A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them

Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes.Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers.Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit β2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas.Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas.

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

Genetic diversity within leukemia-associated immunophenotype-defined subclones in AML

Acute myeloid leukemia (AML) is a highly heterogeneous disease showing dynamic clonal evolution patterns over time. Various subclones may be present simultaneously and subclones may show a different expansion pattern and respond differently to applied therapies. It is already clear that immunophenotyping and genetic analyses may yield overlapping, but also complementary information. Detailed information on the genetic make-up of immunophenotypically defined subclones is however scarce. We performed error-corrected sequencing for 27 myeloid leukemia driver genes in 86, FACS-sorted immunophenotypically characterized normal and aberrant subfractions in 10 AML patients. We identified three main scenarios. In the first group of patients, the two techniques were equally well characterizing the malignancy. In the second group, most of the isolated populations did not express aberrant immunophenotypes but still harbored several genetic aberrancies, indicating that the information obtained only by immunophenotyping would be incomplete. Vice versa, one patient was identified in which genetic mutations were found only in a small fraction of the immunophenotypically defined malignant populations, indicating that the genetic analysis gave an incomplete picture of the disease. We conclude that currently, characterization of leukemic cells in AML by molecular and immunophenotypic techniques is complementary, and infer that both techniques should be used in parallel in order to obtain the most complete view on the disease.

Novel Molecular Determinants of Response or Resistance to Immune Checkpoint Inhibitor Therapies in Melanoma

BackgroundImmune checkpoint inhibitor (ICI) therapy dramatically prolongs melanoma survival. Currently, the identified ICI markers are sometimes ineffective. The objective of this study was to identify novel determinants of ICI efficacy.MethodsWe comprehensively curated pretreatment somatic mutational profiles and clinical information from 631 melanoma patients who received blockade therapy of immune checkpoints (i.e., CTLA-4, PD-1/PD-L1, or a combination). Significantly mutated genes (SMGs), mutational signatures, and potential molecular subtypes were determined. Their association with ICI responses was assessed simultaneously.ResultsWe identified 27 SMGs, including four novel SMGs (COL3A1, NRAS, NARS2, and DCC) that are associated with ICI efficacy and well-known driver genes. COL3A1 mutations were associated with improved ICI overall survival (hazard ratio (HR): 0.64, 95% CI: 0.45–0.91, p = 0.012), whereas immune resistance was observed in patients with NRAS mutations (HR: 1.42, 95% CI: 1.10–1.82, p = 0.006). The presence of the tobacco smoking-related signature was significantly correlated with inferior prognoses (HR: 1.42, 95% CI: 1.11–1.82, p = 0.005). In addition, the signature resembling that of alkylating agents and a newly discovered signature both exhibited extended prognoses (both HR < 1, p < 0.05). Based on the activities of the extracted 6 mutational signatures, we identified one immune subtype that was significantly associated with better ICI outcomes (HR: 0.44, 95% CI: 0.23–0.87, p = 0.017).ConclusionWe uncovered several novel SMGs and re-annotated mutational signatures that are linked to immunotherapy response or resistance. In addition, an immune subtype was found to exhibit favorable prognoses. Further studies are required to validate these findings.

Age influences on the molecular presentation of tumours

Cancer is often called a disease of aging. There are numerous ways in which cancer epidemiology and behaviour change with the age of the patient. The molecular bases for these relationships remain largely underexplored. To characterise them, we analyse age-associations in the nuclear and mitochondrial somatic mutational landscape of 20,033 tumours across 35 tumour-types. Age influences both the number of mutations in a tumour (0.077 mutations per megabase per year) and their evolutionary timing. Specific mutational signatures are associated with age, reflecting differences in exogenous and endogenous oncogenic processes such as a greater influence of tobacco use in the tumours of younger patients, but higher activity of DNA damage repair signatures in those of older patients. We find that known cancer driver genes such as CDKN2A and CREBBP are mutated in age-associated frequencies, and these alter the transcriptome and predict for clinical outcomes. These effects are most striking in brain cancers where alterations like SUFU loss and ATRX mutation are age-dependent prognostic biomarkers. Using three cancer datasets, we show that age shapes the somatic mutational landscape of cancer, with clinical implications.

Assessment of the Molecular Heterogeneity of E-Cadherin Expression in Invasive Lobular Breast Cancer

Mutations and loss of E-cadherin protein expression define the vast majority of invasive lobular carcinomas. In a subset of these cases, the heterogeneous expression of E-cadherin is observed either as wild-type (strong membranous) expression or aberrant expression (cytoplasmic expression). However, it is unclear as to whether the two components would be driven by distinct genetic or epigenetic alterations. Here, we used whole genome DNA sequencing and methylation array profiling of two separately dissected components of nine invasive lobular carcinomas with heterogeneous E-cadherin expression. E-cadherin negative and aberrant/positive components of E-cadherin heterogeneous tumours showed a similar mutational, copy number and promoter methylation repertoire, suggesting they arise from a common ancestor, as opposed to the collision of two independent tumours. We found that the majority of E-cadherin heterogeneous tumours harboured CDH1 mutations in both the E-cadherin negative and aberrant/positive components together with somatic mutations in additional driver genes known to be enriched in both pure invasive carcinomas of no special type and invasive lobular breast cancers, whereas these were less commonly observed in CDH1 wild-type tumours. CDH1 mutant tumours also exhibited a higher mutation burden as well as increased presence of APOBEC-dependent mutational signatures 2 and 13 compared to CDH1 wild-type tumours. Together, our results suggest that regardless of E-cadherin protein expression, tumours showing heterogeneous expression of E-cadherin should be considered as part of the spectrum of invasive lobular breast cancers.

Novel ratio-metric features enable the identification of new driver genes across cancer types

An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG.

Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.