DeePathNet: A Transformer-Based Deep Learning Model Integrating Multiomic Data with Cancer Pathways.

DeePathNet integrates cancer-specific biological pathways using transformer-based deep learning for enhanced cancer analysis. It outperforms existing models in predicting drug responses, cancer types, and subtypes. By enabling pathway-level biomarker discovery, DeePathNet represents a significant advancement in cancer research and could lead to more effective treatments.

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities.

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome.

Opportunities for pharmacoproteomics in biomarker discovery.

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics.

Machine learning for multi-omics data integration in cancer.

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data.

Role of POT1 in Human Cancer.

Telomere abnormalities facilitate cancer development by contributing to genomic instability and cellular immortalization. The Protection of Telomeres 1 (POT1) protein is an essential subunit of the shelterin telomere binding complex. It directly binds to single-stranded telomeric DNA, protecting chromosomal ends from an inappropriate DNA damage response, and plays a role in telomere length regulation.

Strategies to enable large-scale proteomics for reproducible research.

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells.

Mutational processes of distinct POLE exonuclease domain mutants drive an enrichment of a specific TP53 mutation in colorectal cancer.

Cancer genomes with mutations in the exonuclease domain of Polymerase Epsilon (POLE) present with an extraordinarily high somatic mutation burden. In vitro studies have shown that distinct POLE mutants exhibit different polymerase activity. Yet, genome-wide mutation patterns and driver mutation formation arising from different POLE mutants remains unclear.

Scarcity of Recurrent Regulatory Driver Mutations in Colorectal Cancer Revealed by Targeted Deep Sequencing.

Background: Genetic testing of cancer samples primarily focuses on protein-coding regions, despite most mutations arising in noncoding DNA. Noncoding mutations can be pathogenic if they disrupt gene regulation, but the benefits of assessing promoter mutations in driver genes by panel testing has not yet been established. This is especially the case in colorectal cancer, for which few putative driver variants at regulatory elements have been reported.

JMJD6 is a tumorigenic factor and therapeutic target in neuroblastoma.

Chromosome 17q21-ter is commonly gained in neuroblastoma, but it is unclear which gene in the region is important for tumorigenesis. The JMJD6 gene at 17q21-ter activates gene transcription. Here we show that JMJD6 forms protein complexes with N-Myc and BRD4, and is important for E2F2, N-Myc and c-Myc transcription.

Lymphocyte-Specific Chromatin Accessibility Pre-determines Glucocorticoid Resistance in Acute Lymphoblastic Leukemia.

Glucocorticoids play a critical role in the treatment of lymphoid malignancies. While glucocorticoid efficacy can be largely attributed to lymphocyte-specific apoptosis, its molecular basis remains elusive. Here, we studied genome-wide lymphocyte-specific open chromatin domains (LSOs), and integrated LSOs with glucocorticoid-induced RNA transcription and chromatin modulation using an in vivo patient-derived xenograft model of acute lymphoblastic leukemia (ALL).

Analysis of 7,815 cancer exomes reveals associations between mutational processes and somatic driver mutations.

Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types.

Finding cancer driver mutations in the era of big data research.

In the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour.

Proteogenomic analysis prioritises functional single nucleotide variants in cancer samples.

Massively parallel DNA sequencing enables the detection of thousands of germline and somatic single nucleotide variants (SNVs) in cancer samples. The functional analysis of these mutations is often carried out through predictions, with further downstream experimental validation rarely performed. Here, we examine the potential of using mass spectrometry-based proteomics data to further annotate the function of SNVs in cancer samples.

The interaction between cytosine methylation and processes of DNA replication and repair shape the mutational landscape of cancer genomes.

Methylated cytosines (5mCs) are frequently mutated in the genome. However, no studies have yet comprehensively analysed mutation-methylation associations across cancer types. Here we analyse 916 cancer genomes, together with tissue type-specific methylation and replication timing data.

The Histone Methyltransferase DOT1L Promotes Neuroblastoma by Regulating Gene Transcription.

Myc oncoproteins exert tumorigenic effects by regulating expression of target oncogenes. Histone H3 lysine 79 (H3K79) methylation at Myc-responsive elements of target gene promoters is a strict prerequisite for Myc-induced transcriptional activation, and DOT1L is the only known histone methyltransferase that catalyzes H3K79 methylation. Here, we show that N-Myc upregulates DOT1L mRNA and protein expression by binding to the DOT1L gene promoter.

Differential DNA repair underlies mutation hotspots at active promoters in cancer genomes.

Promoters are DNA sequences that have an essential role in controlling gene expression. While recent whole cancer genome analyses have identified numerous hotspots of somatic point mutations within promoters, many have not yet been shown to perturb gene expression or drive cancer development. As such, positive selection alone may not adequately explain the frequency of promoter point mutations in cancer genomes.

WDR5 Supports an N-Myc Transcriptional Complex That Drives a Protumorigenic Gene Expression Signature in Neuroblastoma.

MYCN gene amplification in neuroblastoma drives a gene expression program that correlates strongly with aggressive disease. Mechanistically, trimethylation of histone H3 lysine 4 (H3K4) at target gene promoters is a strict prerequisite for this transcriptional program to be enacted. WDR5 is a histone H3K4 presenter that has been found to have an essential role in H3K4 trimethylation.

The search for cis-regulatory driver mutations in cancer genomes.

With the advent of high-throughput and relatively inexpensive whole-genome sequencing technology, the focus of cancer research has begun to shift toward analyses of somatic mutations in non-coding cis-regulatory elements of the cancer genome. Cis-regulatory elements play an important role in gene regulation, with mutations in these elements potentially resulting in changes to the expression of linked genes. The recent discoveries of recurrent TERT promoter mutations in melanoma, and recurrent mutations that create a super-enhancer regulating TAL1 expression in T-cell acute lymphoblastic leukaemia (T-ALL), have sparked significant interest in the search for other somatic cis-regulatory mutations driving cancer development.