Spatial Distribution of Private Gene Mutations in Clear Cell Renal Cell Carcinoma

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive diversity across tumors and patients. Resolving the interactions among mutations and recovering recurrent evolutionary processes may offer greater opportunities for successful therapeutic strategies. To this end, we present a novel probabilistic framework, called TreeMHN, for the joint inference of exclusivity patterns and recurrent trajectories from a cohort of intra-tumor phylogenetic trees. Through simulations, we show that TreeMHN outperforms existing alternatives that can only focus on one aspect of the task. By analyzing datasets of blood, lung, and breast cancers, we find the most likely evolutionary trajectories and mutational patterns, consistent with and enriching our current understanding of tumorigenesis. Moreover, TreeMHN facilitates the prediction of tumor evolution and provides probabilistic measures on the next mutational events given a tumor tree, a prerequisite for evolution-guided treatment strategies.

Deciphering intratumor heterogeneity in clear cell renal cell carcinoma utilizing clinicopathologic and molecular platforms

Clear cell renal cell carcinoma (CCRCC) is a common renal malignancy known for its lethality and chromosome 3p aberrancies associated with loss of VHL. It has been shown that additional prognostic molecular markers exist in other transcriptional modifiers such as BAP1 and SETD2. Molecular heterogeneity has been described between primary and metastatic sites as well as genetic diversity in spatial tumor analysis; however, morphologic and proteogenomic heterogeneity information is lacking. We assessed 77 nephrectomy specimens with a diagnosis of CCRCC for morphologic architectural patterns including nodular growth patterns and variations in WHO/ISUP grade. Evaluation of highly heterogeneous areas with immunohistochemical (IHC) staining for BAP1, UCHL1, SETD2, and CAIX was performed and correlated with morphologic and histology data. Ultimately, high variability in the morphologic and histological findings matched the complexity of the IHC findings. Alterations in expression of CAIX and UCHL1 correlated with alterations in transcriptional regulators BAP1 and SETD2 within the tumor. High-grade morphology, such as eosinophilia, were areas enriched for alteration of biomarker expression. This highly complex data set of morphologic and biomarker characteristics highlights the heterogeneity of morphology amongst high-grade CCRCC tumors.

Identification and validation of an immune-related gene prognostic signature for clear cell renal carcinoma

Clear Cell Renal Carcinoma (ccRCC) accounts for nearly 80% of renal carcinoma cases, and immunotherapy plays an important role in ccRCC therapy. However, the responses to immunotherapy and overall survival for ccRCC patients are still hard to predict. Here, we constructed an immune-related predictive signature using 19 genes based on TCGA datasets. We also analyzed its relationships between disease prognosis, infiltrating immune cells, immune subtypes, mutation load, immune dysfunction, immune escape, etc. We found that our signature can distinguish immune characteristics and predict immunotherapeutic response for ccRCC patients with better prognostic prediction value than other immune scores. The expression levels of prognostic genes were determined by RT-qPCR assay. This signature may help to predict overall survival and guide the treatment for patients with ccRCC.

Systematic Review With Meta-Analysis: Diagnostic, Prognostic and Clinicopathological Significance of CircRNA Expression in Renal Cancer

Background

Renal cancer (RC) is one of the most common malignant tumors of the urinary system, and molecular targets for the specific diagnosis and treatment of RC have been widely explored. The purpose of this study was to systematically analyze circular RNAs (circRNAs), which may serve as novel tumor markers in terms of the diagnosis, prognosis and clinicopathological characteristics of RC.

Methods

PubMed and Web of Science were systematically searched for literature as up to July 30, 2021. All included studies were evaluated by the evaluation system, and the results were satisfactory. Hazard ratios (HRs) and odds ratios (ORs) were used to assess the association of circRNAs with diagnostic and clinicopathological indicators. The sensitivity (SEN), specificity (SPE), positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the summary receiver operating characteristic curve (AUC) were combined to evaluate the diagnostic performance of circRNAs in RC.

Results

We included 22 studies that met the criteria, including 18 that were prognostic, 4 that were diagnostic, and 12 that were clinicopathologically relevant. In terms of prognosis, we found that upregulated circRNAs were positively associated with poor overall survival in patients with RC (HR=1.63, 95% CI=1.43–1.85). In terms of diagnosis, the combined SEN, SPE and AUC of circRNAs in the diagnosis of RC were 0.82, 0.84 and 0.89 (0.86–0.91), respectively. In terms of clinicopathological features, upregulated circRNAs were associated with the Fuhrman grade (OR=0.641, 95% CI=0.471–0.873), T stage (OR=0.236, 95% CI=0.141–0.396), TNM stage (OR=0.225, 95% CI=0.158–0.321) and lymphatic metastasis (OR=0.329, 95% CI=0.193–0.560).

Conclusion

Our meta-analysis confirms that circRNAs may be candidate biomarkers for the diagnosis, prognosis, and clinicopathological indicators of RC.

Statistical tests for intra-tumour clonal co-occurrence and exclusivity

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

Tumours typically display high levels of heterogeneity, not only between different tumours but also within a single one. Intra-tumour heterogeneity results from an evolutionary process, giving rise to different populations of cancer cells known as clones. How clones interact may affect tumour evolution, which in turn determines disease progression and treatment outcome. In practice, we may observe pairs of mutations that co-occur in clones or exclude each other more often than we would expect for a given cohort of patient samples. Exclusive pairs are suggestive that clones carrying one or the other mutation may cooperate in the evolutionary process. Targeting only one of them may then suffice to alter the tumour evolution. Therefore it is critical to have statistical methods which allow us to identify such pairs. GeneAccord is a novel statistical testing framework we developed especially to identify pairs of genes altered in distinct clones of the same tumour. Accounting for the evolutionary dependencies among clones emerged as critical to adequately control testing errors. In a cohort of 123 acute myeloid leukaemia patients, GeneAccord identified one clonally co-occurring and eight clonally exclusive gene pairs. The latter predominantly involved genes of key signalling pathways.

A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma

Purpose

To construct a novel radiogenomics biomarker based on hypoxic-gene subset for the accurate prognostic prediction of clear cell renal cell carcinoma (ccRCC).

Materials and Methods

Initially, we screened for the desired hypoxic-gene subset by analysis using the GSEA database. Through univariate and multivariate cox regression hazard ratio analysis, survival-related hypoxia genes were identified, and a genomics signature was constructed in the TCGA database. Building on this, a hypoxia-gene related radiogenomics biomarker (prediction of hypoxia-genes signature by contrast-enhanced CT radiomics) was constructed in the TCIA-KIRC database by extracting features in the venous phase of contrast-enhanced CT images, selecting features using the mRMR and LASSO algorithms, and building logistic regression models. Finally, we validated the prognostic capability of the new biomarker for patients with ccRCC in an independent validation cohort at Huashan Hospital of Fudan University, Shanghai, China.

Results

The hypoxia-related genomics signature consisting of five genes (IFT57, PABPN1, RNF10, RNF19B and UBE2T) was shown to be significantly associated with survival for patients with ccRCC in the TCGA database, delineated by grouping of the signature expression as either low- or high-risk. In the TCIA database, we constructed a radiogenomics biomarker consisting of 13 radiomics features that were optimal predictors of hypoxia-gene signature expression levels (low- or high-risk) in patients at each institution, that demonstrated AUC values of 0.91 and 0.91 in the training and validation groups, respectively. In the independent validation cohort at Huashan Hospital, our radiogenomics biomarker was significantly associated with prognosis in patients with ccRCC (p=0.0059).

Conclusions

The novel prognostic radiogenomics biomarker that was constructed achieved excellent correlation with prognosis in both the cohort of TCGA/TCIA-KIRC database and the independent validation cohort of Huashan hospital patients with ccRCC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.