Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling.

BACKGROUND: There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery. METHODS: We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues. RESULTS: We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues. CONCLUSIONS: Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy.

The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.

Adaptive immune signature in HER2-positive breast cancer in NCCTG (Alliance) N9831 and NeoALTTO trials.

Trastuzumab acts in part through the adaptive immune system. Previous studies showed that enrichment of immune-related gene expression was associated with improved outcomes in HER2-positive (HER2+) breast cancer. However, the role of the immune system in response to lapatinib is not fully understood. Gene expression analysis was performed in 1,268 samples from the North Central Cancer Treatment Group (NCCTG) N9831 and 244 samples from the NeoALTTO trial. In N9831, enrichment of CD45 and immune-subset signatures were significantly associated with improved outcomes. We identified a novel 17-gene adaptive immune signature (AIS), which was found to be significantly associated with improved RFS among patients who received adjuvant trastuzumab (HR 0.66, 95% CI 0.49-0.90, Cox regression model p = 0.01) but not in patients who received chemotherapy alone (HR 0.96, 95% CI 0.67-1.40, Cox regression model p = 0.97). This result was validated in NeoALTTO. Overall, AIS-low patients had a significantly lower pathologic complete response (pCR) rate compared with AIS-high patients (χ2 p < 0.0001). Among patients who received trastuzumab alone, pCR was observed in 41.7% of AIS-high patients compared with 9.8% in AIS-low patients (OR of 6.61, 95% CI 2.09-25.59, logistic regression model p = 0.003). More importantly, AIS-low patients had a higher pCR rate with an addition of lapatinib (51.1% vs. 9.8%, OR 9.65, 95% CI 3.24-36.09, logistic regression model p < 0.001). AIS-low patients had poor outcomes, despite receiving adjuvant trastuzumab. However, these patients appear to benefit from an addition of lapatinib. Further studies are needed to validate the significance of this signature to identify patients who are more likely to benefit from dual anti-HER2 therapy. ClinicalTrials.gov Identifiers: NCT00005970 (NCCTG N9831) and NCT00553358 (NeoALTTO).

Differential Peripheral Blood Glycoprotein Profiles in Symptomatic and Asymptomatic COVID-19.

Glycosylation is the most common form of post-translational modification of proteins, critically affecting their structure and function. Using liquid chromatography and mass spectrometry for high-resolution site-specific quantification of glycopeptides coupled with high-throughput artificial intelligence-powered data processing, we analyzed differential protein glycoisoform distributions of 597 abundant serum glycopeptides and nonglycosylated peptides in 50 individuals who had been seriously ill with COVID-19 and in 22 individuals who had recovered after an asymptomatic course of COVID-19. As additional comparison reference phenotypes, we included 12 individuals with a history of infection with a common cold coronavirus, 16 patients with bacterial sepsis, and 15 healthy subjects without history of coronavirus exposure. We found statistically significant differences, at FDR < 0.05, for normalized abundances of 374 of the 597 peptides and glycopeptides interrogated between symptomatic and asymptomatic COVID-19 patients. Similar statistically significant differences were seen when comparing symptomatic COVID-19 patients to healthy controls (350 differentially abundant peptides and glycopeptides) and common cold coronavirus seropositive subjects (353 differentially abundant peptides and glycopeptides). Among healthy controls and sepsis patients, 326 peptides and glycopeptides were found to be differentially abundant, of which 277 overlapped with biomarkers that showed differential expression between symptomatic COVID-19 cases and healthy controls. Among symptomatic COVID-19 cases and sepsis patients, 101 glycopeptide and peptide biomarkers were found to be statistically significantly abundant. Using both supervised and unsupervised machine learning techniques, we found specific glycoprotein profiles to be strongly predictive of symptomatic COVID-19 infection. LASSO-regularized multivariable logistic regression and K-means clustering yielded accuracies of 100% in an independent test set and of 96% overall, respectively. Our findings are consistent with the interpretation that a majority of glycoprotein modifications observed which are shared among symptomatic COVID-19 and sepsis patients likely represent a generic consequence of a severe systemic immune and inflammatory state. However, there are glycoisoform changes that are specific and particular to severe COVID-19 infection. These may be representative of either COVID-19-specific consequences or susceptibility to or predisposition for a severe course of the disease. Our findings support the potential value of glycoproteomic biomarkers in the biomedical understanding and, potentially, the clinical management of serious acute infectious conditions.

Serum Glycoprotein Markers in Nonalcoholic Steatohepatitis and Hepatocellular Carcinoma.

Fatty liver disease progresses through stages of fat accumulation and inflammation to nonalcoholic steatohepatitis (NASH), fibrosis and cirrhosis, and eventually hepatocellular carcinoma (HCC). Currently available diagnostic tools for HCC lack sensitivity and specificity. In this study, we investigated the use of circulating serum glycoproteins to identify a panel of potential prognostic markers that may be indicative of progression from the healthy state to NASH and further to HCC. Serum samples were processed and analyzed using a novel high-throughput glycoproteomics platform. Our initial dataset contained healthy, NASH, and HCC serum samples. We analyzed 413 glycopeptides, representing 57 abundant serum proteins, and compared among the three phenotypes. We studied the normalized abundance of common glycoforms and found 40 glycopeptides with statistically significant differences in abundances in NASH and HCC compared to controls. Summary level relative abundances of core-fucosylated, sialylated, and branched glycans containing glycopeptides were higher in NASH and HCC as compared to controls. We replicated some of our findings in an independent set of samples of individuals with benign liver conditions and HCC. Our results may be of value in the management of liver diseases. Data generated in this work can be downloaded from MassIVE (https://massive.ucsd.edu) with identifier MSV000088809.

Novel plasma glycoprotein biomarkers predict progression-free survival in surgically resected clear cell renal cell carcinoma.

INTRODUCTION: Limited data exists on utilization of protein post-translational modifications as biomarkers for clear cell renal cell carcinoma (ccRCC). We employed high-throughput glycoproteomics to evaluate differential expression of glycoprotein-isoforms as novel markers for ccRCC progression-free survival (PFS). METHODS: Plasma samples were obtained from 77 patients treated surgically for ccRCC. Glycoproteomic analyses were carried out after liquid chromatography tandem mass spectrometry. Age-adjusted Cox proportional hazard models were constructed to evaluate PFS. Optimized Harrell's C-index was employed to dichotomize the collective for the construction of Kaplan-Meier curves. RESULTS: The average length of follow-up was 3.4 (range: 0.04-9.83) years. Glycoproteomic analysis identified 39 glycopeptides and 14 non-glycosylated peptides that showed statistically significant (false discovery rate P ≤ 0.05) differential expression associated with PFS. Five of the glycosylated peptides conferred continuous hazard ratio (HR) of > 6 (range 6.3-11.6). These included prothrombin A2G2S glycan motif (HR = 6.47, P = 9.53E-05), immunoglobulin J chain FA2G2S2 motif (HR = 10.69, P = 0.001), clusterin A2G2 motif (HR = 7.38, P = 0.002), complement component C8A A2G2S2 motif (HR = 11.59, P = 0.002), and apolipoprotein M glycopeptide with non-fucosylated and non-sialylated hybrid-type glycan (HR = 6.30, P = 0.003). Kaplan-Meier curves based on dichotomous expression of these five glycopeptides resulted in hazard ratios of 3.9 to 10.7, all with P-value < 0.03. Kaplan-Meyer plot using the multivariable model comprising 3 of the markers yielded HR of 11.96 (P < 0.0001). CONCLUSION: Differential glyco-isoform abundance of plasma proteins may be a useful source of biomarkers for the clinical course and prognosis of ccRCC.

Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer's disease.

Selective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer's disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

Latent trait modeling of tau neuropathology in progressive supranuclear palsy.

Progressive supranuclear palsy (PSP) is the second most common neurodegenerative Parkinsonian disorder after Parkinson's disease, and is characterized as a primary tauopathy. Leveraging the considerable clinical and neuropathologic heterogeneity associated with PSP, we measured tau neuropathology as quantitative traits to perform a genome-wide association study (GWAS) within PSP to identify genes and biological pathways that underlie the PSP disease process. In 882 PSP cases, semi-quantitative scores for phosphorylated tau-immunoreactive coiled bodies (CBs), neurofibrillary tangles (NFTs), tufted astrocytes (TAs), and tau threads were documented from 18 brain regions, and converted to latent trait (LT) variables using the R ltm package. LT analysis utilizes a multivariate regression model that links categorical responses to unobserved covariates allowing for a reduction of dimensionality, generating a single, continuous variable to account for the multiple lesions and brain regions assessed. We first tested for association with PSP LTs and the top PSP GWAS susceptibility loci. Significant SNP/LT associations were identified at rs242557 (MAPT H1c sub-haplotype) with hindbrain CBs and rs1768208 (MOBP) with forebrain tau threads. Digital microscopy was employed to quantify phosphorylated tau burden in midbrain tectum and red nucleus in 795 PSP cases and tau burdens were used as quantitative phenotypes in GWAS. Top associations were identified at rs1768208 with midbrain tectum and red nucleus tau burden. Additionally, we performed a PSP LT GWAS on an initial cohort, a follow-up SNP panel (37 SNPs, P < 10-5) in an extended cohort, and a combined analysis. Top SNP/LT associations were identified at SNPs in or near SPTBN5/EHD4, SEC13/ATP2B2, EPHB1/PPP2R3A, TBC1D8, IFNGR1/OLIG3, ST6GAL1, HK1, CALB1, and SGCZ. Finally, testing for SNP/transcript associations using whole transcriptome and whole genome data identified significant expression quantitative trait loci at rs3088159/SPTBN5/EHD4 and rs154239/GHRL. Modeling tau neuropathology heterogeneity using LTs as quantitative phenotypes in a GWAS may provide substantial insight into biological pathways involved in PSP by affecting regional tau burden.

Validation of the NSABP/NRG Oncology 8-Gene Trastuzumab-benefit Signature in Alliance/NCCTG N9831.

Our objective was to validate the NSABP 8-gene trastuzumab-benefit signature, developed and initially validated in NRG Oncology/NSABP B-31 in Alliance/NCCTG N9831. The B-31 and N9831 trials demonstrated the benefit of adding trastuzumab to chemotherapy in the adjuvant setting for HER2+ breast cancer patients. NSABP investigators utilized gene expression profiles of N9831 patients (N = 892) to blindly assign patients to large-, moderate-, or no-trastuzumab benefit groups and then NCCTG investigators assessed the degree of trastuzumab benefit using Cox models adjusted for age, nodes, estrogen receptor/progesterone receptor status, tumor size, and grade. Hazard ratios and 2-sided P values for recurrence-free survival of the predicted large- (n = 387), moderate- (n = 401), and no-benefit (n = 104) groups, based on the 8-gene signature were 0.47 (95% CI = 0.31 to 0.73, P < .001), 0.60 (95% CI = 0.39 to 0.92, P = .02), and 1.54 (95% CI = 0.59 to 4.02, P = .38), respectively (P interaction  = .02), providing validation of the 8-gene signature in an independent study.

8q24 clear cell renal cell carcinoma germline variant is associated with VHL mutation status and clinical aggressiveness.

BACKGROUND: The four most commonly-mutated genes in clear cell renal cell carcinoma (ccRCC) tumors are BAP1, PBRM1, SETD2 and VHL. And, there are currently 14 known RCC germline variants that have been reproducibly shown to be associated with RCC risk. However, the association of germline genetics with tumor genetics and clinical aggressiveness are unknown. METHODS: We analyzed 420 ccRCC patients from The Cancer Genome Atlas. Molecular subtype was determined based on acquired mutations in BAP1, PBRM1, SETD2 and VHL. Aggressive subtype was defined clinically using Mayo SSIGN score and molecularly using the ccA/ccB gene expression subtype. Publically-available Hi-C data were used to link germline risk variants with candidate target genes. RESULTS: The 8q24 variant rs35252396 was significantly associated with VHL mutation status (OR = 1.6, p = 0.0037) and SSIGN score (OR = 1.9, p = 0.00094), after adjusting for multiple comparisons. We observed that, while some germline variants have interactions with nearby genes, some variants demonstrate long-range interactions with target genes. CONCLUSIONS: These data further demonstrate the link between rs35252396, HIF pathway and ccRCC clinical aggressiveness, providing a more comprehensive picture of how germline genetics and tumor genetics interact with respect to tumor development and progression.

Association of Genetic Variants at TRPC6 With Chemotherapy-Related Heart Failure.

Background: Our previous GWAS identified genetic variants at six novel loci that were associated with a decline in left ventricular ejection fraction (LVEF), p < 1 × 10-5 in 1,191 early breast cancer patients from the N9831 clinical trial of chemotherapy plus trastuzumab. In this study we sought replication of these loci. Methods: We tested the top loci from the GWAS for association with chemotherapy-related heart failure (CRHF) using 26 CRHF cases from N9831 and 984 patients from the Mayo Clinic Biobank which included CRHF cases (N = 12) and control groups of patients treated with anthracycline +/- trastuzumab without HF (N = 282) and patients with HF that were never treated with anthracycline or trastuzumab (N = 690). We further examined associated loci in the context of gene expression and rare coding variants using a TWAS approach in heart left ventricle and Sanger sequencing, respectively. Doxorubicin-induced apoptosis and cardiomyopathy was modeled in human iPSC-derived cardiomyocytes and endothelial cells and a mouse model, respectively, that were pre-treated with GsMTx-4, an inhibitor of TRPC6. Results: TRPC6 5' flanking variant rs57242572-T was significantly more frequent in cases compared to controls, p = 0.031, and rs61918162-T showed a trend for association, p = 0.065. The rs61918162 T-allele was associated with higher TRPC6 expression in the heart left ventricle. We identified a single TRPC6 rare missense variant (rs767086724, N338S, prevalence 0.0025% in GnomAD) in one of 38 patients (2.6%) with CRHF. Pre-treatment of cardiomyocytes and endothelial cells with GsMTx4 significantly reduced doxorubicin-induced apoptosis. Similarly, mice treated with GsMTx4 had significantly improved doxorubicin-induced cardiac dysfunction. Conclusions: Genetic variants that are associated with increased TRPC6 expression in the heart and rare TRPC6 missense variants may be clinically useful as risk factors for CRHF. GsMTx-4 may be a cardioprotective agent in patients with TRPC6 risk variants. Replication of the genetic associations in larger well-characterized samples and functional studies are required.

PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring.

Mass spectrometry (MS) based proteomics has become an indispensable component of modern molecular and cellular biochemistry analysis. Multiple reaction monitoring (MRM) is one of the most well-established MS techniques for molecule detection and quantification. Despite its wide usage, there lacks an accurate computational framework to analyze MRM data, and expert annotation is often required, especially to perform peak integration. Here we propose a deep learning method PB-Net (Peak Boundary Neural Network), built upon recent advances in sequential neural networks, for fully automatic chromatographic peak integration. To train PB-Net, we generated a large dataset of over 170,000 expert annotated peaks from MS transitions spanning a wide dynamic range, including both peptides and intact glycopeptides. Our model demonstrated outstanding performances on unseen test samples, reaching near-perfect agreement (Pearson's r 0.997) with human annotated ground truth. Systematic evaluations also show that PB-Net is substantially more robust and accurate compared to previous state-of-the-art peak integration software. PB-Net can benefit the wide community of mass spectrometry data analysis, especially in applications involving high-throughput MS experiments. Codes and test data used in this work are available at https://github.com/miaecle/PB-net. SIGNIFICANCE: Human annotations serve an important role in accurate quantification of multiple reaction monitoring (MRM) experiments, though they are costly to collect and limit analysis throughput. In this work we proposed and developed a novel technique for the peak-integration step in MRM, based on recent innovations in sequential deep learning models. We collected in total 170,000 expert-annotated MRM peaks and trained a set of accurate and robust neural networks for the task. Results demonstrated a substantial improvement over the current state-of-the-art software for mass spectrometry analysis and comparable level of accuracy and precision as human annotators.

Concordance of PD-1 and PD-L1 (B7-H1) in paired primary and metastatic clear cell renal cell carcinoma.

OBJECTIVES: Previous studies noted discordance of programmed death-1 (PD-1) and one of its ligands (PD-L1) across patient-matched primary and metastatic clear cell renal cell carcinoma (ccRCC). There are inconsistencies if the primary or metastatic tumor has higher expression, and whether metastatic tumor expression is associated with patient outcome. Thus, we examined PD-1 and PD-L1 in patient-matched tumors using a large number of ccRCC patients with long follow-up. MATERIALS AND METHODS: We analyzed PD-1 and PD-L1 using immunohistochemistry in patient-matched primary and metastatic tumors from 110 ccRCC patients. Concordance was assessed among longitudinal metastatic tumors, as well as across patient-matched primary and metastatic tumors. Cox proportional hazards regression was used to evaluate the associations of metastatic tumor expression with cancer-specific survival. RESULTS: We observed inter-metastatic tumor heterogeneity of PD-1 in 25 (69%) of the 36 patients and of PD-L1 in seven (19%) patients. Concordance between patient-matched primary and metastatic tumors was 73% (Kappa = 0.16, 95% CI: -0.003-0.32). Similarly, concordance of PD-L1 between metastatic and patient-matched primary tumors was 78% (Kappa = 0.27, 95% CI: 0.09-0.46). Both markers demonstrated higher expression in primary vs metastatic tumors. Metastatic tumor expression of PD-1 was significantly associated with metastatic location (P < .0001) and ccRCC-specific survival (HR = 2.15, 95% CI: 1.06-4.36, P = .035). CONCLUSIONS: The expression of PD-1 and PD-L1 is discordant across patient-matched ccRCC tumors, with higher expression in primary tumors. Higher PD-1 expression was associated with metastatic location and lower cancer-specific survival. If validated, these results highlight the importance of evaluating these biomarkers in metastatic tissue specifically.

Incidence of Late Relapses in Patients With HER2-Positive Breast Cancer Receiving Adjuvant Trastuzumab: Combined Analysis of NCCTG N9831 (Alliance) and NRG Oncology/NSABP B-31.

PURPOSE: Recent trials have shown potential benefit of extended adjuvant endocrine therapy and relatively high risk of recurrence (RoR) after 5 years in hormone receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer. Although risk of late relapse in HR+ HER2- breast cancer is fairly well defined, the risk in HER2-positive (HER2+) breast cancer treated with adjuvant trastuzumab-based chemotherapy remains largely unknown. METHODS: We included 3,177 patients with HER2+ breast cancer treated with adjuvant chemotherapy alone or with trastuzumab from the North Central Cancer Treatment Group N9831 (ClinicalTrials.gov identifier: NCT00005970) and National Surgical Adjuvant Breast and Bowel Project B-31 (ClinicalTrials.gov identifier: NCT00004067) trials. RESULTS: Overall, HR+ breast cancer was significantly associated with improved recurrence-free survival (RFS) during the first 5 years (hazard ratio, 0.65; 95% CI, 0.56 to 0.77; P < .001). Among patients treated with trastuzumab, cumulative hazard for RFS was lower in patients with HR+ HER2+ breast cancer during the first 5 years (10.96% v 17.48%; hazard ratio, 0.60; 95% CI, 0.45 to 0.79; P < .001). However, there was no significant difference in RFS based on HR status during years 5 to 10 (hazard ratio, 1.32; 95% CI, 0.93 to 1.88; P = .12). A comparable degree of trastuzumab benefit was observed in HR+ and HR- breast cancers ( P for interaction = .87). Furthermore, we observed low RoR in years 5 to 10 among patients with HR+ HER2+ breast cancer: 3.23% in patients without lymph node involvement (N0) and 6.39% in patients with involvement of one to three lymph nodes (N1). CONCLUSION: The benefit of adjuvant trastuzumab persists for a long time. A distinct pattern of recurrence was observed between HR+ and HR- HER2+ disease but with similar degree of benefit from adjuvant trastuzumab. RoR in years 5 to 10 in HR+ HER2+ breast cancer is low, particularly in patients with N0 or N1 disease.

Site-Specific Glycosylation Quantitation of 50 Serum Glycoproteins Enhanced by Predictive Glycopeptidomics for Improved Disease Biomarker Discovery.

Analysis of serum protein glycovariants has the potential to identify new biomarkers of human disease. However, the inability to rapidly quantify glycans in a site-specific fashion remains the major barrier to applying such biomarkers clinically. Advancements in sample preparation and glycopeptide quantification are thus needed to better bridge glycoscience with biomarker discovery research. We present here the successful utilization of several sample preparation techniques, including multienzyme digestion and glycopeptide enrichment, to increase the repertoire of glycopeptides that can be generated from serum glycoproteins. These techniques combined with glycopeptide retention time prediction and UHPLC-QqQ conditions optimization were then used to develop a dynamic multiple-reaction monitoring (dMRM)-based strategy to simultaneously monitor over 100 glycosylation sites across 50 serum glycoproteins. In total, the abundances of over 600 glycopeptides were simultaneously monitored, some of which were identified by utilizing theoretically predicted ion products and presumed m/ z values. The dMRM method was found to have good sensitivity. In the targeted dMRM mode, the limit of quantitation (LOQ) of nine standard glycoproteins reached femtomole levels with dynamic ranges spanning 3-4 orders of magnitude. The dMRM-based strategy also showed high reproducibility with regards to both instrument and sample preparation performance. The high coverage of the serum glycoproteins that can be quantitated to the glycopeptide level makes this method especially suitable for the biomarker discovery from large sample sets. We predict that, in the near future, biomarkers, such as these, will be deployed clinically, especially in the fields of cancer and autoimmunity.

Effects of Age and Immune Landscape on Outcome in HER2-Positive Breast Cancer in the NCCTG N9831 (Alliance) and NSABP B-31 (NRG) Trials.

PURPOSE: Young age has been shown to be an independent predictor of poor outcome in breast cancer. In HER2-positive breast cancer, the effects of aging remain largely unknown. EXPERIMENTAL DESIGN: A total of 4,547 patients were included [3,132 from North Central Cancer Treatment Group (NCCTG) N9831 and 1,415 from National Surgical Adjuvant Breast and Bowel Project (NSABP) B-31]. Pathologic stromal tumor-infiltrating lymphocyte (sTIL) and molecular tumor infiltrating lymphocyte (mTIL) signatures were evaluated. RESULTS: In NCCTG N9831, comparable benefit of trastuzumab was observed in all patients [age ≤ 40; HR, 0.43; 95% confidence interval (CI), 0.28-0.66; P < 0.001; and age > 40; HR, 0.56; 95% CI, 0.45-0.69; P < 0.001]. Similar results were observed in NSABP B-31 (age ≤ 40; HR, 0.45; 95% CI, 0.29-0.68; P < 0.001; and age > 40; HR, 0.42; 95% CI, 0.33-0.54; P < 0.001). Among patients who received chemotherapy alone, younger age was associated with poor outcome in the hormone receptor-positive subset, but not the hormone receptor-negative subset, in both trials. Although there was no association between sTILs and age, a small, but significant increase in mTIL CD45 and some immune subset signatures were observed. Among patients who received chemotherapy alone, patients over 40 years of age with lymphocyte-predominant breast cancer had excellent outcome, with 95% remaining recurrence free at 15 years. CONCLUSIONS: Among patients treated with trastuzumab, there was no significant difference in outcome related to age. Our study suggests that trastuzumab can negate the poor prognosis associated with young age.

Divergent brain gene expression patterns associate with distinct cell-specific tau neuropathology traits in progressive supranuclear palsy.

Progressive supranuclear palsy (PSP) is a neurodegenerative parkinsonian disorder characterized by tau pathology in neurons and glial cells. Transcriptional regulation has been implicated as a potential mechanism in conferring disease risk and neuropathology for some PSP genetic risk variants. However, the role of transcriptional changes as potential drivers of distinct cell-specific tau lesions has not been explored. In this study, we integrated brain gene expression measurements, quantitative neuropathology traits and genome-wide genotypes from 268 autopsy-confirmed PSP patients to identify transcriptional associations with unique cell-specific tau pathologies. We provide individual transcript and transcriptional network associations for quantitative oligodendroglial (coiled bodies = CB), neuronal (neurofibrillary tangles = NFT), astrocytic (tufted astrocytes = TA) tau pathology, and tau threads and genomic annotations of these findings. We identified divergent patterns of transcriptional associations for the distinct tau lesions, with the neuronal and astrocytic neuropathologies being the most different. We determined that NFT are positively associated with a brain co-expression network enriched for synaptic and PSP candidate risk genes, whereas TA are positively associated with a microglial gene-enriched immune network. In contrast, TA is negatively associated with synaptic and NFT with immune system transcripts. Our findings have implications for the diverse molecular mechanisms that underlie cell-specific vulnerability and disease risk in PSP.

Chromoanasynthesis is a common mechanism that leads to ERBB2 amplifications in a cohort of early stage HER2+ breast cancer samples.

BACKGROUND: HER2 positive (HER2+) breast cancers involve chromosomal structural alterations that act as oncogenic driver events. METHODS: We interrogated the genomic structure of 18 clinically-defined HER2+ breast tumors through integrated analysis of whole genome and transcriptome sequencing, coupled with clinical information. RESULTS: ERBB2 overexpression in 15 of these tumors was associated with ERBB2 amplification due to chromoanasynthesis with six of them containing single events and the other nine exhibiting multiple events. Two of the more complex cases had adverse clinical outcomes. Chromosomes 8 was commonly involved in the same chromoanasynthesis with 17. In ten cases where chromosome 8 was involved we observed NRG1 fusions (two cases), NRG1 amplification (one case), FGFR1 amplification and ADAM32 or ADAM5 fusions. ERBB3 over-expression was associated with NRG1 fusions and EGFR and ERBB3 expressions were anti-correlated. Of the remaining three cases, one had a small duplication fully encompassing ERBB2 and was accompanied with a pathogenic mutation. CONCLUSION: Chromoanasynthesis involving chromosome 17 can lead to ERBB2 amplifications in HER2+ breast cancer. However, additional large genomic alterations contribute to a high level of genomic complexity, generating the hypothesis that worse outcome could be associated with multiple chromoanasynthetic events.

Replication of progressive supranuclear palsy genome-wide association study identifies SLCO1A2 and DUSP10 as new susceptibility loci.

BACKGROUND: Progressive supranuclear palsy (PSP) is a parkinsonian neurodegenerative tauopathy affecting brain regions involved in motor function, including the basal ganglia, diencephalon and brainstem. While PSP is largely considered to be a sporadic disorder, cases with suspected familial inheritance have been identified and the common MAPT H1haplotype is a major genetic risk factor. Due to the relatively low prevalence of PSP, large sample sizes can be difficult to achieve, and this has limited the ability to detect true genetic risk factors at the genome-wide statistical threshold for significance in GWAS data. With this in mind, in this study we genotyped the genetic variants that displayed the strongest degree of association with PSP (P<1E-4) in the previous GWAS in a new cohort of 533 pathologically-confirmed PSP cases and 1172 controls, and performed a combined analysis with the previous GWAS data. RESULTS: Our findings validate the known association of loci at MAPT, MOBP, EIF2AK3 and STX6 with risk of PSP, and uncover novel associations with SLCO1A2 (rs11568563) and DUSP10 (rs6687758) variants, both of which were classified as non-significant in the original GWAS. CONCLUSIONS: Resolving the genetic architecture of PSP will provide mechanistic insights and nominate candidate genes and pathways for future therapeutic intervention strategies.

Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes.

Anthracycline-induced cardiotoxicity (ACT) is a key limiting factor in setting optimal chemotherapy regimes, with almost half of patients expected to develop congestive heart failure given high doses. However, the genetic basis of sensitivity to anthracyclines remains unclear. We created a panel of iPSC-derived cardiomyocytes from 45 individuals and performed RNA-seq after 24 hr exposure to varying doxorubicin dosages. The transcriptomic response is substantial: the majority of genes are differentially expressed and over 6000 genes show evidence of differential splicing, the later driven by reduced splicing fidelity in the presence of doxorubicin. We show that inter-individual variation in transcriptional response is predictive of in vitro cell damage, which in turn is associated with in vivo ACT risk. We detect 447 response-expression quantitative trait loci (QTLs) and 42 response-splicing QTLs, which are enriched in lower ACT GWAS [Formula: see text]-values, supporting the in vivo relevance of our map of genetic regulation of cellular response to anthracyclines.