bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract 1588: Germline variation in DNA repair genes and risk of Barrett's esophagus and esophageal adenocarcinoma
Authors: Buas Matthew F. , Yan Li , Peng Xuan , Qi Qianya , Chen Jianhong , Thrift Aaron , He Qianchuan , Onstad Lynn , Gharahkhani Puya , MacGregor Stuart , Vaughan Thomas L. , Madeleine Margaret M.
Published: August 2019

Incidence of esophageal adenocarcinoma (EA) has risen substantially in Western countries over recent decades. Established risk factors for EA and its precursor lesion, Barrett’s esophagus (BE), include reflux, obesity, and tobacco smoking. Inherited genetic variation also influences disease risk, although only a limited number of susceptibility loci have been identified. Genomic analyses of EA tumors have revealed a distinctive mutational signature, high mutational burden, and extensive somatic chromosome alterations, features also observed in high-risk BE tissue. To explore whether germline variation in DNA repair-related genes may be associated with altered disease susceptibility, we analyzed data from a recent meta-analysis of genome-wide association studies (GWAS) encompassing 4,112 EA cases, 6,167 BE cases, and 17,159 controls, representing the largest sample size assembled for these conditions. Using a gene-based testing approach (VEGAS2), we assessed 263 DNA repair-related genes and found that variation in NEIL2, a mediator of base excision repair (BER), was significantly associated with risk of BE (P=1.4×10-5, q<0.05). No other gene-level associations with BE or EA survived correction for multiple comparisons. SNP-level analysis of 239 polymorphisms at the NEIL2 locus revealed six variants strongly associated with altered risk of BE (P<5×10-5, q<0.01), with the index SNP classified as an intronic eQTL for NEIL2 in esophageal tissue. Four of these SNPs were also associated with risk of EA (P<0.05), with odds ratios in the same direction and of similar magnitude. Our results provide evidence that germline genetic variation in a DNA glycosylase enzyme (NEIL2) may influence risk of BE/EA, and suggest a potential novel biological role for altered BER in BE/EA pathogenesis.

Citation Format: Matthew F. Buas, Li Yan, Xuan Peng, Qianya Qi, Jianhong Chen, Aaron Thrift, Qianchuan He, Lynn Onstad, Puya Gharahkhani, Stuart MacGregor, Thomas L. Vaughan, Margaret M. Madeleine. Germline variation in DNA repair genes and risk of Barrett's esophagus and esophageal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1588.
Abstract 159: Chemokine receptor expression profiling of lymph node invasive versus lymph node non-invasive melanomas
Authors: Bushhouse David Z. , Hargadon Kristian M.
Published: August 2019

Regional lymph nodes are one of the first sites of melanoma metastasis during tumor progression, and lymph node invasion by tumor cells is a poor prognostic factor in melanoma patients. We have employed models of lymph node invasive and lymph node non-invasive murine melanomas to investigate the potential role of chemokine/chemokine receptor signaling pathways in the regulation of tumor cell trafficking to regional lymph nodes. PCR array analyses identified several differentially expressed chemokine and chemokine receptor genes in the lymph node invasive B16-F1 melanoma versus the lymph node non-invasive D5.1G4 melanoma. Among these differentially expressed genes, Cxcr3, Cxcr6, Cxcr7, Cx3cr1, Cxcl10, and Cx3cl1 were all significantly upregulated at the RNA level in B16-F1 melanoma. We are currently validating these differential gene expression data at the protein level, and these findings will provide key insights into the potential role of these chemokines and chemokine receptors in the metastasis of melanoma cells to regional lymph nodes.

Citation Format: David Z. Bushhouse, Kristian M. Hargadon. Chemokine receptor expression profiling of lymph node invasive versus lymph node non-invasive melanomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 159.
Abstract 1592: Genome-wide meta-analysis of keratinocytic cancers identifies 26 novel risk loci
Authors: Liyanage Upekha E. , Law Matthew H. , Han Xikun , An Jiyuan , Ong Jue-Sheng , Gharahkhani Puya , Iles Mark M. , Neale Rachel E. , Olsen Catherine , Macgregor Stuart , Whiteman David C.
Published: August 2019

Background: Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) (collectively referred to as keratinocytic skin cancers (KC)) are the most common forms of cancer in fair-skinned people. KCs account for the highest expenditure of any cancer type in the Australian healthcare system. Knowledge about the genetic architecture of skin cancer may help elucidate the biology of the disease.

Methods: We conducted a genome-wide meta-analysis of KC, including 28248 cases and 353855 controls from United Kingdom, United States and Australian populations. We performed LD score regression to determine the genetic correlation between KC, BCC, SCC, and melanoma.

Results: We identified 41 independent genome-wide significant SNPs associated with KC risk, 16 of which have not been reported previously. A further 10 loci were implicated using gene-based tests. New loci included common variants in BRCA2 (rare variants increase risk of various cancers) and CTLA4 (immune response; antibodies targeting CTLA-4 are used to treat melanoma). We found a strong genetic correlation between BCC and SCC, 0.93 (95% confidence interval [CI] 0.71 - 1.16). The genetic correlation between melanoma and BCC was 0.66 (95% CI: 0.41 - 0.91). Comparatively lower genetic correlations were identified between melanoma and SCC 0.43 (95% CI: 0.12 - 0.74), and melanoma and KC 0.52 (95% CI: 0.27 - 0.76).

Conclusions: We have identified novel genetic loci associated with KC risk which will lead to a greater understanding of the biology of KC. Future work will leverage the genetic correlations between BCC, SCC and melanoma to further advance our understanding of these cancers.

Citation Format: Upekha E. Liyanage, Matthew H. Law, Xikun Han, Jiyuan An, Jue-Sheng Ong, Puya Gharahkhani, Mark M. Iles, Rachel E. Neale, Catherine Olsen, Stuart Macgregor, David C. Whiteman. Genome-wide meta-analysis of keratinocytic cancers identifies 26 novel risk loci [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1592.
Abstract 1595: Functional genomic analysis of 18q21.1 locus identifies potential functional variants and genes for colorectal cancer
Authors: Wang Jifeng , Guo Xingyi , Qu Shimian , Bauer Joshua A. , Long Jirong , Zheng Wei , Cai Qiuyin
Published: August 2019

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the world. Genome-wide association studies have identified approximately 50 genetic risk loci for CRC. We have previously identified SNP rs7229639, located in the second intronic region of the SMAD7 gene at locus 18q21.1, in relation to CRC risk in Asians. In this study, we performed functional genomic analysis to identify potential functional variants and target genes for this association. Functional annotation analysis using epigenetic data from ENCODE and Roadmap projects showed functional evidence of promoter and/or enhancer activities for four highly correlated potential functional SNPs, including rs7229639, rs77544449, rs112540150 and rs57668004. Using in vitro luciferase reporter assays in colon cancer HCT116 and PKO cells, we showed that the risk alleles of rs7229639 and rs77544449 increase promoter or enhancer activities, compared to reference alleles. The risk alleles of both SNPs also affect the ability of interacting with nuclear proteins in the electrophoretic mobility shift assays, compared to reference alleles. To identify candidate target genes for this risk locus, we performed cis-expression quantitative trait loci (eQTL) analysis using transcriptome data from the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) and Colonmics. We identified putative target gene LIPG, located 500 kb downstream of rs7229639, in all three datasets. No associations were observed for SMAD7, or eight other genes in the region, via eQTL analysis. To investigate the biological functions of LIPG gene in colorectal cancer, we knock-downed LIPG gene expression using siRNA in HCT116 and PKO cells, which resulted in a significant decrease in cell proliferation, colony formation, migration and invasion in both HCT116 and PKO cells. These results suggest that LIPG gene plays a significant role in colorectal tumorigenesis via disruption of cell behaviors. Taken together, results from our study suggest that the association between the rs7229639/18q21.1 locus and CRC risk may be mediated through SNPs that regulate expression of the LIPG gene.

Citation Format: Jifeng Wang, Xingyi Guo, Shimian Qu, Joshua A. Bauer, Jirong Long, Wei Zheng, Qiuyin Cai. Functional genomic analysis of 18q21.1 locus identifies potential functional variants and genes for colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1595.
Abstract 1596: Genome-wide gene-environmental interaction and random survival forest analyses: Insulin resistance and breast cancer risk
Authors: Jung Su Yon , Yu Herbert , Pellegrini Matteo , Papp Jeanette , Sobel Eric , Zhang Zuo-Feng
Published: August 2019

Purpose: Obesity–insulin connections have been considered potential risk factors for postmenopausal breast cancer, and the association between insulin resistance (IR) genotypes and phenotypes can be modified by obesity-lifestyle factors, affecting breast cancer risk. We explored the role of IR in those pathways at the genome-wide level. With identified IR genetic factors and selected lifestyles, we further generated risk profiles for breast cancer risk.

Methods: Using large-scale cohort data from postmenopausal women in the Women’s Health Initiative Database for Genotypes and Phenotypes Study, our previous genome-wide association gene–behavior interaction study identified 58 loci for associations with IR phenotypes (homeostatic model assessment–IR, hyperglycemia, and hyperinsulinemia). We evaluated those single-nucleotide polymorphisms (SNPs) and additional 31 lifestyles in relation to breast cancer risk by conducting a two-stage multimodal random survival forest analysis.

Results: We identified the most predictive genetic and lifestyle variables in overall and subgroup analyses (stratified by body mass index [BMI], exercise, and dietary fat intake). Two SNPs (LINC00460 rs17254590 and MKLN1 rs117911989), endogenous and exogenous factors related to lifetime cumulative exposure to estrogen, BMI, and dietary alcohol intake were the most common influential factors across the analyses. Individual SNPs did not have significant associations with breast cancer, but the combined SNPs and lifestyles synergistically increased the risk of breast cancer in a dose-dependent manner. In subgroup analysis, we further stratified women by the two strongest variables (durations of oral contraceptive [OC] and opposed estrogen use). Shorter-duration OC and ever-opposed estrogen users with both risks of genotypes and lifestyles had greater risk (2.5 times and 6.3 times, respectively) than their counterparts without risks in both. This indicates the combined effect of SNPs and lifestyles and the joint effect with OC use and opposed estrogen on breast cancer risk.

Conclusions: Our findings may contribute to more accurate prediction of breast cancer and suggest intervention strategies for those women with genetic risk to reduce their breast cancer risk.

Citation Format: Su Yon Jung, Herbert Yu, Matteo Pellegrini, Jeanette Papp, Eric Sobel, Zuo-Feng Zhang. Genome-wide gene-environmental interaction and random survival forest analyses: Insulin resistance and breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1596.
Abstract 1640: Deep learning classification of neuro-oncology medical documents
Authors: Wells Michael , Sabedot Thais , Malta Tathiane , Snyder James , Poisson Laila , Noushmehr Houtan
Published: August 2019

Introduction

Precision medicine and big data for cancer discovery requires well curated indexed critical health care data, however to date limited resources exist that successfully parse out unstructured clinical data in neuro-oncology. Current practice relies on time consuming manual extraction by researchers or clinicians resulting in data inconsistency and limitation in data set volume. Rule-based natural language processing algorithms could be used for simple consistent text, but medical documents are created longitudinally by multiple people across long periods of time resulting in inconsistencies and semantic heterogeneity that render rule-based techniques insufficient.

Methods

We applied a deep learning text classification method to multiple clinical document categories including clinical pathology reports and a text based clinical database spanning 17 years of clinical narratives with approximately 4000 unique cases. For this study we identified clinically relevant molecular criteria for glioma outlined in the WHO 2016 CNS classification of tumors including IDH mutation, MGMT methylation, and 1p19q co-deletion status. Using a convolutional neural network with two densely connected layers of 30 rectified linear nodes we were able to classify patients into their respected molecular cohort with an accuracy of 98%.

Conclusion

Parsing of unstructured text based clinical narratives and pathology reports using convolutional neural networks is a promising method to extract heterogeneous molecular data in neuro-oncology for large scale data analysis.

Citation Format: Michael Wells, Thais Sabedot, Tathiane Malta, James Snyder, Laila Poisson, Houtan Noushmehr. Deep learning classification of neuro-oncology medical documents [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1640.
Abstract 1641: Generation and evaluation of medical synthetic data
Authors: Goncalves Andre R. , Ray Priyadip , Soper Braden , Myneni Madhumita , Stevens Jennifer L. , Coyle Linda M. , Sales Ana Paula
Published: August 2019

While machine learning (ML) has shown some promise in medical research, its actual impact has been limited relative to other application domains. One reason for this disparity is the lack of high-quality, patient-level data available to the broader ML research community. Such datasets are often not made available due to protections around patient privacy. To overcome these obstacles, high-quality, synthetic datasets could be leveraged to accelerate methodological developments in the application of ML to biomedical research. Clinical data in the form of electronic health records present a rich data source to be used for synthetic data generation. Such data can be high dimensional and predominantly categorical, which poses multiple challenges from a modeling perspective. In this paper, we evaluate four classes of synthetic data generation techniques, as well as several metrics for evaluating the quality of the synthetic data. While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets from the publicly available Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast cancer cases diagnosed in the year of 2010, which includes over 26000 individual cases. Finally, we discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of synthetic medical data.

Citation Format: Andre R. Goncalves, Priyadip Ray, Braden Soper, Madhumita Myneni, Jennifer L. Stevens, Linda M. Coyle, Ana Paula Sales. Generation and evaluation of medical synthetic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1641.
Abstract 1648: Deep learning tissue "fingerprints" to identify patients and predict clinical subtypes of breast cancer from digital pathology images
Authors: Rawat Rishi , Sha Fei , Shibata Darryl , Ruderman Daniel , Agus David
Published: August 2019

Inspired by facial recognition, we define an analogous problem in digital pathology called tissue matching. The objective is to match histology image patches to the patient. Unlike many classification problems in pathology, tissue matching comes with its own ground truth, and does not require subjective annotation. Using tissue-matching via deep neural networks, we developed strategies to overcome common sources of noise in histology data, including H&E and slide scanner variability. We demonstrate that the typical approach of increasing the training set size, in the current study up to 12,000 tissue cores, yields only moderate gains in test accuracy, while including a loss term promoting style-invariance cooperatively improves the impact of scaling training set size. Our best neural network learned features that matched patients from the test set of H&E image patches with 93% accuracy (n=104 patients, baseline accuracy <1%). Leveraging this network’s identification ability, we applied its internal representation as a feature vector (tissue fingerprint) to apply to other problems in pathology. We found that a fingerprint-based classifier could predict estrogen receptor status from breast cancer whole-slides on two separate cohorts with high accuracy (AUCROC=0.89), a sizeable improvement over previous approaches. This work presents a novel, scalable, and transparent approach to train neural networks to identify distinctive features of pathology images. These features have demonstrated utility in classifying biomarkers from morphology and have the potential to assist in a number of difficult tasks in pathology, including tissue subtype classification and patient tumor similarity assessments based on morphometry.

Citation Format: Rishi Rawat, Fei Sha, Darryl Shibata, Daniel Ruderman, David Agus. Deep learning tissue "fingerprints" to identify patients and predict clinical subtypes of breast cancer from digital pathology images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1648.
Abstract 1652: Evaluating cell lines andorganoidsas models for metastatic cancer through integrative analysis of open genomic data
Authors: Liu Ke , Newbury Patrick , Glicksberg Benjamin , Zeng William , Andrechek Eran , Chen Bin
Published: August 2019

Metastasis is the most common cause of cancer-related death and, as such, there is an urgent need to discover new therapies to treat metastasized cancers. Cancer cell lines are widely-used models to study cancer biology and test drug candidates. However, it is still unknown to what extent they adequately resemble the disease in patients. The recent accumulation of large-scale genomic data in cell lines, mouse models, and patient tissue samples provides an unprecedented opportunity to evaluate the suitability of cell lines for metastatic cancer research. In this work, we used breast cancer as a case study. The comprehensive comparison of the genetic profiles of 57 breast cancer cell lines with those of metastatic breast cancer samples revealed substantial genetic differences. In addition, we identified cell lines that more closely resemble different subtypes of metastatic breast cancer. Surprisingly, a combined analysis of mutation, copy number variation and gene expression data suggested that MDA-MB-231, the most commonly used triple negative cell line for metastatic breast cancer research, had little genomic similarity with Basal-like metastatic breast cancer samples. We further compared cell lines with organoids, a new type of preclinical model which are becoming more popular in recent years. We found that organoids outperformed cell lines in resembling the transcriptome of metastatic breast cancer samples. However, additional differential expression analysis suggested that both types of models could not mimic the effects of tumor microenvironment and meanwhile had their own bias towards modeling specific biological processes. Our work provides a guide of cell line selection in metastasis-related study and sheds light on the potential of organoids in translational research.

Note: This abstract was not presented at the meeting.

Citation Format: Ke Liu, Patrick Newbury, Benjamin Glicksberg, William Zeng, Eran Andrechek, Bin Chen. Evaluating cell lines andorganoidsas models for metastatic cancer through integrative analysis of open genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1652.
Abstract 1655: A structural approach for prioritizing variants in personalized Oncology
Authors: Wong Siao-Han , Benedikt Brors
Published: August 2019

In personalized oncology, treatment strategies take individualized genomic aberrations into consideration and relies heavily on how the cancer genomes are interpreted. With immense efforts put into cohort studies, knowledge of pathogenicity at the gene level had been accumulated. On the other hand, the increasing amount of personalized clinical trials and the accompanying need of interpreting pathogenecity at the sub-gene level emphasize the importance of resolving heterogeneous significance within genes.

There had been some initiatives where people identified mutation hotspots within genes, looking at either highly recurrent single residues or mutation clusters. The hotspot clusters can be inferred from protein sequences as well as from protein structures, where residues distant in sequence may be brought together through protein folding. Nonetheless, these information are not well utilized in routine clinical settings yet.

In this study, we aim to build a consensus list of prevalent mutational events for molecular stratification of patients. This is achieved by collecting sequence and structural mutation hotspots from different algorithms, and further combine structural information to identify their neighboring residues in protein structures. Specifically, around 1400 residue hotspots are collected from CancerHotspots and DominoEffect. An additional of around 250 structural hotspots are identified by three structural algorithms, namely 3DHotSpots, HotMAPS and HotSpot3D. These results are mostly derived from TCGA PanCancer cohorts, with CancerHotspots included more other studies in the analysis. With this consensus information in place, we further set up the mechanism to map from genomic variants to protein sequence and protein structures. This enables us to annotate the variants found in patient genome and see whether they co-localize with hotspot residues, in their structural neighborhood, or in structural informative domains like interaction interfaces.

In summary, a higher prevalence of variants might imply higher significance and more well-studied roles than other low prevalent mutant alleles. Transferring this knowledge from the public cohorts helps researchers quickly prioritize variants found in patient genomes. Annotating structural significance of variants further provides us with more confidence in identifying driving events. With these procedures automated, the goal is to help facilitate clinical evaluations to meet the growing number of patients recruited to personalized oncology trials.

Citation Format: Siao-Han Wong, Brors Benedikt. A structural approach for prioritizing variants in personalized Oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1655.
Abstract 1656: Systematic identification of cancer therapeutic liabilities through adaptive functional proteomics
Authors: Li Jun , Zhao Wei , Lu Yiling , Mills Gordon B. , Liang Han
Published: August 2019

Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to evaluate biomarkers and mechanisms underlying sensitivity and resistance to cancer therapy. This rapidly maturing quantitative antibody-based assay can assess a large number of protein markers in many samples in a cost-effective, sensitive and high-throughput manner. MD Anderson Cancer Center has been a leader in the technology, and our platform currently contains ~300 protein markers, covering all major signaling pathways. We have generated and compiled the RPPA data of cell lines that were treated with >150 compounds under multi-dosage, replicates or triplicates up to 8 time points in 2D/3D cell culture. In total, we have >25,000 perturbed samples profiled using our RPPA platform. From these adaptive RPPA data, we developed computational algorithms to infer therapeutic liabilities including synthetic lethality and related combined therapies in a systematic way. Representative examples include PARP and MEK inhibitor combination showing synergistic activities in RAS mutant tumors. We further built an integrated data portal that contains user-friendly analytic and visualization tools for a broad biomedical community to utilize these data.

Citation Format: Jun Li, Wei Zhao, Yiling Lu, Gordon B. Mills, Han Liang. Systematic identification of cancer therapeutic liabilities through adaptive functional proteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1656.
Abstract 1658: Performance evaluation of assembly based structural variation discovery in paired tumor normal samples
Authors: Xiao Chunlin
Published: August 2019

Structural variations (SVs) are well known to contribute to genetic diversity of human populations, affect biological functions, and cause various human disorders. However, accurately identifying SVs with correct sizes and locations in the human genome, particularly in cancer samples, remains a challenge due to the complexities of the human genome, limitations of sequencing technologies, and drawbacks of analysis methods. Recent advancement of next-generation sequencing technologies has significantly reduced the sequencing cost, while substantially increased the lengths of the sequencing reads. Therefore, using de novo assembly based approaches for discovering a full spectrum of SVs in human genome becomes tempting. While various assembly methods have been developed and proposed for general use by the community, the relative efficiency and predictive accuracy of SVs calling based on these assembly methods have not been fully evaluated. In this study, we applied several popular de novo assembly tools to the sequencing read data that were generated using multiple sequencing technologies with technical replicates for a paired tumor-normal samples. Assemblies and SVs callsets were generated, and repeatability in the SVs of the technical replicates and reproducibility across sequencing sites were evaluated. Results showed that there were substantial variabilities between SVs callsets based on various assemblies within sequencing platform and cross sequencing sites. We also observed considerable variabilities between assembly based SVs callsets and alignment based SVs callsets. The sources of these variabilities will be further discussed. These results allow better understanding of the impacts of de novo assembly methods on SVs calling, thus providing a better insight to precision medicine.

Citation Format: Chunlin Xiao. Performance evaluation of assembly based structural variation discovery in paired tumor normal samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1658.
Abstract 166: MMP12 promotes EMT-mediated tumor aggressiveness in lung cancer cells
Authors: Han Na-Kyung , Lee Yoon-Jin , Lee Hae-June
Published: August 2019

Epithelial mesenchymal transition (EMT) plays a pivotal role in cancer progression and metastasis. Matrix metalloproteinases (MMPs) have a major role in remodeling the extracellular matrix (ECM) and are implicated in the invasion and metastasis of various malignancies. MMP12 is involved in many pathological processes including cancer, but has not been fully understood yet. To investigate the role of MMP12 in lung cancer cells, we examined the effect of overexpression or knockdown of MMP12 in A549 cells and measured the protein expression level of EMT markers. MMP12 overexpression induced considerable EMT phenotypic changes compared to the control group, both in vitro and in vivo. The mRNA levels of EMT markers, snail2/slug, snail1, zeb1, TGFβ-1, and fibronectin, increased significantly upon MMP12 overexpression. We observed that changes in the expression level of ECM components such as, collagen, lamin, and proteoglycans led to MMP12 overexpression-induced EMT. These alterations promoted the aggressiveness of the A549 cells, evident from changes in functional properties of the cell, such as proliferation, migration, invasion, and adhesion capacity. On the contrary, MMP12 suppression abrogated malignancy of A549 cells. MMP12 knockdown in A549 cells led to decreased mRNA expression of EMT marker proteins associated with adherence, invasion, and migration. Our results suggest that MMP12 is a key regulator of EMT in non-small cell lung cancer (NSCLC). MMP12 overexpression in A549 cells results in changes in the expression of EMT markers and ECM composition. Interestingly, the dynamic changes in the ECM components by MMP12 overexpression cause EMT reprogramming and induce malignant functional properties. Thus, we suggest that the control of MMP12 expression can be a potential target for new therapeutic strategies of NSCLC.

Citation Format: Na-Kyung Han, Yoon-Jin Lee, Hae-June Lee. MMP12 promotes EMT-mediated tumor aggressiveness in lung cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 166.
Abstract 1664: Comparison of RNA sequencing data generated from formalin-fixed, paraffin-embedded (FFPE) papillary thyroid carcinoma samples using different library preparation methods
Authors: Dragon Julie , Barrantes Ramiro , Hoffman Jessica , Tighe Scott
Published: August 2019

Comparison of RNA sequencing data generated from formalin-fixed, paraffin-embedded (FFPE) papillary thyroid carcinoma samples using different library preparation methods. Julie Dragon, Ramiro Barrantes, Jessica Hoffman, and Scott Tighe.

Genomics research requires a significant sample size to provide robust biological signal, leaving researchers clamoring for access to large sample sets. As this research continues to expand into the clinical arena, the demand to sequence RNA from banked tissue samples, such as formalin-fixed paraffin-embedded blocks, is unavoidable. Readily available, these samples represent a wealth of both patients and disease variation. However, recovering DNA and RNA from such samples can be challenging depending on age of the sample block and fixture protocols, which influence nucleic acid degradation and ultimately our ability to synthesize RNA-Seq libraries. To fulfill the need for increased recovery of quality reads, several manufacturers have developed solutions to address these challenges, including FFPE-specific extraction kits, as well as library synthesis and quality control reagents.

In this study, we analyzed the quality and outcome of RNA-Seq data generated from RNA from two library synthesis kits applied to FFPE-derived human thyroid tumors with storage times from 4-12 years. The data processing pipeline included adaptor and poor quality base trimming (trimommatic), removal of rRNA reads (sortMeRNA), genomic alignment to hg19 (HiSat2), and read distribution and alignment quality assessment (RNASeqQC) (Adiconis and Levin, 2013). Differential expression was evaluated using DESeq2 (Love et al, 2014).

Results indicated that of the reagents tested, it is possible to improve capture of the usable RNA data, and biological signal, with improved control of rRNA depletion and read duplication with newly developed library preparation kits specifically designed for FFPE samples. Moreover, our analysis illustrates some features of the tested synthesis kits that enable one to perform better for FFPE samples with highly varying quality, and quality metrics to consider when evaluating FFPE samples for sequencing.

Citation Format: Julie Dragon, Ramiro Barrantes, Jessica Hoffman, Scott Tighe. Comparison of RNA sequencing data generated from formalin-fixed, paraffin-embedded (FFPE) papillary thyroid carcinoma samples using different library preparation methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1664.
Abstract 1670: A hidden markov modeling approach for identifying tumor subclones in next-generation sequencing studies
Authors: Zhu Bin , Choo-Wosoba Hyoyoung , Albert Paul
Published: August 2019

Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous with a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying main and subclone genotypes across the genome is important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of the subclones. In this paper, we propose subHMM, a hidden Markov model-based approach that estimates both subclone region as well as region-specific subclone genotype and clonal proportion. We specify a hidden state variable representing the conglomeration of clonal genotypes and subclone status. We propose a two-step algorithm for parameter estimation, where in the first step, a standard hidden Markov model with this conglomerated state space is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods. We apply subHMM to a renal cell carcinoma dataset from The Cancer Genome Atlas. In addition, we conduct simulation studies that show the good performance of the proposed approach.

Citation Format: Bin Zhu, Hyoyoung Choo-Wosoba, Paul Albert. A hidden markov modeling approach for identifying tumor subclones in next-generation sequencing studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1670.
Abstract 1671: Characterization of a gene signature predictive of cancer patient survival prognosis and chemo-response
Authors: Tran Paul , Li Shuchun , Liu Hai-Tao , Tran Lynn , Purohit Sharad , Dun Boying , She Jin-Xiong
Published: August 2019

Precision oncology aims to match cancer patients with drugs based on their unique molecular profiles. The MATCH trial results indicate ~80% of cancer patients do not benefit from screening cancer patients for actionable genomic aberrations. We aim to identify potentially druggable gene signatures that are molecular drivers of poor survival outcome for these cancer patients. We analyze data from The Cancer Genome Atlas (TCGA) to identify patient groups with differential survival characteristics using gene expression data. We applied a univariate Cox regression model to identify genes associated with patient survival and identified a 67 gene signature of highly correlated genes significant for cell cycle related gene ontologies on over-representation analysis. Patients from the TCGA dataset whose tumors have a higher gene expression score are more likely to have worse survival prognosis compared to tumors with a lower gene expression score, demonstrated through Kaplan-Meier survival analysis and univariate and multivariate Cox proportional hazard models. This suggests we have identified a co-regulated gene expression network associated with cellular proliferation which predicts overall and recurrence-free survival in many of the cancers in the TCGA dataset. We further analyzed data from the NCI-60 Human Tumor Cell Lines Screen and found the cellular proliferation gene signature expression is correlated with response to cell cycle inhibitors. We then validated this finding using resistant cell lines and gene knockdown models in vitro. This gene signature may provide an important tool for selecting patients for chemotherapy administration.

Citation Format: Paul Tran, Shuchun Li, Hai-Tao Liu, Lynn Tran, Sharad Purohit, Boying Dun, Jin-Xiong She. Characterization of a gene signature predictive of cancer patient survival prognosis and chemo-response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1671.
Abstract 1674: Prediction of survival based on tumor size dynamics and new lesions in EGFR mutation-positive non-small cell lung cancer patients treated with gefitinib or carboplatin and paclitaxel
Authors: Dunyak James , Buros-Novik Jacqueline , Novik Eric , Yan Hong , Al-Huniti Nidal
Published: August 2019

Objectives: Dynamics of tumor size have long been used for the clinical diagnosis, staging, prognosis and treatment of non-small cell lung cancer (NSCLC). The aim of this work was to develop a joint statistical framework to interrogate the longitudinal tumor burden, the appearance of new lesions, and changes in therapy following progression in a population of NSCLC patients treated with an EGFR inhibitor gefinitib to predict, given early results, time to progression (PFS) and overall survival at both population and individual levels.

Methods: The model builds on a previously developed joint model for tumor size and survival. The tumor size model was extended to estimate the probability of a new lesion. For the survival submodel, two additional biomarkers were evaluated: the derivative of the sum of longest diameters (SLD) and the probability of new lesions. The model was fitted in a Bayesian framework using STAN. Model was developed from 434 EGFR+, NSCLC patients treated with gefitinib or carboplatin+paclitaxel (‘IPASS’, NCT00322452). Predictive performance was evaluated on a cohort of 102 EGFR+ NSCLC patients (‘IFUM’, NCT01203917).

Results: Overall, the model fit well to the observed data and the predictive performance was strong in both within- and out-of-sample evaluations. Surprisingly, SLD derivative and new-lesion association parameters appear to negatively impact model performance, indicating further improvement of these submodels might be necessary. The model recapitulates response evaluation criteria in solid tumors (RECIST) outcomes with nearly 90% accuracy. When comparing progression-specific hazards, it appears the drug effect is mediated by tumor size dynamics rather than new lesion incidence.

Conclusion: This Bayesian joint model of tumor size and survival accurately recapitulates the RECIST-based outcomes and generates well calibrated predictions of survival. It offers insights regarding the relative predictive value of the components of RESICT in NSCLC.

Citation Format: James Dunyak, Jacqueline Buros-Novik, Eric Novik, Hong Yan, Nidal Al-Huniti. Prediction of survival based on tumor size dynamics and new lesions in EGFR mutation-positive non-small cell lung cancer patients treated with gefitinib or carboplatin and paclitaxel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1674.
Abstract 1678: Accurate detection of expressed variation in RNA-seq
Authors: Mose Lisle E. , Marron David , Parker Joel S.
Published: August 2019

Somatic variant detection has long been an area of great interest and critical importance in cancer research and treatment. DNA-Seq based somatic variant calling pipelines have been maturing over the last several years and although there is still room for improvement, several methods have been developed capable of accurately detecting somatic variants from matched tumor/normal DNA. RNA-Seq has become a widely used tool for measuring gene and isoform abundance, identifying alternative splicing, and detection of gene fusions. Detection of expressed variation is receiving growing interest, however computational methods to detect this variation have received far less attention than that of DNA-Seq. We have developed an optimized RNA-Seq pipeline based upon the ABRA2 realigner capable of accurately detecting expressed somatic variation in RNA-Seq.

We applied this pipeline to the TCGA Breast Cancer dataset. To assess the impact of identification of expressed variation as an indicator of variant significance, we compared the expression state of the top 20 cohort wide significantly mutated genes (SMG) as identified by The Cancer Genome Atlas Network (2012) with all other genes. At the median for SMGs, 23 somatic variants are expressed with over 90% of variants detected in DNA being expressed. For non-SMGs, the median number of expressed variants is 1 and the median fraction of expressed variants is 50%. Among non-SMGs, a total of six genes exceed the median total number of expressed variants and fraction of expressed variants for SMGs (AHNAK, CHD4, ERBB2, FASN, FOXA1 and MYH9). All of these variants have been previously implicated in breast cancer, however none of these variants were among the cohort wide SMGs identified in the original study and only FOXA1 was identified as being a subtype specific SMG (ER+,ER+/HER2-). Notably, variants in the massive TTN gene are found to be coding 73% of the time, but expressed in only 8% of cases. This is substantially less than the 90% found in SMGs and a likely indicator that TTN mutations are passenger variants. The median and third quartile RNA variant allele frequency (VAF) for SMGs is .41 and .69 versus .32 and .46 for DNA. By comparison, the median and third quartile VAF for non-SMGS was 0 and .27 in RNA and .20 and .30 for DNA. The increased VAF for RNA SMGs is a likely indicator of selection and can potentially further be used to identify variants of significance. We believe these results demonstrate the utility of variant expression as a potentail tool to aid in assessment of variant significance.

Citation Format: Lisle E. Mose, David Marron, Joel S. Parker. Accurate detection of expressed variation in RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1678.
Abstract 169: Uncovering a myosin phosphatase regulator in pancreatic tumor cell mechanics and behavior
Authors: Angstadt Shantel M.
Published: August 2019

Pancreatic ductal adenocarcinoma (PDAC) composes more than 80% of all pancreatic cancers and about 95% of exocrine pancreatic cancers. Patients typically present with symptoms post metastatic spread, which contributes to its overall lethality as the 3rd leading cause of cancer-related deaths with a median 5-year survival rate of about 8%. We are investigating the role of the myosin phosphatase targeting subunit 1 (MYPT1) in PDAC. MYPT1 serves as an interaction platform between a catalytic phosphatase subunit and the phosphorylated regulatory light chain (MLC) of myosin, allowing for MLC dephosphorylation and myosin inactivation. MYPT1 has been shown to elicit an antibody response in PDAC patients treated with a cytokine-secreting whole tumor cell vaccine. Additionally, MYPT1 has been shown to be upregulated in PDAC cells through immunohistological staining of surgically resected patient samples and in established PDAC cell lines. As a major regulator of non-muscle myosin II, MYPT1 has implications in cancer cell shape control, adhesion, and migration. To elucidate its implications, we are characterizing tumor cell phenotype with and without MYPT1 expression using the CRISPR genome editing system. We are assessing MYPT1’s effect on cell mechanics (e.g. cortical tension), if MYPT1 responds to applied mechanical stresses, and how MYPT1 affects each of the non-muscle myosin II paralogs’ ability to respond to these mechanical stresses. We are characterizing MYPT1’s impact on cell and tissue behavior, including cell growth, migratory, and invasive phenotypes in culture and in mouse models. Overall, our work will provide insight into MYPT1’s role in cancer cell behavior and response to its surrounding environment. The work will also provide insight for early investigations into MYPT1’s targetability and immunogenic role in PDAC immunotherapy.

Citation Format: Shantel M. Angstadt. Uncovering a myosin phosphatase regulator in pancreatic tumor cell mechanics and behavior [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 169.