Gingivobuccal complex oral squamous cell carcinoma (GBC-OSCC) is an aggressive malignancy with high mortality often preceded by premalignant lesions, including leukoplakia. Previous studies have reported genomic drivers in OSCC, but much remains to be elucidated about DNA methylation patterns across different stages of oral carcinogenesis. There is a serious lack of biomarkers and clinical application of biomarkers for early detection and prognosis of gingivobuccal complex cancers. Hence, in search of novel biomarkers, we measured genome-wide DNA methylation in 22 normal oral tissues, 22 leukoplakia, and 74 GBC-OSCC tissue samples. Both leukoplakia and GBC-OSCC had distinct methylation profiles as compared to normal oral tissue samples. Aberrant DNA methylation increases during the different stages of oral carcinogenesis, from premalignant lesions to carcinoma. We identified 846 and 5111 differentially methylated promoters in leukoplakia and GBC-OSCC, respectively, with a sizable fraction shared between the two sets. Further, we identified potential biomarkers from integrative analysis in gingivobuccal complex cancers and validated them in an independent cohort. Integration of genome, epigenome, and transcriptome data revealed candidate genes with gene expression synergistically regulated by copy number and DNA methylation changes. Regularised Cox regression identified 32 genes associated with patient survival. In an independent set of samples, we validated eight genes (FAT1, GLDC, HOXB13, CST7, CYB5A, MLLT11, GHR, LY75) from the integrative analysis and 30 genes from previously published reports. Bisulfite pyrosequencing validated GLDC (P = 0.036), HOXB13 (P < 0.0001) promoter hypermethylation, and FAT1 (P < 0.0001) hypomethylation in GBC-OSCC compared to normal controls. Our findings identified methylation signatures associated with leukoplakia and gingivobuccal complex cancers. The integrative analysis in GBC-OSCC identified putative biomarkers that enhance existing knowledge of oral carcinogenesis and may potentially help in risk stratification and prognosis of GBC-OSCC.

Nonalcoholic steatohepatitis (NASH)-induced hepatocellular carcinoma (HCC) and its precursor, nonalcoholic fatty liver disease (NAFLD) are an unmet health issue due to widespread obesity. We assessed copy number changes of genes associated with hepatocarcinogenesis and oxidative pathways at a single-cell level. Eleven patients with NASH-HCC and 11 patients with NAFLD were included. Eight probes were analyzed using multiplex interphase fluorescence in situ hybridization (miFISH), single-cell imaging and phylogenetic tree modelling: Telomerase reverse transcriptase ( TERT ), C-Myc ( MYC ), hepatocyte growth factor receptor tyrosine kinase ( MET ), tumor protein 53 ( TP53 ), cyclin D1 ( CCND1 ), human epidermal growth factor receptor 2 ( HER2 ), the fragile histidine triad gene ( FHIT ) and FRA16D oxidoreductase ( WWOX ). Each NASH-HCC tumor had up to 14 distinct clonal signal patterns indicating multiclonality, which correlated with high tumor grade. Changes frequently observed were TP53 losses, 45%; MYC gains, 36%; WWOX losses, 36%; and HER2 gains, 18%. Whole-genome duplications were frequent (82%) with aberrant tetraploid cells evolving from diploid ancestors. Non-tumorous NAFLD/NASH biopsies did not harbor clonal copy number changes. Fine mapping of NASH-HCC using single-cell multiplex FISH shows that branched tumor evolution involves genome duplication and that multiclonality increases with tumor grade. The loss of oxidoreductase WWOX and HER2 gains could be potentially associated with NASH-induced hepatocellular carcinoma.

Motivation: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. Results: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. Availability and implementation: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_ deconvolution. Contact: russells@andrew.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell-type-specific gene expression in each sample from bulk expression, and LIRICS (LIgand Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand-receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method, CIBERSORTx. Second, analyzing the TCGA, we uncover cell-type-specific interactions of mismatch-repair-deficient tumors that are associated with their higher anti-PD1 response rates, including specific T-cell co-stimulating interactions that enhance immunotherapy response independently of the tumors mutation burden levels. Finally, we identify a subset of ligand-receptor interactions in the melanoma TME that predict patient response to anti-PD1 therapy better than recently published transcriptomics-based methods.

MOTIVATION:Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS:In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION:Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

ObjectiveHepatocellular carcinoma (HCC) represents a typical inflammation-associated cancer. Tissue resident innate lymphoid cells (ILCs) have been suggested to control tumour surveillance. Here, we studied how the local cytokine milieu controls ILCs in HCC.DesignWe performed bulk RNA sequencing of HCC tissue as well as flow cytometry and single-cell RNA sequencing of enriched ILCs from non-tumour liver, margin and tumour core derived from 48 patients with HCC. Simultaneous measurement of protein and RNA expression at the single-cell level (AbSeq) identified precise signatures of ILC subgroups. In vitro culturing of ILCs was used to validate findings from in silico analysis. Analysis of RNA-sequencing data from large HCC cohorts allowed stratification and survival analysis based on transcriptomic signatures.ResultsRNA sequencing of tumour, non-tumour and margin identified tumour-dependent gradients, which were associated with poor survival and control of ILC plasticity. Single-cell RNA sequencing and flow cytometry of ILCs from HCC livers identified natural killer (NK)-like cells in the non-tumour tissue, losing their cytotoxic profile as they transitioned into tumour ILC1 and NK-like-ILC3 cells. Tumour ILC composition was mediated by cytokine gradients that directed ILC plasticity towards activated tumour ILC2s. This was liver-specific and not seen in ILCs from peripheral blood mononuclear cells. Patients with high ILC2/ILC1 ratio expressed interleukin-33 in the tumour that promoted ILC2 generation, which was associated with better survival.ConclusionOur results suggest that the tumour cytokine milieu controls ILC composition and HCC outcome. Specific changes of cytokines modify ILC composition in the tumour by inducing plasticity and alter ILC function.

Background Engineered versions of adeno-associated virus (AAV) are commonly used in gene therapy but evidence revealing a potential oncogenic role of natural AAV in hepatocellular carcinoma (HCC) has raised concerns. The frequency of potentially oncogenic integrations has been reported in only a few populations. AAV infection and host genome integration in another type of liver cancer, cholangiocarcinoma (CCA), has been studied only in one cohort. All reported oncogenic AAV integrations in HCC come from strains resembling the fully sequenced AAV2 and partly sequenced AAV13. When AAV integration occurs, only a fragment of the AAV genome is detectable in later DNA or RNA sequencing. The integrated fragment is typically from the 3’ end of the AAV genome, and this positional bias has been only partly explained. Three research groups searched for evidence of AAV integration in HCC RNAseq samples in the Cancer Genome Atlas (TCGA) but reported conflicting results. Results We collected and analyzed whole transcriptome and viral capture DNA sequencing in paired tumor and non-tumor samples from two liver cancer Asian cohorts from Thailand ( N = 147, 47 HCC and 100 intrahepatic cholangiocarcinoma (iCCA)) and Mongolia ( N = 70, all HCC). We found only one HCC patient with a potentially oncogenic integration of AAV, in contrast to higher frequency reported in European patients. There were no oncogenic AAV integrations in iCCA patients. AAV genomic segments are present preferentially in the non-tumor samples of Thai patients. By analyzing the AAV genome positions of oncogenic and non-oncogenic integrated fragments, we found that almost all the putative oncogenic integrations overlap the X gene, which is present and functional only in the strain AAV2 among all fully sequenced strains. This gene content difference could explain why putative oncogenic integrations from other AAV strains have not been reported. We resolved the discrepancies in previous analyses of AAV presence in TCGA HCC samples and extended it to CCA. There are 12 TCGA samples with an AAV segment and none are in Asian patients. AAV segments are present in preferentially in TCGA non-tumor samples, like what we observed in the Thai patients. Conclusions Our findings suggest a minimal AAV risk of hepatocarcinogenesis in Asian liver cancer patients. The partial genome presence and positional bias of AAV integrations into the human genome has complicated analysis of possible roles of AAV in liver cancer.

Abstract The goal of this study is to develop a new method to characterize taxonomically which microbes are present intracellularly in which cell types in the tumor microenvironment. The tumor microbiome impacts many aspects of tumor development including tumorigenesis, mutagenesis, response to chemotherapy and the immune response. A recent computational approach showed that bacterial reads found in sequencing tumor samples and taxon abundances are predictive of cancer type. Recent experimental evidence suggests that some bacteria in the tumor microbiome may reside intracellularly. We sought to answer two questions: 1) which microbes, especially bacteria, reside intracellularly in a tumor sample and 2) in which cell types do these microbes preferentially reside? We developed a computational pipeline, CSI-Microbes (computational identification of Cell-type-Specific Intracellular Microbes), to identify cell-type-specific intracellular microbes from single cell RNA-seq (scRNA-seq) data. CSI-Microbes builds on the observation that in contrast to extracellular microbes and contaminants, some cell-type-specific intracellular microbes would exhibit differential abundance between cell types. For validation, we show that CSI-Microbes identifies Salmonella as the only differentially abundant microbe in a scRNA-seq dataset in the subset of immune cells that were deliberately infected with Salmonella. Next, we recapitulate known findings that Merkel cell polyomavirus and Fusobacterium exist intracellularly in tumor cells in Merkel cell carcinoma and in colorectal carcinoma, respectively. We then apply CSI-Microbes to analyze scRNA-seq data from numerous cancer types. We find that Streptomyces is differentially abundant in the tumor cells of both breast and head-and-neck cancer. We identify three bacterial genera and four fungal genera that are differentially abundant specifically in the tumor cells of melanoma samples. We additionally find evidence for the reactivation of herpesvirus in the plasma cells of a patient with basal cell carcinoma after immune checkpoint blockade therapy. These sequence-based findings raise the possibility that these tumor-specific intracellular microbes may play functional roles in tumor metabolism and drug response in these cancer types. As such, they also may be a source of neo-antigens, which can potentially be targeted using T cell therapies. In sum, CSI-Microbes offers a new way for fast, single cell RNA-seq based identification of likely intracellular bacteria living within specific cell populations in tumors, markedly extending upon previous studies aimed at inferring microbial abundance from bulk tumor samples. Citation Format: Welles Robinson, Fiorella Schischlik, E. Michael Gertz, Alejandro A. Schäffer, Eytan Ruppin. Identifying the landscape of intratumoral microbes via a single cell transcriptomic analysis [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PO093.

Motivation Recent advances in single cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program (ILP), or a constraint satisfaction program (CSP), which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain (MCMC) or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. Results We introduce PhISCS-BnB, a Branch and Bound algorithm to compute the most likely perfect phylogeny (PP) on an input genotype matrix extracted from a SCS data set. PhISCS-BnB not only offers an optimality guarantee, but is also 10 to 100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a large melanoma data set derived from the sub-lineages of a cell line involving 24 clones with 3574 mutations, which returned the optimal tumor phylogeny in less than 2 hours. The resulting phylogeny also agrees with bulk exome sequencing data obtained from in vivo tumors growing out from the same cell line. Availability https://github.com/algo-cancer/PhISCS-BnB

Inferring subject ancestry using genetic data is an important step in genetic association studies, required for dealing with population stratification. It has become more challenging to infer subject ancestry quickly and accurately since large amounts of genotype data, collected from millions of subjects by thousands of studies using different methods, are accessible to researchers from repositories such as the database of Genotypes and Phenotypes (dbGaP) at the National Center for Biotechnology Information (NCBI). Study-reported populations submitted to dbGaP are often not harmonized across studies or may be missing. Widely-used methods for ancestry prediction assume that most markers are genotyped in all subjects, but this assumption is unrealistic if one wants to combine studies that used different genotyping platforms. To provide ancestry inference and visualization across studies, we developed a new method, GRAF-pop, of ancestry prediction that is robust to missing genotypes and allows researchers to visualize predicted population structure in color and in three dimensions. When genotypes are dense, GRAF-pop is comparable in quality and running time to existing ancestry inference methods EIGENSTRAT, FastPCA, and FlashPCA2, all of which rely on principal components analysis (PCA). When genotypes are not dense, GRAF-pop gives much better ancestry predictions than the PCA-based methods. GRAF-pop employs basic geometric and probabilistic methods; the visualized ancestry predictions have a natural geometric interpretation, which is lacking in PCA-based methods. Since February 2018, GRAF-pop has been successfully incorporated into the dbGaP quality control process to identify inconsistencies between study-reported and computationally predicted populations and to provide harmonized population values in all new dbGaP submissions amenable to population prediction, based on marker genotypes. Plots, produced by GRAF-pop, of summary population predictions are available on dbGaP study pages, and the software, is available at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/Software.cgi.

The phenotypic effect of perturbing a gene’s activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) – synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. Here, we describe a novel data-driven approach called EnGIne, that applied to TCGA data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes’ tissue-specificity and differences in patients’ response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications. The GI network is accessible online via a web portal [https://amagen.shinyapps.io/cancerapp/].

Intratumor heterogeneity is a major challenge in cancer treatment. To decipher patterns of chromosomal heterogeneity, we analyzed six colorectal cancer cell lines by multiplex interphase FISH (miFISH). The mismatch-repair-deficient cell lines DLD-1 and HCT116 had the most stable copy numbers, whereas aneuploid cell lines (HT-29, SW480, SW620 and H508) displayed a higher degree of instability. We subsequently assessed the clonal evolution of single cells in two colorectal carcinoma cell lines, SW480 and HT-29, which both have aneuploid karyotypes but different degrees of chromosomal instability. The clonal compositions of the single cell-derived daughter lines, as assessed by miFISH, differed for HT-29 and SW480. Daughters of HT-29 were stable, clonal, with little heterogeneity. Daughters of SW480 were more heterogeneous, with the single cell-derived daughter lines separating into two distinct populations with different ploidy (hyper-diploid and near-triploid), morphology, gene expression and tumorigenicity. To better understand the evolutionary trajectory for the two SW480 populations, we constructed phylogenetic trees which showed ongoing instability in the daughter lines. When analyzing the evolutionary development over time, most single cell-derived daughter lines maintained their major clonal pattern, with the exception of one daughter line that showed a switch involving a loss of APC. Our meticulous analysis of the clonal evolution and composition of these colorectal cancer models shows that all chromosomes are subject to segregation errors, however, specific net genomic imbalances are maintained. Karyotype evolution is driven by the necessity to arrive at and maintain a specific plateau of chromosomal copy numbers as the drivers of carcinogenesis.

We previously proposed that changes in the efficiency of protein translation are associated with autism spectrum disorders (ASDs). This hypothesis connects environmental factors and genetic factors because each can alter translation efficiency. For genetic factors, we previously tested our hypothesis using a small set of ASD-associated genes, a small set of ASD-associated variants, and a statistic to quantify by how much a single nucleotide variant (SNV) in a protein coding region changes translation speed. In this study, we confirm and extend our hypothesis using a published set of 1,800 autism quartets (parents, one affected child and one unaffected child) and genome-wide variants. Then, we extend the test statistic to combine translation efficiency with other possibly relevant variables: ribosome profiling data, presence/absence of CpG dinucleotides, and phylogenetic conservation. The inclusion of ribosome profiling abundances strengthens our results for male-male sibling pairs. The inclusion of CpG information strengthens our results for female-female pairs, giving an insight into the significant gender differences in autism incidence. By combining the single-variant test statistic for all variants in a gene, we obtain a single gene score to evaluate how well a gene distinguishes between affected and unaffected siblings. Using statistical methods, we compute gene sets that have some power to distinguish between affected and unaffected siblings by translation efficiency of gene variants. Pathway and enrichment analysis of those gene sets suggest the importance of Wnt signaling pathways, some other pathways related to cancer, ATP binding, and ATP-ase pathways in the etiology of ASDs.

Desirée Schubert, Marie-Christine Klein, Sarah Hassdenteufel, Andrés CaballeroOteyza, Linlin Yang, Michele Proietti, Alla Bulashevska, Janine Kemming, Johannes Kühn, Sandra Winzer, Stephan Rusch, Manfred Fliegauf, Alejandro A. Schäffer, Stefan Pfeffer, Roger Geiger, Adolfo Cavalié, Hongzhi Cao, Fang Yang, Yong Li, Marta Rizzi, Hermann Eibel, Robin Kobbe, Amy L. Marks, Brian P. Peppers, Robert W. Hostoffer, Jennifer M. Puck, Richard Zimmermann, Bodo Grimbacher

Background Identification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2. Results We detected over 19,000 independent lead cis -eQTLs and over 6000 independent lead trans -eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans -eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans -eQTLs, each targeting the expression of sets of six to 229 distinct trans -eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes. Conclusions These findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci.

Abstract We previously studied synchronous Ductal Carcinomas in Situ (DCIS) and Invasive Ductal Carcinomas (IDC) using a novel approach of multiplexing FISH probes that allows us to simultaneously assess the copy numbers of up to 20 loci within intact nuclei providing new insights into tumor clonality and heterogeneity. A high degree of chromosomal instability already in DCIS, and frequently, but not always, a direct clonal evolution from DCIS to IDC was detected. We now ask whether this degree of instability is also present in DCIS that did not progress to IDC and are therefore analyzing FFPE material from 20 patients with either low-grade and high-grade DCIS who did not present with invasive breast carcinoma during their follow-up (5-10 years), in addition to 10 patients who presented with synchronous IDC. The multiplex FISH assay used targets five oncogenes (COX2, MYC, CCND1, HER2, ZNF217) and three tumor suppressor genes (DBC2, CDH1, TP53) frequently altered in breast carcinomas. To date, we have analyzed two paired cases of synchronous DCIS and IDC, DCIS-IDC 3 and 4, and three cases of DCIS without IDC, OP-DCIS 1, 4 and 5. The paired cases exhibited very similar aberration patterns for synchronous DCIS and IDC indicating the invasive carcinoma is closely related to the DCIS lesion. Specifically, the major clones in DCIS 3 (low-grade) and IDC 3 showed a diploid tumor cell population with a gain of COX2, and losses of DBC2, MYC, TP53, and HER2. Of note, the only difference observed was a gain of ZNF217 in the DCIS which was not seen in the major clone of the invasive carcinoma. The other paired case, DCIS 4 (high-grade) and IDC 4, showed major clones of triploid tumor cell populations with gains of COX2, CCND1 and MYC and losses of DBC2, CDH1, TP53 and ZNF217. The three DCIS cases without synchronous or subsequent IDC exhibited varying degrees of aberration and complexity patterns in their clonal populations. Case OP-DCIS 1, a high-grade DCIS, revealed a tetraploid cell population which showed a major clone with an amplification of CCND1 and a MYC gain combined with losses of DBC2, CDH1, TP53 and HER2. A low-grade DCIS, OP-DCIS 4, exhibited a diploid cell population with a major clone showing loss of CDH1 as the only aberration. The third case OP-DCIS 5 (low-grade DCIS) exhibited a diploid cell population with one major clone showing losses of CDH1, MYC, DBC2, TP53, HER2 and CCND1. Our preliminary observations show a tendency of diploid lesions with a predominance of loss patterns for low-grade DCIS, while high-grade DCIS seem to reveal higher ploidy with more complex gain and loss patterns. However, also low-grade DCIS with mainly loss patterns progress to invasive cancers as seen in case DCIS-IDC3. We expect that the analysis of the remaining cases will further elucidate the dynamics of DCIS lesions which will hopefully help to assess and stratify progression risk in patients with DCIS. Citation Format: Irianna Marie Torres, Leanora Hernandez, Jausheng Tzeng, Russell Schwartz, Alejandro Schaffer, Edward Gertz, Stephen Brower, Miguel Sanchez, Gert Auer, Kerstin Heselmeyer-Haddad, Ried Thomas. Single-cell genetic analysis of ductal carcinoma in situ with and without synchronous invasive breast cancer by multiplex FISH delineates specific patterns of tumor clonality and heterogeneity [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 2908. doi:10.1158/1538-7445.AM2017-2908

Abstract Tumor type specific genomic imbalances are the defining feature of carcinomas. It remains elusive to which extent intratumor heterogeneity and the clonal composition of cancer cell populations influences the genetic makeup and the phenotype of tumors. Many studies aimed at understanding the idiosyncrasies of cancer rely on the analysis of cell lines derived from primary tumors. In an attempt to decipher patterns of chromosomal heterogeneity we analyzed the widely used colorectal cancer cell lines DLD-1, HCT116, H508, SW620, HT-29 and SW480 using FISH and gene expression profiling. First, we applied a four color probe set that targets the oncogenes EGFR, CCND1, CDX2 and TERC and enumerated 200 metaphase spread, which included chromosome counts, and 5,000 interphase cells. Not surprisingly, the mismatch repair deficient cell lines DLD-1 and HCT116 were the most stable, whereas the aneuploid cell lines displayed a higher degree of instability. These results prompted us to apply our recently developed multiplex interphase FISH approach that allows copy number analysis of 12 gene loci in individual cells to 11 single cell derived clones from both HT-29 and SW480. SW480 was considerably more instable than HT-29. The parental lines consisted of major clones, which were propagated in the single cell derived lines. These analyses were complemented by gene expression profiling of the single cell derived clones and the parental cell lines using the Nanostring technology which measures expression levels of 770 cancer-associated genes. The SW480 clones separated into two distinct clusters that reflect discrete ploidy levels that were also present in the parental line. We then explored whether mutations of the BRAF (HT-29) and KRAS (SW480) genes observed in the parental lines were maintained in the derived clones; this was the case in all instances. Finally, we reconstructed phylogenetic trees of tumor evolution using a specifically developed algorithm, termed FISHTrees. Our meticulous analysis of the clonal composition of these colorectal cancer models shows that, despite a certain degree of chromosomal instability, specific genomic imbalances are maintained. The results challenge the concept of ongoing chromosomal instability in cancer cell populations. This suggests a karyotype evolution that is driven by the necessity to arrive at a different plateau of chromosomal copy number as the driving force of carcinogenesis. Citation Format: Darawalee Wangsa, Madison Schiefer, Daniel Bronder, Hesed Padilla-Nash, Irianna Torres, Lidia Warner, Yue Hu, E Michael Gertz, Russell Schwartz, Alejandro A. Schäffer, Daniela Hirsch, Timo Gaiser, Rüdiger Meyer, Jordi Camps, Kerstin Heselmeyer-Haddad, Thomas Ried. The clonal composition of colorectal cancer cell lines is defined by the maintenance of specific genomic imbalances, not by ongoing chromosomal instability [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 4387. doi:10.1158/1538-7445.AM2017-4387

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Background: Primary antibody deficiencies (PADs) are the most frequent primary immunodeficiencies in human subjects. The genetic causes of PADs are largely unknown. Sec61 translocon alpha 1 subunit (SEC61A1) is the major subunit of the Sec61 complex, which is the main polypeptide-conducting channel in the endoplasmic reticulum membrane. SEC61A1 is a target gene of spliced X-box binding protein 1 and strongly induced during plasma cell (PC) differentiation. Objective: We identified a novel genetic defect and studied its pathologic mechanism in 11 patients from 2 unrelated families with PADs. Methods: Whole-exome and targeted sequencing were conducted to identify novel genetic mutations. Functional studies were carried out ex vivo in primary cells of patients and in vitro in different cell lines to assess the effect of SEC61A1 mutations on B-cell differentiation and survival. Results: We investigated 2 families with patients with hypogammaglobulinemia, severe recurrent respiratory tract infections, and normal peripheral B-and T-cell subpopulations. On in vitro stimulation, B cells showed an intrinsic deficiency to develop into PCs. Genetic analysis and targeted sequencing identified novel heterozygous missense (c.254T>A, p.V85D) and nonsense (c.1325G>T, p.E381*) mutations in SEC61A1, segregating with the disease phenotype. SEC61A1-V85D was deficient in cotranslational protein translocation, and it disturbed the cellular calcium homeostasis in HeLa cells. Moreover, SEC61A1-V85D triggered the terminal unfolded protein response in multiple myeloma cell lines. Conclusion: We describe a monogenic defect leading to a specific PC deficiency in human subjects, expanding our knowledge about the pathogenesis of antibody deficiencies.

Ductal Carcinoma In Situ (DCIS) is a precursor lesion of Invasive Ductal Carcinoma (IDC) of the breast. Investigating its temporal progression could provide fundamental new insights for the development of better diagnostic tools to predict which cases of DCIS will progress to IDC. We investigate the problem of reconstructing a plausible progression from single-cell sampled data of an individual with synchronous DCIS and IDC. Specifically, by using a number of assumptions derived from the observation of cellular atypia occurring in IDC, we design a possible predictive model using integer linear programming (ILP). Computational experiments carried out on a preexisting data set of 13 patients with simultaneous DCIS and IDC show that the corresponding predicted progression models are classifiable into categories having specific evolutionary characteristics. The approach provides new insights into mechanisms of clonal progression in breast cancers and helps illustrate the power of the ILP approach for similar problems in reconstructing tumor evolution scenarios under complex sets of constraints.