Citations

Abstract Lung cancer is the deadliest cancer in the United States, contributing approximately 25% of all cancer deaths. Lung cancer risk is well-documented to increase in response to environmental factors, particularly tobacco smoking. As one might expect for a complex trait, there is also a significant genetic risk component in lung cancer, though it is not very well studied. We obtained whole exome sequencing (WES) genotype data from Washington University on 204 subjects from 25 extended families. These individuals were recruited from families with a history of lung cancer and previous analyses showed these 25 families be informative. The purpose of this study is to identify potential risk variants for lung cancer by performing genetic linkage analysis. Quality control was performed on the sequence data, filtering on parameters such as depth (less than 10), genotype quality (less than 10), missingness, and Mendelian inconsistencies. Identity-by-descent (IBD) values were also calculated to verify correct familial relationships. Quality control procedures left approximately 500,000 SNVs and indels for analysis. We performed two-point parametric linkage analysis assuming an autosomal dominant mode of inheritance with a disease allele frequency of 1%, a 10% penetrance for carriers and a 1% penetrance for non-carriers. Two discrete sets of linkage analyses were performed. One was a variant-based analysis, which evaluated linkage between the phenotype and individual SNVs or indels. The second was gene-based analysis, which created a multi-allelic pseudomarker corresponding to a gene from haplotypes of rare variants (minor allele frequency <= 0.01) located within that particular gene. Two-point linkage analysis was then performed on the pseudomarkers. While the variant-based analysis did not identify any genome-wide significant results, several were identified by the gene-based analysis. The highest HLOD scores were both greater than the genome-wide significance level of HLOD = 3.3 and were located on two regions of chromosome 1q: 1q42.13-43 and 1q21.2-21.1. In both regions, the significant HLOD scores clustered around known cancer genes. At the 1q42.13-43 region, signals centered on the cancer-implicated genes OBSCN and RYR2 (also a known mesothelioma gene), while at 1q21.2-21.1 the signals centered on five genes in the neuroblastoma breakpoint family (NBPF) genes, a cluster of recently duplicated genes. NBPF genes have previously been implicated in a variety of different cancers including lung cancer. We are currently performing additional analyses to corroborate the significant results, as well as examining the individual families to determine which families are driving the significant signals. Citation Format: Anthony M. Musolf, Haiming Sun, Bilal A. Moiz, Diptasri Mandal, Mariza de Andrade, Colette Gaba, Ping Yang, Yafang Li, Ming You, Elena Y. Kupert, Marshall W. Anderson, Ann G. Schwartz, Susan M. Pinney, Christopher I. Amos, Joan E. Bailey-Wilson. Whole exome sequencing identifies significantly linked regions on multiple chromosomes in families with a history of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3276.

Abstract Background: Although the efficacy of different immunotherapeutic agents varies greatly across tumor types, little is known about the role of different metastatic tumor microenvironments in modulating the expression of immune-checkpoints like PD-L1. Using metastatic breast cancer as a model, this work explored whether metastatic lesions derived from different patients, but colonizing the same host target organ, showed organ-specific immune signatures. Methods: Snap frozen metastatic lesions collected from 30 breast cancer patients enrolled in a prospective phase II multi-omic precision medicine clinical trial (“Side-Out II”) were used for this analysis. Sites of metastasis were: liver (LI) (n=10), skin/chest wall (SC) (n=10), and other lesions (OL) (n=10) [including lymph nodes (LN) (n=4), lung (LU) (n=3), and intra-abdominal lesions (IA) (n=3)]. Pure tumor epithelia were isolated from each specimen via Laser Capture Microdissection (LCM). PD-L1 expression (Antibody Clone E1L3N) was quantitatively measured using the Reverse Phase Protein Microarray (RPPA), a high-throughput, multiplex immunoassay that provides operator-independent quantitative data starting from a small amount of biological material. Proportion of patients with PD-L1 expression above the median and the 75th percentile of the population was compared across metastatic sites. Results: PD-L1 expression had a >5 fold dynamic range across breast cancer-derived metastatic lesions with different distribution across metastatic sites. Specifically, PD-L1 expression of 6/10 LIs, 5/10 OLs (including 2 LU and 3 LN positive lesions), and 4/10 SC metastases was greater than the population median of all metastases combined. LI metastases were the most represented group above the 75th percentile of the population (4/10), followed by OS (2/10) (including 1 LU and 1 LN positive lesions), and SC (1/10). Of the 15 lesions above the population median, 6 were LI (40%), 5 were OS (33%), and 4 were SC (27%); of the 7 metastases above the 75th percentile, 4 were LI (57%), 2 were OS (29%), and 1 was SC (14%). Conclusions: The LCM-RPPA workflow can capture PD-L1 protein expression on a continuous quantitative scale and provide a broader understanding of the dynamic range of expression. Our data suggest that organ specific microenvironments in which metastatic lesions develop may strongly influence overall PD-L1 tumor cell expression. If different metastatic host organs can modulate PD-L1 expression, location of the metastatic lesion should be kept in consideration when selecting patients that are candidates for immunotherapy. Such hypotheses should be further validated in prospective clinical trial where quantitative PD-L1 expression of different metastatic lesions is evaluated along with response to targeted immuno-oncology compounds. Citation Format: Mariaelena Pierobon, K Alex Hodge, Elisa Baldelli, Steven Anthony, Nicholas J. Robert, Donald W. Northfelt, Mohammad J. Jahanzeb, Linda Vocila, Lance A. Liotta, Bryant Dunetz, Emanuel F. Petricoin. PD-L1 expression differs across cancer metastatic sites from breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3129.

PurposeThe purpose of this study was to estimate the radiation dose to the lung and breast as well as the effective dose from tube current modulated (TCM) lung cancer screening (LCS) scans across a range of patient sizes.MethodsMonte Carlo (MC) methods were used to calculate lung, breast, and effective doses from a low‐dose LCS protocol for a 64‐slice CT that used TCM. Scanning parameters were from the protocols published by AAPM's Alliance for Quality CT. To determine lung, breast, and effective doses from lung cancer screening, eight GSF/ICRP voxelized phantom models with all radiosensitive organs identified were used to estimate lung, breast, and effective doses. Additionally, to extend the limited size range provided by the GSF/ICRP phantom models, 30 voxelized patient models of thoracic anatomy were generated from LCS patient data. For these patient models, lung and breast were semi‐automatically segmented. TCM schemes for each of the GSF/ICRP phantom models were generated using a validated method wherein tissue attenuation and scanner limitations were used to determine the TCM output as a function of table position and source angle. TCM schemes for voxelized patient models were extracted from the raw projection data. The water equivalent diameter, Dw, was used as the patient size descriptor. Dw was estimated for the GSF/ICRP models. For the thoracic patient models, Dw was extracted from the DICOM header of the CT localizer radiograph. MC simulations were performed using the TCM scheme for each model. Absolute organ doses were tallied and effective doses were calculated using ICRP 103 tissue weighting factors for the GSF/ICRP models. Metrics of scanner radiation output were determined based on each model's TCM scheme, including CTDIvol, dose length product (DLP), and CTDIvol, Low Att, a previously described regional metric of scanner output covering most of the lungs and breast. All lung and breast doses values were normalized by scan‐specific CTDIvol and CTDIvol, Low Att. Effective doses were normalized by scan‐specific CTDIvol and DLP. Absolute and normalized doses were reported as a function of Dw.ResultsLung doses normalized by CTDIvol, Low Att were modeled as an exponential relationship with respect to Dw with coefficients of determination (R2) of 0.80. Breast dose normalized by CTDIvol, Low Att was modeled with an exponential relationship to Dw with an R2 of 0.23. For all eight GSF/ICRP phantom models, the effective dose using TCM protocols was below 1.6 mSv. Effective doses showed some size dependence but when normalized by DLP demonstrated a constant behavior.ConclusionLung, breast, and effective doses from LCS CT exams with TCM were estimated with respect to patient size. Normalized lung dose can be reasonably estimated with a measure of a patient size such as Dw and regional metric of CTDIvol covering the thorax such as CTDIvol, Low Att, while normalized breast dose can also be estimated with a regional metric of CTDIvol but with a larger degree of variability than observed for lung. Effective dose normalized by DLP can be estimated with a constant multiplier.

AbstractWe aimed to validate the prognostic association of p16 expression in ovarian high‐grade serous carcinomas (HGSC) and to explore it in other ovarian carcinoma histotypes. p16 protein expression was assessed by clinical‐grade immunohistochemistry in 6525 ovarian carcinomas including 4334 HGSC using tissue microarrays from 24 studies participating in the Ovarian Tumor Tissue Analysis consortium. p16 expression patterns were interpreted as abnormal (either overexpression referred to as block expression or absence) or normal (heterogeneous). CDKN2A (which encodes p16) mRNA expression was also analyzed in a subset (n = 2280) mostly representing HGSC (n = 2010). Association of p16 expression with overall survival (OS) was determined within histotypes as was CDKN2A expression for HGSC only. p16 block expression was most frequent in HGSC (56%) but neither protein nor mRNA expression was associated with OS. However, relative to heterogeneous expression, block expression was associated with shorter OS in endometriosis‐associated carcinomas, clear cell [hazard ratio (HR): 2.02, 95% confidence (CI) 1.47–2.77, p < 0.001] and endometrioid (HR: 1.88, 95% CI 1.30–2.75, p = 0.004), while absence was associated with shorter OS in low‐grade serous carcinomas (HR: 2.95, 95% CI 1.61–5.38, p = 0.001). Absence was most frequent in mucinous carcinoma (50%), and was not associated with OS in this histotype. The prognostic value of p16 expression is histotype‐specific and pattern dependent. We provide definitive evidence against an association of p16 expression with survival in ovarian HGSC as previously suggested. Block expression of p16 in clear cell and endometrioid carcinoma should be further validated as a prognostic marker, and absence in low‐grade serous carcinoma justifies CDK4 inhibition.

Abstract Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P &lt; 2.2 × 10−6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10−7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P &lt; 1.47 × 10−3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis. Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419–30. ©2018 AACR.

Significance Mechanical forces are important for normal gastrointestinal tract function. The enterochromaffin cells in the gastrointestinal epithelium have been proposed, but not previously shown, to be specialized sensors that convert forces into serotonin release, and serotonin released from these cells is important for normal gastrointestinal secretion and motility. The findings in this study show that some enterochromaffin cells are indeed mechanosensitive, and that they use mechanosensitive Piezo2 channels to generate an ionic current that is critical for the intracellular Ca 2+ increase, serotonin release, and epithelial fluid secretion.

Abstract BACKGROUND Reducing length of stay (LOS) in a safe manner has the potential to save significant costs for the care of patients undergoing elective lumbar spine surgery. Due to the relative absence on weekends of staff required for discharging patients to rehabilitation or nursing facilities, we hypothesize that patients undergoing lumbar surgery later in the week have a longer LOS than their counterparts. OBJECTIVE To analyze the effect of day of the week for lumbar surgery on LOS. METHODS Patients undergoing surgery for lumbar degenerative disease were prospectively enrolled in the multicenter quality and outcomes database registry. A multivariable proportional odds regression model was built with LOS as the outcome of interest and patient and surgical variables as covariates. RESULTS A total of 11 897 patients were analyzed. Among those discharged home, the regression analysis demonstrated significantly higher odds of longer LOS for patients undergoing surgery on Friday as compared to Monday ( P < .001). Among those discharged to a facility, there were significantly higher odds of longer LOS for patients undergoing surgery on Wednesday ( P < .001), Thursday ( P < .001), and Friday ( P = .002) as compared to Monday. CONCLUSION The findings of this study suggest that lumbar patients undergoing fusions and those discharged to a facility have longer LOS when surgery is later in the week. Scheduling these patients for surgery earlier in the week and ensuring adequate resources for patient disposition on weekends may lead to LOS reduction and cost savings for hospitals, payers, and patients alike.