MHC-I molecule during tumor development. To understand the effect that B2M and HLA MHC-I molecule mutations have on mutagenesis, we analyzed the accumulation of mutations in patients from The Cancer Genome Atlas according to their MHC-I molecule mutation status. Results Somatic B2M and HLA mutations in microsatellite stable tumors were associated with higher overall mutation burden and a larger fraction of HLA-binding neoantigens when compared to B2M and HLA wild type tumors. B2M and HLA mutations were highly enriched in patients with microsatellite instability. B2M mutations tended to occur relatively early during patients’ respective tumor development, whereas HLA mutations were either early or late events. In addition, B2M and HLA mutated patients had higher levels of immune infiltration by natural killer and CD8+ T cells and higher levels of cytotoxicity. Conclusions Our findings add to a growing body of evidence that somatic B2M and HLA mutations are a mechanism of immune evasion by demonstrating that such mutations are associated with a higher load of neoantigens that should be presented via MHC-I.

Background Immune checkpoint blockade (ICB) with antibodies inhibiting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) (or its ligand (PD-L1)) can stimulate immune responses against cancer and have revolutionized the treatment of tumors. The influence of host germline genetics and its interaction with tumor neoantigens remains poorly defined. We sought to determine the interaction between tumor mutational burden (TMB) and the ability of a patient’s major histocompatibility complex class I (MHC-I) to efficiently present mutated driver neoantigens in predicting response ICB. Methods Comprehensive genomic profiling was performed on 83 patients with diverse cancers treated with ICB to determine TMB and human leukocyte antigen-I (HLA-I) genotype. The ability of a patient’s MHC-I to efficiently present mutated driver neoantigens (defined by the Patient Harmonic-mean Best Rank (PHBR) score (with lower PHBR indicating more efficient presentation)) was calculated for each patient. Results The median progression-free survival (PFS) for PHBR score < 0.5 vs. ≥ 0.5 was 5.1 vs. 4.4 months ( P  = 0.04). Using a TMB cutoff of 10 mutations/mb, the stable disease >  6 months/partial response/complete response rate, median PFS, and median overall survival (OS) of TMB high/PHBR high vs. TMB high/PHBR low were 43% vs. 78% ( P  = 0.049), 5.8 vs. 26.8 months ( P  = 0.03), and 17.2 months vs. not reached ( P  = 0.23), respectively. These findings were confirmed in an independent validation cohort of 32 patients. Conclusions Poor presentation of driver mutation neoantigens by MHC-I may explain why some tumors (even with a high TMB) do not respond to ICB.

Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy. Human leukocyte antigen loss of heterozygosity (HLA LOH) is an important mechanism of immune escape in patients with cancer. Here the authors design and validate a machine learning algorithm with subclonal sensitivity for the identification of HLA LOH from paired tumor-normal sequencing data.

Circulating tumor DNA (ctDNA)-based response assessment is appealing but limited by conventional analytical thresholds. We utilized a whole genome sequencing based, tumor-informed ultrasensitive ctDNA assay which tracked ~1800 somatic mutations to analyze 227 longitudinal plasma samples from 39 patients with advanced/metastatic cancers receiving immune checkpoint inhibitors (ICIs). ctDNA was detected from 2.0-239,315 PPM (median limit of detection: 1.77 PPM), with 33% of positive detections below 100 PPM. Early molecular response, defined as >50% ctDNA reduction or sustained ctDNA negativity from baseline to first follow-up, strongly predicted improved progression-free survival (PFS) (hazard ratio (HR) = 0.09, 95% CI: 0.02-0.39, p = 0.001) and was independently prognostic of PFS. Molecular complete response (mCR), defined as any ctDNA clearance, predicted overall survival and PFS, with 1-year PFS of 87% in mCR patients versus 16% in non-mCR patients (HR = 0.14, 95% CI: 0.04-0.50, p = 0.003). The high-sensitivity ctDNA monitoring may enable precise, real-time evaluation of ICI response to guide clinical decision-making.

BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

BackgroundHuman Leukocyte Antigen (HLA) genes are critical for the presentation of neoantigens to the immune system by cancer cells. Deletion of HLA alleles, known as HLA loss of heterozygosity (LOH), has been highlighted as a key immune escape mechanism. Validated algorithms to detect HLA LOH from sequencing data are critical for exploring the biological impact of HLA LOH and assessing its utility as a clinical biomarker.MethodsWe developed DASH (Deletion of Allele-Specific HLAs), a machine learning algorithm trained on data from 279 patients on the ImmunoID NeXT Platform using features that account for probe capture variability between alleles and incorporate information from the regions flanking each HLA gene. To understand the contribution of boosted sequencing in the HLA region of the ImmunoID NeXT Platform, we performed an in silico downsampling analysis. To assess DASH’s performance at variable tumor purities and HLA LOH subclonalities we identified three tumor-normal cell lines with HLA LOH and created in silico mixtures. Furthermore, after designing patient-specific primers for 21 patients that target specific alleles, we applied digital PCR (dPCR) to validate the HLA allele copy number status of the patients. Finally, we applied DASH to 611 patients spanning 15 tumor types.ResultsIn cross validation analyses across patient samples, DASH achieved 98.7% specificity and 92.9% sensitivity while LOHHLA, a widely used algorithm, only reached 94.3% and 78.8%, respectively (figure 1). Downsampling analyses demonstrated that DASH benefits significantly from the boosted HLA sequencing on the ImmunoID NeXT Platform, dropping 0.06 in F-score after downsampling to the sequencing depth of other exome platforms. In cell line mixture analyses, DASH demonstrates greater than 99% specificity across all tumor purity and sub-clonality levels and greater than 98% sensitivity for above 27% tumor purity. Moreover, DASH demonstrated 100% sensitivity and specificity in dPCR experiments across 21 tumor samples with stable controls. We applied DASH to a large pan-cancer cohort and found that 18% of patients had HLA LOH (figure 2). We identified strong associations between HLA LOH and genomic instability. Moreover, we demonstrated relationships between HLA LOH and markers of immune pressure, such as a correlation with CD274 (PD-1) expression and allele-specific neoantigen enrichment for deleted HLA alleles.ConclusionsDASH, a highly sensitive HLA LOH algorithm that has been extensively validated using cross validation, in silico downsampling, cell line mixtures and dPCR, has demonstrated the widespread impact of HLA LOH in a large pan-cancer cohort.Abstract 79 Figure 1Bar plots showing the sensitivity and specificities scores across ImmunoID NeXT cross validation samples between LOHHLA (blue) and DASH (green).Abstract 79 Figure 2Bar plots denoting the number of patients and the frequency of HLA LOH in each tumor type cohort. 95% confidence intervals are shown with the thin dark grey bars. Only cohorts with at least 10 patients are shown

BackgroundTypical liquid biopsy panels offer a limited understanding of tumor biology, potentially under-representing the heterogeneity of resistance in late-stage cancers. Here, diminished scope can result in undetected, therapeutically-relevant biomarkers which respond dynamically to treatment, as well as potentially missed resistance mechanisms and pathway-level events. To address the challenges associated with identifying multiple concurrent heterogeneous resistance mechanisms in individual patients, we evaluated longitudinal exome-scale tumor-informed cell-free DNA (cfDNA) data from head and neck squamous cell carcinoma (HNSCC) patients receiving anti-PD1 therapy.MethodsPre- and post-intervention matched tumor, normal and plasma samples were retrospectively obtained from 15 stage II-IV HNSCC patients. Following baseline sample collection, all patients received a single dose of nivolumab or pembrolizumab. The primary tumor was then resected approximately one month later when possible, or a second biopsy collected where resection was impractical. Paired tumor and normal samples were then profiled using ImmunoID NeXT Platform®, an augmented exome/transcriptome platform and analysis pipeline. Exome-scale cfDNA profiling of matched plasma samples was performed using the NeXT Liquid BiopsyTM platform to detect somatic variants.ResultsPatient neoantigen presentation score (NEOPSTM) rapidly and significantly contracted following therapy (p=.00098). Novel neoantigens arising post-treatment which were predicted to be presented on lost HLA alleles were significantly higher in patients with longer overall survival (p=.019). Variant detection across same-patient serial cfDNA samples revealed significantly correlated VAFs (R=.62, p<.0001) despite significant contraction of mutational burden in solid tumor (p=.0039), suggesting complex clonal/subclonal dynamics. Investigation of the evolving tumor and cfDNA subclonal architecture revealed significant association between decreasing cellular prevalence and NOTCH signaling (q=.001) and the innate immune system (q=.002), while increasing cellular prevalence was associated with p53 signalling (q=.02) and hypoxia (q=.02). These findings were complimented by transcriptomic data which showed significant enrichment of multiple immune pathways across treatment.ConclusionsWe found that immune checkpoint blockade precipitates rapid evolution of the HNSCC tumor microenvironment. By leveraging comprehensive, tumor-informed liquid biopsy data we were able to identify contracting cellular populations enriched for NOTCH pathway mutations. Longer OS following either intervention was associated with an expansion of novel neoantigens predicted to be presented by lost HLA alleles. Our results suggest that tumor-informed liquid biopsy provides a more robust understanding of therapeutic response and resistance mechanisms than that attainable with typical liquid biopsy panels alone.Ethics ApprovalThis study obtained ethics approval from Human Subjects Research at Stanford University. ID number is 40425. All participants gave informed consent prior to enrollment.

BackgroundMetastatic gastrointestinal stromal tumors (GIST) are lethal tumors of the GI tract characterized by gain of function mutations in KIT or PDGFRα. Transient first-line control is achieved through the inhibition of tyrosine kinase signaling using the KIT inhibitor imatinib, though most patients progress after 2–3 years. Progression through successive lines of therapy results in a molecularly heterogeneous disease with diverse subtypes, driven by distinct collections of exon-specific KIT mutations which directly inform therapy decisions. To address the unmet need of comprehensive understanding of GIST, we used tumor-informed whole exome liquid biopsy to identify and track the evolution of multiple concurrent heterogeneous resistance mechanisms in individual patients receiving tyrosine kinase inhibitors (TKIs).MethodsBaseline matched tumor, normal and longitudinal plasma samples were obtained from 15 metastatic, heavily pretreated GIST patients. Following baseline sample collection, all patients received systemic TKI therapy, and were monitored until disease progression. Paired tumor and normal samples were profiled using the ImmunoID NeXT Platform®, an augmented exome/transcriptome platform and analysis pipeline which generates comprehensive tumor and immune information. Exome-scale cfDNA profiling of matched plasma samples was performed using the NeXT Liquid BiopsyTM platform to detect somatic variants.ResultsBaseline solid tumor WES confirmed primary sensitizing KIT mutations in all 15 (100%) patients, and secondary KIT mutations in 7/15 patients (47%). Serial plasma whole exome sequencing identified evolution and expansion of clones harboring newly formed, druggable, exon-specific KIT mutations which evolved prior to identification of tumor progression using standard imaging techniques. In addition to these variants, we detected node-specific enrichment of PI3K-AKT and MAPK pathway mutations in plasma of patients with shorter overall survival (OS), which may contribute to the observed immune evasion. Accompanying these changes, we also detected significant association between gene copy-number profiles and duration of OS (P = 0.0097). Investigation of immune signatures using univariate cox modeling revealed a significant association between TCRβ diversity and reduced OS (HR = 2.55, log rank P = 0.04).ConclusionsComprehensive genomic profiling (WES and RNA-Seq) of paired tumor tissue and WES of serially collected ctDNA identified evolving druggable KIT mutations and other molecular alterations which preceded clinical disease progression. These findings suggest liquid biopsy-based monitoring of late-stage GIST malignancies may be useful for early identification of treatment resistance, providing treatment guidance prior to traditional approaches.Ethics ApprovalEthics approval was granted by the MD Anderson Human Research Protection Program, and all participants gave informed consent prior to participation.