Roeland Verhaak

SOX2 is an amplified lineage-survival oncogene in lung and esophageal squamous cell carcinomas

Lineage-survival oncogenes are activated by somatic DNA alterations in cancers arising from the cell lineages in which these genes play a role in normal development. Here we show that a peak of genomic amplification on chromosome 3q26.33 found in squamous cell carcinomas (SCCs) of the lung and esophagus contains the transcription factor gene SOX2, which is mutated in hereditary human esophageal malformations, is necessary for normal esophageal squamous development, promotes differentiation and proliferation of basal tracheal cells and cooperates in induction of pluripotent stem cells. SOX2 expression is required for proliferation and anchorage-independent growth of lung and esophageal cell lines, as shown by RNA interference experiments. Furthermore, ectopic expression of SOX2 here cooperated with FOXE1 or FGFR2 to transform immortalized tracheobronchial epithelial cells. SOX2-driven tumors show expression of markers of both squamous differentiation and pluripotency. These characteristics identify SOX2 as a lineage-survival oncogene in lung and esophageal SCC.

Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A-TCF7L2 fusion

Prior studies have identified recurrent oncogenic mutations in colorectal adenocarcinoma and have surveyed exons of protein-coding genes for mutations in 11 affected individuals. Here we report whole-genome sequencing from nine individuals with colorectal cancer, including primary colorectal tumors and matched adjacent non-tumor tissues, at an average of 30.7× and 31.9× coverage, respectively. We identify an average of 75 somatic rearrangements per tumor, including complex networks of translocations between pairs of chromosomes. Eleven rearrangements encode predicted in-frame fusion proteins, including a fusion of VTI1A and TCF7L2 found in 3 out of 97 colorectal cancers. Although TCF7L2 encodes TCF4, which cooperates with β-catenin in colorectal carcinogenesis, the fusion lacks the TCF4 β-catenin–binding domain. We found a colorectal carcinoma cell line harboring the fusion gene to be dependent on VTI1A-TCF7L2 for anchorage-independent growth using RNA interference-mediated knockdown. This study shows previously unidentified levels of genomic rearrangements in colorectal carcinoma that can lead to essential gene fusions and other oncogenic events.

Integrative analysis of the melanoma transcriptome

Global studies of transcript structure and abundance in cancer cells enable the systematic discovery of aberrations that contribute to carcinogenesis, including gene fusions, alternative splice isoforms, and somatic mutations. We developed a systematic approach to characterize the spectrum of cancer-associated mRNA alterations through integration of transcriptomic and structural genomic data, and we applied this approach to generate new insights into melanoma biology. Using paired-end massively parallel sequencing of cDNA (RNA-seq) together with analyses of high-resolution chromosomal copy number data, we identified 11 novel melanoma gene fusions produced by underlying genomic rearrangements, as well as 12 novel readthrough transcripts. We mapped these chimeric transcripts to base-pair resolution and traced them to their genomic origins using matched chromosomal copy number information. We also used these data to discover and validate base-pair mutations that accumulated in these melanomas, revealing a surprisingly high rate of somatic mutation and lending support to the notion that point mutations constitute the major driver of melanoma progression. Taken together, these results may indicate new avenues for target discovery in melanoma, while also providing a template for large-scale transcriptome studies across many tumor types.

Predicting drug susceptibility of non–small cell lung cancers based on genetic lesions

Somatic genetic alterations in cancers have been linked with response to targeted therapeutics by creation of specific dependency on activated oncogenic signaling pathways. However, no tools currently exist to systematically connect such genetic lesions to therapeutic vulnerability. We have therefore developed a genomics approach to identify lesions associated with therapeutically relevant oncogene dependency. Using integrated genomic profiling, we have demonstrated that the genomes of a large panel of human non-small cell lung cancer (NSCLC) cell lines are highly representative of those of primary NSCLC tumors. Using cell-based compound screening coupled with diverse computational approaches to integrate orthogonal genomic and biochemical data sets, we identified molecular and genomic predictors of therapeutic response to clinically relevant compounds. Using this approach, we showed that v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations confer enhanced Hsp90 dependency and validated this finding in mice with KRAS-driven lung adenocarcinoma, as these mice exhibited dramatic tumor regression when treated with an Hsp90 inhibitor. In addition, we found that cells with copy number enhancement of v-abl Abelson murine leukemia viral oncogene homolog 2 (ABL2) and ephrin receptor kinase and v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) (SRC) kinase family genes were exquisitely sensitive to treatment with the SRC/ABL inhibitor dasatinib, both in vitro and when it xenografted into mice. Thus, genomically annotated cell-line collections may help translate cancer genomics information into clinical practice by defining critical pathway dependencies amenable to therapeutic inhibition.

A pan-cancer proteomic perspective on The Cancer Genome Atlas

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumors. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyze 3,467 patient samples from 11 TCGA “Pan-Cancer” diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data is integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumor lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumor lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

TCPA: a resource for cancer functional proteomics data

To the Editor: Functional proteomics represents a powerful approach to understand the pathophysiology and therapy of cancer. However, comprehensive cancer proteomic data have been relatively limited. As a part of The Cancer Genome Atlas (TCGA) Project and other efforts, we have generated protein expression data over a large number of tumor and cell line samples using reverse-phase protein arrays (RPPAs). RPPA is a quantitative, antibody-based technology that can assess multiple protein markers in many samples in a cost-effective, sensitive and highthroughput manner1,2. This technology has been extensively validated for both cell line and patient samples3–5, and its applications range from building reproducible prognostic models6 to generating experimentally verified mechanistic insights7. Our RPPA profiling platform includes extensively validated antibodies to nearly 200 proteins and phosphoproteins (Supplementary Methods and Supplementary Table 1). We are in the process of extending it to 500 independent proteins, covering all major signaling pathways, including PI3K, MAPK, mTOR, TGF-b, WNT, cell cycle, apoptosis, DNA damage, Hippo and Notch pathways. The current data release covers 4,379 tumor samples and consists of three parts (Supplementary Table 2). These are (i) TCGA tumor tissue sample sets: 3,467 samples from 11 cancer types, to be extended to 25 cancer types; (ii) independent tumor tissue sample sets: one endometrial tumor set (244 samples)7 and two ovarian tumor sets (99 and 130 samples, respectively)6, with other independent sets to be added soon; and (iii) tumor cell lines: 439 samples in four cell line sets, including both baseline and drug-treated cell lines. To our knowledge, this represents the largest publicly available collection of cancer functional proteomics data with parallel DNA and RNA data. To facilitate broad access to these RPPA data sets, we developed a user-friendly data portal, The Cancer Proteome Atlas (TCPA; http://bioinformatics.mdanderson.org/main/ TCPA:Overview). TCPA provides six modules: Summary, My Protein, Download, Visualization, Analysis and Cell Line (Fig. 1, i). The Summary module provides an overview of the RPPA data with detailed descriptions of each set (Fig. 1, ii). The Download module allows users to obtain any RPPA data set for analysis through a tree-view interface (Fig. 1, iii). The My Protein module provides detailed information about each RPPA protein: protein name, corresponding gene symbol, antibody status and source for the antibody. Users can examine the expression pattern of a protein of interest across different tumor types (for example, HER2 expression shown in Fig. 1, iv). The Visualization module provides two ways to examine global protein expression patterns in a specific RPPA data set . One is through a “next-generation clustered heat map” (Fig. 1, v), which allows users to zoom, navigate and scrutinize clustering patterns of samples or proteins and link those patterns to relevant biological information sources. The other is through a network view (Fig. 1, vi), which overlays the correlation between any two interacting partners in the protein interaction network (curated in the Human Protein Reference Database8). The Analysis module provides three analysis methods. (i) For correlation analysis, given a user-specified data set, correlations between any pair of proteins are presented in a table (Fig. 1, vii). Users can search the results by protein name, rank correlations or visualize the scatter plot of a correlation of interest (for example, there is a strong correlation between PKC-a and its phosphorylated form PKC-a_pS657 in endometrial cancer, as shown in Fig. 1, vii). (ii) For differential analysis, differentially expressed protein markers between two tumor types or subtypes can be identified. Given user-defined comparison groups, the Krzywinski and Cairo reply: We are in full agreement with the core of Katz’s argument that “distortion,” “embellishment,” “concealment” and “unrepresentative displays” have no place in principled communication of scientific information1. There is no controversy here—Katz extrapolates our storytelling metaphor beyond the intended scope of our column and argues against a position we did not take. The Points of View series offers effective strategies for visual presentation of complex data. The scope of the Storytelling column2 was limited to the construction of multipanel figures, which summarize as much as they support detailed exposition of the text. The column did not address how this text should be composed or the broad subject of motivation and design of scientific experiments. We described an approach to structure the flow of concepts and data across panels in a figure as a way to achieve a narrative, not confabulation. The design of visual communication requires a distinct approach because we organize and interpret images very differently than words (Gestalt principles of perception3). Whereas text is a natural place for nuance and alternative interpretations, multiple lines of argument in a figure can easily interfere with our perception of all its parts. Our suggestion to “leave out detail that does not advance the plot” speaks to controlling the amount of information to avoid an incomprehensible image and deferring it to the text, where it can be more suitably framed. To interpret it as “inconvenient truths are [to be] swept away” is a misrepresentation. Readers often look to the abstract and then the figures to provide them with an initial impression and overview of the findings. These are not the only elements that are reported, merely the first elements to be read. At each step, from abstract to figure to text, the level of detail is expanded to accommodate the preparedness of the reader to assimilate new information. It is often impossible to “do justice to experimental complexities and their myriad of interpretations” with a figure. We support Katz’s position that authors should include all the details necessary to appreciate, understand and reproduce the science through the use of visual and written communication that is clear, concise and thoughtful.

Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.

The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes

Although individual pseudogenes have been implicated in tumor biology, the biomedical significance and clinical relevance of pseudogene expression have not been assessed in a systematic way. Here we generate pseudogene expression profiles in 2,808 patient samples of seven cancer types from The Cancer Genome Atlas RNA-seq data using a newly developed computational pipeline. Supervised analysis reveals a significant number of pseudogenes differentially expressed among established tumor subtypes; and pseudogene expression alone can accurately classify the major histological subtypes of endometrial cancer. Across cancer types, the tumor subtypes revealed by pseudogene expression show extensive and strong concordance with the subtypes defined by other molecular data. Strikingly, in kidney cancer, the pseudogene-expression subtypes not only significantly correlate with patient survival, but also help stratify patients in combination with clinical variables. Our study highlights the potential of pseudogene expression analysis as a new paradigm for investigating cancer mechanisms and discovering prognostic biomarkers.

Glioblastoma-Derived Epidermal Growth Factor Receptor Carboxyl-Terminal Deletion Mutants Are Transforming and Are Sensitive to EGFR-Directed Therapies

Genomic alterations of the epidermal growth factor receptor (EGFR) gene play a crucial role in pathogenesis of glioblastoma multiforme (GBM). By systematic analysis of GBM genomic data, we have identified and characterized a novel exon 27 deletion mutation occurring within the EGFR carboxyl-terminus domain (CTD), in addition to identifying additional examples of previously reported deletion mutations in this region. We show that the GBM-derived EGFR CTD deletion mutants are able to induce cellular transformation in vitro and in vivo in the absence of ligand and receptor autophosphorylation. Treatment with the EGFR-targeted monoclonal antibody, cetuximab, or the small molecule EGFR inhibitor, erlotinib, effectively impaired tumorigenicity of oncogenic EGFR CTD deletion mutants. Cetuximab in particular prolonged the survival of intracranially xenografted mice with oncogenic EGFR CTD deletion mutants, compared with untreated control mice. Therefore, we propose that erlotinib and, especially, cetuximab treatment may be a promising therapeutic strategy in GBM patients harboring EGFR CTD deletion mutants.

Molecular subtypes of glioblastoma are relevant to lower grade glioma.

Background Gliomas are the most common primary malignant brain tumors in adults with great heterogeneity in histopathology and clinical course. The intent was to evaluate the relevance of known glioblastoma (GBM) expression and methylation based subtypes to grade II and III gliomas (ie. lower grade gliomas). Methods Gene expression array, single nucleotide polymorphism (SNP) array and clinical data were obtained for 228 GBMs and 176 grade II/II gliomas (GII/III) from the publically available Rembrandt dataset. Two additional datasets with IDH1 mutation status were utilized as validation datasets (one publicly available dataset and one newly generated dataset from MD Anderson). Unsupervised clustering was performed and compared to gene expression subtypes assigned using the Verhaak et al 840-gene classifier. The glioma-CpG Island Methylator Phenotype (G-CIMP) was assigned using prediction models by Fine et al. Results Unsupervised clustering by gene expression aligned with the Verhaak 840-gene subtype group assignments. GII/IIIs were preferentially assigned to the proneural subtype with IDH1 mutation and G-CIMP. GBMs were evenly distributed among the four subtypes. Proneural, IDH1 mutant, G-CIMP GII/III s had significantly better survival than other molecular subtypes. Only 6% of GBMs were proneural and had either IDH1 mutation or G-CIMP but these tumors had significantly better survival than other GBMs. Copy number changes in chromosomes 1p and 19q were associated with GII/IIIs, while these changes in CDKN2A, PTEN and EGFR were more commonly associated with GBMs. Conclusions GBM gene-expression and methylation based subtypes are relevant for GII/III s and associate with overall survival differences. A better understanding of the association between these subtypes and GII/IIIs could further knowledge regarding prognosis and mechanisms of glioma progression.