Single-cell multi-omics of human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation

Somatic mutations in cancer genes have been ubiquitously detected in clonal expansions across healthy human tissue, including in clonal hematopoiesis. However, mutated and wildtype cells are morphologically and phenotypically similar, limiting the ability to link genotypes with cellular phenotypes. To overcome this limitation, we leveraged multi-modality single-cell sequencing, capturing the mutation with transcriptomes and methylomes in stem and progenitors from individuals with DNMT3A R882 mutated clonal hematopoiesis. DNMT3A mutations resulted in myeloid over lymphoid bias, and in expansion of immature myeloid progenitors primed toward megakaryocytic-erythroid fate. We observed dysregulated expression of lineage and leukemia stem cell markers. DNMT3A R882 led to preferential hypomethylation of polycomb repressive complex 2 targets and a specific sequence motif. Notably, the hypomethylation motif is enriched in binding motifs of key hematopoietic transcription factors, serving as a potential mechanistic link between DNMT3A R882 mutations and aberrant transcriptional phenotypes. Thus, single-cell multi-omics pave the road to defining the downstream consequences of mutations that drive human clonal mosaicism.

Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability

ABSTRACT There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.

Harnessing Synthetic Lethal Interactions for Personalized Medicine

Two genes are said to have synthetic lethal (SL) interactions if the simultaneous mutations in a cell lead to lethality, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells but leave normal cells intact. The applicability of translating this concept into clinics has been demonstrated by three drugs that have been approved by the FDA to target PARP for tumors bearing mutations in BRCA1/2. This article reviews applications of the SL concept to translational cancer medicine over the past five years. Topics are (1) exploiting the SL concept for drug combinations to circumvent tumor resistance, (2) using synthetic lethality to identify prognostic and predictive biomarkers, (3) applying SL interactions to stratify patients for targeted and immunotherapy, and (4) discussions on challenges and future directions.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistatic interactions and their quantitative effect on tumor evolution remains a challenge. We developed a method to quantify COnditional SELection on the Excess of Nonsynonymous Substitutions (Coselens) in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identified 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection accounts for 25-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario, where gene-specific across-pathway interactions shape differentiated cancer subtypes.

The insertion and dysregulation of transposable elements in osteosarcoma and their association with patient event-free survival

The dysregulation of transposable elements (TEs) has been explored in a variety of cancers. However, TE activities in osteosarcoma (OS) have not been extensively studied yet. By integrative analysis of RNA-seq, whole-genome sequencing (WGS), and methylation data, we showed aberrant TE activities associated with dysregulations of TEs in OS tumors. Specifically, expression levels of LINE-1 and Alu of different evolutionary ages, as well as subfamilies of SVA and HERV-K, were significantly up-regulated in OS tumors, accompanied by enhanced DNA repair responses. We verified the characteristics of LINE-1 mediated TE insertions, including target site duplication (TSD) length (centered around 15 bp) and preferential insertions into intergenic and AT-rich regions as well as intronic regions of longer genes. By filtering polymorphic TE insertions reported in 1000 genome project (1KGP), besides 148 tumor-specific somatic TE insertions, we found most OS patient-specific TE insertions (3175 out of 3326) are germline insertions, which are associated with genes involved in neuronal processes or with transcription factors important for cancer development. In addition to 68 TE-affected cancer genes, we found recurrent germline TE insertions in 72 non-cancer genes with high frequencies among patients. We also found that +/− 500 bps flanking regions of transcription start sites (TSS) of LINE-1 (young) and Alu showed lower methylation levels in OS tumor samples than controls. Interestingly, by incorporating patient clinical data and focusing on TE activities in OS tumors, our data analysis suggested that higher TE insertions in OS tumors are associated with a longer event-free survival time.

A Robust Feature Extraction and Deep Learning Approach for Cancer Gene Prognosis

Mutated genes are one of the prominent factors in origination and spread of cancer disease. Here we have used Genomic signal processing methods to identify the patterns that differentiate cancer and non-cancerous genes. Furthermore, Deep learning algorithms were used to model a system that automatically predicts the cancer gene. Unlike the existing methods, two feature extraction modules are deployed to extract six attributes. Power Spectral Density based module was used to extract statistical parameters like Mean, Median, Standard deviation, Mean Deviation and Median Deviation. Adaptive Functional Link Network (AFLN) based filter module was used to extract Normalized Mean Square Error (NMSE). The uniqueness of this paper is identification of six input features that differentiates cancer genes. In this work artificial neural network is developed to predict cancer genes. Comparison is done on three sets of datasets with 6 attributes, 5 attributes and one attribute. We performed all the training and testing on the Tensorflow using the Keras library in Python using Google Colab. The developed approach proved its efficiency with 6 attributes attaining an accuracy of 98% for 150 epochs. The ANN model was also compared with existing work and attained a 10 fold cross validation accuracy of 96.26% with an increase of 1.2%.

Genome Chaos, Information Creation, and Cancer Emergence: Searching for New Frameworks on the 50th Anniversary of the “War on Cancer”

The year 2021 marks the 50th anniversary of the National Cancer Act, signed by President Nixon, which declared a national “war on cancer.” Powered by enormous financial support, this past half-century has witnessed remarkable progress in understanding the individual molecular mechanisms of cancer, primarily through the characterization of cancer genes and the phenotypes associated with their pathways. Despite millions of publications and the overwhelming volume data generated from the Cancer Genome Project, clinical benefits are still lacking. In fact, the massive, diverse data also unexpectedly challenge the current somatic gene mutation theory of cancer, as well as the initial rationales behind sequencing so many cancer samples. Therefore, what should we do next? Should we continue to sequence more samples and push for further molecular characterizations, or should we take a moment to pause and think about the biological meaning of the data we have, integrating new ideas in cancer biology? On this special anniversary, we implore that it is time for the latter. We review the Genome Architecture Theory, an alternative conceptual framework that departs from gene-based theories. Specifically, we discuss the relationship between genes, genomes, and information-based platforms for future cancer research. This discussion will reinforce some newly proposed concepts that are essential for advancing cancer research, including two-phased cancer evolution (which reconciles evolutionary contributions from karyotypes and genes), stress-induced genome chaos (which creates new system information essential for macroevolution), the evolutionary mechanism of cancer (which unifies diverse molecular mechanisms to create new karyotype coding during evolution), and cellular adaptation and cancer emergence (which explains why cancer exists in the first place). We hope that these ideas will usher in new genomic and evolutionary conceptual frameworks and strategies for the next 50 years of cancer research.

SpaceX: Gene Co-expression Network Estimation for Spatial Transcriptomics

Motivation: The analysis of spatially-resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. Results: We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

Pan-Genomic Sequencing Reveals Actionable CDKN2A/2B Deletions and Kataegis in Anaplastic Thyroid Carcinoma

Anaplastic thyroid carcinoma (ATC) is a lethal malignancy characterized by poor response to conventional therapies. Whole-genome sequencing (WGS) analyses of this tumor type are limited, and we therefore interrogated eight ATCs using WGS and RNA sequencing. Five out of eight cases (63%) displayed cyclin-dependent kinase inhibitor 2A (CDKN2A) abnormalities, either copy number loss (n = 4) or truncating mutations (n = 1). All four cases with loss of the CDKN2A locus (encoding p16 and p14arf) also exhibited loss of the neighboring CDKN2B gene (encoding p15ink4b), and displayed reduced CDKN2A/2B mRNA levels. Mutations in established ATC-related genes were observed, including TP53, BRAF, ARID1A, and RB1, and overrepresentation of mutations were also noted in 13 additional cancer genes. One of the more predominant mutational signatures was intimately coupled to the activity of Apolipoprotein B mRNA-editing enzyme, the catalytic polypeptide-like (APOBEC) family of cytidine deaminases implied in kataegis, a focal hypermutation phenotype, which was observed in 4/8 (50%) cases. We corroborate the roles of CDKN2A/2B in ATC development and identify kataegis as a recurrent phenomenon. Our findings pinpoint clinically relevant alterations, which may indicate response to CDK inhibitors, and focal hypermutational phenotypes that may be coupled to improved responses using immune checkpoint inhibitors.

Mixed Pulmonary Adenocarcinoma and Atypical Carcinoid: A Report of Two Cases of a Non-codified Entity With Biological Profile

Pulmonary carcinoids combined with a non-neuroendocrine component have rarely been described, and this histological subtype is not included as a specific entity in the current World Health Organization classification of pulmonary neoplasms. Here, we described the molecular and histological features of two rare cases of mixed lung neoplasms, composed of atypical carcinoid and adenocarcinoma. The targeted next-generation sequencing analysis covering single nucleotide variations, copy number variations, and transcript fusions in a total of 161 cancer genes of the two different tumor components shows a similar molecular profile of shared and private gene mutations. These findings suggest their monoclonal origin from a transformed stem/progenitor tumor cell, which acquires a divergent differentiation during its development and progression and accumulates novel, specific mutations.