Citations

Investigating the most likely causal variants identified by fine‐mapping analyses may improve the power to detect gene–environment interactions. We assessed the interplay between 70 single nucleotide polymorphisms identified by genetic fine‐scale mapping of susceptibility loci and 11 epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER) positive (ER+) and ER negative (ER–) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene–environment interactions were identified as noteworthy (BFDP < 0.80) when assuming a true prior interaction probability of 0.01. The strongest interaction result in relation to overall breast cancer risk was found between CFLAR‐rs7558475 and current smoking (ORint = 0.77, 95% CI: 0.67–0.88, pint = 1.8 × 10−4). The interaction with the strongest statistical evidence was found between 5q14‐rs7707921 and alcohol consumption (ORint =1.36, 95% CI: 1.16–1.59, pint = 1.9 × 10−5) in relation to ER– disease risk. The remaining two gene–environment interactions were also identified in relation to ER– breast cancer risk and were found between 3p21‐rs6796502 and age at menarche (ORint = 1.26, 95% CI: 1.12–1.43, pint =1.8 × 10−4) and between 8q23‐rs13267382 and age at first full‐term pregnancy (ORint = 0.89, 95% CI: 0.83–0.95, pint = 5.2 × 10−4). While these results do not suggest any strong gene–environment interactions, our results may still be useful to inform experimental studies. These may in turn, shed light on the potential interactions observed.

463 Background: The genomic assessment of cancer has been revolutionized by next-generation sequencing and is increasingly being applied in the clinic to guide therapeutic decision-making. Time to reporting of results, specimen quantity, and analyte quality have constrained initial clinical applications to gene panels and whole exome based strategies. Methods: Patients underwent surgical resection, excisional or core biopsies, or bone marrow biopsy. Samples were analyzed by whole genome or exome sequencing and RNA sequencing, bioinformatics analysis, and therapeutic target prioritization by a multi-disciplinary Clinical Genomics Board. All prioritized targets were CLIA validated using Sanger sequencing, RT-qPCR, FISH, or IHC as appropriate. Treatment was delivered using off-label FDA approved drugs, clinical trials, or single patient INDs. Results: We have enrolled 40 patients with advanced, treatment-refractory cancers of whom sequencing data is available on 33. The initial 6 patients were evaluated in a non-CLIA pilot phase and 27 in the CLIA enabled phase. Upon availability of the initial report, identified targets of putative therapeutic relevance were then prioritized by the CGB in 22/27 patients (81%) for subsequent CLIA validation. Eleven patients have been treated with genomically selected therapy with partial response in 3/10 assessed patients. A testicular cancer patient had aberrations in two testes specific genes, a copy number gain in TSSK6 and a novel gene fusion between thyroid hormone receptor associated protein 3 (THRAP3) and Tektin 2 (TEKT2). Additionally, a case of papillary renal cell carcinoma had an amplification of YAP1 and a previously unreported P287T mutation in CCND1, suggesting potential benefit with a CDK4/6 inhibitor. Treatment is ongoing and results will be reported. Conclusions: Integrated whole genome analysis in a CLIA setting is feasible. Integration of SNV, copy number and transcriptional data may allow for selection of putative driver genes to enhance targeted therapy decisions. Barriers for future broader implementation include the need for reduced time from biopsy to report and availability of therapies.

Abstract Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103–14. ©2016 AACR.

Abstract The aim of this study was to estimate the effect of exposure to secondhand tobacco smoke on the incidence of lung adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA). Data from seven case–control studies participating in the International Lung Cancer Consortium (ILCCO) were pooled, resulting in 625 cases of AIS/MIA and 7,403 controls, of whom 170 cases and 3,035 controls were never smokers. Unconditional logistic regression was used to estimate adjusted ORs (ORadj) and 95% confidence intervals (CI), controlling for age, sex, race, smoking status (ever/never), and pack-years of smoking. Study center was included in the models as a random-effects intercept term. Ever versus never exposure to secondhand tobacco smoke was positively associated with AIS/MIA incidence in all subjects (ORadj = 1.48; 95% CI, 1.14–1.93) and in never smokers (ORadj = 1.45; 95% CI, 1.00–2.12). There was, however, appreciable heterogeneity of ORadj across studies (P = 0.01), and the pooled estimates were largely influenced by one large study (40% of all cases and 30% of all controls). These findings provide weak evidence for an effect of secondhand tobacco smoke exposure on AIS/MIA incidence. Further studies are needed to assess the impact of secondhand tobacco smoke exposure using the newly recommended classification of subtypes of lung adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 24(12); 1902–6. ©2015 AACR.

Abstract Background: Breast tumors exhibit intratumor heterogeneity resulting in targeted therapy resistance and other challenges in disease management. To address the sources of heterogeneity, we performed a unique, in-depth analysis of clonal architecture in primary chemoradiation-naïve breast cancers. We combined DNA content-based flow cytometry and ploidy analysis with aCGH and whole exome next-generation sequencing (NGS) in multiple biopsies from the tumors and involved lymph nodes (LNs). Material and methods: We sorted nuclei from distinct populations of diploid, tetraploid, and aneuploid cells in surgical tumor samples from three chemoradiation-naïve patients. Each sorted tumor cell population was interrogated with aCGH and whole exome NGS. In Patient #1, we sorted and interrogated the genomes of tumor populations from 12 fresh frozen sections morphologically mapped from within a HER2+, ER+, PR- primary invasive ductal carcinoma (IDC) of histological grade 3 with LN involvement and 2-3 sections from 2 out of 5 LNs. In Patient #2, 11 morphologically mapped fresh frozen sections were analyzed from a grade 2, ER+, PR+, HER2+, BRCA2 mutant LN- IDC. In parallel, matching samples were processed for IHC assays. In Patient #3 biopsies from a grade 2 ER+, PR+ primary tumor were profiled. Results: The 18 primary and LN biopsies from Patient #1 fell into 6 distinct ploidy groups albeit with aberrant but homogenous aCGH profiles, characterized by SARC amplification and homozygous deletions in ROBO1 and ROBO2. In contrast a dominant ploidy was identified throughout 10 biopsies in Patient #2 but with heterogeneous aCGH profiles. Patient #3 had a clonal homozygous deletion in Numb in each of 4 tumor biopsies. Mutation profiles obtained through exome sequencing further confirmed that ploidy was the main driver in Patient #1 whereas copy number aberrations played the key role in Patient #2 with the BRCA2 mutation (R3129X). A dendrogram based on exome variant calls of the aneuploid populations in Patient #1 strongly correlated with ploidy group and further revealed the specific clonal population characterized by a 5N ploidy and homozygous mutations in TP53 and PIK3CA as the progenitor to the ploidies present in the distant LNs. Strikingly, patients #1 and #2 were HER2 wild type across their clonal populations, contradicting IHC staining in a single core biopsy. Conclusions:Rather than inferring the presence of distinct tumor cell populations, our flow-sorting based approach of first identifying clonal populations and then interrogating their genomes, provides an objective method of exploring the sources and clinical significance of tumor heterogeneity. Our approach of clonal analysis has broad implications in the study of tumor heterogeneity and the identification of drivers in breast and other solid tumors that can advance more effective treatment and clinical management of patients with this disease. Citation Format: MARIACARLA ANDREOZZI, Princy Francis, Elizabeth Lenkiewicz, Mia Champion, Brady Laughlin, Karen Anderson, Heather Cunliffe, Ann E. McCullough, Michael T. Barrett, Barbara Pockaj. Deep clonal profiling identifies distinct mechanisms of heterogeneity and evolution in breast cancer. [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 LB-175. doi:10.1158/1538-7445.AM2015-LB-175

Abstract Background: Genomic assessment of cancer has been revolutionized by Next-Generation sequencing and is increasingly being applied to guide clinical prognostic and therapeutic decision-making. Initial clinical applications have been limited to gene panels and whole exome based strategies due to challenges with time to reporting of results, specimen quantity, analyte quality, and ethical-legal-social issues (ELSI). Methods: Excisional or core tumor biopsies or bone marrow biopsies were obtained from consenting participants. Nucleic acid was extracted from fresh or frozen samples followed by WGS/WES/RNASeq on the Illumina HiSeq2000 or HiSeq2500 and bioinformatics analysis. Therapeutic targets were then prioritized by a multi-disciplinary Genomic Tumor Board (GTB) and CLIA validated using Sanger sequencing, RT-qPCR, FISH and/or IHC. Treatment was delivered using on/off-label FDA approved drugs, available clinical trials and single patient INDs. Results: We consented 64 and enrolled 35 patients with advanced, treatment-refractory cancers. Median age was 59 (range 27-91) with 62% male. The majority had ECOG scores of 1 (94%). A median of 79 (range 28-8891) potentially functional somatic point mutations were identified in each case. One to two mutations/case were further identified as therapeutically targetable in 60% of the cases. The median time from tissue acquisition to CLIA validated results was 116 days (range 42-282) with CLIA validation of targets achieved in 21 of 22 patients. Genomic, target directed treatment was ultimately instituted in 13 patients utilizing: on/off label FDA approved drugs (n = 9), clinical trial (n = 3) and single patient IND (n = 1). Preliminary clinical efficacy was noted in 5 patients (2 PR, 3 SD). Integration of WES, long-insert WGS and RNA-Seq identified a case with an ERRFI1 mutant allele present in only 11% of the DNA reads while 82% of the RNA-Seq reads had the mutant transcript. The patient was treated with erlotinib and achieved a partial response by RECIST criteria. In another case, a fusion between FGFR2-MGEA5 was observed in WES and RNA-Seq data leading to prioritization as a drug target. Reasons for patients not receiving targeted therapy included: inability to access treatment (n = 1), death prior to intended treatment (n = 3), results returned after death (n = 3), no targets identified (n = 2) and decision to pursue alternate therapy (n = 5). Conclusions: Integrating whole genome analysis in a CLIA setting is not only feasible, but also valuable, in the prioritization and selection of potential targeted therapies for patients with advanced tumors. Continued barriers to broad application include the need for shorter time to reporting as well as broad availability of therapies through basket studies. Citation Format: Mitesh J. Borad, Jan Egan, Mia Champion, Katherine Hunt, Robert McWilliams, Ann McCullough, Jessica Aldrich, Sara Nasser, Winnie Liang, Michael Barrett, David Craig, Ramesh Ramanathan, John Carpten, A. Keith Stewart, Alan Bryce. Implementation of CLIA enabled integrated whole genome (WGS)/exome (WES)/transcriptome (RNAseq) next-gen sequencing to identify therapeutically relevant targets in advanced cancer patients. [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 CT112. doi:10.1158/1538-7445.AM2015-CT112

Abstract Background: Next generation sequencing (NGS) has revealed that the genetic profiles of individual tumors are highly heterogeneous due to their subclonal composition. Tumor heterogeneity has profound clinical implications affecting differences in treatment response and therapeutic resistance. Methods: An estrogen receptor positive, Her2 normal breast cancer patient underwent definitive surgical treatment with modified radical mastectomy. Multiple samples were obtained from the primary cancer and metastatic lymph nodes. Flow cytometry based methods were used to isolate and classify distinct cell subpopulations according to their ploidy, and samples were processed for whole exome sequencing (WES). Comparative Genomic Hybridization (CGH) methods were used to identify somatic unique copy-number alterations (CNAs). WES-Single Nucleotide Variations (SNVs) were used to infer subclonal phylogenetic relationships using Maximum Parsimony methods and annotated with variation-cluster-barcodes (vc-barcodes) generated using a variation Bayesian mixture model (VBMM) approach that focuses on copy-number neutral sequence variations for subclone identification (SciClone). Our comparative phylogenetic-VBMM clustering method was used to identify CNA and/or SNV drivers that are key in differentiating subclonal populations at multiple tumor sites. Results: CNA-neutral WES-SNVs from 25 subclonal populations (isolated from 4 distinct sites) clustered into 6 major groups and were used to generate vc-barcodes. A phylogenetic reconstruction of subclonal architecture annotated with vc-barcode information expedited identification of key variations underlying the differentiation of subclones at distinct tumor sites. For example, we identified an amplification event involving the Anaplastic Lymphoma Kinase (ALK) gene that was more frequent in primary biopsy subclones (12/17, ∼71%) in comparison to metastatic subclones (2/6, ∼34%). As the ALK amplification is lost, we find that most of the metastatic subclones also acquire a predicted damaging mutation in this oncogene (4/6, ∼67%). In addition, we identified a potential driver metastatic cell lineage that carries the ALK amplification in the absence of the nonsynonymous mutation. Conclusions: We developed a hybrid clustering methodology and used it to reconstruct the subclonal architecture in primary and metastatic tumors from a single breast cancer patient. Our methods have identified several CNAs and/or SNVs underlying subclonal differentiation, as well as potential biomarkers of disease progression and indicators of emerging resistance. Application of these methods to larger cohorts and types of tumors should be conducted to ascertain more precise estimates of the predictive accuracy of our processes. Citation Format: Mia D. Champion, Princy Francis, Barbara A. Pockaj, Michael T. Barrett. Hybrid clustering methodologies to distinguish CNAs and/or SNVs that drive subclonal differentiation in samples from a breast cancer patient primary tumor and metastatic lymph nodes. [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 4861. doi:10.1158/1538-7445.AM2015-4861