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Title: A Genome-Wide Systematic Analysis Reveals Different and Predictive Proliferation Expression Signatures of Cancerous vs. Non-Cancerous Cells}
Article:
Abstract:
Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups. Here we employ a complementary approach and assign each gene a cancer Proliferation Index( cPI) that quantifies the association between its expression levels and growth rate measurements across 60 cancer cell lines. Reassuringly, genes found essential in cancer gene knockdown screens exhibit significant positive cPI values, while tumor suppressors exhibit significant negative cPI values. Cell cycle, DNA replication, splicing and protein production related processes are positively associated with cancer proliferation, while cellular migration is negatively associated with it – in accordance with the well known “go or grow” dichotomy. A parallel analysis of genes' non-cancerous proliferation indices( nPI) across 224 lymphoblastoid cell lines reveals surprisingly marked differences between cancerous and non-cancerous proliferation. These differences highlight genes in the translation and spliceosome machineries as selective cancer proliferation-associated proteins. A cross species comparison reveals that cancer proliferation resembles that of microorganisms while non-cancerous proliferation does not. Furthermore, combining cancerous and non-cancerous proliferation signatures leads to enhanced prediction of patient outcome and gene essentiality in cancer. Overall, these results point to an inherent difference between cancerous and non-cancerous proliferation determinants, whose understanding may contribute to the future development of novel cancer-specific anti-proliferative drugs.
Introduction:
Cancer is one of the leading causes of death worldwide and it is estimated that 12. 7 million new cancer cases and 7. 6 million cancer deaths occurred in 2008[1]. One of the hallmarks of cancer is uncontrolled cellular proliferation[2],[3]. Understanding the determinants of cancer proliferation is an important task not only from a biological but also from a clinical stance – as the basis towards introducing new cancer therapies. Indeed, many chemotherapeutic agents target rapidly proliferating cells to fight cancer. However, these agents have additional detrimental effects on the non-cancerous, but proliferating tissues. In an attempt to find new anti-cancer drug targets, various studies in recent years used short hairpin RNA( shRNA) techniques and screened thousands of genes to find those that are essential for cancer growth and proliferation and are therefore putative targets for clinical intervention in cancer[4]–[7]. While this approach was shown to be powerful for the analysis of biological processes, shRNA screens have a variety of limitations such as off-target effects, partial knock down of the target genes and more[8]–[10]. Furthermore, as these are essentiality screens, they are less adequate to highlight genes involved in biological processes that have functional backups. Here we set to explore the determinants of cancer proliferation by employing a complementary, computational approach, studying the association between gene expression and growth rate measurements on a set of cancer samples. Previous studies that analyzed cancer proliferation using expression data were limited in their extent, focusing on a small set of genes: Ross et al.[11] found that the expression of ribosomal proteins is highly correlated with doubling times of cancer cell lines and Gaur et al.[12] performed a similar analysis in order to find microRNAs whose expression is correlated with the doubling times of cancer cell lines. Others had defined and studied a set of proliferating genes based on prior knowledge from the literature( Gene Ontology( GO) annotation)[13]. Going beyond these earlier investigations, we present the first genome-wide analysis of cancer proliferation based on gene expression and growth rate measurements of 12690 genes across 60 cancer cell lines( the NCI-60 panel). We identify the genes and cellular processes that are related to cancer proliferation and determine whether their expression levels are positively or negatively associated with proliferation. By performing a similar analysis on non-cancerous proliferating human cells, we find marked differences between the genes whose expression is associated with cancerous and non-cancerous proliferation. This work lays the basis for future identification of selective anti-cancer drug targets.
Discussion:
In the current work we identify gene signatures of cancer proliferation, based on a large scale analysis of gene expression and growth rate measurements of 12690 genes across the NCI-60 panel. The results show that the emerging cPI signature is highly relevant, having clear functional correlates. Our analysis uncovers genes and cellular processes that are associated with proliferation, either positively or negatively, in various cancers. Our non-cancerous analysis reveals marked differences between cancerous and non-cancerous proliferation. Currently, we lack additional datasets that include both expression and growth rate measurements of non-cancerous cells or cell lines to further support and generalize these results. Recently, Im et al.[42] calculated growth rates for HapMap samples based on a different experimental method and a model termed mixed effects model averaging( MEM). They report a correlation of R = 0. 30 between their MEM growth rates and the growth rates directly measured by Choy et al.[31]( and used in the current study). In their paper, Im et al. also report a measure that is analogous to our nPI measure, and is based on the association between MEM values and expression levels on 176 HapMap samples measured in their lab( 136 of them also appear in our dataset). We used their measure( termed here nPI-MEM) to see if it can support our results. Overall, the results obtained by nPI-MEM are weaker than those obtained by nPI. Several reasons may account for this. First, gene expression of the same sample may very across time and as opposed to Choy et al., growth rate and expression measurements of each cell line were not necessarily taken in the same time. In addition, Choy et al. used direct measurements of cell lines' growth rate while Im et al. used an estimator which is based on a model and also on indirect measurements of growth rate. Nevertheless and given all these limitations, we still find that the nPI-MEM is more similar to nPI than to cPI. Thus, we find that nPI-MEM shows significant correlation to nPI( R = 0. 16, P-value = 1. 32e-43; 7443 genes). The correlation between nPI-MEM and cPI is significant but weaker( R = 0. 065, P-value = 1. 80e-8). When focusing on a subset of 2187 genes whom Im et al. defined as associated( either positively or negatively) with proliferation, the correlation between nPI and nPI-MEM becomes much stronger and much higher( R = 0. 294, P-value = 1. 58e-44) as compared to the correlation between cPI and nPI-MEM on the same set of genes( R = 0. 118, P-value = 3. 73e-8). Additional and detailed results are described in Text S1 and in Table S6. In addition to the support for the nPI measure from the independent measurements of Im et al., we find additional support by the fact that combining the two proliferation signatures( cPI and nPI) has an added value in cancer research: the differential proliferation measure dPI can better predict a likelihood of a gene to be essential to cancer proliferation( Figure 3) as compared to each of the proliferation measures( cPI and nPI) alone and also has predictive power in patient prognosis( Figure 5). Compared with genome-wide knockdown genetic screens, our approach is less sensitive to the potential masking by backups of a gene's contribution to proliferation. Nevertheless, it obviously has its own limitations. Similar to other high-throughput techniques, there is experimental noise in the measurements. Obviously, a correlation between expression and growth rate does not necessarily imply causality and may reflect indirect associations. The two different approaches, which exhibit a significant overlap in their predictions, are complementary, and together lay the foundation for small scale studies to validate specific emerging predictions of interest. Specifically, we find biological processes and machineries that are enriched with genes with high dPI values that might be used as putative selective anti-cancer drug targets. These include members of the translation( mainly ribosomal proteins) and the spliceosome machineries( Table S4). Notably, previous studies already suggested these machineries as targets in cancer therapy. Thus, targeting translation initiation[43],[44] and elongation[45] cause a therapeutic response in various tumors. Although the ribosome plays an important role also in non-cancerous cells, extensive reduction in ribosomal activity may be more tolerated in non-cancerous cell proliferation but not in cancer cells where there is a greater demand for it[46]. Indeed, targeting ribosomal RNA genes selectively kills B lymphoma cells while maintaining a viable wild-type B cell population[47]. Furthermore, FDA recently approved the ribosome targeting drug SYNRIBO( omacetaxine mepesuccinate), for specific cases of chronic myeloid leukemia( CML). In addition, various compounds that inhibit cancer cells growth target the spliceosome[48]. The results presented here lay a genome-scale view for future studies aimed at teasing apart the differences between non-cancerous and cancerous proliferation, paving the way towards novel selective cancer therapeutics.
One of the hallmarks of cancer is uncontrolled cellular proliferation , and therefore many anti-cancer drugs aim to disrupt cancer proliferation . However , some of these drugs ( e . g . , chemotherapeutic agents ) affect normal proliferating cells as well , resulting in undesirable side effects . Understanding the differences between cancerous and non-cancerous proliferation can help us design new selective drugs that kill cancer cells without harming normal cells . In this work , we use genome scale gene expression and growth rate measurements across 60 cancer cell lines ( NCI-60 ) to uncover genetic determinants of cancerous proliferation . In parallel , gene expression and growth rate measurements of non-cancerous cell lines allow us to uncover determinants of non-cancerous proliferation . Notably , we find marked differences between the cancerous and non-cancerous proliferation . The two proliferation signatures can be used jointly to enhance the prediction of patient outcome in cancer . Notably , we find that certain genes in the translation and spliceosome machineries are involved in cancerous proliferation but not in non-cancerous proliferation , highlighting them as putative selective anti-cancer drug targets .