Rebecca Kusko

Premature expression of a muscle fibrosis axis in chronic HIV infection

BackgroundDespite the success of highly active antiretroviral therapy (HAART), HIV infected individuals remain at increased risk for frailty and declines in physical function that are more often observed in older uninfected individuals. This may reflect premature or accelerated muscle aging.MethodsSkeletal muscle gene expression profiles were evaluated in three uninfected independent microarray datasets including young (19 to 29 years old), middle aged (40 to 45 years old) and older (65 to 85 years old) subjects, and a muscle dataset from HIV infected subjects (36 to 51 years old). Using Bayesian analysis, a ten gene muscle aging signature was identified that distinguished young from old uninfected muscle and included the senescence and cell cycle arrest gene p21/Cip1 (CDKN1A). This ten gene signature was then evaluated in muscle specimens from a cohort of middle aged (30 to 55 years old) HIV infected individuals. Expression of p21/Cip1 and related pathways were validated and further analyzed in a rodent model for HIV infection.ResultsWe identify and replicate the expression of a set of muscle aging genes that were prematurely expressed in HIV infected, but not uninfected, middle aged subjects. We validated select genes in a rodent model of chronic HIV infection. Because the signature included p21/Cip1, a cell cycle arrest gene previously associated with muscle aging and fibrosis, we explored pathways related to senescence and fibrosis. In addition to p21/Cip1, we observed HIV associated upregulation of the senescence factor p16INK4a (CDKN2A) and fibrosis associated TGFβ1, CTGF, COL1A1 and COL1A2. Fibrosis in muscle tissue was quantified based on collagen deposition and confirmed to be elevated in association with infection status. Fiber type composition was also measured and displayed a significant increase in slow twitch fibers associated with infection.ConclusionsThe expression of genes associated with a muscle aging signature is prematurely upregulated in HIV infection, with a prominent role for fibrotic pathways. Based on these data, therapeutic interventions that promote muscle function and attenuate pro-fibrotic gene expression should be considered in future studies.

Comprehensive Genomic Profiling of the Lung Transcriptome in Emphysema and Idiopathic Pulmonary Fibrosis Using RNA-Seq

Methods 87 LGRC lung tissue samples were sequenced on the Illumina GAIIx, generating 75 nt paired-end reads and approximately 30-40 million reads per sample. Using gapped aligner Tophat, an average of 85% of reads aligned to hg19. Gene expression was quantified using Cufflinks and Ensembl59 known gene annotation (n = 24,249 genes). All lung tissue samples used in this study, as well as additional LGRC lung tissue samples, were run on Agilent V2 human whole genome arrays and Agilent V3 human miRNA microarrays.

Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery

The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data.

Abstract 789: Leveraging transcriptomic and genomic data to better select models for preclinical oncology therapeutic development to identify cell lines most similar to patient tumors

Cancer cell lines represent the front line of new compound testing, and results from these experiments often decide which compounds go on for further testing. Genomic context plays a critical role in drug response and now genomic data for tumors and cell lines are widely available. However, cell lines are often chosen based on ease of access, literature prevalence, and ease of culture. We combined gene expression and CNV/mutation profiling from four pancreatic cancer tumor datasets (GSE21501, GSE28735, ICGC, TCGA,) and three pancreatic cancer cell line datasets (Klijn et al, Collisson et al, and CCLE) to identify which cell lines best match patient tumors. CNV comparison revealed that popular cell lines do not always have the best CNV correlation with tumors: when comparing pancreatic cancer tumors to cell lines, the citations of the top five cell lines by CNV correlation were less than 10% of the pancreatic cancer cell line total. Next we filtered for driver mutations including SMAD4 and CDKN2A using mutation scoring algorithms and clustered tumors and cell lines. We found that many cell lines with few citation counts clustered readily amongst tumors (such as L33). Leveraging the hypothesis that different hits in the same pathway can have a similar downstream effect, we combined CNV, expression and mutation data and clustered cell lines together with tumors based on overall aberrations in MSigDB cancer pathways. L33 and YAPC clustered near tumors while the majority of other cell lines clustered together. To identify coexpressed gene clusters, we ran WGCNA individually in all seven datasets and discovered modules consistent in cell line and tumor datasets using iGraph. One of the most interesting modules (interferon regulated genes) is expressed highly in the majority of tumors profiled. About half of cell lines also express this module highly, suggesting that they may be more ideal models for high interferon expression tumors than other cell lines. Here we present evidence demonstrating that certain cell lines mimic pancreatic tumor genomes more closely while others represent patterns of genomic features not commonly observed in vivo. We also show that certain biologically relevant tumor subtypes may be better represented by some cell lines than others. Our analysis highlights the emerging role of genomics in advancing the clinical success of therapeutic trials. Citation Format: Yoonjeong Cha, Adam Labradorf, Joseph Perez-Rogers, Brian Haas, Andrew Lysaght, Brian Weiner, Fadi Towfic, Kevin Fowler, Benjamin Zeskind, Sarah Kolitz, Badri Vardarajan, Maxim Artyomov, Rebecca L. Kusko. Leveraging transcriptomic and genomic data to better select models for preclinical oncology therapeutic development to identify cell lines most similar to patient tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 789.

Abstract A1-66: Leveraging Gene Expression in the Bronchial Airway to Develop a Nasal Biomarker for Early Detection of Lung Cancer

Rationale: Lung cancer results in five times more deaths per year than car accidents in the United States. Approximately 57% of lung cancers diagnosed this year will be diagnosed at a late stage and these patients will exhibit a 5-year survival rate of only 4%. Annual screening of high-risk current and former smokers by chest-CT can reduce cancer mortality, however this procedure has a 95% false positive rate. It is therefore critical to develop methods to rapidly and accurately determine which patients with nodules on chest CT have lung cancer and potentially spare those with benign disease an unnecessary invasive procedure. We have previously demonstrated that specific gene expression alterations in cytologically normal bronchial epithelial cells from patients with lung cancer can be leveraged to form a clinically informative lung cancer biomarker in the population of patients undergoing bronchoscopy for suspect lung cancer. We hypothesized that there might be similar expression differences in nasal epithelium and that these could form the basis of a less invasive test that could be applied more broadly to individuals with screen detected nodules on chest CT. Methods: Bronchial (n=676) and nasal (n=280) epithelial brushings were collected from current and former smokers undergoing bronchoscopy for clinical suspicion of lung cancer within the AEGIS clinical trial. 271 subjects had matched bronchial and nasal samples. RNA was extracted and hybridized to Affymetrix Human Gene ST 1.0 Arrays. To establish a connection between bronchial and nasal epithelial gene expression signal for cancer, we first applied the bronchial gene expression-based diagnostic test, BronchoGen, directly to our nasal cohort. Gene Set Enrichment Analysis was then used to determine the concordance of cancer signal between the bronchial and nasal epithelium. To develop the nasal gene expression biomarker for lung cancer detection, we examined the correlation of each gene between the bronchial and nasal epithelium as well as the significance of each gene9s association with cancer in each tissue. Genes passing our selection criteria were passed to a biomarker discovery pipeline in which we examined the performance of different biomarker algorithm configurations (e.g. feature-selection algorithms, classification algorithms, and other biomarker parameters) using cross-validation. Results: Direct application of BronchoGen to our nasal cohort resulted in an AUC of 0.64 on a set of NE samples (n=110) with a matched bronchial sample in the training set used to develop the test. On an independent set of nasal samples (n=109), BronchoGen achieved an AUC of 0.67. Gene Set Enrichment Analysis revealed high levels of concordance between cancer-associated nasal and bronchial gene expression. Using a cross-validation approach, we found that nasal biomarkers built from sets of genes showing significant correlation (p Conclusions: Given the larger sample size, more isolated location in the airway, and higher RIN scores that characterize the bronchial cohort, we sought to leverage bronchial airway epithelial gene-expression to inform which genes in the nasal epithelium should be indicative of the presence of cancer. We have shown that gene expression in the nasal epithelium reflects the presence of lung cancer and can serve as a diagnostic biomarker. We have further demonstrated concordance between bronchial and nasal airway gene expression differences associated with lung cancer. These results suggest the potential to develop a robust nasal gene expression biomarker for lung cancer diagnosis that leverages cancer-associated gene expression differences occurring at other airway sites. Citation Format: Joseph F. Perez-Rogers, Joseph Gerrein, Christina Anderlind, Rebecca L. Kusko, Joshua D. Campbell, Teresa W. Wang, Kate Porta, Duncan Whitney, Avrum Spira, Marc Lenburg. Leveraging Gene Expression in the Bronchial Airway to Develop a Nasal Biomarker for Early Detection of Lung Cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-66.

Abstract 1574: Leveraging bronchial airway gene expression to develop a nasal biomarker for lung cancer detection

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Rationale: Using nasal gene expression to predict the presence of lung cancer would offer a less invasive alternative to diagnostic approaches we have pioneered using bronchial airway epithelial (BE) gene expression. We have previously demonstrated that cytologically normal BE and nasal epithelial (NE) cells harbor gene expression differences that reflect tobacco-related lung disease and that these changes in the BE form the basis of a clinically informative lung cancer biomarker. Given the concordance of BE and NE gene-expression, we hypothesized that gene signatures associated with the presence of lung cancer extend from the airway to the nose and that lung cancer associated BE gene-expression could be leveraged to develop more accurate nasal lung cancer biomarkers. Methods: BE (n = 676) and NE (n = 280) brushings were collected from current and former smokers undergoing bronchoscopy for clinical suspicion of lung cancer. We leveraged two methods to determine the concordance between BE and NE gene-expression signal for cancer. First we applied the bronchial gene expression-based diagnostic test directly to our nasal cohort. Second, we used Gene Set Enrichment Analysis (GSEA) to quantify the relationship between the BE and NE. To develop the nasal gene expression biomarker, we examined the correlation of each gene between the BE and NE. Genes passing our selection criteria were passed to a biomarker discovery pipeline in which we examined the performance of different biomarker algorithm configurations using cross-validation. Results: Direct application of the bronchial airway gene-expression classifier to an independent set of nasal samples (n = 109) resulted in an AUC of 0.67. GSEA revealed high concordance (p<0.001) between cancer-associated nasal and bronchial gene expression profiles from the same patients. Using a cross-validation approach, we found that nasal biomarkers built from sets of genes showing significant correlation (p<0.05) between the BE and NE as well as significance for cancer in both tissues (p<0.05) perform better, on average, than biomarkers built from genes significant for cancer (p<0.05) in the NE alone. Conclusions. We have demonstrated concordance between BE and NE gene expression differences associated with lung cancer. We have further shown that gene expression in the NE reflects the presence of lung cancer and can serve as a diagnostic biomarker. These results demonstrate the feasibility of leveraging cancer-associated gene expression changes throughout the airway to develop a minimally invasive and robust nasal gene expression biomarker for lung cancer diagnosis. Citation Format: Joseph Perez-Rogers, Joseph Gerrein, Christina Anderlind, Xiaohui Xiao, Hanqiao Liu, Rebecca Kusko, Joshua Campbell, Teresa Wang, Yuriy Alekseyev, Gang Liu, Kate Porta, Duncan Whitney, Avrum Spira, Marc Lenburg. Leveraging bronchial airway gene expression to develop a nasal biomarker for lung cancer detection. [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 1574. doi:10.1158/1538-7445.AM2015-1574

Abstract 4741: Improving pancreatic cancer drug discovery by leveraging genomics to select better in vitro models

Currently, pancreatic cancer has an estimated 5-year survival rate of only 5-6%. The projection that pancreatic cancer will be the second leading cause of cancer related death by 2020 compounded by the numerous clinical trial failures precipitates the need for novel approaches to accelerate progress in new medicine development. Cell lines used for screening pre-clinical compounds prior to animal models and human testing are usually chosen based on ease of access and literature prevalence. However, the constellation of genomic derangements in cell lines commonly used for in vitro studies may not be representative of pancreatic cancer. In this study, we leveraged copy number variation (CNV) and targeted sequencing data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) to predict optimal cell lines that mirror pancreatic cancer genomes most closely. We calculated the frequency of each CCLE pancreatic cancer cell line in literature and compared this to how well each cell line recapitulates the pancreatic cancer population. Unsurprisingly, we observed that CCLE pancreatic cancer cell lines overall have more frequent CNVs and mutations than TCGA pancreatic cancer tumors. This observation is likely due to inherent genomic instability of cell lines and underscores the importance of using low passage cells. Next, we directly compared the median per gene CNV values in TCGA pancreatic cancer tumors and pancreatic cancer cell lines in CCLE. Contrary to our expectation, the top five cell lines by CNV correlation with TCGA pancreatic tumors represented only 6% out of all literature search hits for all CCLE pancreatic cancer cell lines, indicating the availability of more optimal cell lines from a genomics perspective. Additionally, we leveraged targeted sequencing data to compare the most frequent mutations with medium to high Mutation Assessor scores in TCGA pancreatic cancer tumors to CCLE pancreatic cancer cell lines. The seven most common mutations by this method in TCGA pancreatic cancer tumors were: KRAS, TP53, MYH8, TAOK2, PCDH15, ATRX, and CDKN2A. Using hierarchical clustering based on the presence or absence of these 7 mutations in pancreatic cancer CCLE cell lines and TCGA tumors, we showed that some cell lines readily clustered amongst TCGA tumors (such as BXPC3), while others occupied discrete branches of the dendrogram exclusive of most TCGA tumors such as PK1 and PANC1. This implies that while some cell line mimic pancreatic tumor mutations closely, others represent mutation constellations not commonly observed in patients. It is possible to apply this method to other cancer types, given consideration for potentially different cancer biology. In summary, our work reports that many popular pancreatic cancer cell lines harbor distinct genomic aberration profiles from pancreatic cancer tumors and highlights the emerging role of genomics in advancing the clinical success of therapeutic trials. Citation Format: Yoonjeong Cha, Andrew Lysaght, Rain Cui, Brian Weiner, Sarah Kolitz, Fadi Towfic, Jason Funt, Kevin Fowler, Badri Vardarajan, Maxim Artyomov, Benjamin Zeskind, Rebecca Kusko. Improving pancreatic cancer drug discovery by leveraging genomics to select better in vitro models. [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 4741. doi:10.1158/1538-7445.AM2015-4741

Abstract 3554: Gene and miRNA expression networks specific to never smoker lung adenocarcinoma

While smoking is well recognized as a major risk factor for lung cancer, there is a growing incidence of lung cancer in never smokers which is in turn the fifth leading cause of cancer-related death worldwide. Never smokers (NS) who develop lung cancer exhibit disparate profiles of somatic mutations and clinical responses to targeted therapy relative to lung cancer arising in current or former “ever” smokers (ES), suggesting that ES and NS lung cancer arise through distinct molecular processes. We therefore sought to characterize mRNA and miRNA expression differences specific to NS adenocarcinoma (AdC) to gain insights into the molecular differences underlying NS and ES AdC carcinogenesis. Total RNA was isolated from matched pairs of lung AdC tumor and adjacent histologically normal tissue obtained from 22 subjects (8 NS, 14 ES). Large and small RNA libraries were sequenced on the Illumina HiSeq 2000. Tumor-specific gene and miRNA expression differences between NS and ES were identified using linear mixed-effects ANOVA. MiRConnx was used to construct miRNA-mRNA networks. We identified 120 mRNA and 15 miRNA whose expression was modified uniquely in NS lung AdC. In the predicted miRNA-mRNA regulatory network, additional analysis pinpointed modulation of the development and cellular metabolism canonical pathway within genes connected to several of the differentially expressed miRNA (GATHER, p In summary, the construction of a miRNA-mRNA regulatory network has enabled us to identify molecular alterations that may be specific to NS lung AdC. Ultimately, these findings may serve to broaden the landscape of personalized therapeutic and treatment options by identifying targetable molecular interactions and therapeutic drug candidates for lung AdC in never smokers. Citation Format: Rebecca Kusko, Carly Garrison, Teresa Wang, Josh Campbell, Joseph Perez-Rogers, Lingqi Luo, Jennifer Beane, Gang Liu, Humam Kadara, Steven Belinsky, Marc E. Lenburg, Avrum Spira. Gene and miRNA expression networks specific to never smoker lung adenocarcinoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3554. doi:10.1158/1538-7445.AM2014-3554

Abstract 2352: Mapping the airway-wide molecular field of injury in smokers with lung cancer

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Lung cancer mortality is the leading cause of cancer death in the United States in part because diagnosis occurs after regional or distant metastasis of the disease. Identifying effective early detection biomarkers is crucial for improving lung cancer clinical management. Moreover, molecular biomarkers for early disease detection may provide insight into the molecular pathways associated with disease development and progression. Our lab has shown that smoking-induced gene expression alterations are mirrored in the epithelia of the mainstem bronchus, buccal and nasal cavity. We have additionally demonstrated that gene-expression profiles in cytologically normal mainstem bronchial epithelium can serve as an early diagnostic biomarker for lung cancer. Here we expand on our previous work by spatially mapping the molecular field of injury throughout the entire respiratory tract in smokers with lung cancer. Using Affymetrix Gene ST 2.0 arrays, we profiled genome-wide gene-expression in 1) lung lesions and adjacent normal lung obtained from smokers undergoing surgical resection, 2) epithelial brushings obtained at intraoperative bronchoscopy from the nasal epithelium, main carina and ipsilateral and contralateral proximal and distal bronchi (relative to the location of the resected lung lesion), and 3) epithelial brushings obtained at lobectomy from sub-segmental bronchus (adjacent to tumor). Linear modeling approaches comparing the airways and tumors of patients with cancer to those with benign lung disease were used to explore relationships in cancer-specific gene-expression alterations across sites within the respiratory tract. We found that genes upregulated in the small airways leading to the tumor were enriched in genes upregulated in the mainstem bronchus and main carina of smokers with lung cancer. In addition, genes upregulated in the bronchus and main carina of smokers with lung cancer showed enrichment among cancer associated genes elevated in the nose. Furthermore, a linear mixed effects model uncovered genes and pathways which change in expression in a gradient-like manner as distance from the tumor increases. Our findings suggest that the molecular field of injury encompasses airway-wide alterations throughout the entire respiratory tract of smokers with lung cancer as well as gradient profiles that change with respect to proximity of the nearby tumor. These molecular alterations may ultimately serve as early detection biomarkers for lung cancer and provide new insights into early stages of lung carcinogenesis. Citation Format: Rebecca Kusko, Christina Anderlind, Gerald Wang, Sherry Zhang, W. Dean Wallace, Tonya Wasler, Michael Ebright, Melinda M. Garcia, Rosana Eisenberg, Gina Lee, Gang Liu, David Elashoff, Neda Kalhor, Cesar Moran, Reza Mehran, Junya Fujimoto, Pierre P. Massion, Steven Dubinett, Ignacio Wistuba, Marc Lenburg, Humam Kadara, Avrum Spira. Mapping the airway-wide molecular field of injury in smokers with lung cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2352. doi:10.1158/1538-7445.AM2014-2352

Abstract 1948: Shared and distinct microRNA-expression alterations in lung adenocarcinoma from smokers vs. nonsmokers.

Rationale: While lung adenocarcinoma (ADC) is predominantly associated with the exposure to tobacco smoke, 10-15% of cases arise in never smokers. Small non-coding RNAs such as microRNAs (miRNAs) often act as oncogenes or tumor suppressors to regulate gene expression during disease, and may provide the critical insight needed to address the clinical and molecular disparities consistently observed in ADC of smokers and never smokers. We therefore sought to characterize the similarities and differences in the tumor-associated miRNA transcriptome between lung tumors from active, former, and never smokers. Methods: Total RNA was isolated from paired lung adenocarcinomas (purity ≥ 70%) and adjacent-normal lung tissues resected from 32 subjects with varied smoking statuses (n=8 active; n=11 former; n=13 never). Subjects were matched for gender and age. Small RNA libraries were generated and multiplexed 7-8 per lane for sequencing on the Illumina HiSeq 2000. Through a custom miRNA sequencing analysis pipeline, reads were trimmed, size-selected, and mapped to hg19 using Bowtie. Counts per mature miRNA from aligned reads were computed using Bedtools and a list of genomic features retrieved from miRBase v17. Differential expression analysis was conducted using a likelihood ratio test between two linear models: one adjusting for tumor and smoking status, another with an additional interaction term. Results: Small RNA sequencing generated an average of 10 million high quality miRNA reads per sample. Among the 1906 mature miRNAs examined, 554 miRNAs had at least an average of 20 counts across all samples. We identified 97 miRNAs (q Conclusions: Using small RNA sequencing, we have identified miRNAs that are markedly dysregulated in primary lung ADC tissues as compared to their histologically normal, adjacent counterparts. Subsets of these profiles are both shared and distinct between lung cancer cases that arise in smokers and nonsmokers. The ongoing integration of miRNA and large RNA sequencing data generated from these samples will inform our understanding of mechanisms that are specific to carcinogenesis in the presence or absence of tobacco smoke exposure. These results may yield novel targeted therapies for smoking or nonsmoking-specific ADC subtypes. Citation Format: Teresa Wang, Joshua Campbell, Rebecca Kusko, Lingqi Luo, Carmen Tellez, Gang Liu, Ji Xiao, Marc Lenburg, Steven Belinsky, Avrum Spira. Shared and distinct microRNA-expression alterations in lung adenocarcinoma from smokers vs. nonsmokers. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1948. doi:10.1158/1538-7445.AM2013-1948