Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor-positive, lymph node-negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clinicopathologic data. Hence, we developed a model that uses routine pathologic parameters to estimate Oncotype DX recurrence score (ODX RS) and independently tested its ability to predict ODX RS in clinical samples. Patients and MethodsWe retrospectively reviewed ordered ODX RS and pathology reports from five institutions (n = 1,113) between 2006 and 2013. We used locally performed histopathologic markers (estrogen receptor, progesterone receptor, Ki-67, human epidermal growth factor receptor 2, and Elston grade) to develop models that predict RS-based risk categories. Ordering patterns at one site were evaluated under an integrated decision-making model incorporating clinical treatment guidelines, immunohistochemistry markers, and ODX. Final locked models were independently tested (n = 472). Results Distribution of RS ConclusionThe proposed model accurately predicts high-and low-risk RS categories (. 25 or # 25) in a majority of cases. Integrating histopathologic and molecular information into the decision-making process allows refocusing the use of new molecular tools to cases with uncertain risk.

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Background Our previous study using single nucleotide polymorphism (SNP) array identified significant differences in copy number variation (CNV) between tumor subtypes. This study was conducted to extend our previous findings, using both Fluorescent In-Situ Hybridization (FISH) and quantitative PCR (qPCR), and to investigate the possible relationship with major thyroid cancer driver mutations, and their association with aggressive features in follicular thyroid tumors. Methods For the validation, we obtained paraffin blocks of all cases with CNVs identified in our prior study (7 samples with Ch12 amplification, and 6 samples with Ch22 deletion), as well as 6 control samples with no CNV, for FISH analysis and qPCR. We used centromeric and distal probes to differentiate partial vs whole chromosome alterations. We then extended the study to characterize the relation between CNV and driver mutations and possible associations with aggressive features in a larger cohort of tumors, consisting of: 35 follicular adenoma (FA), 19 non-aggressive follicular variant papillary thyroid carcinoma (FVPTC), 22 aggressive FVPTC, 16 non-aggressive follicular carcinoma (FC), and 15 aggressive FC. Results Our FISH and qPCR results confirmed our earlier CNV results in this expanded sample cohort. Furthermore, FISH indicated that in each case, CNV involved the entire chromosome rather than sub-chromosomal alterations. Ch12 amplification was significantly more prevalent in FA and non-aggressive tumor than aggressive tumor subtypes (p-value <0.01). The HRAS mutation was predominantly detected in aggressive FCs, but the low number of events precluded reaching statistical significance. Conclusion Our study suggests that Ch12 amplification is associated with indolent tumors. Since FAs are a common source of inconclusive pre-operative cytopathology, a molecular signature such as Ch12 amplification that identifies follicular tumors with little aggressive potential could be useful. Further independent external testing is warranted. CNV and RAS pattern across follicular thyroid tumor subtypesChromosome 12 AmplificationChromosome 22 DeletionNRAS mutationHRAS mutationFA40% (14/35)17.1% (6/35)17.1% (6/35)0.3% (1/35)Non-aggressive FVPTC15.8% (3/19)15.8% (3/19)36.8% (7/19)0% (0/19)Aggressive FVPTC4.5% (1/22)22.7% (5/22)18.2% (4/22)0% (0/22)Non-aggressive FC6.3% (1/16)6.3% (1/16)50% (8/16)0% (0/16)Aggressive FC6.7% (1/15)26.7% (4/15)53.3% (8/15)26.7% (4/15) Citation Format: Hyun-seok Kim, Kathleen Wilsbach, Aurelien Marti, Alireza Najafian, Alan K. Meeker, James R. Eshleman, Justin A. Bishop, Martha Zeiger, Christopher B. Umbricht. DNA copy number variation and driver mutation patterns of follicular thyroid tumors. [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 4268. doi:10.1158/1538-7445.AM2015-4268

Background: Cancers exploit changes in both genomic copy number and DNA methylation to promote growth and escape tumor-suppressor pathways. Under the 2-hit hypothesis, a single gene is likely to be altered by multiple mechanisms at the same time and integrated analysis can sharpen the focus on the most likely drivers. Requiring larger amounts of input material and imposing additional costs may limit multiplatform analysis, especially in studies using archival tissues with long clinical follow-up information where the nucleic acids are degraded and yields are generally lower than are obtained from fresh tissues. Taking advantage of similarities between methylation arrays and SNP arrays, we developed Epicopy, a robust computational method to identify DNA copy number variation (CNV) using high-density Illumina Human Methylation 450K methylation microarrays, thereby delivering two complementary genetic and epigenetic profiles from a single chip. Methods: Epicopy was developed using data from thyroid carcinoma samples arrayed by The Cancer Genome Atlas (TCGA) and subsequently validated on breast and lung small cell carcinoma TCGA datasets. Using Epicopy, we identified circumstances where CNV information can be reliably measured by methylation microarrays. Results: Using TCGA SNP microarrays as the gold standard to assess the performance of methylation derived CNV data from the thyroid, breast, and lung small cell carcinoma datasets, we showed that Epicopy is able to detect CNVs identified by SNP arrays at a sensitivity of 0.69 and specificity of 0.90. Frequently occurring CNVs identified using Genomic Identification of Significant Targets in Cancer (GISTIC), were identified with even higher accuracy. Conclusion: Epicopy provides a robust method to obtain both copy number and methylation information from a single methylation microarray experiment and will add value to methylation microarrays at no additional cost to the user. Tools to highlight regions of high sensitivity and specificity will also be provided to help users decide on the feasibility of using Epicopy to identify CNVs in regions of interest. Epicopy is implemented in the R statistical language and will be made available as a freestanding package as part of the Bioconductor bioinformatics software project. Citation Format: Soonweng Cho, Hyun-seok Kim, Leslie M. Cope, Christopher B. Umbricht. Epicopy: Measuring DNA copy number variation using high-density methylation microarrays. [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 4873. doi:10.1158/1538-7445.AM2015-4873