Biomolecular Events in Cancer Revealed by Attractor Metagenes

Abstract

Mining gene expression profiles has proven valuable for identifying signatures serving as surrogates of cancer phenotypes. However, the similarities of such signatures across different cancer types have not been strong enough to conclude that they represent a universal biological mechanism shared among multiple cancer types. Here we present a computational method for generating signatures using an iterative process that converges to one of several precise attractors defining signatures representing biomolecular events, such as cell transdifferentiation or the presence of an amplicon. By analyzing rich gene expression datasets from different cancer types, we identified several such biomolecular events, some of which are universally present in all tested cancer types in nearly identical form. Although the method is unsupervised, we show that it often leads to attractors with strong phenotypic associations. We present several such multi-cancer attractors, focusing on three that are prominent and sharply defined in all cases: a mesenchymal transition attractor strongly associated with tumor stage, a mitotic chromosomal instability attractor strongly associated with tumor grade, and a lymphocyte-specific attractor.

Immunological metagene signatures derived from immunogenic cancer cell death associate with improved survival of patients with lung, breast or ovarian malignancies: A large-scale meta-analysis

“…11 In order to address this probability, we first analyzed the sets of genes showing highly correlated expression with the genes composing the cancer type-independent ICD-derived metagene (i.e. CXCR3, PRF1, CASP1) across all three cancer-types (Fig.…”

“…5 Furthermore, transcriptomic analysis is capable of revealing 'multi-gene expression patterns' or 'metagenes' (i.e., aggregate patterns of gene expressions like a cluster of genes, exhibiting or stratified to exhibit collective high expression) as prognostic biomarkers. [10][11][12][13] Interestingly, recent progress in the fields of prognostic metagene biology in cancer has suggested that initial consensus-metagenes identified through biomarker discovery approaches may subsequently exhibit the ability to act as 'attractor'-metagenes for a further set of 'convergent'-metagenes. 11 More specifically certain consensus-metagenes (acting as attractor-metagenes), may have the ability to associate with, or, be 'converged' upon by, a further sets of co-expressed genes, with close but not necessarily identical relationship (which can be termed as convergentmetagenes).…”

“…[10][11][12][13] Interestingly, recent progress in the fields of prognostic metagene biology in cancer has suggested that initial consensus-metagenes identified through biomarker discovery approaches may subsequently exhibit the ability to act as 'attractor'-metagenes for a further set of 'convergent'-metagenes. 11 More specifically certain consensus-metagenes (acting as attractor-metagenes), may have the ability to associate with, or, be 'converged' upon by, a further sets of co-expressed genes, with close but not necessarily identical relationship (which can be termed as convergentmetagenes). 11 Thus, this data-mining process has the ability to not only characterize defined biological process-specific metagenes but also help in discovery of new sets of co-expressing genes with putative prognostic impact.…”

“…11 More specifically certain consensus-metagenes (acting as attractor-metagenes), may have the ability to associate with, or, be 'converged' upon by, a further sets of co-expressed genes, with close but not necessarily identical relationship (which can be termed as convergentmetagenes). 11 Thus, this data-mining process has the ability to not only characterize defined biological process-specific metagenes but also help in discovery of new sets of co-expressing genes with putative prognostic impact. Owing to such developments, it has been proposed that apart from a single high-powered, prospective, randomized controlled trial, retrospective meta-analysis of publicly available microarray datasets from multiple clinical studies is a powerful (if not exhaustive) datamining methodology for discovery as well as assessment of novel prognostic biomarkers.…”

An important role for Myb-MuvB and its target gene KIF23 in a mouse model of lung adenocarcinoma

“…However, many mitotic genes that are expressed at elevated levels in tumors are part of recently published chromosomal instability (CIN) signatures that predict the clinical outcome of various cancer types independent of the cell cycle score. [1][2][3] Thus elevated expression of some of mitotic genes could play a causal role in tumor initiation or progression due to the abnormal execution of mitosis resulting in defects in chromosome segregation and aneuploidy. Mouse models have confirmed that the overexpression of mitotic proteins such as the spindle checkpoint protein MAD2 or the kinetochore protein HEC1 is sufficient to generate aneuploidy and to induce various tumors, including lung adenomas.…”