GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images.

Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISHGoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISHGoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISHGoIFISH is freely available at www.sourceforge.net/projects/goifish/.This is the final published version. It is also available from Genome Biology at http://genomebiology.com/2014/15/8/442

Identification of fusion genes in breast cancer by paired-end RNA-sequencing

Background Until recently, chromosomal translocations and fusion genes have been an underappreciated class of mutations in solid tumors. Next-generation sequencing technologies provide an opportunity for systematic characterization of cancer cell transcriptomes, including the discovery of expressed fusion genes resulting from underlying genomic rearrangements. Results We applied paired-end RNA-seq to identify 24 novel and 3 previously known fusion genes in breast cancer cells. Supported by an improved bioinformatic approach, we had a 95% success rate of validating gene fusions initially detected by RNA-seq. Fusion partner genes were found to contribute promoters (5' UTR), coding sequences and 3' UTRs. Most fusion genes were associated with copy number transitions and were particularly common in high-level DNA amplifications. This suggests that fusion events may contribute to the selective advantage provided by DNA amplifications and deletions. Some of the fusion partner genes, such as GSDMB in the TATDN1-GSDMB fusion and IKZF3 in the VAPB-IKZF3 fusion, were only detected as a fusion transcript, indicating activation of a dormant gene by the fusion event. A number of fusion gene partners have either been previously observed in oncogenic gene fusions, mostly in leukemias, or otherwise reported to be oncogenic. RNA interference-mediated knock-down of the VAPB-IKZF3 fusion gene indicated that it may be necessary for cancer cell growth and survival. Conclusions In summary, using RNA-sequencing and improved bioinformatic stratification, we have discovered a number of novel fusion genes in breast cancer, and identified VAPB-IKZF3 as a potential fusion gene with importance for the growth and survival of breast cancer cells

Intratumor heterogeneity defines treatment-resistant HER2+ breast tumors.

Targeted therapy for patients with HER2-positive (HER2+) breast cancer has improved overall survival, but many patients still suffer relapse and death from the disease. Intratumor heterogeneity of both estrogen receptor (ER) and HER2 expression has been proposed to play a key role in treatment failure, but little work has been done to comprehensively study this heterogeneity at the single-cell level. In this study, we explored the clinical impact of intratumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations. Using combined immunofluorescence and in situ hybridization on tissue sections followed by a validated computational approach, we analyzed more than 13 000 single tumor cells across 37 HER2+ breast tumors. The samples were taken both before and after neoadjuvant chemotherapy plus HER2-targeted treatment, enabling us to study tumor evolution as well. We found that intratumor heterogeneity for HER2 copy number varied substantially between patient samples. Highly heterogeneous tumors were associated with significantly shorter disease-free survival and fewer long-term survivors. Patients for which HER2 characteristics did not change during treatment had a significantly worse outcome. This work shows the impact of intratumor heterogeneity in molecular diagnostics for treatment selection in HER2+ breast cancer patients and the power of computational scoring methods to evaluate in situ molecular markers in tissue biopsies

A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers

Somatic rearrangements contribute to the mutagenized landscape of cancer genomes. Here, we systematically interrogated rearrangements in 560 breast cancers by using a piecewise constant fitting approach. We identified 33 hotspots of large (>100 kb) tandem duplications, a mutational signature associated with homologous-recombination-repair deficiency. Notably, these tandem-duplication hotspots were enriched in breast cancer germline susceptibility loci (odds ratio (OR) = 4.28) and breast-specific 'super-enhancer' regulatory elements (OR = 3.54). These hotspots may b

Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity.

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution

Amplified Loci on Chromosomes 8 and 17 Predict Early Relapse in ER-Positive Breast Cancers

Adjuvant hormonal therapy is administered to all early stage ER+ breast cancers, and has led to significantly improved survival. Unfortunately, a subset of ER+ breast cancers suffer early relapse despite hormonal therapy. To identify molecular markers associated with early relapse in ER+ breast cancer, an outlier analysis method was applied to a published gene expression dataset of 268 ER+ early-stage breast cancers treated with tamoxifen alone. Increased expression of sets of genes that clustered in chromosomal locations consistent with the presence of amplicons at 8q24.3, 8p11.2, 17q12 (HER2 locus) and 17q21.33-q25.1 were each found to be independent markers for early disease recurrence. Distant metastasis free survival (DMFS) after 10 years for cases with any amplicon (DMFS  = 56.1%, 95% CI  = 48.3–63.9%) was significantly lower (P  = 0.0016) than cases without any of the amplicons (DMFS  = 87%, 95% CI  = 76.3% –97.7%). The association between presence of chromosomal amplifications in these regions and poor outcome in ER+ breast cancers was independent of histologic grade and was confirmed in independent clinical datasets. A separate validation using a FISH-based assay to detect the amplicons at 8q24.3, 8p11.2, and 17q21.33-q25.1 in a set of 36 early stage ER+/HER2- breast cancers treated with tamoxifen suggests that the presence of these amplicons are indeed predictive of early recurrence. We conclude that these amplicons may serve as prognostic markers of early relapse in ER+ breast cancer, and may identify novel therapeutic targets for poor prognosis ER+ breast cancers

Simulated ablation for detection of cells impacting paracrine signalling in histology analysis

Intra-tumour phenotypic heterogeneity limits accuracy of clinical diagnostics and hampers the efficiency of anti-cancer therapies. Dealing with this cellular heterogeneity requires adequate understanding of its sources, which is extremely difficult, as phenotypes of tumour cells integrate hardwired (epi)mutational differences with the dynamic responses to microenvironmental cues. The later comes in form of both direct physical interactions, as well as inputs from gradients of secreted signalling molecules. Furthermore, tumour cells can not only receive microenvironmental cues, but also produce them. Despite high biological and clinical importance of understanding spatial aspects of paracrine signaling, adequate research tools are largely lacking. Here, a partial differential equation (PDE)–based mathematical model is developed that mimics the process of cell ablation. This model suggests how each cell might contribute to the microenvironment by either absorbing or secreting diffusible factors, and quantifies the extent to which observed intensities can be explained via diffusion-mediated signalling. The model allows for the separation of phenotypic responses to signalling gradients within tumour microenvironments from the combined influence of responses mediated by direct physical contact and hardwired (epi)genetic differences. The method is applied to a multi-channel immunofluorescence in situ hybridisation (iFISH)–stained breast cancer histological specimen, and correlations are investigated between: HER2 gene amplification, HER2 protein expression and cell interaction with the diffusible microenvironment. This approach allows partial deconvolution of the complex inputs that shape phenotypic heterogeneity of tumour cells and identifies cells that significantly impact gradients of signalling molecules

Patients with cell cycle pathway activation or outliers patterns consistent with amplification of 17q12, 17q21.33-q25.1, 8p11.2 and 8q24.3 show poor outcome under tamoxifen treatment.

<p>A) Kaplan-Meier curves of the samples in the primary dataset (GSE6532) enriched for over-expressed cell cycle genes versus the rest of samples that don’t show this feature. Patients with cell cycle activated genes show a significant decrease in distant metastasis free survival rate (HR  = 9.71, 95% CI  = 3.3–28.6; P<0.0001). B) Kaplan-Meier curves of the ER+ samples in the primary dataset (GSE6532) stratified by presence of putative amplicons in 17q12, 17q21.33-q25.1, 8p11.2 and 8q24.3. Patients that show any one of the chromosomal amplifications have significantly higher relapse rates when compared to samples without any amplifications: 17q12 (HR  = 4.09, 95% CI  = 3.84–21.99; P  = 6.3e−07), 17q21.33– q25.1 (HR  = 3.14, 95% CI  = 2.17–13.62; P  = 3.0e−04), 8p11.2 (HR  = 3.75, 95% CI  = 3.18–18.31; P  = 5.7e−06), and 8q24.3 (HR  = 4.29, 95% CI  = 4.32–34.08; P  = 2.2e−06). C) Analysis of combined gene expression data of 624 ER+ breast cancers from multiple published data sets. Outlier analysis was performed to identify cases with evidence of amplification at 17q12, 17q22, 8p11.2, and 8q24.3 and those without evidence of any amplification. Kaplan-Meier curves of relapse free survival for ER+ samples with each of the four amplicons, and samples containing no amplicon are plotted: 17q12 (HR  = 2.30, 95% CI  = 1.45–3.64; P  = 4.0e−04), 17q22 (HR  = 3.07, 95% CI  = 1.99–4.73; P<1.0e−04), 8p11.2 (HR  = 1.96, 95% CI  = 1.23–3.13; P  = 4.9e−3), 8q24.3 (HR  = 2.38, 95% CI  = 1.60–3.55; P<1.0e−04) D) Kaplan-Meier curves of overall survival for the ER+ samples in the test CGH dataset (GSE22133) with each of the 4 amplicons, as well as samples that don’t have any of the chromosomal amplifications. Analysis of the CGH data identified amplification peaks at each of the four regions that overlap with the previously identified loci. Patients that show any one of the chromosomal amplifications have significantly higher event rates than those without any of the amplifications: 17q12 (HR  = 2.61, 95% CI  = 1.51–5.51; P  = 6.8e−04), 17q22 (HR  = 3.02, 95% CI  = 1.76–5.18; P  = 7.3e−05), 8p11.2 (HR  = 2.65, 95% CI  = 1.48–4.74; P  = 1.3e−03), and 8q24.3 (HR  = 2.12, 95% CI  = 1.24–3.65; P  = 6.7e−03). Log-rank tests were used to calculate all the P values.</p

Over-expressed genes in chromosomal regions 17q12, 17q21.33-q25.1, 8p11.2 and 8q24.3 associated with early relapse in ER+ breast cancers treated with tamoxifen.

<p>List of genes associated with early relapse on chromosomes 8 and 17. Highlighted in bold are cancer related genes of interest.</p