MR4Cancer: a web server prioritizing master regulators for cancer

Comprehensive molecular characterization of TFE3-rearranged renal cell carcinoma

TFE3-rearranged renal cell cancer (tRCC) is a rare form of RCC that involves chromosomal translocation of the Xp11.2 TFE3 gene. Despite its early onset and poor prognosis, the molecular mechanisms of the pathogenesis of tRCC remain elusive. This study aimed to identify novel therapeutic targets for patients with primary and recurrent tRCC. We collected 19 TFE3-positive RCC tissues that were diagnosed by immunohistochemistry and subjected them to genetic characterization to examine their genomic and transcriptomic features. Tumor-specific signatures were extracted using whole exome sequencing (WES) and RNA sequencing (RNA-seq) data, and the functional consequences were analyzed in a cell line with TFE3 translocation. Both a low burden of somatic single nucleotide variants (SNVs) and a positive correlation between the number of somatic variants and age of onset were observed. Transcriptome analysis revealed that four samples (21.1%) lacked the expected fusion event and clustered with the genomic profiles of clear cell RCC (ccRCC) tissues. The fusion event also demonstrated an enrichment of upregulated genes associated with mitochondrial respiration compared with ccRCC expression profiles. Comparison of the RNA expression profile with the TFE3 ChIP-seq pattern data indicated that PPARGC1A is a metabolic regulator of the oncogenic process. Cell proliferation was reduced when PPARGC1A and its related metabolic pathways were repressed by its inhibitor SR-18292. In conclusion, we demonstrate that PPARGC1A-mediated mitochondrial respiration can be considered a potential therapeutic target in tRCC. This study identifies an uncharacterized genetic profile of an RCC subtype with unique clinical features and provides therapeutic options specific to tRCC.

BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.

Immune-related risk score: An immune-cell-pair-based prognostic model for cutaneous melanoma

Background

Melanoma is among the most malignant immunologic tumor types and is associated with high mortality. However, a considerable number of melanoma patients cannot benefit from immunotherapy owing to individual differences. This study attempts to build a novel prediction model of melanoma that fully considers individual differences in the tumor microenvironment.

Methods

An immune-related risk score (IRRS) was constructed based on cutaneous melanoma data from The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was used to calculate immune enrichment scores of 28 immune cell signatures. We performed pairwise comparisons to obtain scores for cell pairs based on the difference in the abundance of immune cells within each sample. The resulting cell pair scores, in the form of a matrix of relative values of immune cells, formed the core of the IRRS.

Results

The area under the curve (AUC) for the IRRS was over 0.700, and when the IRRS was combined with clinical information, the AUC reached 0.785, 0.817, and 0.801 for the 1-, 3-, and 5-year survival, respectively. Differentially expressed genes between the two groups were enriched in staphylococcal infection and estrogen metabolism pathway. The low IRRS group showed a better immunotherapeutic response and exhibited more neoantigens, richer T-cell receptor and B-cell receptor diversity, and higher tumor mutation burden.

Conclusion

The IRRS enables a good prediction of prognosis and immunotherapy effect, based on the difference in the relative abundance of different types of infiltrating immune cells, and could provide support for further research in melanoma.

Serial Analysis of Gene Mutations and Gene Expression during First-Line Chemotherapy against Metastatic Colorectal Cancer: Identification of Potentially Actionable Targets within the Multicenter Prospective Biomarker Study REVEAL

Simple Summary

The emergence of resistant cells remains a major obstacle for chemotherapy treatment of metastatic colorectal cancers. Improvement of the therapeutic response requires a thorough understanding of the mechanisms of resistance as well as informative biomarkers. In the REVEAL study, we have systematically compared the mutational patterns and expression profiles of primary tumor specimens before and after first-line chemotherapy treatment in the metastatic situation. In addition, we analyzed liquid biopsies pre, during, and after treatment. Alterations in gene expression appeared as the major drivers of chemotherapy resistance. We identified a gene expression signature differentiating primary tumors and metastases and validated this signature in two independent patient cohorts. Moreover, we evaluated the expression of two signature genes, SFRP2 and SPP1, as prognostic and potentially druggable biomarkers.

Abstract

Most metastatic colorectal cancer (mCRC) patients succumb to refractory disease due to secondary chemotherapy resistance. To elucidate the molecular changes associated with secondary resistance, we recruited 64 patients with mCRC and hepatic metastases before standard first-line chemotherapy between 2014 and 2018. We subjected DNA from primary tumor specimens (P), hepatic metastasis specimens after treatment (M), and liquid biopsies (L) taken prior to (pre), during (intra), and after (post) treatment to next generation sequencing. We performed Nanostring expression analysis in P and M specimens. Comparative bioinformatics and statistical analysis revealed typical mutational patterns with frequent alterations in TP53, APC, and KRAS in P specimens (n = 48). P and pre-L (n = 42), as well as matched P and M (n = 30), displayed a similar mutation spectrum. In contrast, gene expression profiles classified P (n = 31) and M (n = 23), distinguishable by up-regulation of immune/cytokine receptor and autophagy programs. Switching of consensus molecular subtypes from P to M occurred in 58.3% of cases. M signature genes SFRP2 and SPP1 associated with inferior survival, as validated in an independent cohort. Molecular changes during first-line treatment were detectable by expression profiling rather than by mutational tumor and liquid biopsy analyses. SFRP2 and SPP1 may serve as biomarkers and/or actionable targets.

Transcriptional Regulation of Cancer Immune Checkpoints: Emerging Strategies for Immunotherapy

The study of immune evasion has gained a well-deserved eminence in cancer research by successfully developing a new class of therapeutics, immune checkpoint inhibitors, such as pembrolizumab and nivolumab, anti-PD-1 antibodies. By aiming at the immune checkpoint blockade (ICB), these new therapeutics have advanced cancer treatment with notable increases in overall survival and tumor remission. However, recent reports reveal that 40-60% of patients fail to benefit from ICB therapy due to acquired resistance or tumor relapse. This resistance may stem from increased expression of co-inhibitory immune checkpoints or alterations in the tumor microenvironment that promotes immune suppression. Because these mechanisms are poorly elucidated, the transcription factors that regulate immune checkpoints, known as "master regulators", have garnered interest. These include AP-1, IRF-1, MYC, and STAT3, which are known to regulate PD/PD-L1 and CTLA-4. Identifying these and other potential master regulators as putative therapeutic targets or biomarkers can be facilitated by mining cancer literature, public datasets, and cancer genomics resources. In this review, we describe recent advances in master regulator identification and characterization of the mechanisms underlying immune checkpoints regulation, and discuss how these master regulators of immune checkpoint molecular expression can be targeted as a form of auxiliary therapeutic strategy to complement traditional immunotherapy.

A framework for identifying dysregulated chromatin regulators as master regulators in human cancer

MOTIVATION

Chromatin regulators (CRs) are frequently dysregulated to reprogram the epigenetic landscape of the cancer genome. However, the underpinnings of the dysregulation of CRs and their downstream effectors remain to be elucidated.

RESULTS

Here, we designed an integrated framework based on multi-omics data to identify candidate master regulatory CRs affected by genomic alterations across eight cancer types in The Cancer Genome Atlas. Most of them showed consistent activated or repressed (i.e. oncogenic or tumor-suppressive) roles in cancer initiation and progression. In order to further explore the insight mechanism of the dysregulated CRs, we developed an R package ModReg based on differential connectivity to identify CRs as modulators of transcription factors (TFs) involved in tumorigenesis. Our analysis revealed that the connectivity between TFs and their target genes (TGs) tended to be disrupted in the patients who had a high expression of oncogenic CRs or low-expression of tumor-suppressive CRs. As a proof-of-principle study, 14 (82.4%) of the top-ranked 17 driver CRs in liver cancer were able to be validated by literature mining or experiments including shRNA knockdown and dCas9-based epigenetic editing. Moreover, we confirmed that CR SIRT7 physically interacted with TF NFE2L2, and positively modulated the transcriptional program of NFE2L2 by affecting ∼64% of its TGs.

AVAILABILITY AND IMPLEMENTATION

ModReg is freely accessible at http://cis.hku.hk/software/ModReg.tar.gz.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.