Abstract 1334: A diagnostic platform for precision cancer therapy enabling composite biomarkers by combining tumor and immune features from an enhanced exome and transcriptome

There is an increasing need for more advanced, composite biomarkers that can model the complex systems biology driving response and resistance to cancer therapy. However, many cancer diagnostic platforms to date, with their focus on mutational changes in a relatively small panel of genes, provide limited data to support integrative, multidimensional biomarkers that can better predict immunotherapy response.

To enable the identification of composite biomarkers that combine tumor- and immune-related information from both DNA and RNA, we have developed ImmunoID NeXT, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune system from a single FFPE sample, across all of the approximately 20,000 genes. By co-optimizing assay and analytics design, we enable sensitive evaluation of clinically-relevant cancer biomarkers from >=25ng of co-extracted DNA/RNA, while also providing a broader evaluation of neoantigens, HLA typing and LOH, antigen processing machinery (APM), TCR/BCR repertoire, immune expression signatures, tumor-infiltrating lymphocytes (TILs), oncoviruses, and germline variants. Leveraging this expansive feature set, we developed methods that combine individual analytes to construct composite biomarker scores that correlate with immunotherapy response.

Validation of ImmunoID NeXT demonstrated high sensitivity and specificity to somatic and structural variants across ~20,000 genes at allelic fractions as low as 5%, with clinical diagnostic reporting on actionable mutations (SNVs, indels, CNAs, fusions) in 248 cancer-driver genes that have been boosted further for higher sensitivity, as well as reporting on TMB and MSI status. For neoantigen prediction, immuno-peptidomic data from monoallelic HLA-transfected cell lines were used to train neural networks to predict pMHC binding with higher precision than public tools. For TCRα/β analysis in FFPE tumor samples, strong correlation with targeted TCR kit results was shown (R^2>0.9 and >0.94). For TILs, we developed signatures for eight immune cell types, demonstrating concordance with orthogonal immunofluorescence methods. We achieved genotyping accuracy of 99.1% for HLA Class I, and 95% for HLA Class II, and have developed and verified the performance of a tool for HLA LOH detection. In a cohort of 55 late-stage melanoma patients, the integration of neoantigen burden, HLA LOH, and APM mutational data formed a composite neoantigen score that more accurately predicted response to checkpoint blockade than other markers such as TMB.

With ImmunoID NeXT, we have developed a broad diagnostic platform that can be leveraged for the development of advanced composite biomarkers (and novel resistance mechanisms) that combine both tumor and immune features from DNA and RNA; enabling more accurate stratification of patient response to immunotherapy. The platform has been validated and optimized for use with limited FFPE tissue samples, making it ideal for both research and clinical applications.

Citation Format: Robert Peter Power, Gabor Bartha, Jason Harris, Sean M. Boyle, Eric Levy, Pamela Milani, Prateek Tandon, Paul McNitt, Mandy Lee, Massimo Morra, Sejal Desai, Sebastian Salvidar, Michael J. Clark, Christian Haudenschild, Sekwon Jang, John West, Richard Chen. A diagnostic platform for precision cancer therapy enabling composite biomarkers by combining tumor and immune features from an enhanced exome and transcriptome [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1334.

Abstract 2512: Pan-cancer characterization of the tumor and immune microenvironment facilitates identification of cancer-specific biological signatures

Background: A better understanding of the characteristics of cancer across different indications is required to drive the development of personalized treatments, inform therapy decisions, and improve outcomes. Integrating data from the tumor and the immune system can enable the identification of comprehensive biological signatures and composite biomarkers for the improved stratification of responders/progressors. Here, we describe a pan cancer study, including an enhanced whole-exome and transcriptome sequencing approach, across over 500 samples representing 13 tumor types, analyzed at high depth using the ImmunoID NeXT platform.

Methods: We sequenced paired tumor-normal samples on the ImmunoID NeXT platform, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune microenvironment from a single FFPE sample, across all of the approximately 20,000 genes. For each sample, we analyzed a broad set of features focused on both the tumor and immune system. From DNA, we profiled small variants, CNAs, MSI status, oncoviruses, HLA LOH, and neoantigens. From RNA, we profiled gene expression, small variants, fusions, TILs, TCR, BCR, and immune signatures. Integrated analyses assessing the impact of each feature, both within and across tumor types, were performed across the cohort.

Results: Through immunogenomic analysis we identified striking differences in both tumor and TME profiles across cancer types. In addition to mutation and neoantigen burden, by. we also computed a composite neoantigen score for each sample, which we have shown in a separate melanoma study can be a stronger predictor of response to immunotherapy. The composite neoantigen score integrates neoantigen prediction with mechanisms of tumor escape that can affect neoantigen presentation, providing a more accurate model of the antigen presentation biology. We also looked at the distribution of HLA LOH using our DASH algorithm and found differences in LOH frequency between tumor types. For example, we found HLA LOH to be five timesmore common in lung cancer than breast cancer. Further, we profiled immune gene signatures, including Gejewski and Ribas signatures, highlighting varied immune activation across cancer types. Analysis of somatic alterations in pathways controlling cell growth, PI3K/AKT signaling, apoptosis, and other canonical pathways revealed malignancy-specific alteration frequencies. The varying frequency, and combination of these alterations is indicative of a complex hierarchy of cross-talk between pathways, which operates in a cancer specific manner.

Conclusions: We performed a broad integrated analysis of the tumor and immune microenvironment for over 500 samples across 13 different tumor types using the ImmunoID NeXT platform. This comprehensive profiling revealed significant differences between cancer types beyond mutational burden, including neoantigen burden, immune microenvironment differences, and incidence of putative tumor escape mechanisms

Citation Format: Sean Michael Boyle, Charles Abbott, Eric Levy, Rachel Marty Pyke, Dattatreya Mellacheruvu, Simo Zhang, Mengyao Tan, Rena McClory, John West, Richard Chen. Pan-cancer characterization of the tumor and immune microenvironment facilitates identification of cancer-specific biological signatures [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2512.

Abstract 4278: A composite neoantigen score is more strongly associated with therapeutic response than tumor mutational burden in a cohort of late-stage anti-PD-1-treated melanoma patients

Checkpoint inhibitor therapy has demonstrated meaningful, if varied antitumor activity, with patient response influenced by a variety of biological factors, including complex interactions between the tumor and immune system. Thus, it is of increasing interest to identify composite biomarkers integrating multiple biological features to better predict immunotherapy response. In this study we use a comprehensive tumor immungenomics profiling platform to examine the effectiveness of our composite neoantigen score for stratifying patient response to checkpoint blockade therapy compared to tumor mutational burden and other biomarkers.Pre-treatment tumor/normal samples from 55 unresectable, stage III/IV melanoma patients who underwent anti-PD-1 therapy were characterized to assess factors influencing response. RECIST criteria were used to evaluate tumor response to therapy, with a median follow-up of 18 months. For each patient, a single paired FFPE tumor and normal blood sample was collected and profiled using Personalis' ImmunoID NeXT platform; an augmented exome/transcriptome platform and analysis pipeline, which produces comprehensive tumor mutation information, gene expression quantification, neoantigen characterization, HLA typing and LOH, TCR repertoire profiling and tumor microenvironment profiling. These data were then analyzed together with clinical outcome, and a composite neoantigen score computed for each patient along with other biomarkers such as tumor mutational burden (TMB).In this cohort, an elevated pretreatment composite neoantigen score combining neoantigen predictions adjusted based on resistance mechanisms that affect neoantigen presentation on the MHC complex was more strongly predictive of response to PD-1 blockade than TMB alone. This was true for both response and non-response via RECIST criteria and progression free survival. We also found that the composite neoantigen score was a stronger predictor of patient response when compared to neoantigen burden alone. Additionally, we observed increased response to anti-PD-1 therapy in patients with elevated pretreatment TCR clonality. Combining the composite neoantigen score and TCR clonality data revealed a significant association with non-response to therapy. Patients with high composite neoantigen score and TCR clonality that failed to achieve complete response revealed potential resistance mechanisms to anti-PD-1 therapy. Specifically, we identified patients with high expression of IDO1 or CTLA4, which may facilitate PD-1-independent immune escape. Additionally, we found patients with mutations within the antigen presentation machinery (APM), likely leading to loss of surface expression of the proteins, and in the case of B2M mutations, improper HLA class I folding and antigen presentation. These APM mutations likely result in reduced neoantigen presentation in these patients, facilitating tumor escape.In summary, our composite neoantigen score which integrates multiple components of MHC class I presentation into a single score, is more significantly associated with response to therapy than individual biomarkers such as tumor mutational burden. These findings highlight the promise of composite biomarkers for the optimization of anti-PD-1 therapy patient selection.

Citation Format: Charles W. Abbott, Eric Levy, Rachel Marty Pyke, Rena McClory, Sekwon Jang, Richard Chen, Sean Boyle. A composite neoantigen score is more strongly associated with therapeutic response than tumor mutational burden in a cohort of late-stage anti-PD-1-treated melanoma patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4278.

Exome scale liquid biopsy characterization of putative neoantigens and genomic biomarkers pre- and post anti-PD-1 therapy in squamous cell carcinoma of the head and neck

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Background: The reduced scope, and number of genes profiled by typical liquid biopsy panels can result in missed biomarkers including neoantigens, which may change with treatment, as well as potentially undetected resistance mechanisms and pathways beyond the scope of targets typically captured by panels. To address these limitations, we used a whole-exome scale liquid biopsy monitoring platform, NeXT Liquid Biopsy, to analyze head and neck squamous cell carcinoma (HNSCC) patients that have received anti-PD1 therapy. Presently, we sought to (1) monitor neoantigen changes in cfDNA as a complement to tumor biopsy-derived neoantigens, (2) compare the impact of tumor escape mechanisms, including HLA-LOH, on neoantigens identified in tissue and cfDNA and (3) to identify novel biological signatures that combine information from both solid tumor and liquid biopsies. Methods: Pre- and post-intervention matched normal, tumor and plasma samples were collected from a cohort of 12 patients with HNSCC. Following baseline sample collection all patients received a single dose of nivolumab, followed by resection approximately one month later when feasible, or a second biopsy where resection was impractical. Solid tumor and matched normal samples were profiled using ImmunoID NeXT, an augmented exome/transcriptome platform and analysis pipeline. Exome-scale somatic variants were identified in cfDNA from plasma samples using the NeXT Liquid Biopsy platform. Data from these two platforms were compared with corresponding clinical findings. Results: Concordant somatic events were detected between plasma and tumor at pre- and post-treatment timepoints. Neoantigens predicted to arise from these somatic events were reduced in solid tumor post-treatment, but increased in cfDNA, when compared to pre-treatment timepoints. HLA LOH was identified in a number of subjects, likely resulting in reduced neoepitope presentation in those cases. Immune cell infiltration increased in the tumor following treatment, with no changes to the CD8+/Treg cell ratio, suggesting consistent immunoregulation. Conclusions: Exome-wide neoantigen burden was reliably predicted from cfDNA, providing additional insight complementing data from solid tumor. Analyzing HLA LOH, and neoantigen burden from both solid and liquid biopsies together over the course of treatment creates a more comprehensive profile of therapeutic response and resistance mechanisms in HNSCC patients missed with typical liquid biopsy panels.

Abstract A051: A comprehensive, highly accurate genomics platform for precision immunotherapy: Simultaneously characterize tumors and the TME from a single FFPE sample

Immunogenomic profiling of the tumor and the tumor microenvironment (TME) is critical for identifying new biomarkers of immunotherapy response, understanding resistance, and enabling the development of personalized immunotherapies. However, running a comprehensive array of biomarker assays for each patient sample is often impractical given limited sample quantity, processing complexity, and prohibitive cost. To address these challenges, we developed a novel, augmented exome and transcriptome-based platform that simultaneously characterizes the tumor and TME from a single FFPE sample. We co-optimized the design of our sequencing assays and analytics to increase performance for the detection of somatic SNVs, indels, CNAs, and fusions across ~20,000 genes, as well as the evaluation of neoantigens, expression signatures, HLA typing and LOH, TCR/BCR repertoires, oncoviruses, tumor-infiltrating lymphocytes (TILs), clinically-actionable mutations, tumor mutational burden (TMB), and MSI status. We developed novel methods to sequence difficult regions of the exome and to extend coverage to key immunogenomic biomarkers. Analytic pipelines were designed to utilize assay optimizations to achieve higher accuracy than with other platforms. We then validated the platform for diagnostic and therapeutic use. With as little as 50ng of DNA per FFPE sample and co-extracted RNA, this platform completely covers between 17% to 40% more genes compared to a non-augmented exome, thus increasing sensitivity to somatic mutations and putative neoantigens. For neoantigen performance, we generated immune-peptidomic data from mono-allelic HLA transfected cell-lines and trained neural networks to predict neoepitope binding to MHC, demonstrating a higher precision (0.88) across alleles than publicly available tools (<0.7). For TCR alpha and beta clonotype profiling in tumor samples, we demonstrate strong correlation with the results from a targeted TCR kit (R2>0.9 and >0.94, respectively). For TILs, we developed signatures for CD4, CD8 T-cells, and other immune cells, demonstrating concordance with synthetic and CyTOF-derived validation sets. For HLA typing, we achieve an accuracy of 99.1% for HLA Class I, and 95% for HLA Class II typing calls, and have developed a novel tool for HLA LOH detection. We demonstrate sensitive detection of HPV, EBV, HCV, HTLV, and KSHV in known samples, and accurate MSI and TMB assessment. Finally, for diagnostic reporting, we achieve high sensitivity and specificity for clinically-reportable mutations comparable with diagnostic cancer panels. With this platform, we have developed a novel immunogenomics platform that can characterize both the tumor and TME from a single sample. By co-optimizing our assay and analytics for immuno-oncology, we enhance biomarker sensitivity compared to non-optimized genomics assays. Validation of the platform extends its use to diagnostics and personalized immunotherapy development.

Citation Format: Robert Power, Gabor Bartha, Jason Harris, Sean Michael Boyle, Eric Levy, Pamela Milani, Prateek Tandon, Robin Li, Manjula Chinnappa, Paul McNitt, Rena McClory, Massimo Morra, Sebastian Saldivar, Michael Clark, Christian Haudenschild, Erin Newburn, Christelle Johnson, John West. A comprehensive, highly accurate genomics platform for precision immunotherapy: Simultaneously characterize tumors and the TME from a single FFPE sample [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A051. doi:10.1158/1535-7163.TARG-19-A051

Acylated Ghrelin and The Regulation of Lipid Metabolism in The Intestine

Acylated ghrelin (AG) is a gastrointestinal (GI) peptide mainly secreted by the stomach that promotes cytosolic lipid droplets (CLD) hypertrophy in adipose tissues and liver. However, the role of AG in the regulation of lipid metabolism in the intestine remains unexplored. This study aimed at determining whether AG influences CLD production and chylomicron (CM) secretion in the intestine. The effects of AG and oleic acid on CLD accumulation and CM secretion were first investigated in cultured Caco-2/15 enterocytes. Intestinal lipid metabolism was also studied in Syrian Golden Hamsters submitted to conventional (CD) or Western (WD) diets for 8 weeks and continuously administered with AG or physiological saline for the ultimate 2 weeks. In cultured Caco-2/15 enterocytes, CLD accumulation influenced CM secretion while AG reduced fatty acid uptake. In WD hamsters, continuous AG treatment amplified chylomicron output while reducing postprandial CLD accumulation in the intestine. The present study supports the intimate relationship between CLD accumulation and CM secretion in the intestine and it underlines the importance of further characterizing the mechanisms through which AG exerts its effects on lipid metabolism in the intestine.

Undernutrition in childhood: Clinically based assessment tools and biological markers: Where are we and where should we go?

Despite its association with poor clinical outcomes and increased hospital costs, as of today undernutrition still goes undetected in paediatric hospitals. The reported prevalence of undernutrition in paediatric patients varies considerably. This disparity is partly due to the diversity of methods for its detection and assessment, as well as to the lack of consensus regarding its definition. Several methods, based on varied combinations of morphology characteristics, estimated nutritional intakes and medical conditions have been developed during the last 25 years. However, these tools suffer from poor sensitivity and selectivity particularly in acute conditions. Also while having their own merit, these tools mainly view malnutrition from the energy standpoint, disregarding assessment of specific micronutrients such as minerals and vitamins. In this position paper we make the point that in the era of personalized medicine, present technology offers the possibility of going beyond the traditional nutritional tools for assessing patients' status, and propose the measurement of selected micronutrients and allied metabolic markers in nutritional workup schemes adapted to each clinical condition.

Abstract 3377: T-cell receptor repertoire profiling using an augmented transcriptome

Immunotherapy is growing as one of the most promising therapeutic approaches in clinical oncology practice. This brings with it an increasing need for comprehensive immuno-genomic profiling of tumors to better understand the interaction with the immune system. This includes profiling of the T and B-cell receptor repertoires (TCR/BCR), which has traditionally not been feasible with an exome/transcriptome platform.

To address these challenges, we developed ImmunoID NeXT, an augmented, immuno-oncology optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and tumor microenvironment (TME) from limited FFPE tumor biopsies, including the TCR alpha, beta, gamma and delta chains and BCR heavy and light chains. We show how this platform accurately profiles abundant clones, and can be applied to understand the diversity and activity of the adaptive immune system.

We characterize the performance of ImmunoID NeXT at profiling TCR beta from RNA. We analyze the reproducibility of clones identified using replicates of PBMC and FFPE samples, and assess the concordance of top clones from a standalone TCR sequencing approaches to ours. Then, we test LOD by diluting well-characterized clonal T-cell line samples into PBMCs.

We also analyze patient-derived FFPE tumors to understand the profiles of tumor-infiltrating immune repertoires. First, we compare TCR beta profiling with IHC quantification of CD3+ cells in both tumor and adjacent normal tissues. Finally, to better understand both B and T cell infiltration, we profile the intra-sample heterogeneity of BCR and TCR in a set of tumors.

Between replicates of PBMC samples, abundances for shared clones have high concordance (R2>0.98). We observe strong concordance of the abundances for shared clones between adjacent curls of a tumor FFPE sample (R2>0.87), showing that our approach is robust to degraded FFPE samples. Compared to a standalone approach, we identify over 93% of the top 1000 clones, with highly concordant abundances (R2>0.94). Assessing LOD in dilutions of T cell lines into a PBMC sample, we are able to identify clones present at over 0.00032% RNA by mass.

In our analysis of T-cell infiltration, we find a significant T-cell population in normal tissues. We also compare TCR read counts detected by ImmunoID NeXT in tumor and normal tissues with IHC results. Finally, we find substantial inter-sample variations in the number of TCR and BCR clones in tumors.

ImmunoID NeXT has been designed to enable sensitive detection of abundant TCR and BCR clones in addition to comprehensive biomarkers from exome/transcriptome data. We demonstrate that our platform is reproducible, sensitive, and concordant with the top-abundance clones derived from targeted TCR methods, as well as feasible with FFPE samples. Finally, we highlight how immune repertoire results from ImmunoID NeXT can be used to gain understanding about the immunological composition of the TME.

Citation Format: Eric Levy, Pamela Milani, Sean M. Boyle, Shujun Luo, Gabor Bartha, Charles Abbott, Rena McClory, Robin Li, John West, Richard Chen. T-cell receptor repertoire profiling using an augmented transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3377.

Abstract 4695: Development and validation of an accurate exome-scale cfDNA detection platform

Background: Neoantigens are increasingly critical as biomarkers for immunotherapy response to checkpoint blockade therapy and as therapeutic targets for neoantigen-based personalized cancer vaccines. Accurate identification of neoantigens requires comprehensive exome and transcriptome sequencing of both a tumor biopsy and the matched normal samples to enable identification of putative neoantigens - which occur across the genome.

Methods: However, as tumor biopsy samples cannot always be obtained, and because tumor heterogeneity can result in an incomplete set of neoantigens from a single biopsy, we developed ImmunoID NeXT circulating tumor DNA (ctDNA) Exome to (1) identify neoantigens in cell free DNA (cfDNA) as a complement to tumor biopsy derived neoantigens and (2) track neoantigens in the cfDNA post immuno-therapy treatment. To demonstrate the utility of the ImmunoID NeXT ctDNA Exome for both identification and monitoring of neoantigens directly from cfDNA, we have performed two extensive studies. Firstly, we obtained cfDNA from 2 healthy donors, mixed them to create somatic variants with AFs down to 0.5% with analytical sensitivity calculated against >10,000 variants. Secondly, we have performed extensive cfDNA testing in cancer patients to assess our capabilities across tumor types. Finally, and very importantly, we utilized multi-location primary and metastatic tumor profiling to demonstrate the ImmunoID NeXT cfDNA platform can be applied to profile tumor heterogeneity.

Results: In our healthy donor mixes, we observed our ImmunoID NeXT cfDNA platform can detect de novo “gold set” SNVs with a sensitivity of 90% down to an allele frequency of 2%. For monitoring applications we are able to detect SNVs with a sensitivity of 92% down to an allele frequency of 0.5%. When tested across a range of tumor types including melanoma, lung, and colorectal, ImmunoID NeXT repeatedly detected high concordance for somatic events shared between tumor and cfDNA. In tumor samples allele frequencies ranged from10% to 100%, and through cfDNA interrogation 60%-98% of these events were accurately detected in the plasma. Finally, we were able to reproducibly detect ctDNA, which were not present in the primary tumor sample, in subsequent primary tumor biopsy curls or metastases in multiple tumor types, demonstrating our ctDNA platform can effectively monitor tumor heterogeneity.

Conclusion: These results show the accuracy of the ImmunoID NeXT platform for detecting and profiling ctDNA somatic events. Further, these results demonstrate the potential of using a comprehensive ctDNA Exome to identify and monitor neoantigens as a complement to the results from sequencing of the tumor biopsy alone.

Citation Format: Simo V. Zhang, Charles Abbott, Robin Li, Eric Levy, Shujun Luo, Robert Power, Rena McClory, John West, Richard Chen, Sean Michael Boyle. Development and validation of an accurate exome-scale cfDNA detection platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4695.

Abstract 905: Comprehensive immunogenomic profiling of anti-PD-1 treated melanoma patients reveals subject-specific tumor escape mechanisms

Background: Despite the remarkable response of some melanoma patients to checkpoint inhibitor therapy, the majority of patients do not achieve complete response. It is of great interest to identify biomarkers and mechanisms that influence immunotherapy effectiveness. Here we apply our comprehensive tumor immunogenomics platform (ImmunoID NeXT) to identify potential biomarkers of response to checkpoint blockade therapy related to both the tumor and tumor microenvironment.

Methods: We characterized the immunogenomics of 50 stage III/IV melanoma patients who have undergone anti-PD-1 therapy to assess potential factors influencing response. Tumor response to therapy was evaluated using RECIST criteria with a median follow-up of 12 months. Immuno-genomic profiling was performed using Personalis’ ImmunoID NeXT platform; an augmented exome/transcriptome platform and analysis pipeline which from a single paired tumor FFPE and normal blood sample yielded comprehensive tumor mutation information, gene expression quantification, neoantigen characterization, TCR repertoire profiling, HLA typing and tumor microenvironment profiling. The molecular information of the tumors was then analyzed together with their corresponding clinical response.

Results: Through comprehensive immunogenomic profiling we demonstrated that higher TCR clonality in pre-treatment biopsy was predictive of response to PD-1 blockade, and significantly associated with improved progression free survival. We also observed increased response to anti-PD-1 treatment in patients with elevated pretreatment neoantigen burden. Further investigation of patients with high neoantigen burden and TCR clonality that failed to achieve complete response revealed potential resistance mechanisms to anti-PD-1 therapy. Specifically, we identified two patients with high expression of IDO1 or CTLA4, which may facilitate immune escape in a PD-1 independent manner. Additionally, we found two patients with mutations in their antigen presentation machinery (APM). The first patient had two independent HLA mutations in HLA-A and HLA-B, leading to the likely loss of surface expression of the proteins. In the second APM mutation patient we observed a high frequency (80% AF) frameshift variant in B2M, which potentially prevents proper HLA class I folding and antigen presentation. These APM mutations suggest reduced neoantigen presentation in these patients, which are probable mechanisms for tumor escape.

Conclusions: In summary, our comprehensive cancer immunogenomic analysis shows that genomic and immune profiling of pretreatment patient samples can identify biomarkers and resistance mechanisms to immune checkpoint blockade, suggesting the potential efficacy of these as a combinatorial biomarker to optimize patient selection for anti-PD-1 therapy.

Citation Format: Charles W. Abbott, Sean M. Boyle, Eric Levy, Rena McClory, Sekwon Jang, Richard Chen. Comprehensive immunogenomic profiling of anti-PD-1 treated melanoma patients reveals subject-specific tumor escape mechanisms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 905.

Is there a relationship between vitamin D nutritional status and metabolic syndrome in childhood acute lymphoblastic leukemia survivors? A PETALE study

BACKGROUND

Treatment of childhood acute lymphoblastic leukemia (cALL) has reached unprecedented success leading to survival rates reaching 90%. This is regrettably linked to increased risk of developing long-term health-related sequels into early adulthood.

OBJECTIVE

This study aims at assessing the relationship between the vitamin D status and metabolic biomarkers in PETALE, a well-characterized cohort of cALL survivors.

RESULTS

We demonstrate that 15.9% of the study participants exhibited 3 or more metabolic syndrome (MetS) risk factors. We also show a direct relationship between s25OHD₃ and plasma HDL-Cholesterol concentrations in female but not male participants.

CONCLUSION

Our data, from a metabolically well-described cohort, support a modest role for vitamin D in lipid metabolism in childhood leukemia survivors. The major outcome of this study is the strong association between HDL-Cholesterol concentration and s25OHD₃ only in female subjects, thereby conveying vitamin D a gender-specific cardio-protective effect. cALL survivors represent a population at higher risk for secondary diseases. For this reason thorough nutritional evaluation, including vitamin D should be part of the regular follow-up.

Abstract 5710: Molecular profiling of anti-PD-1 treated melanoma patients reveals importance of assessing neoantigen burden and tumor escape mechanisms for clinical treatment

Despite the remarkable response of some melanoma patients to checkpoint inhibitor therapy, significant numbers of patients do not achieve complete response. To understand this differential response, there is an increasing interest in identifying biomarkers and mechanisms that influence immunotherapy effectiveness. In this study, we characterize the immuno-genomics of tumors from a series of melanoma patients that have received anti-PD-1 checkpoint inhibitors to assess potential factors influencing response.

To better understand mechanisms of anti-PD-1 response, we sequenced and genomically profiled tumors from 19 stage III and IV melanoma patients where response was evaluated using RECIST criteria. Of the 19 patients, there were 5 complete responders (CR), 8 partial responders (PR), and 6 progressive disease (PD) patients. Immuno-genomic profiling was performed using Personalis' ACE ImmunoID platform, an augmented exome/transcriptome platform and analysis pipeline that allows for assessment of tumor mutations, neoantigens, HLA typing, gene expression quantification, tumor micro-environment, and tumor escape mechanisms. The molecular information for each of the 19 melanoma patient samples was then analyzed together with the corresponding clinical response to anti-PD-1 therapy.

We identified 3 outlier patients, which, while having very high neoantigen burden, did not achieve complete response (2 PR & 1 PD). One of these patients had extremely high expression of IDO1, which may facilitate immune escape in a PD-1 independent manner. Two independent HLA mutations in HLA-A and HLA-B (stop-gain mutation and splice site mutation, respectively) were found in the second patient, leading to the likely loss of surface expression of two classes of HLA-A and HLA-B proteins. If these three high neoantigen burden individuals with proposed tumor escape mechanisms are removed from consideration, we found a highly significant association between neoantigen burden and response to anti-PD-1 therapy (PD + PR vs CR, P = 0.00046). We also observed that, in our cohort, response to anti-PD-1 therapy was more accurately predicted by neoantigen burden than mutational burden.

In conclusion, we observed a strong correlation between response to anti-PD-1 therapy in melanoma patients and neoantigen burden when tumor escape mechanisms are considered. In our patients, we saw highly suggestive resistance mechanisms that involve perturbations to elements of the antigen presenting machinery and checkpoint blockade. This highlights the potential importance of broad immuno-genomic profiling of patients that are candidates for receiving immunotherapy. We are continuing to increase our cohort size to observe both how well the neoantigen burden holds to anti-PD-1 response and to identify additional mechanisms for immune evasion.

Citation Format: Jie Wang, Sean Michael Boyle, Christina Lee, Eric Levy, Zeid Rusan, Sekwon Jang, Richard Chen. Molecular profiling of anti-PD-1 treated melanoma patients reveals importance of assessing neoantigen burden and tumor escape mechanisms for clinical treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5710.

Abstract 2245: Deconvolution of diverse immune cell populations within tumors using ACE Transcriptome

Comprehensive tumor immuno-genomic characterization is becoming an important tool for identifying new biomarkers correlated with patient response to immunotherapy. Both the abundance and composition of tumor-infiltrating lymphocytes have been associated with tumor progression and patient outcome. Prior studies have shown that an enrichment of cytotoxic immune cells, and depletion of immunosuppressive cells, can indicate better clinical outcome.

While common lab approaches are used to profile tumor samples for the presence and enumeration of immune cell types, these approaches can be limited by the number of markers and throughput. Thus, there is interest in using an accurate computational method leveraging NGS data to more comprehensively profile the distribution of immune cells in the tumor microenvironment. Through the use of marker genes that are expressed in a cell type specific manner, it is possible to computationally predict the fractions of multiple cell types in a mixed sample. This approach relies on an input of reference gene expression signatures, which are used to estimate the proportions of the cell types represented in the reference. The generation and validation of these reference signatures are critical for ensuring the accuracy of the results on a given platform.

We have used the ACE Cancer Transcriptome, from the ACE ImmunoID platform, to produce high-quality gene expression profiles of purified immune cells representing many lineages, including B cells, monocytes, NK cells, and T cells. These profiles were used to create reference signatures of immune cell type specific genes, enabling quantification of their cellular abundances. In addition, we test the accuracy of the deconvolution approach on mixtures of immune cells. Finally, we validate the approach on tumor samples with immune cells quantified by widely accepted orthogonal methods. Through these results, we show that deconvolution of TILs through our ACE Cancer Transcriptome can be a robust platform for elucidating the composition of immune cells in a tumor sample.

Citation Format: Eric Levy, Sean M. Boyle, Pamela Milani, Shujun Luo, John West, Richard Chen. Deconvolution of diverse immune cell populations within tumors using ACE Transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2245.

Abstract 1292: Methods of improving accuracy of neoantigen identification for therapeutic and diagnostic use in immuno-oncology

Background:Neoantigens are increasingly critical in immuno-oncology as a therapeutic target for neoantigen-based personalized cancer vaccines and as a potential biomarker for immunotherapy response. However, the methods for identifying which neoepitopes are more likely to provoke an immune response remains an important challenge for improving both the effectiveness of neoantigen-based vaccines and enabling the potential use of neoantigens as a biomarker in immunotherapy.

Methods:We sought to improve overall neoantigen identification performance by systematically improving critical components of our ACE ImmunoID assays and neoantigen pipeline. Personalis' Accuracy and Content Enhanced (ACE) technology was developed to fill critical gaps in conventional exome and transcriptome sequencing that can lead to missed neoantigens. To improve MHC-epitope binding prediction, we trained neural networks on mass spectrometry derived MHC-epitope binding data. This is in contrast to other MHC binding algorithms that have been primarily trained using in vitro competitive binding data, which suffer from having not been processed, loaded, nor shuttled natively into the HLA binding domain. HLA typing, a key input into the neoantigen prediction algorithms, was improved through exome augmentation of the HLA region with an optimized HLA typing algorithm. Other enhancements include RNA based somatic variant calling, peptide phasing, transcript isoform estimation, and identification of indel and fusion derived neoepitopes.

Results:Our ACE augmented exome demonstrates high sensitivity and specificity for SNVs, indels, and fusions at MAF >=10%. These are all variant types that result in putative neoantigens. Further, we show that our augmented ACE transcriptome can achieve high sensitivity for RNA derived variants and can be an important filter for putative neoantigens. When compared with commercially available MHC binding algorithms for specific HLA alleles, our MHC binding prediction algorithm consistently achieves a higher overall sensitivity and specificity than other tools. For example, our MHC class I-epitope binding prediction algorithm demonstrated an aggregative precision value of 0.88 across HLA alleles, as opposed to 0.50 for other widely used tools. To assess overall HLA-typing performance, we performed a blinded clinical HLA typing validation demonstrating 98% and 95% concordance with Class I and II HLA results (respectively) from clinical testing. We also show instances where peptide phasing, SNP, indel and fusion-derived neoepitopes are important for more accurate and comprehensive neoantigen identification.

Citation Format: Sean Michael Boyle, Ravi Alla, Ryan Wang, Eric Levy, Gabor Bartha, Jason Harris, Robert McCord, Rena McClory, John West, Richard Chen. Methods of improving accuracy of neoantigen identification for therapeutic and diagnostic use in immuno-oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1292.