Citations

ABSTRACTX‐chromosome inactivation (XCI) epigenetically silences transcription of an X chromosome in females; patterns of XCI are thought to be aberrant in women's cancers, but are understudied due to statistical challenges. We develop a two‐stage statistical framework to assess skewed XCI and evaluate gene‐level patterns of XCI for an individual sample by integration of RNA sequence, copy number alteration, and genotype data. Our method relies on allele‐specific expression (ASE) to directly measure XCI and does not rely on male samples or paired normal tissue for comparison. We model ASE using a two‐component mixture of beta distributions, allowing estimation for a given sample of the degree of skewness (based on a composite likelihood ratio test) and the posterior probability that a given gene escapes XCI (using a Bayesian beta‐binomial mixture model). To illustrate the utility of our approach, we applied these methods to data from tumors of ovarian cancer patients. Among 99 patients, 45 tumors were informative for analysis and showed evidence of XCI skewed toward a particular parental chromosome. For 397 X‐linked genes, we observed tumor XCI patterns largely consistent with previously identified consensus states based on multiple normal tissue types. However, 37 genes differed in XCI state between ovarian tumors and the consensus state; 17 genes aberrantly escaped XCI in ovarian tumors (including many oncogenes), whereas 20 genes were unexpectedly inactivated in ovarian tumors (including many tumor suppressor genes). These results provide evidence of the importance of XCI in ovarian cancer and demonstrate the utility of our two‐stage analysis.

Purpose The purpose of this meta‐analysis was to determine overall objective graft failure rate, failure rate by graft type (allograft vs autograft reconstruction), instrumented laxity, and patient outcome scores following revision anterior cruciate ligament (ACL) reconstruction. Outcomes of interest were collected for all studies meeting the study inclusion criteria, but lower‐level studies (level III/IV) were not pooled for quantitative synthesis due to high levels of heterogeneity in these study populations. Methods A comprehensive search strategy was performed to identify studies reporting outcomes of revision ACL reconstruction. The primary outcome reported was graft failure. A meta‐analysis comparing rate of failure by graft type was conducted using a random effects model. Studies also reported patient clinical outcome scores, including International Knee Documentation Committee (IKDC), Lysholm, and knee injury and osteoarthritis outcome scores (KOOS) and graft laxity. Results Eight studies with 3,021 patients (56% male, 44% female) with an average age of 30 ± 4 years and mean follow‐up time of 57 months were included. The overall objective failure rate was 6% (95% confidence interval [CI], 1.8%‐12.3%). Mean instrumented laxity as side‐to‐side difference was 2.5 mm (95% CI, 1.9‐3.1 mm). Mean IKDC subjective score was 76.99 (95% CI, 76.64‐77.34), mean KOOS symptoms score was 76.73 (95% CI, 75.85‐77.61), and mean Lysholm score was 86.18 (95% CI, 79.08‐93.28). The proportion of patients with IKDC grade A or B was 85% (95% CI, 77%‐91%). When the available data for failure rate were analyzed by graft type, autograft reconstruction had a failure rate of 4.1% (95% CI, 2.0%‐6.9%), similar to allograft reconstruction at 3.6% (95% CI, 1.4%‐6.7%). Conclusions In this meta‐analysis, revision ACL reconstruction had failure rates similar to autograft or allograft reconstruction. Overall outcome scores for revision reconstruction have improved but appear modest when compared with primary ACL reconstruction surgery. Level of Evidence Meta‐analysis of Level II studies, Level II.

General and Critical Review Format: The evolution of clinical practice and medical technology has yielded an increasing number of clinical measures and tests to assess a patient's progression and return to sport readiness after injury. The plethora of available tests may be burdensome to clinicians in the absence of evidence that demonstrates the utility of a given measurement. Objective: Thus, there is a critical need to identify a discrete number of metrics to capture during clinical assessment to effectively and concisely guide patient care. Data Sources: The data sources included Pubmed and PMC Pubmed Central articles on the topic. Therefore, we present a systematic approach to injury risk analyses and how this concept may be used in algorithms for risk analyses for primary anterior cruciate ligament (ACL) injury in healthy athletes and patients after ACL reconstruction. Main Results: In this article, we present the five-factor maximum model, which states that in any predictive model, a maximum of 5 variables will contribute in a meaningful manner to any risk factor analysis. Conclusions: We demonstrate how this model already exists for prevention of primary ACL injury, how this model may guide development of the second ACL injury risk analysis, and how the five-factor maximum model may be applied across the injury spectrum for development of the injury risk analysis.

Abstract Introduction: In females, X-chromosome inactivation (XCI) epigenetically silences transcription of one copy of the X chromosome. Which chromosome is silenced is randomly selected, and is tissue- and cell-specific. While some genes are known to escape XCI under normal conditions, aberrant XCI patterns are thought to occur in female-specific cancers, although the role of XCI in ovarian tumorigenesis and progression is largely unknown. The process of XCI is complex, and integration of gene expression, DNA methylation, and copy number data can inform the XCI status of individual genes and chromosome-wide XCI patterns for individual patients. Methods: We evaluated gene- and chromosome-level patterns of XCI by integrating RNA sequence, copy number alteration, genotype, and DNA methylation data to study XCI escape patterns in tumor samples from 99 ovarian cancer patients. We measured allele-specific expression (ASE) for 397 X-linked genes to identify the active alleles for each tumor. Combining ASE data with knowledge of copy number status, we used a Bayesian beta-binomial mixture model to estimate which genes escaped XCI for each patient, and validated our findings using DNA methylation data. To assess global XCI patterns, we performed cluster analyses on the ASE and methylation data, after adjusting for loss of heterozygosity. We examined the relationship between the clusters and clinical factors, including overall survival and time to recurrence. Results: DNA promoter methylation demonstrated inverse regional correlations with ASE. Cluster analyses using ASE and methylation data demonstrated evidence of two tumor clusters, representing normal XCI and global XCI dysregulation. The dysregulated XCI cluster (N=52) was associated with lower X-inactive specific transcript expression as expected (p<0.01). Patients with XCI dysregulated tumors were higher grade, stage, serous histology and were sub-optimally debulked (p<0.05). These patients also had shorter overall survival time (HR=1.87, p=0.02) and time to recurrence (HR=2.34, p<0.01), although associations were attenuated after covariate adjustment. In 45 tumor samples with sufficient data, we observed escape patterns largely consistent with previous reports of multiple tissue types. When comparing tumor to normal ovarian tissue, eight genes (CXorf23, CXorf36, BRWD3, ELF4, SLITRK4, GABRE, CLCN4, SH3BGRL) showed putative escape in the tumor and two genes (RBBP7, OFD1) showed discrepant tumor inactivation. Conclusions: We identified discrepant gene-level XCI tumor classifications compared to normal tissue and identified a group of patients with chromosome-wide XCI dysregulation associated with worse clinical prognosis. This provides evidence of the role of XCI in ovarian cancer and highlights the need to integrate multiple genomic data types to study XCI. Citation Format: Stacey J. Winham, Nicholas B. Larson, Sebastian M. Armasu, Zachary C. Fogarty, Melissa C. Larson, Kimberly R. Kalli, Kate Lawrenson, Simon Gayther, Brooke L. Fridley, Ellen L. Goode. Integrative analyses of gene expression, DNA methylation, genotype and copy number alterations characterize X-chromosome inactivation in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2420. doi:10.1158/1538-7445.AM2017-2420

Abstract Background: Large-scale genome-wide association studies (GWAS) have identified ~35 loci associated with epithelial ovarian cancer risk. However, these genetic loci together explain only a small portion of the heritability of this malignancy. The large majority of the GWAS-identified variants are located in non-coding regions, thus possible causal genes in these loci remain largely unknown. We performed a transcriptome-wide association study (TWAS) to search for novel genetic loci for ovarian cancer risk and plausible causal genes at GWAS-identified loci. Method: Transcriptome data from normal ovarian tissue samples (n=68) and all tissue samples (n = 369), along with their high-density genotyping data, obtained from only European descendants included in the Genotype-Tissue Expression Project (GTEx), were used to build ovarian linear prediction models and cross-tissue models (to increase statistical power) using the elastic net method. Based on model performance, we evaluated 17,121 genes for their cis-predicted gene expressions in relation to ovarian cancer risk using summary statistics data generated in GWAS of ovarian cancer from 25,509 cases and 40,941 controls. MetaXcan was used to integrate gene expression prediction model with summary statistics. Results: We identified 35 genes with predicted expression levels associated with ovarian cancer risk at P value < 2.2 × 10-6, the Bonferroni corrected significance level for multiple comparisons. Of these, 12 genes at 4 genetic loci are located ≥500kb away from risk SNPs previously reported in GWAS, representing potential novel genetic loci for ovarian cancer risk. The remaining 23 genes at 12 loci are located within known ovarian cancer risk loci. Fifteen of these genes at the 12 loci have not been reported in previous studies. Analyses by ovarian cancer histological subtypes showed that the majority of these 35 genes are associated with serous invasive carcinoma. Several new associations were identified in histological subtype analyses. Conclusion: In this TWAS we identified multiple genes with predicted expressions related to ovarian cancer risk and provide substantial new information to enhance the understanding of ovarian cancer biology and genetics. Citation Format: Yingchang Lu, Joellen M. Schildkraut, Thomas A. Sellers, Lang Wu, Xingyi Guo, Bingshan Li, Y. Ann Chen, Jennifer B. Doherty, Simon Gayther, Ellen L. Goode, Hae Kyung Im, Siddhartha Kar, Kate Lawrenson, Ani W. Manichaikul, Jennifer B. Permuth, Brett M. Reid, Jamie K. Teer, Paul Pharoah, Wei Zheng, Jirong Long. Transcriptome-wide association study among 66,450 women to identify candidate susceptible genes for ovarian cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1308. doi:10.1158/1538-7445.AM2017-1308

Abstract Epithelial ovarian cancer (EOC) is a rare but deadly disease for which there is notably poorer survival in women of African Ancestry (AA) compared to women of European Ancestry (EA). Previous EA-based genome-wide association studies (GWAS) have identified 30 common, low penetrant EOC susceptibility alleles. Using the custom-designed 533,631 SNP Illumina OncoArray and imputation to ~12 million genetic variants in the 1000 Genomes Phase 3, we conducted a GWAS in 755 AA EOC cases, including 537 high-grade serous ovarian cancer (HGSOC) cases, and 1,235 AA controls from the Ovarian Cancer Association Consortium (OCAC). We identified novel susceptibility loci with suggestive evidence of association with EOC, based on a threshold of P<1x10-6, at three distinct loci including 10p15.1 (lead SNP rs4525119, intronic to AKR1C3, P=4.9 x 10-7, minor allele frequency [MAF]=0.33), 3p25.3 (lead SNP rs7643459, intronic to LOC101927394, P=8.4 x 10-7, MAF=0.36), and 4q13.3 (lead SNP rs4286604, 15 kb 3’ of UGT2A1, P=8.5x10-7, MAF=0.27). In analysis of HGSOC, we identified suggestive evidence of association at six distinct loci including 5q11.2 (lead SNP rs37792, 132 kb 5’ of FST, P=6.0x10-8, MAF=0.34), Xq27.2 (lead SNP rs57403204, 81 kb 3' of MAGEC1, P=1.7x10-7, MAF=0.06), 10p15.1 (lead SNP rs79079890, LOC105376360 intronic, P=3.0x10-7, MAF=0.03), 17p25.1 (lead SNP rs66459581, 5 kb 5’ of PRPSAP1, P=5.1x10-7, MAF=0.23), 15p12 (lead SNP rs116046250, GABRG3 intronic, P=8.7x10-7, MAF=0.05), and 4q21.21 (lead SNP rs192876988, 30 kb 3’ of GK2, P=9.2x10-7, MAF=0.05). These SNPs showed no evidence of association with risk in the OCAC EA GWAS including up to 23,543 EOC cases and 29,444 controls. Of the SNPs identified with suggestive evidence of association in our GWAS, two are located near genes known to regulate hormones and previously reported in relation to diseases of the ovary (AKR1C3 and FST), two have been linked to cancer (AKR1C3 and MAGEC1) and one represents testis-specific expression (GK2). Of the 30 SNPs identified previously in a EA EOC GWAS, we observed nominally significant associations (P<0.05) with consistent direction of effect for eight SNPs in AA women. Our investigation presents evidence of (1) variants for EOC shared among EA and AA women and (2) novel EOC risk loci of importance for AA women. Citation Format: Ani Manichaikul, Lauren C. Peres, Xin-Qun Wang, Kate Lawrenson, Sarah Abbott, Ann G. Schwartz, Anna H. Wu, Edward Peters, Patricia G. Moorman, Michele L. Cote, Melissa Bondy, Linda Kelemen, Ellen L. Goode, Jill Barnholtz-Sloan, Simon A. Gather, Andrew Berchuck, Jennifer A. Doherty, Paul Pharoah, Joellen Schildkraut. Identification of novel epithelial ovarian cancer loci in women of African ancestry from the Ovarian Cancer Association Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1307. doi:10.1158/1538-7445.AM2017-1307

AbstractBackgroundCentral nervous system (CNS) relapse in non‐Hodgkin lymphoma (NHL) is a rare but serious complication that carries a poor prognosis. The use of infusional etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab (EPOCH‐R) for frontline treatment of diffuse large B cell lymphoma (DLBCL) is increasing, though little is known about incidence of and risk factors for CNS relapse with this regimenPatients and methodsWe completed a chart review of patients with NHL who received EPOCH‐R as front line therapy. Data obtained included baseline and treatment characteristics including if patients received CNS directed therapy. We measured overall survival (OS), progression free survival (PFS), and progression to CNS involvement.ResultsWe identified 223 patients who met the inclusion criteria, 72% had DLBCL. Of all the patients, 5.8% experienced CNS relapse, and 38.6% were treated with CNS prophylaxis. There was no difference in rate of CNS relapse, OS, or PFS between patients who had and had not received CNS prophylaxis. Patients whose serum lactate dehydrogenase was greater than twice the upper limit of normal at diagnosis and those with extranodal disease were significantly more likely to have CNS relapse (P = .0247 and 0.022, respectively) than their counterparts.ConclusionsThe rate of CNS relapse in this patient population approaches 6%, not significantly different from reports on those receiving R‐CHOP. The results of this study suggest that CNS prophylaxis might be more selectively used among patients treated with EPOCH‐R with certain high‐risk features.

4076 Background: Gallbladder carcinoma (GBC) is commonly diagnosed at an advanced, unresectable disease stage and has a poor prognosis. Comprehensive genomic profiling (CGP) has a developing role in guiding systemic, precision anti-cancer therapy. Methods: We performed hybrid capture-based CGP on FFPE samples for 491 consecutive advanced GBCs. Mean coverage depth was >550X for up to 315 cancer-related genes plus 37 introns from 28 genes frequently rearranged in cancer. All 4 classes of genomic alterations (GA) were detected. Tumor mutational burden (TMB) was determined on up to 1.1 Mb of sequenced DNA. Results: Median age was 64 (range 25-88) and 69% (337/491) of patients were female. 96% (470), 2.9% (14), and 1.4% (7) of GBC patients had the diagnosis of adenocarcinoma, adenosquamous, or carcinoma not specified (NOS), respectively. Commonly altered genes were TP53 (62%), CDNK2A (31%), ARID1A (18%), and SMAD4 (15%), and most of the genetic aberrations (GA) were short variants. Potentially targetable SCNAs (including ERBB genes, MET, FGFRs, and the CCND1-FGF3/4/19 11q13 amplicon) were identified in 21% of cases (Table). Oncogenic BRAF, ALK, or FGFR2/3 rearrangements were found in 7 cases (1.4%). Moreover, 7.8% of cases had BRCA2 or ATM GA, and 0.8% had INI1loss suggesting benefit from PARP or EZH2 inhibitors, respectively. TMB was low; the 25th, 50th, and 75th percentiles were 2.5, 3.8 and 6.3 mutations/Mb, respectively. Less than 1% of cases had microsatellite instability. Radiological response to TKIs and immunotherapy was noted. Conclusions: In addition to the significant opportunity for anti-HER targeted therapies, other subsets of GBC cases harbored kinase GA (particularly SCNA), the 11q13 amplicon, or BRCA2/ ATM/ INI1mutations that are linked to therapeutic benefit. How the frequency of both driver SCNA and DNA repair alterations in GBC can be linked with inflammation awaits additional investigation. [Table: see text]