Contribution of Rare Genetic Variation to Disease Susceptibility in a Large Scandinavian Myositis Cohort

OBJECTIVE

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of complex autoimmune conditions characterized by inflammation in skeletal muscle and extramuscular compartments, and interferon (IFN) system activation. We undertook this study to examine the contribution of genetic variation to disease susceptibility and to identify novel avenues for research in IIMs.

METHODS

Targeted DNA sequencing was used to mine coding and potentially regulatory single nucleotide variants from ~1,900 immune-related genes in a Scandinavian case-control cohort of 454 IIM patients and 1,024 healthy controls. Gene-based aggregate testing, together with rare variant- and gene-level enrichment analyses, was implemented to explore genotype-phenotype relations.

RESULTS

Gene-based aggregate tests of all variants, including rare variants, identified IFI35 as a potential genetic risk locus for IIMs, suggesting a genetic signature of type I IFN pathway activation. Functional annotation of the IFI35 locus highlighted a regulatory network linked to the skeletal muscle-specific gene PTGES3L, as a potential candidate for IIM pathogenesis. Aggregate genetic associations with AGER and PSMB8 in the major histocompatibility complex locus were detected in the antisynthetase syndrome subgroup, which also showed a less marked genetic signature of the type I IFN pathway. Enrichment analyses indicated a burden of synonymous and noncoding rare variants in IIM patients, suggesting increased disease predisposition associated with these classes of rare variants.

CONCLUSION

Our study suggests the contribution of rare genetic variation to disease susceptibility in IIM and specific patient subgroups, and pinpoints genetic associations consistent with previous findings by gene expression profiling. These features highlight genetic profiles that are potentially relevant to disease pathogenesis.

Identification and Functional Characterization of a Novel Susceptibility Locus for Small Vessel Vasculitis with MPO-ANCA

OBJECTIVE

To identify and characterize genetic loci associated with the risk of developing anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV).

METHODS

Genetic association analyses were performed after Illumina sequencing of 1,853 genes and subsequent replication with genotyping of selected SNPs in a total cohort of 1110 Scandinavian cases with granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA) and 1589 controls. A novel AAV-associated SNP was analysed for allele-specific effects on gene expression using luciferase reporter assay.

RESULTS

Proteinase 3 ANCA positive (PR3-ANCA+) AAV was significantly associated with two independent loci in the HLA-DPB1/A1 region (rs1042335, p= 6.3 x 1 0 -61, Odds ratio (OR)= 0.10; rs9277341, p= 1.5 x 1 0 -44, OR = 0.22) and with rs28929474 in the SERPINA1 gene (p= 2.7 x 1 0 -10, OR = 2.9). Myeloperoxidase (MPO)-ANCA+ AAV was significantly associated with the HLA-DQB1/HLA-DQA2 locus (rs9274619, p= 5.4 x 1 0 -25, OR = 3.7) and with a rare variant in the BACH2 gene (rs78275221, p= 7.9 x 1 0 -7, OR = 3.0), the latter a novel susceptibility locus for MPO-ANCA+ GPA/MPA. The rs78275221-A risk allele reduced luciferase gene expression in endothelial cells, specifically, as compared with the non-risk allele.

CONCLUSION

We identified a novel susceptibility locus for MPO-ANCA+ AAV and propose that the associated variant is of mechanistic importance, exerting a regulatory function on gene expression in specific cell types.

Association of Protective HLA-A With HLA-B∗27 Positive Ankylosing Spondylitis

Objectives

To further elucidate the role of the MHC in ankylosing spondylitis by typing 17 genes, searching for HLA-B^∗27 independent associations and assessing the impact of sex on this male biased disease.

Methods

High-confidence two-field resolution genotyping was performed on 310 cases and 2196 controls using an n-1 concordance method. Protein-coding variants were called from next-generation sequencing reads using up to four software programs and the consensus result recorded. Logistic regression tests were applied to the dataset as a whole, and also in stratified sets based on sex or HLA-B^∗27 status. The amino acids driving association were also examined.

Results

Twenty-five HLA protein-coding variants were significantly associated to disease in the population. Three novel protective associations were found in a HLA-B^∗27 positive population, HLA-A^∗24:02 (OR = 0.4, CI = 0.2–0.7), and HLA-A amino acids Leu95 and Gln156. We identified a key set of seven loci that were common to both sexes, and robust to change in sample size. Stratifying by sex uncovered three novel risk variants restricted to the female population (HLA-DQA1^∗04.01, -DQB1^∗04:02, -DRB1^∗08:01; OR = 2.4–3.1). We also uncovered a set of neutral variants in the female population, which in turn conferred strong effects in the male set, highlighting how population composition can lead to the masking of true associations.

Conclusion

Population stratification allowed for a nuanced investigation into the tightly linked MHC region, revealing novel HLA-B^∗27 signals as well as replicating previous HLA-B^∗27 dependent results. This dissection of signals may help to elucidate sex biased disease predisposition and clinical progression.

Molecular pathways in patients with systemic lupus erythematosus revealed by gene-centred DNA sequencing

Objectives

Systemic lupus erythematosus (SLE) is an autoimmune disease with extensive heterogeneity in disease presentation between patients, which is likely due to an underlying molecular diversity. Here, we aimed at elucidating the genetic aetiology of SLE from the immunity pathway level to the single variant level, and stratify patients with SLE into distinguishable molecular subgroups, which could inform treatment choices in SLE.

Methods

We undertook a pathway-centred approach, using sequencing of immunological pathway genes. Altogether 1832 candidate genes were analysed in 958 Swedish patients with SLE and 1026 healthy individuals. Aggregate and single variant association testing was performed, and we generated pathway polygenic risk scores (PRS).

Results

We identified two main independent pathways involved in SLE susceptibility: T lymphocyte differentiation and innate immunity, characterised by HLA and interferon, respectively. Pathway PRS defined pathways in individual patients, who on average were positive for seven pathways. We found that SLE organ damage was more pronounced in patients positive for the T or B cell receptor signalling pathways. Further, pathway PRS-based clustering allowed stratification of patients into four groups with different risk score profiles. Studying sets of genes with priors for involvement in SLE, we observed an aggregate common variant contribution to SLE at genes previously reported for monogenic SLE as well as at interferonopathy genes.

Conclusions

Our results show that pathway risk scores have the potential to stratify patients with SLE beyond clinical manifestations into molecular subsets, which may have implications for clinical follow-up and therapy selection.

The use of plasma proteomic markers to understand the biology of immunotherapy response

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Background: Despite recent successes with immune checkpoint blockade (ICB) in melanoma, the prognosis for most patients remains dire. Whereas small fraction of patients are able to achieve disease control, most do not respond or are limited by immune-related toxicities. Robust non-invasive predictors of ICB response have the potential to guide clinical decision and alter management of patients, however, no such predictors currently exist. Methods: We applied a highly-multiplex Proximity Extension Assay to simultaneously detect > 1000 proteins in the plasma of anti-PD-1 treated melanoma patients. Our cohort comprised 116 patients, 66 responders (R) and 50 non-responders (NR). Additional 65 patients comprised a validation cohort with 30 R and 35 NR, and included 50 patients who developed treatment-related toxicities. Plasma samples were collected at baseline, 6-weeks and 6-months after starting the treatment. A subset of patients had single-cell RNA-seq performed on tumor tissue. Group differences and treatment effects were evaluated by linear model with maximum likelihood estimation for model parameters and Benjamini and Hochberg multiple hypothesis correction. Results: At baseline, 6 significantly differentially expressed (DE) proteins were identified between R and NR. Elevated expression of ST2 and IL-6, two key immunoregulatory proteins were found in NR. At 6-weeks, more dynamic changes occurred and 79 significantly DE proteins were identified between R and NR, including proteins implicated in primary or acquired resistance as IL-8, MIA, TNFR1 and potential novel targets as MCP-4/CCL13, ICOSLG and VEGF. Proteomic changes identified at baseline and 6-weeks were more profound at 6-months, and moreover 238 proteins were confirmed significant between R and NR. Importantly, we were able to leverage these differences to build classifiers of R and NR subsets. We compared mRNA expression of DE proteins within the tumor microenvironment by leveraging scRNAseq data from a subset of these patients. Enriched expression of these genes was uncovered in certain myeloid and exhausted T cell subsets, thus shedding insight into the potential role of these cell subsets in ICB response. Conclusions: Plasma proteomic profiling of anti-PD1 treated patients identified important tumor and immune changes associated with response. Non-invasive means discovery of circulatory protein biomarkers may predict sensitivity to immunotherapy and uncover biological insights underlying primary resistance.

Abstract 4533: Plasma and exosome proteomic profiling for prediction of immunotherapy response and toxicity

Immune checkpoint blockade (ICB) has revolutionized the treatment of many solid tumors, including metastatic melanoma. Despite recent successes, many patients fail to respond or are overcome by severe toxicities that limit further treatment. To date, there are no non-invasive predictors of response and toxicity that can guide treatment decisions. In this work, we perform whole plasma and exosome proteomic profiling to construct a predictive model of immunotherapy response and toxicity, and to glean further biologic insight into the mechanisms underlying resistance to ICB. Whole plasma was analyzed in a cohort of 150 melanoma patients receiving anti-PD1 antibodies (MGH IRB #11-181) at baseline, and on-treatment at 6 week and 6 month time-points. Exosomes were analyzed in 15 of these patients for all time-points. Proteomic analysis was performed using a multiplex proximity extension assay that enabled detection of more than 1000 proteins simultaneously. A linear mixed model with maximum likelihood estimation for model parameters was used to analyze differences between patient groups, and significant differences were determined after Benjamini and Hochberg multiple hypothesis correction. Between plasma baseline and on-treatment time-points, 67 differentially expressed proteins were identified including markers of inflammation such as PD1, CXCL9, CXCL10, CXCL11, IL10, CCL3 and TNFR2. Exosome samples had a distinct protein signature over the treatment period compared to plasma, including differential expression of CXCL16, CCL18, CCL20, and IL6, among others. 41 proteins were differentially expressed in plasma between ICB responders and non-responders including several inflammatory proteins such as CD28, TNFb, MCSFRa and IL8, and others implicated in melanoma resistance, such as MIA and ERBB2. Again, exosome samples had a distinct protein signature between responders and non-responders compared to plasma samples, consisting of CXCL9, CXCL13, CXCL16, CCL19, CD8a, GZMA and CD5 expression. Whereas plasma proteins reflected a myeloid signature, exosome proteins reflected a lymphoid signature, suggesting that the two compartments may capture elements of different immune processes. Integrating data from both plasma and exosome proteomics, we applied machine learning tools to build a predictor of response. Further analysis to look for predictors of toxicity is also currently underway. Overall, our work suggests that plasma and exosome protein signatures are distinct and may reflect unique immunological processes. Proteomic analysis of these compartments may be an effective way for non-invasive liquid biopsy to predict ICB response.

Citation Format: Arnav Mehta, Gyulnara Kasumova, Alvin Shi, Marijana Rucevic, Markus Sallman-Almen, Lina Hultin Rosenberg, Emmett Sprecher, Jacqueline Ohmura, Michelle Kim, David Lieb, Xue Bai, Dennie T. Frederick, Manolis Kellis, Ryan J. Sullivan, Keith T. Flaherty, Nir Hacohen, Genevieve M. Boland. Plasma and exosome proteomic profiling for prediction of immunotherapy response and toxicity [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 4533.

Liquid biopsy using plasma proteomic profiling to reveal predictors of immunotherapy response

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Background: The response of metastatic melanoma to anti-PD1 is heterogeneous. We performed proteomic profiling of patient plasma samples to build a predictor of immunotherapy response and uncover biological insights underlying primary resistance. Methods: 58 metastatic melanoma patients receiving anti-PD1 (Pembrolizumab or Nivolumab) at MGH comprised the initial cohort, and 150 additional patients comprised a validation cohort. Plasma samples were collected (MGH IRB #11-181) at baseline and several on-treatment time-points. Samples were analyzed for 1102 proteins by a multiplex proximity extension assay (Olink Proteomics). A subset of patients had single-cell RNA-seq (Smart-Seq2 protocol) performed on tumor tissue. Group differences and treatment effects were evaluated using linear mixed models with maximum likelihood estimation for model parameters, and Benjamini and Hochberg multiple hypothesis correction. Results: 70 significantly differentially expressed (DE) proteins were identified across the treatment period, including markers of immune activation (PD1, CXCL9, CXCL10, CD25, IL-17a, among others). 38 significantly DE proteins were identified with on-treatment time points between anti-PD1 responders (R) and non-responders (NR), including several implicated in primary or acquired resistance (IL8, MIA, ERBB2, among others). Importantly, we demonstrate the relationship of these serum biomarkers to overall and progression-free survival, and employed statistical learning approaches to build classifiers of treatment response, leveraging early and late on-treatment time points. Analysis of single-cell RNA-seq data (Sade-Feldman et al, Cell, 2018) of tumor tissue from a subset of these patients revealed that gene expression of most proteins predictive of response were enriched among tumor myeloid cells, with the remainder of proteins being reflective of exhausted T cell states. Conclusions: Whole plasma proteomic profiling of anti-PD1 treated patients revealed DE proteins between R and NR that may enable a liquid biopsy to predict anti-PD1 response. These results unveil a putative role of myeloid cells within the tumor microenvironment in anti-PD1 response or primary resistance.

Multivariate meta-analysis of proteomics data from human prostate and colon tumours

Background

There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work.

Results

We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples.

Conclusions

The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.

Association of heterotopic gray matter with seizures: MR imaging. Work in progress

Heterotopic gray matter, which previously had been associated with severe congenital malformations of the brain and developmental delay, was found without these associated conditions. The authors found ten cases of heterotopic gray matter on magnetic resonance (MR) images. The lesions had a signal intensity that was isointense compared with that of gray matter on T1, spin-density, and T2-weighted images. Nine of the ten cases were associated with a seizure disorder. The tenth case, discovered during a workup for metastatic lung disease, was confirmed with pathologic studies. Heterotopic gray matter is the presence of cortical neurons in an abnormal location, which may be periventricular (nodular) or within the white matter (laminar). A knowledge of heterotopic gray matter and its association with seizures may prevent the misinterpretation of findings on MR images.