Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models

An Immune Gene Expression Signature Associated With Development of Human Hepatocellular Carcinoma Identifies Mice That Respond to Chemopreventive Agents

Cirrhosis and chronic inflammation precede development of hepatocellular carcinoma (HCC) in approximately 80% of cases. We investigated immune-related gene expression patterns in liver tissues surrounding early-stage HCCs and chemopreventive agents that might alter these patterns to prevent liver tumorigenesis.We analyzed gene expression profiles of non-tumor liver tissues from 392 patients with early-stage HCC (training set, n=167 and validation set, n=225) and liver tissue from patients with cirrhosis without HCC (n=216, controls) to identify changes in expression of genes that regulate the immune response that could contribute to hepatocarcinogenesis. We defined 172 genes as markers for this deregulated immune response, which we called the immune-mediated cancer field (ICF). We analyzed the expression data of liver tissues from 216 patients with cirrhosis without HCC and investigated the association between this gene expression signature and development of HCC and outcomes of patients (median follow-up 10 years). Human liver tissues were also analyzed by histology. C57BL/6J mice were given a single injection of N-nitrosodiethylamine followed by weekly doses of carbon tetrachloride to induce liver fibrosis and tumorigenesis. Mice were then given orally the multiple tyrosine inhibitor nintedanib or vehicle (controls); liver tissues were collected and histology, transcriptome, and protein analyses were performed. We also analyzed transcriptomes of liver tissues collected from mice on a choline-deficient high-fat diet, which developed chronic liver inflammation and tumors, given orally aspirin and clopidogrel or the anti-inflammatory agent sulindac vs mice on a chow (control) diet.We found the ICF gene expression pattern in 50% of liver tissues from patients with cirrhosis without HCC and in 60% of non-tumor liver tissues from patients with early-stage HCC. The liver tissues with the ICF gene expression pattern had 3 different features: increased numbers of effector T cells; increased expression of genes that suppress the immune response and activation of transforming growth factor beta signaling; or expression of genes that promote inflammation and activation of interferon gamma signaling. Patients with cirrhosis and liver tissues with the immunosuppressive profile (10% of cases) had a higher risk of HCC (hazard ratio, 2.41; 95% 1.21-4.80). Mice with chemically-induced fibrosis or diet-induced steatohepatitis given nintedanib or aspirin and clopidogrel downregulated the ICF gene expression pattern in liver and developed fewer and smaller tumors than mice given vehicle.We identified an immune-related gene expression pattern in liver tissues of patients with early-stage HCC, called the ICF, that associates with risk of HCC development in patients with cirrhosis. Administration of nintedanib or aspirin and clopidogrel to mice with chronic liver inflammation caused loss of this gene expression pattern and developed fewer and smaller liver tumors. Agents that alter immune regulatory gene expression patterns associated with carcinogenesis might be tested as chemopreventive agents in patients with cirrhosis

Integrating clinical, molecular, proteomic and histopathological data within the tissue context : tissunomics

Malignant tumours show a marked degree of morphological, molecular and proteomic heterogeneity. This variability is closely related to microenvironmental factors and the location of the tumour. The activation of genetic alterations is very tissue-dependent and only few tumours have distinct genetic alterations. Importantly, the activation state of proteins and signaling factors is heterogeneous in the primary tumour and in metastases and recurrences. The molecular diagnosis based only on genetic alterations can lead to treatments with unpredictable responses, depending on the tumour location, such as the tumour response in melanomas versus colon carcinomas with mutations. Therefore, we understand that the correct evaluation of tumours requires a system that integrates both morphological, molecular and protein information in a clinical and pathological context, where intratumoral heterogeneity can be assessed. Thus, we propose the term 'tissunomics', where the diagnosis will be contextualised in each tumour based on the complementation of the pathological, molecular, protein expression, environmental cells and clinical data

Cross-platform gene expression signature of human spermatogenic failure reveals inflammatory-like response

BACKGROUND The molecular basis of human testicular dysfunction is largely unknown. Global gene expression profiling of testicular biopsies might reveal an expression signature of spermatogenic failure in azoospermic men. METHODS Sixty-nine individual testicular biopsy samples were analysed on two microarray platforms; selected genes were validated by quantitative real-time PCR and immunohistochemistry. RESULTS A minimum of 188 transcripts were significantly increased on both platforms. Their levels increased with the severity of spermatogenic damage and reached maximum levels in samples with Sertoli-cell-only appearance, pointing to genes expressed in somatic testicular cells. Over-represented functional annotation terms were steroid metabolism, innate defence and immune response, focal adhesion, antigen processing and presentation and mitogen-activated protein kinase K signalling pathway. For a considerable proportion of genes included in the expression signature, individual transcript levels were in keeping with the individual mast cell numbers of the biopsies. When tested on three disparate microarray data sets, the gene expression signature was able to clearly distinguish normal from defective spermatogenesis. More than 90% of biopsy samples clustered correctly into the corresponding category, emphasizing the robustness of our data. CONCLUSIONS A gene expression signature of human spermatogenic failure was revealed which comprised well-studied examples of inflammation-related genes also increased in other pathologies, including autoimmune disease

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies

The ErbB2ΔEx16 splice variant is a major oncogenic driver in breast cancer that promotes a pro-metastatic tumor microenvironment.

Amplification and overexpression of erbB2/neu proto-oncogene is observed in 20-30% human breast cancer and is inversely correlated with the survival of the patient. Despite this, somatic activating mutations within erbB2 in human breast cancers are rare. However, we have previously reported that a splice isoform of erbB2, containing an in-frame deletion of exon 16 (herein referred to as ErbB2ΔEx16), results in oncogenic activation of erbB2 because of constitutive dimerization of the ErbB2 receptor. Here, we demonstrate that the ErbB2ΔEx16 is a major oncogenic driver in breast cancer that constitutively signals from the cell surface. We further show that inducible expression of the ErbB2ΔEx16 variant in mammary gland of transgenic mice results in the rapid development of metastatic multifocal mammary tumors. Genetic and biochemical characterization of the ErbB2ΔEx16-derived mammary tumors exhibit several unique features that distinguish this model from the conventional ErbB2 ones expressing the erbB2 proto-oncogene in mammary epithelium. Unlike the wild-type ErbB2-derived tumors that express luminal keratins, ErbB2ΔEx16-derived tumors exhibit high degree of intratumoral heterogeneity co-expressing both basal and luminal keratins. Consistent with these distinct pathological features, the ErbB2ΔEx16 tumors exhibit distinct signaling and gene expression profiles that correlate with activation of number of key transcription factors implicated in breast cancer metastasis and cancer stem cell renewal

Mucin and Splice Variant Profiles of Pancreatic Adenocarcinoma Predict Patient Survival and Subtyping

PDAC is a pancreatic epithelial malignancy and demonstrates aggressive progression and bleak patient prognosis. Despite decades of research, the evolution of novel diagnostics and intervention modalities for PDAC is stagnant. This dissertation explores the characteristic aberrant and elevated expression of mucins in PDAC. Beginning with the hypothesis that mucins are associated with disease aggressiveness, analysis of PDAC patient survival in TCGA revealed no associations between single mucin expression and patient survival. This led to the underlying issue of PDAC tumor cellularity since this disease demonstrates variability in the proportion of cancer cells within the tumor. Tumor purity assessed with the ABSOLUTE computational algorithm is reported for all patient samples in the TCGA PDAC dataset. Using these purity scores, a mathematical correction of epithelial-specific mucin expression was devised. Again, no significant association between PDAC patient survival and mucin expression was found. Therefore, I investigated combinatorial expression of mucins by Spearman’s nonparametric PCA, which resulted in four groups of mutual expression: Group One= MUC7/12/17, Group Two= MUC1/3/13/19/20, Group Three= MUC6/15/22, and Group Four= MUC2/4/5AC/5B/16/21. These four groups were associated significantly with survival outcomes. To determine the biological implications of vi these four groups, PCA scores for all patients were correlated to whole transcriptomes. Significantly correlated genes were assessed for biological pathway upregulation. The four pathway composites revealed potential pathological signatures unrelated to previous PDAC classifications, representing novel PDAC subtypes. The role of mucin splice variants (SVs) was assessed and correlated to PDAC patient survival. Bioinformatic studies revealed 12 total mucin SVs significantly associated with survival. Better survival was correlated with expression of four MUC1, one MUC13, and one MUC20 SVs. High expression of two MUC4, one MUC15, one MUC16, one MUC21, and one MUC22 SVs were correlated with worse survival. The correlation between MUC4-sv-215 and MUC13-sv-201 SVs and survival were PCR validated in PDAC patient samples. These MUC4Δ6 prognostic findings contributed to in vitro studies and the development of a novel nanoparticle assay that detects MUC4-sv-215 in patient biofluids. The cumulative impact of the results described here may advance the clinical utility of mucins and associated SVs for improved diagnosis of PDAC