Non-coding regulatory variations: the dark matter of pancreatic cancer genomics

Drivers of Gene Expression Dysregulation in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease with a poor prognosis. The functional consequences of common genetic aberrations and their roles in treatment strategies have been extensively reviewed. In addition to these genomic aberrations, consideration of non-genetic drivers of altered oncogene expression is essential to account for the diversity in PDAC phenotypes. In this review we seek to assess our current understanding of mechanisms of gene expression dysregulation. We focus on four drivers of gene expression dysregulation, including mutations, transcription factors, epigenetic regulators, and RNA stability/isoform regulation, in the context of PDAC pathogenesis. Recent studies provide much-needed insight into the role of gene expression dysregulation in dissecting tumor heterogeneity and stratifying patients for the development of personalized treatment strategies.

Dark-matter matters: Discriminating subtle blood cancers using the darkest DNA

The confluence of deep sequencing and powerful machine learning is providing an unprecedented peek at the darkest of the dark genomic matter, the non-coding genomic regions lacking any functional annotation. While deep sequencing uncovers rare tumor variants, the heterogeneity of the disease confounds the best of machine learning (ML) algorithms. Here we set out to answer if the dark-matter of the genome encompass signals that can distinguish the fine subtypes of disease that are otherwise genomically indistinguishable. We introduce a novel stochastic regularization, ReVeaL, that empowers ML to discriminate subtle cancer subtypes even from the same ‘cell of origin’. Analogous to heritability, implicitly defined on whole genome, we use predictability (F₁score) definable on portions of the genome. In an effort to distinguish cancer subtypes using dark-matter DNA, we applied ReVeaL to a new WGS dataset from 727 patient samples with seven forms of hematological cancers and assessed the predictivity over several genomic regions including genic, non-dark, non-coding, non-genic, and dark. ReVeaL enabled improved discrimination of cancer subtypes for all segments of the genome. The non-genic, non-coding and dark-matter had the highest F₁ scores, with dark-matter having the highest level of predictability. Based on ReVeaL’s predictability of different genomic regions, dark-matter contains enough signal to significantly discriminate fine subtypes of disease. Hence, the agglomeration of rare variants, even in the hitherto unannotated and ill-understood regions of the genome, may play a substantial role in the disease etiology and deserve much more attention.

Many subtypes of cancer are unable to be distinguished based on their genomic profiles. With the ever-increasing use of sequencing, we now have the ability to look deeper into the genome and pick up on hidden signals in areas typically considered irrelevant to disease. To overcome the issue of rare variants and the vast amount of heterogeneity found in these non-coding sectors, we introduce a new algorithm capable of correcting for both challenges, ReVeaL. Using this approach, we are able to demonstrate that the non-coding regions of the genome have more signal for distinguishing subtle subtypes of disease compared to all the coding regions. Specifically, we show that the darkest unexplored genomic regions, the non-coding genome with no functional annotation whatsoever in the literature, have the strongest signal. Thus dark-matter does indeed matter and should not be ignored but rather considered for the continued pressing task of finding biomarkers of disease to adequately treat our patients.

Cell Fusion in Human Cancer: The Dark Matter Hypothesis

Current strategies to determine tumor × normal (TN)-hybrid cells among human cancer cells include the detection of hematopoietic markers and other mesodermal markers on tumor cells or the presence of donor DNA in cancer samples from patients who had previously received an allogenic bone marrow transplant. By doing so, several studies have demonstrated that TN-hybrid cells could be found in human cancers. However, a prerequisite of this cell fusion search strategy is that such markers are stably expressed by TN-hybrid cells over time. However, cell fusion is a potent inducer of genomic instability, and TN-hybrid cells may lose these cell fusion markers, thereby becoming indistinguishable from nonfused tumor cells. In addition, hybrid cells can evolve from homotypic fusion events between tumor cells or from heterotypic fusion events between tumor cells and normal cells possessing similar markers, which would also be indistinguishable from nonfused tumor cells. Such indistinguishable or invisible hybrid cells will be referred to as dark matter hybrids, which cannot as yet be detected and quantified, but which contribute to tumor growth and progression.