ATOH1, TFAP2B, and CEACAM6 as Immunohistochemical Markers to Distinguish Merkel Cell Carcinoma and Small Cell Lung Cancer

Merkel cell carcinoma (MCC) and small cell lung cancer (SCLC) can be histologically similar. Immunohistochemistry (IHC) for cytokeratin 20 (CK20) and thyroid transcription factor 1 (TTF-1) are commonly used to differentiate MCC from SCLC; however, these markers have limited sensitivity and specificity. To identify new diagnostic markers, we performed differential gene expression analysis on transcriptome data from MCC and SCLC tumors. Candidate markers included atonal BHLH transcription factor 1 (ATOH1) and transcription factor AP-2β (TFAP2B) for MCC, as well as carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) for SCLC. Immunostaining for CK20, TTF-1, and new candidate markers was performed on 43 MCC and 59 SCLC samples. All three MCC markers were sensitive and specific, with CK20 and ATOH1 staining 43/43 (100%) MCC and 0/59 (0%) SCLC cases and TFAP2B staining 40/43 (93%) MCC and 0/59 (0%) SCLC cases. TTF-1 stained 47/59 (80%) SCLC and 1/43 (2%) MCC cases. CEACAM6 stained 49/59 (83%) SCLC and 0/43 (0%) MCC cases. Combining CEACAM6 and TTF-1 increased SCLC detection sensitivity to 93% and specificity to 98%. These data suggest that ATOH1, TFAP2B, and CEACAM6 should be explored as markers to differentiate MCC and SCLC.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Comprehensive identification of somatic nucleotide variants in human brain tissue

BACKGROUND

Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells.

RESULTS

Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees.

CONCLUSIONS

This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

Identifying and ranking potential driver genes of Alzheimer's disease using multiview evidence aggregation

MOTIVATION

Late onset Alzheimer's disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types.

RESULTS

We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer's. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer's and are enriched in pathways that have been previously associated with the disease.

AVAILABILITY AND IMPLEMENTATION

Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

Abstract P3-05-10: Impact of neoadjuvant systemic therapy on sTIL in breast cancer

Background

Multiple studies have shown a correlation between the presence of stromal tumor infiltrating lymphocytes (sTIL) and breast cancer outcomes. Higher sTIL are associated with higher rates of pCR, DFS, and OS. These findings have been primarily observed in TNBC and in HER2+ rather than in HR+, which overall has lower sTIL. Despite this growing body of prognostic evidence, there is little data comparing sTIL at diagnosis to sTIL after neoadjuvant systemic treatment. Understanding the impact of treatment on sTIL may lead to novel techniques to increase host immune response in the tumor microenvironment. The objective of this study was to correlate sTIL in paired samples before and after standard neoadjuvant systemic therapy, and then to identify the direction and magnitude of change in sTIL along with any associated features.

Methods

Institutional pathology database was queried for invasive breast carcinoma with “yp” in staging designation. From July 2013 to September 2016, there were 122 cases identified. When cases were excluded due to unavailable biopsy slides, there were 61 cases remaining. Needle biopsy and finalsurgical specimens were evaluated using International TIL Working Group 2014 guidelines and reported as a whole number percentage. In specimens with pCR, where there are no applicable guidelines, sTIL were determined by estimating the lymphoplasmacytic infiltrate in the tumor bed. Additional information gathered from the electronic medical record included patient age, clinical TNM stage, breast cancer subtype (HR+, HER2+, TNBC), neoadjuvant systemic therapy regimen (anthracycline, nonanthracycline, Trastuzumab-containing, endocrine), and response to treatment (pCR, residual invasive disease). A negative binomial generalized linear mixed model was used for analysis.

Results

Overall the sTIL declined from biopsy to surgery by about 36% (β = -0.448, S.E. = 0.223, pvalue = 0.045). However, there were 19/61 patients who had an increase in sTIL with neoadjuvant systemic therapy. For this group, the mean biopsy sTIL was 9.7% while the mean surgery sTIL was 28.7%, significantly higher than the mean biopsy (p-value < 0.001). This group was not associated with age, stage, breast cancer subtype, or regimen. We examined our results in the 37/61 patients who did not achieve pCR. In all breast cancer subtypes, this group had an increase from biopsy to surgery in sTIL, with no significant difference between treatment regimens. Increasing age was significantly associated with lower sTIL in both biopsy and surgery specimens (p-value = 0.033). For every decade increase in age, sTIL decreased by about 37%.

Discussion

Overall the sTIL declined from biopsy to surgery. In the group of patients with an increase in sTIL, there was no significant correlation with systemic treatment regimen. There was a nonsignificant trend towards increasing sTIL and residual disease at surgery. We hypothesize that the host immune response declined after tumor eradication in pCR and remained active when residual disease was present. Increasing age was significantly associated with lower sTIL. Further work to identify additional factors associated with sTIL is needed to guide efforts to alter the immune response for improved breast cancer outcomes.

Citation Format: Armaghani A, Asirvatham R, Mramba L, Daily K. Impact of neoadjuvant systemic therapy on sTIL in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-05-10.

Abstract 5116: Identification of new treatment options for Merkel cell carcinoma using high throughput chemical screening

Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine skin cancer with limited treatment options. Approximately 80% of MCC tumors have Merkel cell polyomavirus (MCPyV) DNA integrated into the host genome, and viral oncogenes are thought to drive carcinogenesis. In contrast, MCPyV-negative (MCPyV-) tumors have higher rates of proto-oncogene mutations. Transcriptome differences between MCPyV+ and MCPyV- tumors further suggests divergent underlying pathologies. In order to develop efficacious treatments for MCC and elucidate the pathophysiologies underlying MCPyV+ and MCPyV- MCC, we screened the effects of mechanistically annotated drug libraries on MCC cell viability (CellTiter-Glo). We screened ~4,500 compounds including the NCGC Pharmaceutical Collection of approved and investigational drugs and the oncology-focused MIPE (Mechanism Interrogation PlatE) library against six MCC cell lines (3 MCPyV+ and 3 MCPyV-). We ran follow-up screens on the above cell lines as well as non-MCC control cells to identify agents that specifically reduced viability in MCC. DNA topoisomerase inhibitors, proteasome inhibitors, and PI3K inhibitors potently and efficaciously reduced viability in all MCC cell lines tested. Moreover, using hierarchical clustering we found that viral status impacted pharmacological responses. For example, a subset of HDAC inhibitors were more potent in MCPyV+ than MCPyV- MCC cells. In contrast, a dual RasGAP/ERK inhibitor more potently reduced viability in MCPyV- MCC cells relative to MCPyV+ MCC cells. Together these results identify existing drugs that can be repurposed for treating MCC. Moreover, they help elucidate the distinct pathophysiology driving MCPyV+ and MCPyV- MCC.

Citation Format: Tara Gelb, Daniel Urban, Kenneth Daily, Ying Xiao, Min Shen, Matthew Hall, Isaac Brownell. Identification of new treatment options for Merkel cell carcinoma using high throughput chemical screening [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 5116. doi:10.1158/1538-7445.AM2017-5116

Abstract OT3-04-01: BRE12-158: A phase II randomized controlled trial of genomically directed therapy after preoperative chemotherapy in patients with triple negative breast cancer (TNBC)

Background: About 1/3 of patients with TNBC who receive preoperative therapy will experience a pathological complete response (pCR). Patients with residual disease have a markedly inferior overall survival (OS) compared to those who experience pCR. Recently, the CREATE-X trial demonstrated an improvement in disease free survival (DFS) and OS for post-neoadjuvant capecitebine; although the addition of capecitebine to standard therapy has not previously improved outcome across other non-selected adjuvant or neo-adjuvant trials. Prior data have also demonstrated that the residual tumors are genomically diverse and that these genetic changes are reflected at time of relapse.

Trial Design: This trial is a randomized phase II trial to determine whether a genomically guided therapy in the setting of incomplete response to standard neoadjuvant therapy will improve outcomes compared to standard of care. DNA from archived tumor samples collected at the time of surgery will be extracted and sequenced. The sequencing data will be interrogated for known genomic drivers of sensitivity or resistance to existing FDA approved agents. A cancer genomic tumor board (CGTB) will consider the genomic data along with the patient's prior treatment history, toxicities, and comorbidities and select the optimal therapy. Participants with a CGTB recommendation will be randomized to Experimental Arm A (genomically directed monotherapy) or Control Arm B (standard of care). Participants may have no CGTB recommendation either because sequencing did not identify a matched drug or because the drug was contraindicated and will be assigned to Control Arm B.

Eligibility criteria: Patients must have histologically confirmed TNBC with completion of all definitive local therapy and no evidence of metastatic disease. There must be significant residual disease characterized by >2cm primary tumor, or lymph node positivity or RCB classification II or III. An FFPE tumor block with tumor cellularity >20% is required. All patients must have completed preoperative chemotherapy including a taxane or anthracycline or both.

Specific aims: The Primary Aim is to compare 2-year DFS with a genomically directed therapy vs. standard of care. Secondary Aims include 1-year DFS, 5-year OS, collection of archival specimens for correlative studies, and to describe toxicities. Exploratory Aims are to describe the evolution of genomically directed therapies during the course of the study and to evaluate the drug specific effect on efficacy and toxicity.

Statistical methods: In order to detect an improvement in the fraction of patients free from disease at 2-year from 40% in the control Arm B to 63.2% in the genomically directed Experimental Arm A (corresponding to an HR=0.5), 136 participants will have 80% power to detect a difference in DFS using a two-side log-rank test with 0.05 level of significance.

Present accrual/target accrual: 38 accrued of 136 to be randomized.

Citation Format: Schneider B, Miller KD, Badve S, O'Neil B, Helft P, Chitambar C, Falkson C, Nanda R, McCormick M, Danso M, Blaya M, Langdon R, Lippman M, Paplomata E, Walling R, Thompson M, Robin E, Aggarwal L, Shalaby I, Canfield V, Adesunloye B, Lee T, Daily K, Ma C, Erban J, Radhakrishnan N, Bruetman D, Graham M, Reddy NA, Lynce FC, Radovich M. BRE12-158: A phase II randomized controlled trial of genomically directed therapy after preoperative chemotherapy in patients with triple negative breast cancer (TNBC) [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr OT3-04-01.

Evaluating blood levels of neuron specific enolase, chromogranin A, and circulating tumor cells as Merkel cell carcinoma biomarkers

Background

Merkel cell carcinoma (MCC) is a rare, aggressive neuroendocrine skin cancer. Although used to monitor MCC patients, the clinical utility of neuron-specific enolase (NSE) and chromogranin A (ChrA) blood levels is untested. EpCAM-positive circulating tumor cells (CTC) reflect disease status in several epithelial tumors. Here we investigate the use of NSE and ChrA blood levels and CTC counts as biomarkers for MCC disease behavior.

Methods

NSE and ChrA blood levels from 60 patients with MCC were retrospectively analyzed; 30 patients were additionally screened for CTC. Biomarker values were correlated to clinical parameters.

Results

Despite routine use by some physicians, NSE and ChrA blood levels did not correlate with progression free survival, disease specific survival, or MCC recurrence. We found CTC in 97% of tested MCC patients. CTC counts were elevated in patients with active disease, suggesting their potential use in monitoring MCC.

Conclusion

NSE and ChrA levels were not effective in predicting outcomes or detecting recurrences of MCC. In contrast, CTC counts have potential utility as a biomarker for MCC disease behavior.

Abstract 114: Oncogenomic analysis of Merkel cell carcinoma

We used an integrative oncogenomic approach to investigate the molecular etiology of Merkel Cell Carcinoma (MCC) in an effort to identify novel therapeutic targets and biomarkers for this highly aggressive neuroendocrine skin tumor. Because MCC is rare and poorly understood, there are currently no effective treatment options for patients with advanced disease. Approximately 80% of MCC tumors have Merkel Cell polyomavirus (MCV) DNA integrated into the host genome, and viral oncogenes are thought to drive carcinogenesis. Moreover it has been reported that MCV-negative MCC samples have an increased rate of mutations relative to MCV-positive tumors. Taken together, this suggests that MCV-positive and negative MCCs are driven by different mechanisms. We used whole exome sequencing and bioinformatic analyses to identify somatically dysregulated genes and pathways in MCC tumor samples. Additionally, we used a comparative genomic hybridization array to identify genomic amplifications and deletions. Whole transcriptome profiling was achieved using microarrays and RNA-sequencing. As the same tumor samples were used in each of these platforms, we were able to systematically correlate sequence variants with copy number changes and gene expression to identify high priority targets for further study. Interestingly, despite differences in driver mutations, we found relatively few differences in the global gene expression of MCV-positive and MCV-negative MCC tumors. Overall, our integrative oncogenomic approach has identified novel therapeutic targets for the treatment of MCC that are currently undergoing functional validation and preclinical testing.

Citation Format: Tara Gelb, Kenneth Daily, Amy Coxon, Isaac Brownell. Oncogenomic analysis of Merkel cell carcinoma. [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 114.

Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium

The rigorous characterization of distinct induced pluripotent stem cells (iPSC) derived from multiple reprogramming technologies, somatic sources, and donors is required to understand potential sources of variability and downstream potential. To achieve this goal, the Progenitor Cell Biology Consortium performed comprehensive experimental and genomic analyses of 58 iPSC from ten laboratories generated using a variety of reprogramming genes, vectors, and cells. Associated global molecular characterization studies identified functionally informative correlations in gene expression, DNA methylation, and/or copy-number variation among key developmental and oncogenic regulators as a result of donor, sex, line stability, reprogramming technology, and cell of origin. Furthermore, X-chromosome inactivation in PSC produced highly correlated differences in teratoma-lineage staining and regulator expression upon differentiation. All experimental results, and raw, processed, and metadata from these analyses, including powerful tools, are interactively accessible from a new online portal at https://www.synapse.org to serve as a reusable resource for the stem cell community.

•

Comprehensive portal for diverse iPSC, protocols, metadata, and genomic assays

•

Recurrent CNV occur during reprogramming, impact oncogenes and tumor suppressors

•

DNA methylation is influenced by cell of origin in iPSC

•

PSC X-chromosome inactivation impacts lineage differentiation outcomes

GRHL3/GET1 and trithorax group members collaborate to activate the epidermal progenitor differentiation program

The antagonistic actions of Polycomb and Trithorax are responsible for proper cell fate determination in mammalian tissues. In the epidermis, a self-renewing epithelium, previous work has shown that release from Polycomb repression only partially explains differentiation gene activation. We now show that Trithorax is also a key regulator of epidermal differentiation, not only through activation of genes repressed by Polycomb in progenitor cells, but also through activation of genes independent of regulation by Polycomb. The differentiation associated transcription factor GRHL3/GET1 recruits the ubiquitously expressed Trithorax complex to a subset of differentiation genes.

Human epidermal keratinocyte differentiation provides a highly suitable system to understand how progenitor cells become specialized. Previous work has implicated resolution of repressive histone modifications in the activation of the terminal differentiation gene expression program. Our work shows that this mechanism only accounts for the regulation of a subset of the differentiation gene expression program and that activating histone modifications by Trithorax chromatin modifiers, acting alone or in combination with the release from repressive chromatin changes, is essential. Furthermore, we show that the Trithorax complex is recruited to a subset of differentiation gene promoters by the transcription factor Grhl3, an evolutionarily conserved regulator of the epidermal differentiation program. Altered differentiation is characteristic for several skin diseases, including skin cancer and inflammatory diseases such as psoriasis. While genetic abnormalities play a role in these diseases, the cellular and macro-environment may also alter the course of these diseases through chromatin changes (epigenetics). Understanding the epigenetic regulation of keratinocyte differentiation may in the future lead to the development of new drugs for skin diseases.

Retrotransposon profiling of RNA polymerase III initiation sites

Although retroviruses are relatively promiscuous in choice of integration sites, retrotransposons can display marked integration specificity. In yeast and slime mold, some retrotransposons are associated with tRNA genes (tDNAs). In the Saccharomyces cerevisiae genome, the long terminal repeat retrotransposon Ty3 is found at RNA polymerase III (Pol III) transcription start sites of tDNAs. Ty1, 2, and 4 elements also cluster in the upstream regions of these genes. To determine the extent to which other Pol III-transcribed genes serve as genomic targets for Ty3, a set of 10,000 Ty3 genomic retrotranspositions were mapped using high-throughput DNA sequencing. Integrations occurred at all known tDNAs, two tDNA relics (iYGR033c and ZOD1), and six non-tDNA, Pol III-transcribed types of genes (RDN5, SNR6, SNR52, RPR1, RNA170, and SCR1). Previous work in vitro demonstrated that the Pol III transcription factor (TF) IIIB is important for Ty3 targeting. However, seven loci that bind the TFIIIB loader, TFIIIC, were not targeted, underscoring the unexplained absence of TFIIIB at those sites. Ty3 integrations also occurred in two open reading frames not previously associated with Pol III transcription, suggesting the existence of a small number of additional sites in the yeast genome that interact with Pol III transcription complexes.

MotifMap: integrative genome-wide maps of regulatory motif sites for model species

Background

A central challenge of biology is to map and understand gene regulation on a genome-wide scale. For any given genome, only a small fraction of the regulatory elements embedded in the DNA sequence have been characterized, and there is great interest in developing computational methods to systematically map all these elements and understand their relationships. Such computational efforts, however, are significantly hindered by the overwhelming size of non-coding regions and the statistical variability and complex spatial organizations of regulatory elements and interactions. Genome-wide catalogs of regulatory elements for all model species simply do not yet exist.

Results

The MotifMap system uses databases of transcription factor binding motifs, refined genome alignments, and a comparative genomic statistical approach to provide comprehensive maps of candidate regulatory elements encoded in the genomes of model species. The system is used to derive new genome-wide maps for yeast, fly, worm, mouse, and human. The human map contains 519,108 sites for 570 matrices with a False Discovery Rate of 0.1 or less. The new maps are assessed in several ways, for instance using high-throughput experimental ChIP-seq data and AUC statistics, providing strong evidence for their accuracy and coverage. The maps can be usefully integrated with many other kinds of omic data and are available at http://motifmap.igb.uci.edu/.

Conclusions

MotifMap and its integration with other data provide a foundation for analyzing gene regulation on a genome-wide scale, and for automatically generating regulatory pathways and hypotheses. The power of this approach is demonstrated and discussed using the P53 apoptotic pathway and the Gli hedgehog pathways as examples.