Discovery Sample Set Research Articles

Abstract LB122: Arima-HiC sequencing accurately detects clinically-relevant structural variants in pediatric leukemia samples

Abstract Genetic structural variants (SVs), especially those leading to gene fusions, are well-known oncogenic drivers. These SVs can produce overexpression or loss-of-function of certain genes, or generate chimeric fusion proteins. Thus, they serve as important disease biomarkers across several cancers and can also represent therapeutically targetable alterations. Detecting these SVs at a gene-level resolution can be challenging with lower resolution karyotyping approaches, or even RNA sequencing approaches, due to difficulties with culture, sample stability, low transcript abundance, or low-quality RNA. In addition, the ability of fluorescence in situ hybridization (FISH) techniques to detect gene fusions is limited by the targeted nature of these assays as they depend heavily on the design and selection of each probe. Here we use a novel method, Arima-HiC sequencing, that utilizes DNA to evaluate 12 pediatric leukemia samples and determine this assay’s effectiveness in detecting clinically-relevant SVs. We first selected 5 archived (cryopreserved) pediatric acute myeloid leukemia (AML) samples (archival period range: 1-4 years) known to be either fusion-positive (n=3) or fusion-negative (n=2) via prior clinical genetic testing (i.e., chromosomes, FISH, and/or microarray). All samples underwent Arima-HiC sequencing. Briefly, chromatin digestion, end-labeling, and proximity ligation were performed prior to DNA purification per the Arima-HiC protocol. Purified DNA was next prepared as a short-read sequencing library and sequenced on a HiSeq X. The raw reads were aligned and deduplicated, and SVs were called using HiC-Breakfinder software. For a discovery set, we then additionally selected 7 pediatric leukemia samples—6 precursor B-cell acute lymphoblastic leukemias (ALL) and 1 AML—for Arima-HiC sequencing (as above). These cases had undergone standard-of-care cytogenetic (karyotyping, FISH, microarray) and/or molecular (targeted cancer NGS sequencing panel) testing clinically, and a genetic driver/known gene fusion had not been identified. Using Arima-HiC sequencing, we identified the clinically-relevant SV in each of our 3 fusion-positive AML cases, consistent with the original diagnostic cytogenetic finding (n= 1 RUNX1-RUNX1T1 fusion, 1 CBFB-MYH11 fusion, and 1 CBFA2T3-GLIS2 fusion). The 2 fusion-negative AML cases were also negative for structural gene fusions by HiC data. In our discovery sample set, Arima-HiC sequencing was able to find clinically-relevant SVs that were not previously detected in 3 of 6 samples: a KMT2A-MLLT10 fusion was found in an AML case, and a ZNF384-EP300 fusion was found in each of 2 ALL cases. A ABHD17B-PTK2B fusion was found in another ALL case, although ABHD17B may be a novel partner PTK2B and is undergoing validation. Rearrangements involving KRAS and EGFR were detected in the final two ALL cases, and are also undergoing validation. Overall, this study demonstrates how Arima-HiC sequencing can provide diagnostic value in pediatric leukemia specimens via the identification of clinically relevant SVs. Citation Format: Anthony Schmitt, Shadi Melnyk, Kristin Sikkink, Lisa Lansdon, Tomi Pastinen, Erin Guest, Midhat Farooqi. Arima-HiC sequencing accurately detects clinically-relevant structural variants in pediatric leukemia samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB122.

Profiling of serum oxylipins identifies distinct spectrums and potential biomarkers in young people with very early onset gout.

Oxylipins modulate inflammation via complex pathways. The oxylipin profile in gout remains unexplored. In this study, we systemically profiled oxylipins in young men and identified new oxylipin biomarkers for clinical use in differentiating gout from hyperuricaemia. Oxylipin profiling was performed in 90 men (30 very early onset gout, 30 asymptomatic hyperuricaemia [HU] and 30 normouricaemia [NU], all aged <20 years) divided into discovery and validation sample sets. The dataset was analysed based on orthogonal projection to latent structure-discriminant analysis. Correlation network and pathway enrichment were conducted to reveal potential oxylipin-involved pathways of gout. Candidate oxylipins were further evaluated and optimized in the validation cohort, and differential oxylipin biomarkers combined with or without serum urate were applied to construct diagnostic models. In discovery stage, 21 differential oxylipins in the gout vs HU comparisons and 14 differential oxylipins in the gout vs NU comparisons were discovered. Correlation network analysis was performed and 14(S)-HDHA (14S-hydroxy-4Z,7Z,10Z,12E,16Z,19Z-docosahexaenoic acid) was identified as a hub metabolite in both comparisons. Seven down-regulated oxylipins in the gout vs HU group and five down-regulated oxylipins in the gout vs NU group were validated. Diagnostic models were constructed with the above oxylipins, with 14(S)-HDHA alone having an area under the curve of 1 (95% CI, 1, 1) in both comparisons. Young men with very early onset gout have distinct oxylipin spectrums, especially those derived from arachidonic acid, eicosapentaenoic acid and docosahexaenoic acid. Differential oxylipins could serve as candidate serum biomarkers in differentiating gout from hyperuricaemia.

Fully automated and highly specific plasma β-amyloid immunoassays predict β-amyloid status defined by amyloid positron emission tomography with high accuracy

BackgroundClinicians, researchers, and patients alike would greatly benefit from more accessible and inexpensive biomarkers for neural β-amyloid (Aβ). We aimed to assess the performance of fully automated plasma Aβ immunoassays, which correlate significantly with immunoprecipitation mass spectrometry assays, in predicting brain Aβ status as determined by visual read assessment of amyloid positron emission tomography (PET).MethodsThe plasma Aβ42/Aβ40 ratio was measured using a fully automated immunoassay platform (HISCL series) in two clinical studies (discovery and validation studies). The discovery and validation sample sets were retrospectively and randomly selected from participants with early Alzheimer’s disease (AD) identified during screening for the elenbecestat Phase 3 program.ResultsWe included 197 participants in the discovery study (mean [SD] age 71.1 [8.5] years; 112 females) and 200 in the validation study (age 70.8 [7.9] years; 99 females). The plasma Aβ42/Aβ40 ratio predicted amyloid PET visual read status with areas under the receiver operating characteristic curves of 0.941 (95% confidence interval [CI] 0.910–0.973) and 0.868 (95% CI 0.816–0.920) in the discovery and validation studies, respectively. In the discovery study, a cutoff value of 0.102 was determined based on maximizing the Youden Index, and the sensitivity and specificity were calculated to be 96.0% (95% CI 90.1–98.9%) and 83.5% (95% CI 74.6–90.3%), respectively. Using the same cutoff value, the sensitivity and specificity in the validation study were calculated to be 88.0% (95% CI 80.0–93.6%) and 72.0% (95% CI 62.1–80.5%), respectively.ConclusionsThe plasma Aβ42/Aβ40 ratio measured using the HISCL series achieved high accuracy in predicting amyloid PET status. Since our blood-based immunoassay system is less invasive and more accessible than amyloid PET and cerebrospinal fluid testing, it may contribute to the diagnosis of AD in routine clinical practice.

Whole blood transcriptome analysis in onchocerciasis

Identifying the molecular mechanisms controlling the host’s response to infection with Onchocerca volvulus is important to understand how the human host controls such parasitic infection. Little is known of the cellular immune response upon infection with O. volvulus. We performed a transcriptomic study using PAXgene-preserved whole blood from 30 nodule-positive individuals and 21 non-endemic controls. It was found that of the 45,042 transcripts that were mapped to the human genome, 544 were found to be upregulated and 447 to be downregulated in nodule-positive individuals (adjusted P-value < 0.05). Pathway analysis was performed on this set of differentially expressed genes, which demonstrated an impact on oxidative phosphorylation and protein translation. Upstream regulator analysis showed that the mTOR associated protein RICTOR appears to play an important role in inducing the transcriptional changes in infected individuals. Functional analysis of the genes affected by infection indicated a suppression of antibody response, Th17 immune response and proliferation of activated T lymphocytes. Multiple regression models were used to select 22 genes that could contribute significantly in the generation of a classifier to predict infection with O. volvulus. For these 22 genes, as well as for 8 reference target genes, validated RT-qPCR assays were developed and used to re-analyze the discovery sample set. These data were used to perform elastic net regularized logistic regression and a panel of 7 genes was found to be the best performing classifier. The resulting algorithm returns a value between 0 and 1, reflecting the predicted probability of being infected. A validation panel of 69 nodule-positive individuals and 5 non-endemic controls was used to validate the performance of this classifier. Based on this validation set only, a sensitivity of 94.2% and a specificity of 60.0% was obtained. When combining the discovery test set and validation set, a sensitivity of 96.0% and a specificity of 92.3% was obtained. Large-scale validation approaches will be necessary to define the intended use for this classifier. Besides the use as marker for infection in MDA efficacy surveys and epidemiological transmission studies, this classifier might also hold potential as pharmacodynamic marker in macrofilaricide clinical trials.

Abstract 2137: Differential gene expression identifies a transcriptional regulatory network involving ESR1and PITX1 in invasive epithelial ovarian cancer

Abstract The heterogeneous subtypes and stages of epithelial ovarian cancer (EOC) differ in invasive processes, but the molecular factors that drive aggressive behavior remain nebulous. In this study, we analyzed two types of EOC at opposite ends of the invasiveness spectrum, high-grade serous ovarian cancers (HGSOCs) and serous borderline tumors (SBTs). We identified differentially expressed genes to guide follow on studies of regulatory networks. Initially, in a discovery sample set, we generated transcriptome sequences to identify 11 differentially expressed genes in SBTs and HGSOCs, and a nonoverlapping set of 17 differentially expressed genes stage II and stage III HGSOCs. We validated the classification performance of these gene expression signatures on a validation set of SBT and HGSOC with classification rates of 94 and 98% using the 11-gene and 17-gene sets, respectively. Transcription factor binding sites enriched in the differentially expressed gene promoter regions were examined by Chip-seq analysis in ovarian cancer and other cell lines. We identified a suite of transcription factors implicated in the differential regulation associated with invasiveness, including ERα, RARA, PITX1, FOXA1/FOXF1, and BHLHE41/E40. These data implicate estrogen responsive gene regulatory networks in ovarian cancers and suggest that dynamic regulatory interactions determine malignant EOC outcomes. Citation Format: Yichao Li, Sushil Kumar Jaiswal, Rupleen Kaur, Dana Alsaadi, Xiaoyu Liang, Frank Drews, Julie A. DeLoia, Thomas Krivak, Hanna M. Petrykowska, Valer Gotea, Lonnie Welch, Elnitski Laura. Differential gene expression identifies a transcriptional regulatory network involving ESR1and PITX1 in invasive epithelial ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2137.

Candidate symptomatic markers for predicting violence in schizophrenia: A cross-sectional study of 7711 patients in a Chinese population.

Violent behaviour is an alarming problem among schizophrenia patients. The effects of historical, clinical, and pathological risk factors for violence have been investigated by multiple studies, but consensus has not been achieved. As psychotic symptoms are more direct and intuitive indicators for violence, identifying robustly associated symptoms is a crucial part of the future prediction and precise management of violent patients in clinics. This study aims to identify the psychotic symptoms correlated with violence among schizophrenia patients in a Chinese population. In this cross-sectional study, the medical records of 7711 schizophrenia patients (4711 in the discovery set and 3000 in the validation set) were collected from 1998 to 2010. Their psychotic symptoms were extracted, and the patients were divided into violent and non-violent groups. Multivariate logistic analysis was applied to identify symptoms associated with violence in the discovery set. Eight psychotic symptoms were found to be significantly correlated with violence in schizophrenia. "Destruction of property", "verbal aggression" and "insomnia" increased the risk of violence, while "flat affect", "delusion of persecution", "auditory hallucination", "vagueness of thought" and "poverty of thought" decreased the risk of violence. The regression model was evaluated by receiver operating characteristic (ROC) analysis for its discriminatory performance, achieving area under curve (AUC) values of 0.887 in the discovery sample set and 0.824 in the validation sample set. The correlated symptoms identified by this study can serve as future candidate predictors for violence in schizophrenia, paving the way for precise management of schizophrenia patients in clinics.

Metabolite analysis-aided diagnosis of papillary thyroid cancer.

Thyroid cancer is the most frequent endocrine tumor with a growing incidence worldwide. However, common diagnostic strategy for thyroid cancer classification is hardly to make a proper diagnosis in some cases. To assist classical approach, this study used metabolomics to screen and validate biomarkers from serum and urinary for papillary thyroid cancer (PTC). Overall, 124 untreated PTC, 76 untreated benign thyroid nodule (BTN), and 116 healthy control (HC) were collected in this study. Thirty-six differential metabolites were screened from non-targeted metabolomics with a discovery sample set in comparison with HC and BTN. Serum β-hydroxybutyrate (BHB), docosahexaenoic acid (DHA), 1-methyladenosine (1-MedA), pregnanediol-3-glucuronide (PdG), urinary nicotinic acid mononucleotide (NAM) and xanthosine (Xan) were validated to be significantly differential by targeted metabolomics in validation set. The logistic regression model incorporating six biomarkers had excellent discrimination from receiver-operating characteristics (ROC) analysis, with area under the receiver-operating characteristic curve (AUC) of 0.943 (95% CI 0.902 to 0.983) and 0.952 (95% CI 0.921 to 0.983) for female alone and female + male samples, respectively. The prediction accuracy and false-negative rate in the real setting of one PTC to ten suspicious nodules were 84.7 and 17.7% with the threshold at probablity of 0.5. Results of a double-blind study for PTC and BTN had true positive value of 100% and true negative value of 91.7%. To conclude, BHB, DHA, 1-MedA, PdG, NAM and Xan are suitable biomarkers for PTC, and logistic regression models with the six biomarkers can be potentially used as clinical diagnosis.

FGA isoform as an indicator of targeted therapy for EGFR mutated lung adenocarcinoma.

Epidermal growth factor receptor (EGFR) gene is frequently mutated in non-small cell lung cancer (NSCLC), which can be targeted by EGFR tyrosine kinase inhibitors (TKIs). It is hard, however, to monitor the performance of EGFR-TKI therapy dynamically. Therefore, therapeutic indicators are urgently needed. Novel antibody microarray, containing 41,472 antibodies, was used for comprehensive analyzing of serum samples from 9 normal subjects and 9 EGFR mutated lung adenocarcinoma patients at three EGFR-TKI treatment time points, including before treatment (Baseline), partial response (PR) during treatment, and disease progression (PD) after resistance. Through microarray data analysis, five candidate antibodies were screened out for confirmation in serum samples and the verified one was utilized for candidate protein identification through immunoprecipitation-mass spectrometry strategy. A novel protein, isoform 2 of fibrinogen alpha chain (FGA2), was revealed and verified in the discovery sample set. Its performance as therapy indicator was further evaluated in another pre-validation sample set (n = 60). Our data confirmed that serum FGA2 level was correlated with EGFR-TKI response (p < 0.05). The expression and secretion of FGA2 in hepatocytes were inhibited by EGFR-TKI, partially explaining the downregulation of FGA2 in serum. Our results demonstrate that FGA2 is an indicator of targeted therapy for EGFR mutated lung adenocarcinoma. KEY MESSAGES: Antibody microarray was coupled with mass spectrometry for proteomics research. FGA2 was discovered as an indicator of EGFR-TKI targeted therapy. FGA2's expression/secretion in hepatocytes was dramatically inhibited by EGFR-TKI.

Epigenetic silencing of MEIS2 in prostate cancer recurrence

BackgroundCurrent diagnostic and prognostic tools for prostate cancer (PC) are suboptimal, resulting in overdiagnosis and overtreatment of clinically insignificant tumors. Thus, to improve the management of PC, novel biomarkers are urgently needed.ResultsIn this study, we integrated genome-wide methylome (Illumina 450K DNA methylation array (450K)) and RNA sequencing (RNAseq) data performed in a discovery set of 27 PC and 15 adjacent normal (AN) prostate tissue samples to identify candidate driver genes involved in PC development and/or progression. We found significant enrichment for homeobox genes among the most aberrantly methylated and transcriptionally dysregulated genes in PC. Specifically, homeobox gene MEIS2 (Myeloid Ecotropic viral Insertion Site 2) was significantly hypermethylated (p < 0.0001, Mann-Whitney test) and transcriptionally downregulated (p < 0.0001, Mann-Whitney test) in PC compared to non-malignant prostate tissue in our discovery sample set, which was also confirmed in an independent validation set including > 500 PC and AN tissue samples in total (TCGA cohort analyzed by 450K and RNAseq). Furthermore, in three independent radical prostatectomy (RP) cohorts (n > 700 patients in total), low MEIS2 transcriptional expression was significantly associated with poor biochemical recurrence (BCR) free survival (p = 0.0084, 0.0001, and 0.0191, respectively; log-rank test). Next, we analyzed another RP cohort consisting of > 200 PC, AN, and benign prostatic hyperplasia (BPH) samples by quantitative methylation-specific PCR (qMSP) and found that MEIS2 was significantly hypermethylated (p < 0.0001, Mann-Whitney test) in PC compared to non-malignant prostate tissue samples (AN and BPH) with an AUC > 0.84. Moreover, in this cohort, aberrant MEIS2 hypermethylation was significantly associated with post-operative BCR (p = 0.0068, log-rank test), which was subsequently confirmed (p = 0.0067; log-rank test) in the independent TCGA validation cohort (497 RP patients; 450K data).ConclusionsTo the best of our knowledge, this is the first study to investigate, demonstrate, and independently validate a prognostic biomarker potential for MEIS2 at the transcriptional expression level and at the DNA methylation level in PC.

Identification of PBMC-expressed miRNAs for rheumatoid arthritis

ABSTRACTPost-transcriptional regulation by miRNAs plays an important role in the pathogenesis of rheumatoid arthritis (RA), however, the roles of specific miRNAs in RA pathogenesis remain largely unclear. This study performed dual-omics (miRNA and mRNA) integration analysis and in-depth cellular and molecular functional exploration to identify novel RA-associated miRNAs and to understand their underlying pathogenic mechanism. Based on the miRNA and mRNA expression profiles in peripheral blood mononuclear cells (PBMCs) from a discovery sample set (25 RA cases and 18 healthy controls), 18 differentially expressed miRNAs (DEMIRs) (|Fold-change|>2 and P < 0.05) were identified and corresponding interaction networks of DEMIRs and mRNA were constructed. After the expression validation of the DEMIRs in a validation sample set (35 RA cases and 35 healthy controls), miR-99b-5p was highlighted. The over-expression of newly discovered miR-99b-5p is able to suppress T cell apoptosis, promote cell proliferation and activation, increase expression of proinflammatory cytokines (IL-2, IL-6, TNF-α, and IFN-γ), and inhibit expression of its target genes mTOR and RASSF4. This study comprehensively identified PBMC-expressed miRNAs along with corresponding regulatory networks significant for RA and discovered miR-99b-5p as a novel post-transcriptional mediator involved in RA pathogenesis. The findings improved our understanding of RA pathogenesis and provided novel insights into the molecular mechanisms underlying RA pathogenesis.

Proteomics analysis discovers biomarkers in serum months to years before small cell lung cancer: The HUNT study.

e20095 Background: The high incidence and high mortality rate of small-cell lung cancer (SCLC) calls for identification of methods for early diagnosis. Searching of cancer-related proteins and proteins signature in biofluids is an emerging approach in early diagnostic of malignancies. In the present study we have used proteomics-based profiling of serum collected 2 months to 5 years before SCLC diagnosis to search for early diagnostic biomarkers. Methods: All serum samples in this study were obtained from the Nord-Trøndelag Health Study (HUNT) Research Centre’s Biobank. Discovery sample set (Cohort I): Serum samples from 12 individuals that subsequently developed SCLC and 12 matched controls were obtained. Validation sample set (Cohort II): Serum samples from 5 future SCLC patients and 5 matched controls were obtained. The serum samples in both cohorts were collected in a time frame of 2 months to 5 years before diagnosis. All controls included in the study were matched to the cases for smoking status (pack years and quit time), gender and age, and were cancer-free at least 5 years before blood sampling. All subjects were smokers or ex-smokers. Twenty (20) µl of serum was depleted of its high-abundant proteins and the cleaned-up peptides were analysed by LC- MS with an Orbitrap Elite mass spectrometer. Data were processed/analysed using MaxQuant software. Using Cohort I as training set, proteins most related to diagnosis were identified at first with limma univariate analysis. Reference and equivalent signatures were identified using JADbio with SES as feature selection algorithm. The JADbio matched pipeline with 50 repetitions for performance assessment (AUC with 95% CI) was used. Results: In each serum sample the analysis detected 435 proteins in Cohort I and 609 proteins in Cohort II. Reference signature for 12 SCLC future cases vs 12 matched controls had a conservative AUC of 0.686 (95% CI: 0.557, 0.765) in the discovery cohort. Three proteins in the reference and equivalent signatures were also found to be differentially expressed in validation Cohort II; all three proteins discriminated small-cell lung cancer patients from their respective controls (AUC 0.757-0.826 in the discovery Cohort I and AUC 0.68-0.84 in the validation Cohort II). Conclusions: The results indicate that differential levels of a few proteins in serum may help detecting SCLC 2 months to 5 years prior to clinical diagnosis. This is one of the first large-scale proteomics screening studies of pre-diagnostic serum of future SCLC patients. Further validation studies are in progress.

Autoantibody Biomarker Discovery in Primary Open Angle Glaucoma Using Serological Proteome Analysis (SERPA)

Glaucoma is an optic neurological disorder and the leading cause of irreversible blindness worldwide, with primary open angle glaucoma (POAG) as its most prevalent form. An early diagnosis of the disease is crucial to prevent loss of vision. Mechanisms behind glaucoma pathogenesis are not completely understood, but disease related alterations in the serological autoantibody profile indicate an immunologic component. These changes in immunoreactivity may serve as potential biomarkers for glaucoma diagnostics. We aimed to identify novel disease related autoantibodies targeting antigens in the trabecular meshwork as biomarkers to support early detection of POAG. We used serological proteome analysis (SERPA) for initial autoantibody profiling in a discovery sample set. The identified autoantibodies were validated by protein microarray analysis in a larger cohort with 60 POAG patients and 45 control subjects. In this study, we discovered CALD1, PGAM1, and VDAC2 as new biomarker candidates. With the use of artificial neural networks, the panel of these candidates and the already known markers HSPD1 and VIM was able to classify subjects into POAG patients and non-glaucomatous controls with a sensitivity of 81% and a specificity of 93%. These results suggest the benefit of these potential autoantibody biomarkers for utilization in glaucoma diagnostics.

Abstract P4-07-02: Expression of miR-18a and miR-210 in normal breast tissue as candidate markers of breast cancer risk

Abstract Purpose: miRNAs are non-coding RNAs that are abnormally expressed in breast cancer, with critical roles in cancer due to their regulation of large gene networks. miRNA expression in benign high-risk breast tissue has never been evaluated, but it may provide information about early dysregulation events that contribute to breast cancer risk. The contralateral unaffected breast (CUB) of women with unilateral breast cancer is in high-risk for the second primary cancer. Thus, we examined miRNA expression profiles in tumor and the matching CUBs to seek potential miRNA biomarkers for breast cancer risk. Methods: FFPE tissues of breast cancer and their matching CUB tissues were sectioned. The areas of tumor and normal ductal epithelium were outlined and then dissected using laser microdissection system. Total RNA was extracted for miRNA profiling studies. Expression profiles of 754 mature miRNAs were examined using TaqMan Low Density Arrays assays in 30 paired breast cancer and CUB samples (15 with ER+ tumors, 15 with ER- tumors) and 15 reduction mammoplasty (RM) controls, matched by age, race and menopausal status. ANOVA test was performed to examine the differential expression among groups and pairwise comparison with Sidak adjustment was used for multiple comparison. Seven candidate miRNAs were then examined in an independent CUB sample set (20 with ER+ tumors, 20 with ER- tumors) and 20 RM controls. Further independent validation was performed using qRT-PCR in 80 benign breast biopsy (BBB) samples: 40 from women who subsequently developed breast cancer (cases) and 40 from those who did not (controls). Logistic regression analysis and receiver operating characteristic (ROC) analysis were performed using combinations of the expression of multiple miRNAs to establish models discriminating cases from controls. Results: Seven miRNAs (miR-18a, miR-210, miR-214*, miR-124, miR-193a-3p, miR485-3p and miR-671-3p) were found to be differentially expressed in breast cancer and CUB samples vs. RM samples in the discovery sample set. Among them, miR-18a and miR-210 were validated in a second, independent CUB sample set. The expression of miR-18a and miR-210 were significantly higher in tumor (regardless ER status) compared to CUBs and RM controls. The expression levels in CUBs were significantly higher than in RM. We then examined miR expression in case BBB samples, and confirmed that expression of miR-18a and miR-210 were increased compared with controls. ROC analysis using miR-18a and miR-210 discriminated high-risk cases from standard-risk controls with OR 2.44, P = 0.022. Conclusion: The expression of miR-18a and miR-210 were elevated in breast cancer, in matched CUBs, and in BBB predating cancer diagnosis. These data provide strong support for the hypothesis that miR-18a and miR-210 expression in BBB is an indicator of increased risk of breast cancer. Given the high expression in tumors, they are also potential cancer detection biomarkers. Citation Format: Wang J, Shidfar A, Costa FF, Scholtens D, Bischof JM, Sullivan ME, Ivancic D, Soares MB, Khan SA. Expression of miR-18a and miR-210 in normal breast tissue as candidate markers of breast cancer risk [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-07-02.

Discovery and validation of potential urinary biomarkers for bladder cancer diagnosis using a pseudotargeted GC-MS metabolomics method.

Bladder cancer (BC) is the second most prevalent malignancy in the urinary system and is associated with significant mortality; thus, there is an urgent need for novel noninvasive diagnostic biomarkers. A urinary pseudotargeted method based on gas chromatography–mass spectrometry was developed and validated for a BC metabolomics study. The method exhibited good repeatability, intraday and interday precision, linearity and metabolome coverage. A total of 76 differential metabolites were defined in the discovery sample set, 58 of which were verified using an independent validation urine set. The verified differential metabolites revealed that energy metabolism, anabolic metabolism and cell redox states were disordered in BC. Based on a binary logistic regression analysis, a four-biomarker panel was defined for the diagnosis of BC. The area under the receiving operator characteristic curve was 0.885 with 88.0% sensitivity and 85.7% specificity in the discovery set and 0.804 with 78.0% sensitivity and 70.3% specificity in the validation set. The combinatorial biomarker panel was also useful for the early diagnosis of BC. This approach can be used to discriminate non-muscle invasive and low-grade BCs from healthy controls with satisfactory sensitivity and specificity. The results show that the developed urinary metabolomics method can be employed to effectively screen noninvasive biomarkers.

Antibody-based proteomics to identify an apoptosis signature for early recurrence of hepatocellular carcinoma.

BackgroundEarly recurrence after surgical resection is a hallmark of poor prognosis in hepatocellular carcinoma (HCC). To determine the proteomic background of early recurrence of HCC, we focused on apoptosis-related proteins.MethodsSurgically resected tumor tissues were obtained from 80 patients, including HCC tumor tissues, non-tumor tissues, and normal liver tissues. These samples were grouped in the discovery and validation sample sets. The expression level of 192 apoptosis-related proteins was monitored using 247 commercially available antibodies and western blotting. The intensity of protein bands was compared between the tumor and non-tumor tissues as well as between the patients who had recurrence within 2 years after surgery and those who did not.ResultsIn the first screening, we used pooled samples. The intensity of 53 protein bands detected by 37 unique antibodies was higher in tumor tissues compared with normal liver tissues, especially tumor tissues from patients who had recurrence within 2 years after surgery. In the second screening, we examined individual samples used to make the pooled samples. Among the selected bands and antibodies, the intensity of 18 protein bands detected by 11 antibodies was higher in tumor tissues compared with that in normal tissues, especially tumor tissues from the patients with early recurrence after surgery. For the third screening, we examined the samples from newly enrolled patients using these 11 antibodies. Eighteen protein bands detected by six antibodies were selected by using the same criteria. The corresponding antigens included ERK1, PKG, Apaf1, BclX, phosphorylated c-abl, and PIASx1/2.ConclusionsWe screened 192 apoptosis-related proteins using specific antibodies and western blotting. We identified 6 apoptosis-related proteins associated with carcinogenesis and early recurrence in HCC. The biological and clinical significance of the identified proteins are worth further investigation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12014-016-9130-0) contains supplementary material, which is available to authorized users.

Novel epigenetic changes in CDKN2A are associated with progression of cervical intraepithelial neoplasia

ObjectiveTo conduct a comprehensive mapping of the genomic DNA methylation in CDKN2A, which codes for the p16INK4A and p14ARF proteins, and 14 of the most promising DNA methylation marker candidates previously reported to be associated with progression of low-grade cervical intraepithelial neoplasia (CIN1) to cervical cancer. MethodsWe analyzed DNA methylation in 68 HIV-seropositive and negative women with incident CIN1, CIN2, CIN3 and invasive cervical cancer, assaying 120 CpG dinucleotide sites spanning APC, CDH1, CDH13, CDKN2A, CDKN2B, DAPK1, FHIT, GSTP1, HIC1, MGMT, MLH1, RARB, RASSF1, TERT and TIMP3 using the Illumina Infinium array. Validation was performed using high resolution mapping of the target genes with HELP-tagging for 286 CpGs, followed by fine mapping of candidate genes with targeted bisulfite sequencing. We assessed for statistical differences in DNA methylation levels for each CpG loci assayed using univariate and multivariate methods correcting for multiple comparisons. ResultsIn our discovery sample set, we identified dose dependent differences in DNA methylation with grade of disease in CDKN2A, APC, MGMT, MLH1 and HIC1, whereas single CpG locus differences between CIN2/3 and cancer groups were seen for CDH13, DAPK1 and TERT. Only those CpGs in the gene body of CDKN2A showed a monotonic increase in methylation between persistent CIN1, CIN2, CIN3 and cancers. ConclusionOur data suggests a novel link between early cervical disease progression and DNA methylation in a region downstream of the CDKN2A transcription start site that may lead to increased p16INK4A/p14ARF expression prior to development of malignant disease.

Variations in ORAI1 Gene Associated with Kawasaki Disease.

Kawasaki disease (KD; MIM#61175) is a systemic vasculitis syndrome with unknown etiology which predominantly affects infants and children. Recent findings of susceptibility genes for KD suggest possible involvement of the Ca2+/NFAT pathway in the pathogenesis of KD. ORAI1 is a Ca2+ release activated Ca2+ (CRAC) channel mediating store-operated Ca2+ entry (SOCE) on the plasma membrane. The gene for ORAI1 is located in chromosome 12q24 where a positive linkage signal was observed in our previous affected sib-pair study of KD. A common non-synonymous single nucleotide polymorphism located within exon 2 of ORAI1 (rs3741596) was significantly associated with KD (P = 0.028 in the discovery sample set (729 KD cases and 1,315 controls), P = 0.0056 in the replication sample set (1,813 KD cases vs. 1,097 controls) and P = 0.00041 in a meta-analysis by the Mantel-Haenszel method). Interestingly, frequency of the risk allele of rs3741596 is more than 20 times higher in Japanese compared to Europeans. We also found a rare 6 base-pair in-frame insertion variant associated with KD (rs141919534; 2,544 KD cases vs. 2,414 controls, P = 0.012). These data indicate that ORAI1 gene variations are associated with KD and may suggest the potential importance of the Ca2+/NFAT pathway in the pathogenesis of this disorder.

Variations in the FRA10AC1 Fragile Site and 15q21 Are Associated with Cerebrospinal Fluid Aβ1-42 Level.

Proteolytic fragments of amyloid and post-translational modification of tau species in Cerebrospinal fluid (CSF) as well as cerebral amyloid deposition are important biomarkers for Alzheimer’s Disease. We conducted genome-wide association study to identify genetic factors influencing CSF biomarker level, cerebral amyloid deposition, and disease progression. The genome-wide association study was performed via a meta-analysis of two non-overlapping discovery sample sets to identify genetic variants other than APOE ε4 predictive of the CSF biomarker level (Aβ1–42, t-Tau, p-Tau181P, t-Tau:Aβ1–42 ratio, and p-Tau181P:Aβ1–42 ratio) in patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Loci passing a genome-wide significance threshold of P < 5 x 10−8 were followed-up for replication in an independent sample set. We also performed joint meta-analysis of both discovery sample sets together with the replication sample set. In the discovery phase, we identified variants in FRA10AC1 associated with CSF Aβ1–42 level passing the genome-wide significance threshold (directly genotyped SNV rs10509663 P FE = 1.1 x 10−9, imputed SNV rs116953792 P FE = 3.5 x 10−10), rs116953792 (P one-sided = 0.04) achieved replication. This association became stronger in the joint meta-analysis (directly genotyped SNV rs10509663 P FE = 1.7 x 10−9, imputed SNV rs116953792 P FE = 7.6 x 10−11). Additionally, we identified locus 15q21 (imputed SNV rs1503351 P FE = 4.0 x 10−8) associated with CSF Aβ1–42 level. No other variants passed the genome-wide significance threshold for other CSF biomarkers in either the discovery sample sets or joint analysis. Gene set enrichment analyses suggested that targeted genes mediated by miR-33, miR-146, and miR-193 were enriched in various GWAS analyses. This finding is particularly important because CSF biomarkers confer disease susceptibility and may be predictive of the likelihood of disease progression in Alzheimer’s Disease.

DNA-methylation and autoantibodies based cancer diagnosis from body fluids

Special focus and aim of our research activities at AIT, the Austrian Institute of Technology, is to define reliable biomarkers suitable for early and non-invasive disease diagnosis from body fluids such as serum/plasma and saliva. Along a selection of research projects, which are described in more detail underneath, we will present and introduce the broad portfolio of high throughput technologies we successfully apply for diagnostic biomarker discovery and validation. As a first show case of successful non-invasive disease biomarker discovery we will present a study where we investigated and compared the genome wide methylation levels of lung cancer patients, patients suffering from lung fibrosis, patients with COPD (chronic obstructive pulmonary disease), and DNA samples derived from healthy lungs. Along this study we could identify specific methylation patterns for each of these lung diseases. After quantitative PCR validation of 240 disease specific methylation markers in the discovery sample set, the 90 top markers were picked and applied for serum testing (n=204). When we applied gradient boosting classification for differential diagnosis of tested lung diseases and healthy controls an AUC value of 0.95 was reached here to separate cancer from all other non-cancer samples whereas in differential diagnosis of healthy-, COPD and fibrosis patients AUC values of 0.71 and 0.49 were obtained for fibrosis, respectively COPD. Thus in case of COPD the presented method may be used to monitor cancer risk within COPD patients. Our second show case comprises a study where we screened cancer patients’ sera for tumor-specific antibody profiles using an in-house developed 16k protein-microarray. This methodology, which will be described in detail, enabled us to define different tumor-associated antigen (TAA) classifier panels for the big 4 cancer entities (breast, colon, prostate and lung cancer) which all showed very promising classification successes in distinction of patients versus controls. We will further present preliminary data obtained when comparing serum and saliva autoantibody profiles of breast-cancer patients and healthy controls.

Validation of a blood protein signature for non-small cell lung cancer.

BackgroundCT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.ResultsBlood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.ConclusionsSelecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.