Comprehensive Analysis of Driver Genes in Personal Genomes of Clear Cell Renal Cell Carcinoma

PML restrains p53 activity and cellular senescence in clear cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC), the major subtype of RCC, is frequently diagnosed at late/metastatic stage with 13% 5-year disease-free survival. Functional inactivation of the wild-type p53 protein is implicated in ccRCC therapy resistance, but the detailed mechanisms of p53 malfunction are still poorly characterized. Thus, a better understanding of the mechanisms of disease progression and therapy resistance is required. Here, we report a novel ccRCC dependence on the promyelocytic leukemia (PML) protein. We show that PML is overexpressed in ccRCC and that PML depletion inhibits cell proliferation and relieves pathologic features of anaplastic disease in vivo. Mechanistically, PML loss unleashed p53-dependent cellular senescence thus depicting a novel regulatory axis to limit p53 activity and senescence in ccRCC. Treatment with the FDA-approved PML inhibitor arsenic trioxide induced PML degradation and p53 accumulation and inhibited ccRCC expansion in vitro and in vivo. Therefore, by defining non-oncogene addiction to the PML gene, our work uncovers a novel ccRCC vulnerability and lays the foundation for repurposing an available pharmacological intervention to restore p53 function and chemosensitivity.

DrGA: cancer driver gene analysis in a simpler manner

Background

To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient.

Results

This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism.

Conclusions

DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA.

Genomics of Clear-cell Renal Cell Carcinoma: A Systematic Review and Meta-analysis

CONTEXT

Although antiangiogenic treatments and immunotherapies have significantly improved the prognosis of metastatic renal cell carcinoma (RCC), many patients will develop resistance, leading to treatment failure. Genetic tumor heterogeneity is a major cause of this resistance.

OBJECTIVE

To perform a meta-analysis of genomic data for clear-cell RCC obtained from primary tumors and metastases to assess the prevalence of gene mutations and copy number alterations (CNAs).

EVIDENCE ACQUISITION

Articles were selected from Medline and Embase libraries using the search algorithm ("Kidney Neoplasms"[Mesh] OR "Renal Cell Carcinoma") AND ("Genomics"[Mesh] OR "Mutation") from January 1999 to February 2021. A critical review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. Ninety-three publications were selected for inclusion in this meta-analysis.

EVIDENCE SYNTHESIS

Our meta-analysis included a total 14 696 patients, 14 299 primary tumor samples, and 969 metastatic samples. We evaluated the overall and subgroup prevalence of gene mutations and CNAs, including comparisons between primary tumors and metastases. In particular, for metastases we observed that the mutation prevalence was significantly more marked for ten genes compared to primary tumors, with no or little heterogeneity across studies. The VHL mutation prevalence increased significantly from 64% in primary tumors to 75% in metastases (p < 0.001). There was a significant increase in CNA prevalence from primary tumors to metastases for chromosomes 1p36.11, 9p21.3, and 18 in terms of losses, and for chromosomes 1q21.3, 7q36.3, 8q, and 20q11.21 in terms of gains. CDKN2A, also called p16 and involved in cell-cycle progression, is located at the 9p21.3 locus and was lost in 76% of metastatic samples. ASXL1, located on 20p11.21 and amplified in 50% of metastatic RCCs compared to 21% of primary tumors (p < 0.001), is closely linked to BAP1 function.

CONCLUSIONS

Our results underline the added value of preferential biopsies on RCC metastases to fully explore the biology of metastatic disease for therapeutic purposes.

PATIENT SUMMARY

We reviewed the literature on genetic mutations in primary tumors and metastatic lesions in kidney cancer. Our pooled results for all the relevant studies show a higher level of mutations in metastases than in primary tumors. This highlights the importance of taking biopsies of metastases to analyze genetic mutations and potentially guide selection of the most suitable treatment strategy.

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.