Visualizing and interpreting cancer genomics data via the Xena platform

Intratumoral Adipocyte-High Breast Cancer Enrich for Metastatic and Inflammation-Related Pathways but Associated with Less Cancer Cell Proliferation

Cancer-associated adipocytes are known to cause inflammation, leading to cancer progression and metastasis. The clinicopathological and transcriptomic data from 2256 patients with breast cancer were obtained based on three cohorts: The Cancer Genome Atlas (TCGA), GSE25066, and a study by Yau et al. For the current study, we defined the adipocyte, which is calculated by utilizing a computational algorithm, xCell, as "intratumoral adipocyte". These intratumoral adipocytes appropriately reflected mature adipocytes in a bulk tumor. The amount of intratumoral adipocytes demonstrated no relationship with survival. Intratumoral adipocyte-high tumors significantly enriched for metastasis and inflammation-related gene sets and are associated with a favorable tumor immune microenvironment, especially in the ER+/HER2- subtype. On the other hand, intratumoral adipocyte-low tumors significantly enriched for cell cycle and cell proliferation-related gene sets. Correspondingly, intratumoral adipocyte-low tumors are associated with advanced pathological grades and inversely correlated with MKI67 expression. In conclusion, a high amount of intratumoral adipocytes in breast cancer was associated with inflammation, metastatic pathways, cancer stemness, and favorable tumor immune microenvironment. However, a low amount of adipocytes was associated with a highly proliferative tumor in ER-positive breast cancer. This cancer biology may explain the reason why patient survival did not differ by the amount of adipocytes.

Modeling the Diversity of Epithelial Ovarian Cancer through Ten Novel Well Characterized Cell Lines Covering Multiple Subtypes of the Disease

Cancer cell lines are amongst the most important pre-clinical models. In the context of epithelial ovarian cancer, a highly heterogeneous disease with diverse subtypes, it is paramount to study a wide panel of models in order to draw a representative picture of the disease. As this lethal gynaecological malignancy has seen little improvement in overall survival in the last decade, it is all the more pressing to support future research with robust and diverse study models. Here, we describe ten novel spontaneously immortalized patient-derived ovarian cancer cell lines, detailing their respective mutational profiles and gene/biomarker expression patterns, as well as their in vitro and in vivo growth characteristics. Eight of the cell lines were classified as high-grade serous, while two were determined to be of the rarer mucinous and clear cell subtypes, respectively. Each of the ten cell lines presents a panel of characteristics reflective of diverse clinically relevant phenomena, including chemotherapeutic resistance, metastatic potential, and subtype-associated mutations and gene/protein expression profiles. Importantly, four cell lines formed subcutaneous tumors in mice, a key characteristic for pre-clinical drug testing. Our work thus contributes significantly to the available models for the study of ovarian cancer, supplying additional tools to better understand this complex disease.

Clinical and Pre-Clinical Evidence of Carbonic Anhydrase IX in Pancreatic Cancer and Its High Expression in Pre-Cancerous Lesions

Hypoxia is a common phenomenon that occurs in most solid tumors. Regardless of tumor origin, the evolution of a hypoxia-adapted phenotype is critical for invasive cancer development. Pancreatic ductal adenocarcinoma is also characterized by hypoxia, desmoplasia, and the presence of necrosis, predicting poor outcome. Carbonic anhydrase IX (CAIX) is one of the most strict hypoxia regulated genes which plays a key role in the adaptation of cancer cells to hypoxia and acidosis. Here, we summarize clinical data showing that CAIX expression is associated with tumor necrosis, vascularization, expression of Frizzled-1, mucins, or proteins involved in glycolysis, and inevitably, poor prognosis of pancreatic cancer patients. We also describe the transcriptional regulation of CAIX in relation to signaling pathways activated in pancreatic cancers. A large part deals with the preclinical evidence supporting the relevance of CAIX in processes leading to the aggressive behavior of pancreatic tumors. Furthermore, we focus on CAIX occurrence in pre-cancerous lesions, and for the first time, we describe CAIX expression within intraductal papillary mucinous neoplasia. Our review concludes with a detailed account of clinical trials implicating that treatment consisting of conventionally used therapies combined with CAIX targeting could result in an improved anti-cancer response in pancreatic cancer patients.

A user guide for the online exploration and visualization of PCAWG data

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

The Pan-Cancer Analysis of Whole Genomes project generated a vast array of data. In this article, the authors describe five different online resources to enable readers to explore and visualize the data.

Ubiquitin C-terminal hydrolase L1 promotes expression of programmed cell death-ligand 1 in non-small-cell lung cancer cells

Programmed cell death‐ligand 1 (PD‐L1) expressed on cancer cells can cause immune escape of non‐small‐cell lung cancer (NSCLC). Elucidation of the regulatory mechanisms of the PD‐L1 expression is a prerequisite for establishing new tumor immunotherapy strategies. Ubiquitin C‐terminal hydrolase L1 (UCHL1) is a regulator of cellular signaling transduction that is aberrantly expressed in NSCLC. However, it is not known whether UCHL1 regulates the expression of PD‐L1 in NSCLC cells. In the present study, we found that UCHL1 promotes the expression of PD‐L1 in NSCLC cell lines. In addition, UCHL1 expressed in NSCLC cells inhibited activation of Jurkat cells through upregulation of PD‐L1 expression in in vitro experiments. Moreover, UCHL1 upregulates PD‐L1 expression through facilitating activation of the AKT‐P65 signaling pathway. In conclusion, these results indicated that UCHL1 promoted PD‐L1 expression in NSCLC cells. This finding implied that inhibition of UCHL1 might suppress immune escape of NSCLC through downregulation of PD‐L1 expression in NSCLC cells.

Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition

MOTIVATION

The recent development of sequencing technologies revolutionized our understanding of the inner workings of the cell as well as the way disease is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations.

RESULTS

We present the factorized embeddings (FE) model, a self-supervised deep learning algorithm that learns simultaneously, by tensor factorization, gene and sample representation spaces. We ran the model on RNA-Seq data from two large-scale cohorts and observed that the sample representation captures information on single gene and global gene expression patterns. Moreover, we found that the gene representation space was organized such that tissue-specific genes, highly correlated genes as well as genes participating in the same GO terms were grouped. Finally, we compared the vector representation of samples learned by the FE model to other similar models on 49 regression tasks. We report that the representations trained with FE rank first or second in all of the tasks, surpassing, sometimes by a considerable margin, other representations.

AVAILABILITY AND IMPLEMENTATION

A toy example in the form of a Jupyter Notebook as well as the code and trained embeddings for this project can be found at: https://github.com/TrofimovAssya/FactorizedEmbeddings.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Index of Cancer-Associated Fibroblasts Is Superior to the Epithelial-Mesenchymal Transition Score in Prognosis Prediction

In many solid tumors, tissue of the mesenchymal subtype is frequently associated with epithelial–mesenchymal transition (EMT), strong stromal infiltration, and poor prognosis. Emerging evidence from tumor ecosystem studies has revealed that the two main components of tumor stroma, namely, infiltrated immune cells and cancer-associated fibroblasts (CAFs), also express certain typical EMT genes and are not distinguishable from intrinsic tumor EMT, where bulk tissue is concerned. Transcriptomic analysis of xenograft tissues provides a unique advantage in dissecting genes of tumor (human) or stroma (murine) origins. By transcriptomic analysis of xenograft tissues, we found that oral squamous cell carcinoma (OSCC) tumor cells with a high EMT score, the computed mesenchymal likelihood based on the expression signature of canonical EMT markers, are associated with elevated stromal contents featured with fibronectin 1 (Fn1) and transforming growth factor-β (Tgfβ) axis gene expression. In conjugation with meta-analysis of these genes in clinical OSCC datasets, we further extracted a four-gene index, comprising FN1, TGFB2, TGFBR2, and TGFBI, as an indicator of CAF abundance. The CAF index is more powerful than the EMT score in predicting survival outcomes, not only for oral cancer but also for the cancer genome atlas (TCGA) pan-cancer cohort comprising 9356 patients from 32 cancer subtypes. Collectively, our results suggest that a further distinction and integration of the EMT score with the CAF index will enhance prognosis prediction, thus paving the way for curative medicine in clinical oncology.

MeCP2 facilitates breast cancer growth via promoting ubiquitination-mediated P53 degradation by inhibiting RPL5/RPL11 transcription

Methyl-CpG-binding protein 2 (MeCP2) facilitates the carcinogenesis and progression of several types of cancer. However, its role in breast cancer and the relevant molecular mechanism remain largely unclear. In this study, analysis of the Cancer Genome Atlas (TCGA) data that MeCP2 expression was significantly upregulated in breast cancer tissues, and high MeCP2 expression was correlated with poor overall survival. Knockdown of MeCP2 inhibited breast cancer cell proliferation and G1–S cell cycle transition and migration as well as induced cell apoptosis in vitro. Moreover, MeCP2 knockdown suppressed cancer cell growth in vivo. Investigation of the molecular mechanism showed that MeCP2 repressed RPL11 and RPL5 transcription by binding to their promoter regions. TCGA data revealed significantly lower RPL11 and RPL5 expression in breast cancer tissues; additionally, overexpression of RPL11/RPL5 significantly suppressed breast cancer cell proliferation and G1–S cell cycle transition and induced apoptosis in vitro. Furthermore, RPL11 and RPL5 suppressed ubiquitination-mediated P53 degradation through direct binding to MDM2. This study demonstrates that MeCP2 promotes breast cancer cell proliferation and inhibits apoptosis through suppressing RPL11 and RPL5 transcription by binding to their promoter regions.

CATA: a comprehensive chromatin accessibility database for cancer

Accessible chromatin refers to the active regions of a chromosome that are bound by many transcription factors (TFs). Changes in chromatin accessibility play a critical role in tumorigenesis. With the emergence of novel methods like Assay for Transposase-accessible Chromatin Sequencing, a sequencing method that maps chromatin-accessible regions (CARs) and enables the computational analysis of TF binding at chromatin-accessible sites, the regulatory landscape in cancer can be dissected. Herein, we developed a comprehensive cancer chromatin accessibility database named CATA, which aims to provide available resources of cancer CARs and to annotate their potential roles in the regulation of genes in a cancer type-specific manner. In this version, CATA stores 2 991 163 CARs from 23 cancer types, binding information of 1398 TFs within the CARs, and provides multiple annotations about these regions, including common single nucleotide polymorphisms (SNPs), risk SNPs, copy number variation, somatic mutations, motif changes, expression quantitative trait loci, methylation and CRISPR/Cas9 target loci. Moreover, CATA supports cancer survival analysis of the CAR-associated genes and provides detailed clinical information of the tumor samples. Database URL: CATA is available at http://www.xiejjlab.bio/cata/.