Mahoney, Douglas W.

44 Background: We investigated the association of germline genetic variation in hormone biosynthesis and metabolism genes with prostate cancer specific mortality in a cohort of advanced prostate cancer patients. Methods: We successfully genotyped 852 single nucleotide polymorphisms (SNPs) from 97 genes in a cohort of 267 advanced prostate cancer patients at the time of progression to castration recurrence (CRPC) during on-going androgen ablation. Tagging SNPs with minor allele frequency (MAF) of >5% and r2 ≥0.8 were selected from HapMap, NIEHS and Seattle SNP databases. Medical records were queried for cause of death. The primary endpoint of time to prostate cancer specific mortality (PCSM), was pre-defined as time from development of CRPC to death from prostate cancer progression. Principle components analysis was used for gene-levels tests, and to account for multiple testing, we calculated the false discovery rate (FDR). For SNP level results, hazard ratios (HR) and 95% confidence intervals (CI) were estimated using cox regression. Results: The median age of the cohort was 72 years at CRPC. 43% had a Gleason score (GS)=8-10, 33% a GS=7, and 24% a GS<7. After a median follow-up of 1.8 years (IQ range: 0.8–3.3 years), 139 patients died, of which 107 were due to prostate cancer progression. In unadjusted gene level analyses, UGT1A7 (p=0.0059; FDR=0.19), UGT1A10 (p=0.0017; FDR=0.17) and UGT1A3 (p=0.0037; FDR=0.18) were associated with PCSM. After adjusting for age and GS, SNPs strongly associated with PCSM are listed in the Table . Conclusions: Variation in UGT genes involved in hormone metabolism yield prognostic information in CRPC. Further validation is needed to develop these as prognostic biomarkers. [Table: see text] No significant financial relationships to disclose.

14 Background: We evaluated the association of germline variation in genes regulating hormonal pathways with duration of treatment response in prostate cancer patients receiving ADT, focusing on genes in the androgen biosynthesis (the C4 Δ; C5 Δ; C21 CYP pathways and the alternate “backdoor pathway”) and metabolism pathways. Methods: We successfully genotyped 747 single nucleotide polymorphisms (SNPs) from 84 genes in a cohort of 304 patients with advanced prostate cancer who progressed on ADT. Tagging SNPs with a minor allele frequency (MAF) of >5% and r2 ≥0.8 were selected from HapMap, NIEHS and Seattle SNP databases. The primary endpoint was time to ADT failure, and we conducted a gene-level test (principal components), along with individual SNP level tests adjusted for patient age and Gleason score. To account for multiple testing, we calculated the false discovery rate (FDR), and FDRs of 0.10 were considered noteworthy. Results: The median age of the cohort at ADT failure was 72 years (range 65-78 years), and the overall median time to ADT failure was 3.21 years (IQ [25-75]: 1.54-6.20 years). At the gene level, TRMT11 (tRNA methyltransferase 11 homologue) showed the strongest association with time to ADT failure (p<0.0008; adjusted p-value for FDR-0.068). Two of four TRMT11 tag SNPs were associated with time to ADT failure. The first was the intronic SNP rs1268121 (A>G) with a MAF of 15%, for which there was a positive association of median time to ADT failure with the number of variant alleles (p=0.023): 3.1 years for 0, 4.1 years for 1, and 5.9 years for 2 variant alleles. The second was the SNP rs6900796 (A>G) in the 3' untranscribed region with a MAF of 49%, for which the median time to ADT failure was 2.6 years for 0, 2.5 years for 1, and 3.8 years for 2 variant alleles (p=0.023). Four additional genes showed an association with ADT response at the p<0.10 level, including LOC390956, PRMT3, SLC7A6OS, and WBSCR22, although all FDRs were >0.95. Conclusions: Variation in TRMT11 was associated with time to ADT failure. Confirmation of these findings in an independent cohort is needed. No significant financial relationships to disclose.

104 Background: Currently there are no serum predictive markers of response to AA. We used a proteomic based analytic approach to identify candidates. Methods: Serum from three non-localized prostate cancer cohorts was analyzed. The first included15 paired untreated hormone-sensitive “pre-AA” and 3-month “post-AA” specimens; the second included 10 “early AA failure” (median time to AA failure:11 months) and the third included 10 “late AA failure” specimens (median time to AA failure: 95 months). Proteomic analysis was performed with isobaric mass tags for relative and absolute quanititation (iTRAQ) analyzed by reverse-phase liquid chromatography coupled to tandem mass spectrometry (LC/MS/MS). Differentially expressed candidate proteins were identified by comparisons of (i) paired pre/post-AA proteomes and (ii) post-AA proteome with the combined AA failure cohorts at a False Discovery Rate of 0.2. ELISA assays were used to verify candidate markers in a second stored aliquot of first cohort specimens. This cohort was followed for AA failure. Association of post-AA ELISA levels of candidate markers with time to AA failure was performed using Cox proportional hazards regression, summarized as relative risk (RR) for AA failure. Results: Median PSA in pre/post-AA first cohort were 3.15 ng/ml and 0.29 ng/ml. Median PSA in the second and third cohorts were 27.3 and 4.3 ng/ml. Between post-AA and AA failure cohorts, 149 proteins were differentially expressed. Between early and late AA failure 98 proteins were differentially expressed; 47 proteins were common in both comparisons. ELISA assays verified expression levels of 2/47 proteins in the first cohort; zinc alpha-2 macroglobulin (ZAG), and Neuropilin-2 (NPL2). Median change in ZAG decreased by 2073.5 ng/ml (post versus pre-AA) while median change in NPL2 levels increased by 2.9 ng/ml. After a median follow-up of 43 months from the post-AA time-point, 4/15 first cohort subjects had failed AA. The RR of AA failure for ZAG levels below the median change was 3.8 (95% CI: 0.4-37) and 3.0 (95% CI: 0.3-29) for NPL2. Conclusions: A global proteomic analysis identified ZAG and NPL2 as candidate serum predictive markers of AA response which needs further validation.

Abstract Background: Studies of colorectal cancer screening by multitarget stool DNA (MT-sDNA) show false-positive (FP) rates of 7% to 13%. It is unclear whether FP patients are at increased long-term risk of adverse outcomes. Methods: We compared subsequent clinical events among patients with apparent FP MT-sDNA with those in patients reported as true negative (TN). This was a retrospective cohort study of participants in pre-FDA approval MT-sDNA studies having nonadvanced or negative baseline colonoscopy findings from a single referral center. Per-protocol and calibrated cutoffs defined FP and TN groups. From the time of stool collection, we measured differences between FP and TN groups in time to death, subsequent cancer diagnosis, and onset of alarm symptoms. Results: Of 1,050 eligible patients, only 6 were lost to follow-up. Median age was 65.6 years [interquartile range (IQR), 56.8–72.3]; 54% were female. Median follow-up time was 4 years (IQR, 3.5–5.3). Eight aerodigestive (lung and gastrointestinal tract) cancers occurred. FP status by calibrated, but not per-protocol, cutoffs was associated with subsequent aerodigestive cancer; however, cumulative incidence did not exceed SEER expectations from the general population. By any cutoff method, FP status was not associated with mortality or alarm symptoms. Conclusions: Although FP status was associated with long-term aerodigestive cancers, new cases were not temporally related and did not exceed incidence estimates from general population. Impact: These observations do not justify aggressive follow-up evaluation for patients with FP MT-sDNA at this time. Larger studies are needed to confirm these early findings. Cancer Epidemiol Biomarkers Prev; 26(4); 614–21. ©2016 AACR.

Background Sex‐related differences in morbidity and survival in bicuspid aortic valve (BAV) adults are fundamentally unknown. Contemporary studies portend excellent survival for BAV patients identified at early echocardiographic‐clinical stages. Whether BAV adults incur a survival disadvantage throughout subsequent echocardiographic‐clinical stages remains undetermined. Methods and Results Analysis was done of 3 different cohorts of consecutive patients with echocardiographic diagnosis of BAV identified retrospectively: (1) a community cohort of 416 patients with first BAV diagnosis (age 35±21 years, follow‐up 16±7 years), (2) a tertiary clinical referral cohort of 2824 BAV adults (age 51±16 years, follow‐up 9±6 years), and (3) a surgical referral cohort of 2242 BAV adults referred for aortic valve replacement (AVR) (age 62±14 years, follow‐up 6±5 years). For the community cohort, 20‐year risks of aortic regurgitation (AR), AVR, and infective endocarditis were higher in men (all P ≤0.04); for a total BAV‐related morbidity risk of 52±4% vs 35±6% in women ( P =0.01). The cohort's 25‐year survival was identical to that in the general population ( P =0.98). AR independently predicted mortality in women ( P =0.001). Baseline AR was more common in men ( P ≤0.02) in the tertiary cohort, with 20‐year survival lower than that in the general population ( P <0.0001); age‐adjusted relative death risk was 1.16 (95% confidence interval [CI] 1.05‐1.29) for men versus 1.67 (95% CI 1.38‐2.03) for women ( P =0.001). AR independently predicted mortality in women ( P =0.01). Baseline AR and infective endocarditis were higher in men (both ≤0.001) for the surgical referral cohort, with 15‐year survival lower than that in the general population ( P <0.0001); age‐adjusted relative death risk was 1.34 (95% CI 1.22‐1.47) for men versus 1.63 (95% CI 1.40‐1.89) for women ( P =0.026). AR and NYHA class independently predicted mortality in women (both P ≤0.04). Conclusions Within evolving echocardiographic‐clinical stages, the long‐term survival of adults with BAV is not benign, as both men and women incur excess mortality. Although BAV‐related morbidity is higher in men in the community, and AR and infective endocarditis are more prevalent in men, women exhibit a significantly higher relative risk of death in tertiary and surgical referral cohorts, which is independently associated with AR.

Objectives To evaluate the prognostic significance of germline variation in candidate genes in patients with castration‐resistant prostate cancer (CRPC). Methods Germline DNA was extracted from peripheral blood mononuclear cells of patients with CRPC enrolled in a clinically annotated registry. Fourteen candidate genes implicated in either initiation or progression of prostate cancer were tagged using single nucleotide polymorphisms (SNPs) from HapMap with a minor allele frequency of >5%. The primary endpoint was overall survival (OS), defined as time from development of CRPC to death. Principal component analysis was used for gene levels tests of significance. For SNP‐level results the per allele hazard ratios (HRs) and 95% confidence intervals (CIs) under the additive allele model were estimated using Cox regression, adjusted for age at CRPC and Gleason score (GS). Results A total of 240 patients with CRPC were genotyped (14 genes; 84 SNPs). The median (range) age of the cohort was 69 (43–93) years. The GS distribution was 55% with GS ≥8, 32% with GS = 7 and 13% with GS <7 or unknown. The median (interquartile range) time from castration resistance to death for the cohort was 2.67 (1.6–4.07) years (144 deaths). At the gene level, a single gene, JAK2 was associated with OS ( P < 0.01), and 11 of 18 JAK2 SNPs were individually associated with OS after adjustment for age and GS. A multivariate model consisting of age, GS, rs2149556 (HR 0.67; 95% CI 0.38–1.18) and rs4372063 (HR 2.17; 95% CI 1.25–3.76) was constructed to predict survival in patients with CRPC (concordance of 0.69, P < 3.2 × 10 −9 ). Conclusions Germline variation in the JAK2 gene was associated with survival in patients with CRPC and warrants further validation as a potential prognostic biomarker.

Abstract Purpose: Methylated DNA markers can discriminate upper from lower GI neoplasms (Gastroenterology 2013;144(5):S84). It is unknown if organ-specific molecular prediction of GI tumor site can be achieved. Experimental design: An unbiased search for neoplasm markers at each major GI organ site was conducted by reduced representation bisulfite sequencing. Top marker candidates were then blindly validated by methylation specific PCR on independent tissue DNA samples from normal mucosa [17 esophagus (E), 13 stomach (S), 33 pancreas (P), 35 bile duct/liver (BD), 21 colorectal (CR)] and neoplasia [42 E, 43 S, 36 P, 48 BD, 97 CR]. Recursive partitioning (rPart) trees modeled all stepwise combinations of single markers to classify controls vs neoplasms and then neoplasms by site (CR, gastroesophageal or pancreaticobiliary). A 2nd model was designed from tissue data for blinded clinical piloting on 42 independent plasma DNA samples (14 normal and 28 cancers (14 P, 14 CR). Results: From the top 100 markers, 95 were validated and used for rPart modeling. A 3- marker panel (chr12.133484978-5047, BMP3, chr11.123301058-255) was selected for the universal detection of GI neoplasms, as it classified neoplasms and control tissues with 95% accuracy. Two markers (chr7.25896389-501, QKI) then assigned lower vs upper GI neoplasms with 94% accuracy. Finally, 3 markers (PDGFD, ELOVL2, PCBP3) called pancreaticobiliary vs gastroesophageal neoplasms with 94% accuracy. All 8 markers applied in a single model (Table) accurately predicted control vs tumor by site [88% (p &lt;0.0001)]. In plasma, a 2-marker panel (BMP3, QKI) was 78% accurate in assigning patients to cancer or control groups (p&lt;0.0009) and 83% accurate in assigning cancer origin to CR or P sites (p&lt;0.0001). Predicted by ModelTissue SourceControlsColorectal neoplasiaPancreatico-biliary cancerGastro-esophageal neoplasiaControls1169102Colorectal neoplasia18617Pancreatico-biliary cancer10654Gastro-esophageal neoplasia12872 Conclusion: Panels of methylated DNA markers were identified for both universal detection of GI neoplasia and accurate prediction of tumor site. Pilot testing of these markers in plasma supports potential clinical feasibility. Citation Format: John B. Kisiel, William R. Taylor, Tracy C. Yab, Hassan M. Ghoz, Patrick H. Foote, Mary E. Devens, Douglas W. Mahoney, Thomas C. Smyrk, Lisa A. Boardman, Gloria M. Petersen, Navtej S. Buttar, Lewis R. Roberts, Graham P. Lidgard, David A. Ahlquist. Accurate site prediction of gastrointestinal cancer by novel methylated DNA markers: Discovery & validation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4252. doi:10.1158/1538-7445.AM2015-4252

Abstract Purpose: Discriminant markers for pancreatic cancer detection are needed. We sought to identify and validate methylated DNA markers for pancreatic cancer using next-generation sequencing unbiased by known targets. Experimental Design: At a referral center, we conducted four sequential case–control studies: discovery, technical validation, biologic validation, and clinical piloting. Candidate markers were identified using variance-inflated logistic regression on reduced-representation bisulfite DNA sequencing results from matched pancreatic cancers, benign pancreas, and normal colon tissues. Markers were validated technically on replicate discovery study DNA and biologically on independent, matched, blinded tissues by methylation-specific PCR. Clinical testing of six methylation candidates and mutant KRAS was performed on secretin-stimulated pancreatic juice samples from 61 patients with pancreatic cancer, 22 with chronic pancreatitis, and 19 with normal pancreas on endoscopic ultrasound. Areas under receiver-operating characteristics curves (AUC) for markers were calculated. Results: Sequencing identified &gt;500 differentially hyper-methylated regions. On independent tissues, AUC on 19 selected markers ranged between 0.73 and 0.97. Pancreatic juice AUC values for CD1D, KCNK12, CLEC11A, NDRG4, IKZF1, PKRCB, and KRAS were 0.92*, 0.88, 0.85, 0.85, 0.84, 0.83, and 0.75, respectively, for pancreatic cancer compared with normal pancreas and 0.92*, 0.73, 0.76, 0.85*, 0.73, 0.77, and 0.62 for pancreatic cancer compared with chronic pancreatitis (*, P = 0.001 vs. KRAS). Conclusions: We identified and validated novel DNA methylation markers strongly associated with pancreatic cancer. On pilot testing in pancreatic juice, best markers (especially CD1D) highly discriminated pancreatic cases from controls. Clin Cancer Res; 21(19); 4473–81. ©2015 AACR.

Abstract Introduction: Triple negative breast cancers (TNBC) are characterized as lacking estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression and TNBC patients have higher rates of recurrence and death compared with other breast cancer subtypes. For TNBC patients who fail standard chemotherapy, there are a lack of novel drug therapies, given the absence of well-defined molecular targets. Recently, a microarray meta-analysis identified 7 triple negative subtypes, including the validation of the luminal androgen receptor (LAR) positive subtype [Lehmann, 2011]. However, microarray technology is dependent on probe-target specificity and the 7 subtypes have yet to be validated using RNA sequencing data, and the presences of recurrent genomic alterations in the 7 subtypes are unknown. METHODS: We obtained 1106 breast cancer RNA-Seq bam files from The Cancer Genome Atlas (TCGA) and aligned with Tophat v1.3. The PAM50 intrinsic gene signature was used to extract a cohort of 128 TNBC samples. Consensus clustering of genes, greater than 75th percentile of variance, was performed using Kmeans clustering in Spearman's correlation space. A nearest centroid prediction model was developed from genes differentially expressed among the clusters. Eighty independent TNBC RNA sequencing samples were obtained (British Colombia; BC) [Shah, 2012] which were calibrated to our TNBC conditional quantile normalized cohort and sub-typed by our model. RESULTS: Using RNA-Seq gene expression count data, we identified 5 clusters, all of which were stable, including the LAR cluster. Signaling pathway impact analysis (SPIA) implicated cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and regulation of actin cytoskeleton pathways commonly altered in the non-LAR TNBC subtypes. In contrast, cell cycle, ECM-receptor interaction, endocrine regulated calcium reabsorption, and insulin signaling pathways were altered in the LAR versus non-LAR subtypes. Neuroactive ligand-receptor interactions were observed to be altered commonly between all sub-types. We then applied our model to the Shah, et al cohort. In this cohort, the LAR subtype was consistent with Shah's classification of ‘other’ TNBC and contained no basal samples by PAM50 intrinsic modeling. Analysis of sub-type specific mutation data from the BC cohort demonstrates an increased mutational load in ECM-related proteins, particularly the myosins, along with increased TP53 clonality in the non-LAR subtypes. CONCLUSIONS: Using TCGA RNASeq data, we have confirmed the presence of 5 major TNBC subtypes, including the LAR; which was negligible in basal composition by PAM50 intrinsic modeling. SPIA pathway analysis indicates a core set of pathways demonstrating altered expression across the TNBC sub-types and the identification of molecular targets within each subtype is ongoing. Citation Format: Kevin J. Thompson, Xiaojia Tang, Zhifu Sun, Jason P. Sinnwell, Hugues Sicotte, Douglas W. Mahoney, Steven Hart, Peter T. Vedell, Poulami Barman, Jeanette E. Eckel Passow, Eric D. Wieben, James N. Ingle, Judy C. Boughey, Liewei Wang, Richard Weinshilboum, Krishna R. Kalari, Matthew P. Goetz. Molecular classification of triple negative breast cancer via RNA-sequencing data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5592. doi:10.1158/1538-7445.AM2014-5592

Abstract INTRODUCTION Next generation sequencing (NGS) of patients has significantly changed our ways to study cancer genomics as it provides precise estimates of gene expression, fusion transcripts, expressed single nucleotide variants (eSNVs), splice variants and copy number variants. The Breast Cancer Genome Guided Therapy (BEAUTY) is an ongoing clinical study in which RNA sequencing (RNAseq) and whole exome sequencing (WES) are performed prior to, during and after neoadjuvant chemotherapy. Here we report the use of these sequencing technologies to investigate gene expression levels and mutational profiles in a triple negative breast cancer (TNBC) patient enrolled in BEAUTY whose disease did not respond to neoadjuvant paclitaxel and anthracycline/cycphosphamide. METHODS Computational approaches were used to integrate WES and RNAseq data obtained before therapy (V1T), after 12 weekly paclitaxel treatments (V2T) and after anthracycline-based regimen at surgical resection (V3T) to study a single patient with persistent TNBC disease after neoadjuvant chemotherapy. RESULTS Using RNA-Seq data, we identified an inter-chromosomal fusion transcript between chromosome 20 and 22 (GNAS-TTC38) that was highly expressed at V1T, V2T and V3T. We also identified intra-chromosomal fusion transcripts that were expressed at two time points, such as fusion transcript (KANSL1-ARL17A) on chromosome 17 for V1T and V2T and fusion transcript (RBM12B-LINC00535) on chromosome 8 for V2T and V3T time points. Several gene expression changes were also observed. Gene expression analysis of V1T, V2T and V3T tumors was performed. Differential temporal gene expression profiles of 9884 genes that were significant and varying at different time points were obtained for pathway analysis. Pathway analysis of 9884 genes identified up regulation and down regulation of several transcription factors with a fold change of 2x or more. When compared to blood, DNA tumor and RNA-Seq data, we identified 81 common somatic eSNVs that were expressed in both V1T and V2T time points and we are in the process of investigating V3T data. We found alterations of key transporter domains (CD225, Coatamer_beta_C, DUF2435, Dynamitin, EI24, GLTP, LMF1, Porin_3, V-ATPase_C) in our V1T and V2T SNV data. Similar to gene expression analysis, we are in the process of obtaining the list of mutations at various time points to identify driver and passenger mutation candidate genes for this specific TNBC patient. CONCLUSIONS Our initial time-series analysis of eSNV, fusion transcripts and gene expression data demonstrate that intensive analysis for individual patients is feasible. Further investigation of drug transporters and transcription regulators may help develop personalized treatment strategies for patients with disease resistant to current regimens. Citation Format: Krishna R. Kalari, Xiaojia Tang, Kevin J. Thompson, Douglas W. Mahoney, Poulami Barman, Jason P. Sinnwell, Hugues Sicotte, Peter Vedell, Steven N. Hart, Travis J. Dockter, Katie N. Jones, Amy L. Conners, Ann M. Moyer, Daniel W. Visscher, Jia Yu, Bowen Gao, Sarah A. McLaughlin, John A. Copland, Alvaro Moreno-Aspitia, Donald W. Northfelt, Richard J. Gray, Vera J. Suman, Jeanette E. Eckel Passow, Jean-Pierre A. Kocher, Eric D. Wieben, Gianrico Farrugia, Cloann G. Schultz, James N. Ingle, Richard Weinshilboum, Matthew P. Goetz, Liewei Wang, Judy C. Boughey. Analysis of sequencing data to identify potential drug targets for an individual newly diagnosed with basal breast cancer who failed to respond to current standard neoadjuvant chemotherapy. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4185. doi:10.1158/1538-7445.AM2014-4185