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Introduction: The current molecular classification of small cell lung cancer (SCLC) based on expression of four lineage transcription factors, SCLC-A (ASCL1), SCLC-N (NEUROD1), SCLC-P (POU2F3), and SCLC-Y (YAP1) still leaves its major subtype SCLC-A as a large heterogeneous group, necessitating more precise characterization of lineage subclasses. Experimental procedure: To refine the current SCLC classification and to identify specific lineage features of the SCLC subtypes, we performed unsupervised hierarchical clustering of H3K27ac profiles on transcriptional regulators from 25 SCLC cell lines and determined the epigenomic features for each cluster. Functional significance of the transcriptional lineage regulators for the identified cluster was evaluated by cell growth, apoptosis and xenograft using CRISPR-Cas9-mediated deletion. The specific cistromic profiles by ChIP-seq and its functional transcriptional partners using co-immunoprecipitation followed by mass spectrometry were determined to reveal their functional output in the identified subtype. Rb1fl/flTrp53fl/fl and Rb1fl/flTrp53fl/flNkx2-1fl/fl genetic engineered mouse model were generated to explore the function of Nkx2-1 in tumor initiation and differentiation. H3K27ac profiles were further analyzed to reveal 6 human SCLC specimen and 20 mice tumors epigenomic landscapes. Summary: We identified previously uncharacterized epigenomic sub-clusters of the major SCLC-A subtype, named SCLC-A1 and SCLC-A2. SCLC-A1 was characterized by the presence of a super-enhancer at the NKX2-1 locus, which was observed in human SCLC specimens and a murine SCLC model. We found NKX2-1, a dual lung and neural lineage factor, is uniquely relevant in SCLC-A1, where it maintains neural lineage rather than pulmonary epithelial identity. We further found maintenance of this neural identity in SCLC-A1 is mediated by collaborative transcriptional activity with another neuronal transcriptional factor SOX1. ? Conclusions: We comprehensively describe an additional epigenomic heterogeneity of the major SCLC-A subtype, and define SCLC-A1 subtype by the core regulatory circuitry representing NKX2-1 and SOX1 super-enhancers and their functional collaborations to maintain a neuronal linage state. Citation Format: ranran kong, Ayushi S. Patel, Takashi Sato, Seungyeul Yoo, Abhilasha Sinha, Yang Tian, Feng Jiang, Charles A. Powell, Eric Snyder, Jiantao Jiang, Shaomin Li, Hideo Watanabe. Transcriptional circuitry of NKX2-1 and SOX1 defines a previously unrecognized lineage subtype of small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5709.

Patients with head and neck squamous cell carcinomas (HNSCCs) are treated with surgery, radiation, chemotherapy, and limited targeted therapies. Compared to human papillomavirus (HPV)-positive HNSCCs, HPV-negative cases have worse treatment response and prognosis and represent an unmet clinical need. We performed comprehensive proteogenomic characterization of tumor specimens, matched normal adjacent tissues (NATs), and blood samples from 109 HPV-negative HNSCC patients. This cohort is dominated by tumors from oral cavity (45, 41%) and larynx (49, 45%). Somatic mutation and somatic copy number analyses validated previously reported genomic aberrations in HPV-negative HNSCC. Proteomics analysis linked p53 loss of heterozygosity to increased expression of EPCAM, a stemness marker. Additionally, FAT1 truncation mutations were associated with increased expression of proteins involved in keratinization, a key feature of SCC differentiation. Deletions of 3p and 9p led to the loss of genes encoding p16, chemokine receptors, and interferon/JAK/STAT signaling pathway proteins, whereas amplifications of 3q and 11q led to overexpression of proteins involved in cell proliferation and anti-apoptosis pathways. Comparative analysis of tumor and NAT proteomes and phosphoproteomes identified putative diagnostic biomarkers and druggable targets, and proteogenomic integration further identified putative neoantigens. Tumor site-specific characterization associated epigenetic silencing of neurofilaments with laryngeal but not oral cavity SCC. Protein targets of FDA approved or investigational drugs for HNSCC treatment showed high inter-tumor heterogeneity in their protein abundances. DNA copy number and RNA expression level were good surrogates of protein abundance for some targets, such as EGFR and PD-L1, but they failed to reflect protein levels or kinase activities for other targets, such as MMP9 and MTOR. Thus, there is a critical need for protein biomarker-driven treatment stratification. Deconvolution of bulk tumor gene expression data revealed an immune-hot subgroup and an immune-cold subgroup. Immune-hot tumors broadly overexpressed multiple immune checkpoints including PD-L1, IDO1, and CTLA4, underscoring the necessity of combination immune checkpoint inhibition to improve treatment efficacy. Immune-cold tumors were characterized by smoking, chromosomal instability, and activation of the CDK4/6-pRb axis, suggesting they could be targeted by CDK4/6 inhibitors. We also noted that EGFR-amplified tumors frequently harbor copy number aberrations of downstream signaling components of the EGFR pathway. This may explain the low response rate of EGFR-amplified tumors to EGFR inhibitors, and targeting multiple pathway components, including EGFR, PIK3CA and STAT3, may be required for these tumors. In summary, our integrative proteogenomic characterization revealed multiple novel insights into the pathogenesis and treatment of HPV-negative HNSCCs. Citation Format: Chen Huang, Lijun Chen, Yize Li, Sara Savage, Michael Schnaubelt, Felipe V. Leprevost, Marcin Cieslik, Yongchao Dou, Bo Wen, Jonathan T. Lei, Kai Li, Eric Jaehnig, Zhiao Shi, Meenakshi Anurag, Jianbo Pan, Yingwei Hu, Rodrigo V. Eguez, David J. Clark, Matthew Wyczalkowski, Saravana M. Dhanasekaran, Chandan Kumar, Antonio Colaprico, Karsten Krug, Michael Gillette, D. R. Mani, Seungyeul Yoo, Jiayi Ji, Xiaoyu Song, Weiping Ma, Xi Steven Chen, Alex Pico, Nathan J. Edwards, Scott D. Jewell, Mathangi Thiagarajan, Emily S. Boja, Henry Rodriguez, Andrew Sikora, Pei Wang, Matthew Ellis, Gilbert S. Omenn, Li Ding, Alexey I. Nesvizhskii, Adel K. EI-Naggar, Daniel W. Chan, Hui Zhang, Bing Zhang, Clinical Proteomic Tumor Analysis Consortium. Proteogenomics characterization of HPV-negative head and neck squamous cell carcinomas [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5118.

Small cell lung cancer (SCLC) is the most aggressive subtype of lung cancer with a dismal prognosis. The standard-of-care remains to be uniform treatment with chemotherapy and radiotherapy, while emerging evidence suggests its molecular heterogeneity that has been previously under-appreciated. In primary SCLC, the gene loci for Myc family members are amplified mutually exclusively, their expression is correlated with unique neuroendocrine markers and distinct histopathology of xenografts from SCLC cell lines and murine SCLC. In this study, we use integrative genomic and epigenomic analyses to explore a novel role for c-Myc and L-Myc as lineage determining factors to bridge the gap between SCLC molecular subtypes and histological classification. First, we built a novel network using the Bayesian estimation from combined mRNA expression profile datasets for a total of 135 primary SCLC tumors. This revealed distinct transcriptional networks for c-Myc and L-Myc; wherein L-Myc was enriched for neuronal pathways and c-Myc for Notch signaling and epithelial-to-mesenchymal transition. The assay for transposase accessible-chromatin profiling of 3 representative cell lines for each c-Myc and L-Myc, revealed enrichment of biological processes involved in neuronal development for L-Myc expressing cell lines and active Notch signaling in c-Myc expressing cell lines. Together, these analyses implied that c-Myc and L-Myc govern distinct transcriptional programs to impart respective transcriptional networks associated with features unique to SCLC molecular subtypes. Next, we genetically engineered c-Myc amplified SCLC to exchange c-Myc with L-Myc and found L-Myc regulates neuronal associated pathways but was insufficient to induce lineage switch, however; c-Myc was required for the maintenance of NeuroD1-driven lineage state. In contrast, exogenous expression of c-Myc in classical-ASCL1-positive SCLC revealed incompatibility of c-Myc expression in this subtype, and led to suppression of neuronal associated pathways, trans-differentiation to NeuroD1-SCLC accompanied by variant histopathological features. Pharmacological inhibition of neuroendocrine-low associated Notch signaling and its target RE-1 silencing transcription factor (REST), revealed c-Myc-induced suppression of ASCL1 is not mediated by Notch signaling but rather by direct activation of REST expression. Collectively, our findings reveal a previously undescribed role for historically defined general oncogenes, c-Myc and L-Myc, in regulating lineage plasticity across SCLC molecular subtypes as well as histological classes. Citation Format: Ayushi S. Patel, Seungyeul Yoo, Ranran Kong, Takashi Sato, Maya Fridrikh, German Nudelman, Charles A. Powell, Jun Zhu, Hideo Watanabe. Myc family members differentially regulate lineage plasticity in small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1295.

Introduction: Lung cancer is the major cause of cancer-related deaths in United States and lung adenocarcinoma (LUAD) is its most common subtype exhibiting highly variable molecular signature. Identification of patients at high risk of developing invasive tumor is important for their better management. Experimental procedure: We obtained 53 early-stage LUAD patient's tumor samples, histopathologically classified into 32 indolent (adenocarcinoma in situ, minimally invasive and lepidic predominant adenocarcinoma) and 21 invasive (acinar predominant, papillary predominant, micropapillary predominant and solid predominant adenocarcinoma) subtypes and profiled their transcriptomes by RNA sequencing. We clustered these samples in an unsupervised manner followed by additional supervised clustering into 2 groups. Using our signature genes, we categorized 51 lung adenocarcinoma cell lines from Cancer Cell Line Encyclopedia database based on their relative invasiveness scores derived from elastic network trained in our dataset. We chose NCI-H1792, NCI-H1373, SK-LU-1 and NCI-H2009 representing high while HCC1833, Calu3 and NCI-H3255 representing low invasive signature for our in vitro functional assays. Summary: Our clustering significantly overlapped with the groups classified by histology and we defined 21 tumors as “invasive” and 32 as “non-invasive” early stage LUAD. We identified 1,322 differentially expressed genes between two groups with 526 upregulated and 796 downregulated genes in invasive tumors. Among the enriched signature genes, AURKA and AURKB were significantly upregulated in the “invasive” group. We used two potent aurora kinase inhibitors AMG 900 and PF-03814735 to determine the effect of aurora kinase inhibition on invasiveness of LUAD cells. AMG 900 and PF-03814735 significantly inhibited Aurora kinase (Aur-) A and B activity, but did not affect cell viability (IC50 ≥2μM) of all cell lines regardless of their invasiveness score. Of note, both drugs significantly suppressed wound healing, migration and invasion of all four “invasive” cell lines at much lower 100nM. Moreover, the cells with low invasive signature used in the study showed little migration and invasion thus confirming the validity of our elastic network analysis for their relative invasiveness. Conclusion: Here, we have identified a robust gene signature that distinguishes invasive to indolent non-invasive early-stage LUADs, which may lead to development of assays on clinical specimens such as biopsy to identify patients at high risk of developing invasive LUAD at an early stage. This gene signature also suggests Aur-A and B promote invasiveness in early stage LUAD. Our in vitro functional data suggest that targeting Aur-A and B could be a promising approach for management of early stage LUAD patients who are more likely to develop an invasive tumor. Citation Format: Abhilasha Sinha, Seungyeul Yoo, Hideo Watanabe, Jun Zhu, Charles A. Powell. Aurora kinase: A target modulating invasiveness of lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 814.

Introduction: Liver cancer is the second cause of cancer-related mortality worldwide. Currently, there are only two systemic agents able to increase survival in patients at advanced stages (i.e. sorafenib and regorafenib). Median survival of these patients is still poor, which highlights the need for new therapies. Our aim is to identify key regulatory gene networks with oncogenic properties amenable for therapeutic intervention through 1) integration of gene expression and DNA methylation data from human HCC samples followed by 2) functional validation in mice using shRNA screens. Methods: DNA methylation (Illumina HM450) and mRNA expression (Affymetrix human genome U219) data of 215 human HCC samples (Villanueva, Hepatology 2015) were analyzed to identify key gene regulatory networks. A causality test interrogated the impact of cis and trans regulation of promoter methylation on gene expression (Yoo, PLoS Genet 2015). A key regulator gene was defined when it regulated a substantial number of downstream genes (more than 2 standard deviations from the mean predicted trans-regulated downstream genes). Data analysis includes differential gene expression, topological overlap clustering (hierarchical, non-negative factorization [NMF]), and gene annotation. The tumorigenic potential of candidate tumor suppressors was experimentally validated through a positive selection shRNA (short-hairpin RNA) screen in mice (6 shRNAs/gene, 48 shRNAs/pool, 5 mice/library). Results: We identified 116 potential HCC key regulator genes, predicted to regulate expression of between 1,484 and 3,511 downstream genes for each one. Among the key regulators, 60 were classified as potential tumor maintenance genes and 56 as potential tumor suppressors genes based on differential expression with non-tumoral tissue (FDR Conclusions: We have identified 116 putative key regulators of hepatocarcinogenesis by integrative analysis of gene expression and DNA methylation. Functional validation indicates enrichment of bona fide tumor suppressors. Citation Format: Amanda J. Craig, Seungyeul Yoo, Verónica Miguela, Delia D'Avola, Pedro Molina-Sánchez, Josep Llovet, Amaia Lujambio, Jun Zhu, Augusto Villanueva. Integrative molecular analysis of gene expression and methylation reveals 116 putative key regulator genes of human hepatocarcinogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4393. doi:10.1158/1538-7445.AM2017-4393

Gastric cancer is a heterogeneous disease where diverse genetic and epigenetic alternations can accumulate in different molecular and histological subtypes. We applied our recently developed causality test between genome-wide DNA methylation and gene expression profiles to three independent cohorts of gastric tumors (97 in Hong Kong University (HKU), 159 in University of Singapore (Singapore) and 365 samples in TCGA stomach adenocarcinoma (TCGA) ). We focused on methylation variations within CpG islands in promoter regions, where global hypermethylation was observed, and identified 37, 62, and 537 key regulators in HKU, Singapore, and TCGA dataset respectively. There were 5 common key regulators (ADHFE1, CDO1, COX7A1, FSTL1, and TCF21) whose methylation variations had high impact on mRNA level changes of large number of downstream genes in all three dataset where different cohorts were profiled using different platforms. When we compared two dataset, there were 5 common key regulators in HKU and Singapore dataset (Fisher's exact test (FET) p-value = 2.9×e-07), 27 common key regulators in HKU and TCGA dataset (FET p-value = 6.8×e-35), and 30 common key regulators in Singapore and TCGA dataset (FET p-value = 1.0×e-29). By combining these, 52 genes were identified as key regulators within at least two dataset. Several of the key regulators were known for the association between their epigenetic disruption and the disease (for example, BNIP3, CDO1, TCF21, ZSCAN18, and so on) while other genes have not implicated in the gastric cancer previously. More interestingly, the downstream genes of these key regulators were significantly overlapped and the directions of correlation with methylation levels were almost same within the three dataset. Further clustering key regulators based on their downstream genes overlaps revealed that there were two distinct groups of downstream genes commonly regulated by these key regulators and the expression of these two groups were anti-correlated. One group was enriched for cell cycle related genes and the other group was enriched for genes involved in immune responses. This result indicates that cell cycle and immune response functions were inversely regulated by methylation variations of the same set of genes. It is worth to note that methylation patterns of some key regulators were subtype dependent and the subtype specific methylation patterns were only observed in tumor samples, but not in adjacent normal tissues. Based on integrative analysis of genome-wide DNA methylation and gene expression profiles within three independent gastric cancer dataset, we identified a set of key regulators whose methylation changes might play a ‘causal' role in the transcriptional regulation associated with the gastric cancer. Further experiments are needed to validate and dissect these putative candidate genes' roles in tumorigenesis and progression of this complex and heterogeneous disease. Citation Format: Seungyeul Yoo, Suet Yi Leung, Jun Zhu. Integrative analysis of DNA methylation and gene expression data reveals complex regulation of gastric cancer. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Sep 24-27, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2016;76(2 Suppl):Abstract nr B22.

Gene co-expression network analysis has been shown to be effective in identifying functional co-expressed gene modules associated with complex human diseases such as cancer, Alzheimers’ disease, obesity and diabetes. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of modules to be detected and numerical thresholds for defining coexpression/interaction, or do not naturally reproduce the hallmarks of complex systems such as scale-free degree distribution of small-worldness). In order to mitigate these problems as whole or in part, we have developed a novel co-expression network analysis approach, Planar Filtered Network Analysis (PFNA). PFNA utilizes a graph filtering technique to identify “true” interactions by means of embedding candidate interactions on a topological sphere(2, 3). Planar Filtered Networks (PFN) are naturally scale-free, small-world, and are comprised of a number of highly co-expressed gene modules). Furthermore, PFNA allows differential topology analysis of networks from different disease states based on centrality and peripherality metrics). We performed PFNA on the breast cancer data from The Cancer Genome Atlas (TCGA), and identified a number of novel gene modules associated with overall survival of the whole cohort or patient subgroups defined by receptor status (ER, PR, HER2) and PAM50 biomarkers(6). PFNA uncovers not only gene modules enriched for genes in well-known cancer pathways such as cell-cycle and immune response, but also novel modules that prognostically stratify patients with triple negative or basal breast cancers. Notably, several modules involved in G-protein coupled receptor signaling, protocadherin α and β pathways are highly predictive of survival for patients with triple negative disease. Furthermore, the differential topology analysis reveals that breast cancer biomarkers such as PAM50 and poor prognosis gene sets developed by van’t Veer et al(7) are more central in the tumor network than the one based on the matched adjacent normal breast tissues. In summary, PFNA reveals a number of novel high-level molecular features of the highly heterogeneous breast cancer. This novel network analysis method provides a set of unsupervised tools to objectively identify subnetworks that are associated with complex diseases such as breast cancer. 1. R. Albert, A. L. Barabasi. Rev Mod Phys 74, 47. 2. M.Tumminello, T. Aste, T. Di Matteo, R. N. Mantegna. Proc Natl Acad Sci U S A 102,10421. 3. T.Aste, T. Di Matteo, S. T. Hyde. Physica A 346, 20. 4. W.M. Song, T. Di Matteo, T. Aste. Phys RevE Stat Nonlin Soft Matter Phys 85,046115. 5. F.Pozzi, T. Di Matteo, T. Aste. Adv Complex Syst 11, 927. 6. J. S. Parker et al.. Journal of clinical oncology: 27, 1160. 7. L.J. van 't Veer et al.. Nature 415, 530. Citation Format: Won-min Song, Tao Huang, Seungyeul Yoo, EunJee Lee, Yongzhong Zhao, Li Wang, Zhidong Tu, Xudong Dai, Hanna Irie, Jun Zhu, Bin Zhang. Planar filtered gene regulatory networks in breast 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 363. doi:10.1158/1538-7445.AM2014-363