Explaining cancer type specific mutations with transcriptomic and epigenomic features in normal tissues

Tissue-Predisposition to Cancer Driver Mutations

Driver mutations are considered the cornerstone of cancer initiation. They are defined as mutations that convey a competitive fitness advantage, and hence, their mutation frequency in premalignant tissue is expected to exceed the basal mutation rate. In old terms, that translates to "the survival of the fittest" and implies that a selective process underlies the frequency of cancer driver mutations. In that sense, each tissue is its own niche that creates a molecular selective pressure that may favor the propagation of a mutation or not. At the heart of this stands one of the biggest riddles in cancer biology: the tissue-predisposition to cancer driver mutations. The frequency of cancer driver mutations among tissues is non-uniform: for instance, mutations in APC are particularly frequent in colorectal cancer, and 99% of chronic myeloid leukemia patients harbor the driver BCR-ABL1 fusion mutation, which is rarely found in solid tumors. Here, we provide a mechanistic framework that aims to explain how tissue-specific features, ranging from epigenetic underpinnings to the expression of viral transposable elements, establish a molecular basis for selecting cancer driver mutations in a tissue-specific manner.

Brain Tumors: Development, Drug Resistance, and Sensitization - An Epigenetic Approach

In this article, we describe contrasting developmental aspects of paediatric and adult brain tumours. We hypothesize that the formation of cancer progenitor cells, for both paediatric and adult, could be due to epigenetic events. However, the progression of adult brain tumours selectively involves more mutations compared to paediatric tumours. We further discuss epigenetic switches, comprising both histone modifications and DNA methylation, and how they can differentially regulate transcription and expression of oncogenes and tumour suppressor genes. Next, we summarize the currently available therapies for both types of brain tumours, explaining the merits and failures leading to drug resistance. We analyse different mechanisms of drug resistance and the role of epigenetics in this process. We then provide a rationale for combination therapy, which includes epigenetic drugs. In the end, we postulate a concept which describes how a combination therapy could be initiated. The timing, doses, and order of individual drug regimens will depend on the individual case. This type of combination therapy will be part of a personalized medicine which will differ from patient to patient.

Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data

BACKGROUND

Juvenile Pilocytic Astrocytomas (JPAs) are one of the most common pediatric brain tumors, and they are driven by aberrant activation of the mitogen-activated protein kinase (MAPK) signaling pathway. RAF-fusions are the most common genetic alterations identified in JPAs, with the prototypical KIAA1549-BRAF fusion leading to loss of BRAF's auto-inhibitory domain and subsequent constitutive kinase activation. JPAs are highly vascular and show pervasive immune infiltration, which can lead to low tumor cell purity in clinical samples. This can result in gene fusions that are difficult to detect with conventional omics approaches including RNA-Seq.

METHODS

To this effect, we applied RNA-Seq as well as linked-read whole-genome sequencing and in situ Hi-C as new approaches to detect and characterize low-frequency gene fusions at the genomic, transcriptomic and spatial level.

RESULTS

Integration of these datasets allowed the identification and detailed characterization of two novel BRAF fusion partners, PTPRZ1 and TOP2B, in addition to the canonical fusion with partner KIAA1549. Additionally, our Hi-C datasets enabled investigations of 3D genome architecture in JPAs which showed a high level of correlation in 3D compartment annotations between JPAs compared to other pediatric tumors, and high similarity to normal adult astrocytes. We detected interactions between BRAF and its fusion partners exclusively in tumor samples containing BRAF fusions.

CONCLUSIONS

We demonstrate the power of integrating multi-omic datasets to identify low frequency fusions and characterize the JPA genome at high resolution. We suggest that linked-reads and Hi-C could be used in clinic for the detection and characterization of JPAs.

Blood-Derived Extracellular Vesicle-Associated miR-3182 Detects Non-Small Cell Lung Cancer Patients

Simple Summary

Lung cancer is the leading cause of cancer-related death worldwide as patients are burdened with incredibly poor prognosis. Low survival rates are primarily attributed to lack of early detection and, therefore, timely therapeutic interventions. Late diagnosis is essentially caused by absent and non-specific symptoms, and compounded by inadequate diagnostic tools. We show here that a lung cancer biomarker, based on a simple blood test, might provide promising advantages for diagnostic assessment. Small extracellular vesicles (sEVs) are miniscule messengers that carry cancer biomarkers and are easily detected in the blood. We identify that the abundance of a specific micro-RNA, miR-3182, in these sEVs can be detected in the blood of lung cancer patients but not in controls with benign lung conditions. This demonstrates the potential use of miR-3182 as a biomarker for lung cancer diagnosis.

Abstract

With five-year survival rates as low as 3%, lung cancer is the most common cause of cancer-related mortality worldwide. The severity of the disease at presentation is accredited to the lack of early detection capacities, resulting in the reliance on low-throughput diagnostic measures, such as tissue biopsy and imaging. Interest in the development and use of liquid biopsies has risen, due to non-invasive sample collection, and the depth of information it can provide on a disease. Small extracellular vesicles (sEVs) as viable liquid biopsies are of particular interest due to their potential as cancer biomarkers. To validate the use of sEVs as cancer biomarkers, we characterised cancer sEVs using miRNA sequencing analysis. We found that miRNA-3182 was highly enriched in sEVs derived from the blood of patients with invasive breast carcinoma and NSCLC. The enrichment of sEV miR-3182 was confirmed in oncogenic, transformed lung cells in comparison to isogenic, untransformed lung cells. Most importantly, miR-3182 can successfully distinguish early-stage NSCLC patients from those with benign lung conditions. Therefore, miR-3182 provides potential to be used for the detection of NSCLC in blood samples, which could result in earlier therapy and thus improved outcomes and survival for patients.

Nanocarriers as a Tool for the Treatment of Colorectal Cancer

Nanotechnology is a promising tool for the treatment of cancer. In the past decades, major steps have been made to bring nanotechnology into the clinic in the form of nanoparticle-based drug delivery systems. The great hope of drug delivery systems is to reduce the side effects of chemotherapeutics while simultaneously increasing the efficiency of the therapy. An increased treatment efficiency would greatly benefit the quality of life as well as the life expectancy of cancer patients. However, besides its many advantages, nanomedicines have to face several challenges and hurdles before they can be used for the effective treatment of tumors. Here, we give an overview of the hallmarks of cancer, especially colorectal cancer, and discuss biological barriers as well as how drug delivery systems can be utilized for the effective treatment of tumors and metastases.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data

PURPOSE

Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.

PATIENTS AND METHODS

Data from whole-exome sequencing (WES) of The Cancer Genome Atlas (TCGA) patients were used as an input for the artificial neural network (ANN) to predict the anatomical site, iClusters (cell-of-origin patterns) and molecular subtype classifications. The Ohio State University (OSU) and the International Cancer Genome Consortium (ICGC) patients with HPB cancer were included in external validation cohorts. TCGA, OSU and ICGC data were merged, and survival analyses were performed using both the 'classic' survival analysis and a machine learning algorithm (random survival forest).

RESULTS

Although the ANN predicting the anatomical site of the tumour (i.e. cholangiocarcinoma, hepatocellular carcinoma of the liver, pancreatic ductal adenocarcinoma) demonstrated a low accuracy in TCGA test cohort, the ANNs predicting the iClusters (cell-of-origin patterns) and molecular subtype classifications demonstrated a good accuracy of 75% and 82% in TCGA test cohort, respectively. The random survival forest analysis and Cox' multivariable survival models demonstrated that models for HPB cancers that integrated clinical data with molecular classifications (iClusters, molecular subtypes) had an increased prognostic accuracy compared with standard staging systems.

CONCLUSION

The analyses of genetic status (i.e. WES, gene panels) of patients with HPB cancers might predict the classifications proposed by TCGA project and help to select patients suitable to targeted therapies. The molecular classifications of HPB cancers when integrated with clinical information could improve the ability to predict the prognosis of patients with HPB cancer.

A systems genomics approach to uncover the molecular properties of cancer genes

Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped according to mutational landscape (germline and somatically mutated genes), role in cancer initiation (cancer driver genes) or cancer survival (survival genes), as well as being implicated by genome-wide association studies (GWAS genes). For each gene, we also computed feature scores based on all omic features, effectively summarizing how closely a gene resembles cancer genes of the respective class. In general, cancer genes are longer, have a lower GC content, have more isoforms with shorter exons, are expressed in more tissues and have more transcription factor binding sites than non-cancer genes. We found that germline genes more closely resemble single tissue GWAS genes while somatic genes are more similar to pleiotropic cancer GWAS genes. As a proof-of-principle, we utilized aggregated feature scores to prioritize genes in breast cancer GWAS loci and found that top ranking genes were enriched in cancer related pathways. In conclusion, we have identified multiple omic features associated with different classes of cancer genes, which can assist prioritization of genes in cancer gene discovery.

Distribution of cancer genes in human chromosomes

We increasingly recognize the importance of alterations in DNA in the development of cancer. Beginning with the first somatic mutation identified in a urinary bladder carcinoma cell line, recombinant-DNA technology has led to an explosion of this field, bringing a wealth of data, yet to be fully analyzed. As the number of putative cancer genes has grown several groups have compiled lists of cancer genes with the Catalogue Of Somatic Mutations In Cancer, list as one of several highly regarded. With an interest in the distribution of cancer genes in human chromosomes and discerning whether some chromosomes predominated in cancer gene content, we undertook this review of their distribution in the modern-day human genome. We conclude that cancer genes are uniformly distributed across all human chromosomes having been accreted to the evolving human genome likely in a random fashion over the millennia.

Human genes differ by their UV sensitivity estimated through analysis of UV-induced silent mutations in melanoma

We hypothesized that human genes differ by their sensitivity to ultraviolet (UV) exposure. We used somatic mutations detected by genome-wide screens in melanoma and reported in the Catalog Of Somatic Mutations In Cancer. As a measure of UV sensitivity, we used the number of silent mutations generated by C>T transitions in pyrimidine dimers of a given transcript divided by the number of potential sites for this type of mutations in the transcript. We found that human genes varied by UV sensitivity by two orders of magnitude. We noted that the melanoma-associated tumor suppressor gene CDKN2A was among the top five most UV-sensitive genes in the human genome. Melanoma driver genes have a higher UV-sensitivity compared with other genes in the human genome. The difference was more prominent for tumor suppressors compared with oncogene. The results of this study suggest that differential sensitivity of human transcripts to UV light may explain melanoma specificity of some driver genes. Practical significance of the study relates to the fact that differences in UV sensitivity among human genes need to be taken into consideration whereas predicting melanoma-associated genes by the number of somatic mutations detected in a given gene.