A Novel Multiplexed, Image-Based Approach to Detect Phenotypes That Underlie Chromosome Instability in Human Cells

<div><p>Chromosome instability (CIN) is characterized by a progressive change in chromosome numbers. It is a characteristic common to virtually all tumor types, and is commonly observed in highly aggressive and drug resistant tumors. Despite this information, the majority of human CIN genes have yet to be elucidated. In this study, we developed and validated a multiplexed, image-based screen capable of detecting three different phenotypes associated with CIN. Large-scale chromosome content changes were detected by quantifying changes in nuclear volumes following RNAi-based gene silencing. Using a DsRED-LacI reporter system to fluorescently label chromosome 11 within a human fibrosarcoma cell line, we were able to detect deviations from the expected number of two foci per nucleus (one focus/labelled chromosome) that occurred following CIN gene silencing. Finally, micronucleus enumeration was performed, as an increase in micronucleus formation is a classic hallmark of CIN. To validate the ability of each assay to detect phenotypes that underlie CIN, we silenced the established CIN gene, <i>SMC1A</i>. Following <i>SMC1A</i> silencing we detected an increase in nuclear volumes, a decrease in the number of nuclei harboring two DsRED-LacI foci, and an increase in micronucleus formation relative to controls (untreated and si<i>GAPDH</i>). Similar results were obtained in an unrelated human fibroblast cell line. The results of this study indicate that each assay is capable of detecting CIN-associated phenotypes, and can be utilized in future experiments to uncover novel human CIN genes, which will provide novel insight into the pathogenesis of cancer.</p></div

<i>SMC1A</i> Silencing Underlies Increases in Nuclear Volumes in J21 Cells.

<p>(A) A conceptual schematic depicting the relationship between changes in nuclear volume (ovals) and DNA content (2N) that are predicted to occur due to chromosome mis-segregation events occurring during cellular division. (B) Western blot depicting <i>SMC1A</i> silencing following treatment with individual (si<i>SMC1A-1</i>, -<i>2</i>, -<i>3</i> and -<i>4</i>) or pooled (si<i>SMC1A-pool</i>) siRNA duplexes relative to controls (untreated and si<i>GAPDH</i>). α-TUBULIN serves as the loading control. (C) Box-and-whisker plot depicting the distribution range of nuclear volumes measured for the indicated conditions (x-axis). Whiskers delineate the entire distribution range, while the lower, middle and upper horizontal lines of the box identify the 25^th, 50^th and 75^th percentiles, respectively. (D) Bar graph presenting the mean nuclear volumes ± standard deviation (SD) measured for the indicated conditions (x-axis). Highly statistically significant increases in mean nuclear volumes were observed following <i>SMC1A</i> silencing (<i>p</i>-value <0.0001; ****) relative to the untreated controls that were not significant (<i>p</i>-value >0.05; ns) following <i>GAPDH</i> silencing. (E) Box-and-whisker plot depicting the total distribution range and 25^th, 50^th and 75^th percentiles of nuclear volumes measured for each of the individual siRNA duplexes targeting <i>SMC1A</i> or controls. (F) Bar graph depicting the mean nuclear volume ± SD following silencing. Student’s <i>t</i>-tests between untreated controls and each condition revealed statistically significant increases (<i>p</i>-value <0.01; **: <i>p</i>-value <0.0001; ****) in mean nuclear volumes following <i>SMC1A</i> silencing that were not significant (<i>p</i>-value >0.05) following <i>GAPDH</i> silencing (si<i>GAPDH</i>).</p

Comparison of CIN Scores to Untreated Cells.

<p>^AFold increase values refer to the increase in mean CS relative to untreated control.</p><p>^BMann-Whitney tests comparing the distribution of CS values between conditions. A <i>p</i>-value <0.05 is considered statistically significant.</p><p>Comparison of CIN Scores to Untreated Cells.</p

<i>SMC1A</i> Silencing Alters Chromosome 11 Copy Number.

<p>(A) Representative examples of high-resolution, 3D image projections of DsRED-LacI foci (green) within interphase nuclei (red) from J21 cells. The expected number of two DsRED-LacI foci (left; arrowheads) are frequently either lost (e.g. one focus/nucleus) or gained (e.g. three or four foci/nucleus) following <i>SMC1A</i> silencing. Scale bar represents 5 μm. (B) Bar graph presenting the percentage of nuclei harboring the expected number of two foci/nucleus (white), relative to those with losses (gray) or gains (black) in foci. The number of nuclei evaluated is indicated at the base of the column. (C) Histogram presenting the distribution of CIN scores in untreated (black circles), <i>GAPDH</i> (green squares) and <i>SMC1A</i> (red triangles) silenced cells. Note that a CIN score = 0 indicates that a cell harbors the expected number of two DsRED-LacI foci.</p

Micronucleus Formation is Induced Following <i>SMC1A</i> Silencing.

<p>(A) A representative high-resolution, 3D image highlighting a MN (arrowhead). Scale bar represents 5 μm. (B) Bar graph presenting the average number of micronuclei in each condition (x-axis), expressed as a percentage of the total number nuclei analyzed. The fold-increase relative to the untreated control is indicated above each column. (C) Bar graph depicting the average number of micronuclei following <i>SMC1A</i> silencing with individual siRNA duplexes and controls (expressed as a percent). The fold-increase relative to the untreated control is indicated above each column.</p

The temporal dynamics of chromosome instability in ovarian cancer cell lines and primary patient samples

<div><p>Epithelial ovarian cancer (EOC) is the most prevalent form of ovarian cancer and has the highest mortality rate. Novel insight into EOC is required to minimize the morbidity and mortality rates caused by recurrent, drug resistant disease. Although numerous studies have evaluated genome instability in EOC, none have addressed the putative role chromosome instability (CIN) has in disease progression and drug resistance. CIN is defined as an increase in the rate at which whole chromosomes or large parts thereof are gained or lost, and can only be evaluated using approaches capable of characterizing genetic or chromosomal heterogeneity within populations of cells. Although CIN is associated with numerous cancer types, its prevalence and dynamics in EOC is unknown. In this study, we assessed CIN within serial samples collected from the ascites of five EOC patients, and in two well-established ovarian cancer cell models of drug resistance (PEO1/4 and A2780s/cp). We quantified and compared CIN (as measured by nuclear areas and CIN Score (CS) values) within and between serial samples to glean insight into the association and dynamics of CIN within EOC, with a particular focus on resistant and recurrent disease. Using quantitative, single cell analyses we determined that CIN is associated with every sample evaluated and further show that many EOC samples exhibit a large degree of nuclear size and CS value heterogeneity. We also show that CIN is dynamic and generally increases within resistant disease. Finally, we show that both drug resistance models (PEO1/4 and A2780s/cp) exhibit heterogeneity, albeit to a much lesser extent. Surprisingly, the two cell line models exhibit remarkably similar levels of CIN, as the nuclear areas and CS values are largely overlapping between the corresponding paired lines. Accordingly, these data suggest CIN may represent a novel biomarker capable of monitoring changes in EOC progression associated with drug resistance.</p></div

PEO1 and PEO4 Cells Harbor Similar Levels of CIN.

<p><b>(A)</b> Scatter plot depicting the nuclear area distribution for PEO1 (sensitive) and PEO4 (resistant) cells with the interquartile ranges (25^th, 50^th and 75^th percentiles) identified in grey. <b>(B)</b> Scatter plot depicting the CS_C distribution for nuclei in PEO1 and PEO4 cells. <b>(C)</b> Scatter plots presenting the gains and losses of CEP 8 (CS₈; left), 11 (CS₁₁; middle) and 17 (CS₁₇; right) for each nucleus analyzed in PEO1 and PEO4.</p

An Evolutionarily Conserved Synthetic Lethal Interaction Network Identifies FEN1 as a Broad-Spectrum Target for Anticancer Therapeutic Development

<div><p>Harnessing genetic differences between cancerous and noncancerous cells offers a strategy for the development of new therapies. Extrapolating from yeast genetic interaction data, we used cultured human cells and siRNA to construct and evaluate a synthetic lethal interaction network comprised of chromosome instability (CIN) genes that are frequently mutated in colorectal cancer. A small number of genes in this network were found to have synthetic lethal interactions with a large number of cancer CIN genes; these genes are thus attractive targets for anticancer therapeutic development. The protein product of one highly connected gene, the flap endonuclease <em>FEN1</em>, was used as a target for small-molecule inhibitor screening using a newly developed fluorescence-based assay for enzyme activity. Thirteen initial hits identified through <em>in vitro</em> biochemical screening were tested in cells, and it was found that two compounds could selectively inhibit the proliferation of cultured cancer cells carrying inactivating mutations in <em>CDC4</em>, a gene frequently mutated in a variety of cancers. Inhibition of flap endonuclease activity was also found to recapitulate a genetic interaction between <em>FEN1</em> and <em>MRE11A</em>, another gene frequently mutated in colorectal cancers, and to lead to increased endogenous DNA damage. These chemical-genetic interactions in mammalian cells validate evolutionarily conserved synthetic lethal interactions and demonstrate that a cross-species candidate gene approach is successful in identifying small-molecule inhibitors that prove effective in a cell-based cancer model.</p> </div

Mean CS Values in Primary EOC Patient Samples and Cell Line Models.

<p><b>(A)</b> Graph presenting the mCS values calculated for each patient and sample isolated from each patient, and those of the paired EOC cell line models (PEO1/PEO4 and A2780s/A2780cp). Note that the mCS value in each of the initial patient samples is <2 and remains low in all subsequent samples with the exception of EOC16 and EOC140, which exhibit striking increases in mCS values prior to succumbing to the disease. Also, note that the carboplatin resistant cell lines (PEO4 and A2780cp) both exhibit small decreases in mCS values relative to their sensitive counterparts. <b>(B)</b> Timelines presenting the sample collection times for each EOC patient presented in <b>(A)</b> relative to disease progression and treatment(s).</p

CIN is Associated with Primary EOC.

<p><b>(A)</b> Timeline (months) presenting the six collection times (samples B, C, D, E, H and I) from patient EOC18 (who declined treatment options) prior to death. <b>(B)</b> Scatter plot (left) presenting the nuclear area distributions (black circles) and the associated interquartile ranges (25^th, 50^th and 75^th percentiles; red horizontal lines). Cumulative distribution frequency graph (right) depicting nuclear areas arranged from smallest to largest. <b>(C)</b> A scatter plot (left) presenting the CS_C distributions of samples B to I, and the corresponding cumulative CS_C distribution frequencies (right). Note that the absolute values of gains and losses are presented for the combined CS values (CS_C; CEPs 8, 11 and 17), and that a CS_C value of 0 identifies a diploid nucleus for each chromosome evaluated (<i>i</i>.<i>e</i>. 2 copies of CEPs 8, 11 and 17). (<b>D)</b> Scatter plots presenting the gains and losses in CS₈ (left), CS₁₁ (middle), and CS₁₇ (right) for each nucleus analyzed within each sample. Note that both gains (positive values) and losses (negative values) are shown for each individual CEP evaluated. <b>(E)</b> Cumulative distribution frequency graphs for CS₈ (left), CS₁₁ (middle) and CS₁₇ (right).</p