Functional characterization of breast cancer using pathway profiles

Lung squamous cell carcinoma and lung adenocarcinoma differential gene expression regulation through pathways of Notch, Hedgehog, Wnt, and ErbB signalling

Lung malignancies comprise lethal and aggressive tumours that remain the leading cancer-related death cause worldwide. Regarding histological classification, lung squamous cell carcinoma (LUSC) and adenocarcinoma (LUAD) account for the majority of cases. Surgical resection and various combinations of chemo- and radiation therapies are the golden standards in the treatment of lung cancers, although the five-year survival rate remains very poor. Notch, Hedgehog, Wnt and Erbb signalling are evolutionarily conserved pathways regulating pivotal cellular processes such as differentiation, proliferation, and angiogenesis during embryogenesis and post-natal life. However, to date, there is no study comprehensively revealing signalling networks of these four pathways in LUSC and LUAD. Therefore, the aim of the present study was the investigation profiles of downstream target genes of pathways that differ between LUSC and LUAD biology. Our results showed a few co-expression modules, identified through weighted gene co-expression network analysis (WGCNA), which significantly differentiated downstream signaling of Notch, ErbB, Hedgehog, and Wnt in LUSC and LUAD. Among co-expressed genes essential regulators of the cell cycle, DNA damage response, apoptosis, and proliferation have been found. Most of them were upregulated in LUSC compared to LUAD. In conclusion, identified downstream networks revealed distinct biological mechanisms underlying cancer development and progression in LUSC and LUAD that may diversify the clinical outcome of the disease.

Proteomics Profiling of KAIMRC1 in Comparison to MDA-MB231 and MCF-7

Proteomics characterization of KAIMRC1 cell line, a naturally immortalized breast cancer cells, is described in comparison to MCF-7 and MDA-MB-231 breast cancer cells. Quantitative proteomics analysis using the tandem mass tag (TMT)-labeled technique in conjunction with the phosphopeptide enrichment method was used to perform comparative profiling of proteins and phosphoproteins in the three cell lines. In total, 673 proteins and 33 Phosphoproteins were differentially expressed among these cell lines. These proteins are involved in several key cellular pathways that include DNA replication and repair, splicing machinery, amino acid metabolism, cellular energy, and estrogen signaling pathway. Many of the differentially expressed proteins are associated with different types of tumors including breast cancer. For validation, 4 highly significant expressed proteins including S-methyl-5'-thioadenosine phosphorylase (MTAP), BTB/POZ domain-containing protein (KCTD12), Poly (ADP-ribose) polymerase 1 (PARP 1), and Prelamin-A/C were subjected to western blotting, and the results were consistent with proteomics analysis. Unlike MCF-7 and MDA-MB-231, KAIMRC1 showed different phospho- and non-phosphoproteomic phenotypes which make it a potential model to study breast cancer.

Single nucleotide variant profiles of viable single circulating tumour cells reveal CTC behaviours in breast cancer

Circulating tumour cell (CTC) behaviours are distinct from those of bulk tissues. Thus, treatments to eliminate CTCs differ from the regimens followed to reduce the primary tumour and its metastases. Accordingly, comprehensively deciphering the single nucleotide variant (SNV) profiles in CTCs, which partially determine CTC behaviours, is a priority. Using viable CTCs isolated with the oHSV1-hTERT-GFP virus coupled with fluorescence-activated cell sorting (FACS), the whole genome was amplified using the multiple annealing and looping-based amplification cycle (MALBAC) method. CTC behaviours were evaluated using the SNVs found to be recurrently mutated in different cells (termed CTC-shared SNVs). Analysis of the sequencing data of 11 CTCs from 8 patients demonstrated that SNVs accumulated sporadically among CTCs and their matched primary tumours (22 co-occurring mutated genes were identified in the exomes of CTCs and their matched primary tissues and metastases), and 394 SNVs were shared by at least two CTCs. Mutated APC and LRP1B genes co-occurred in CTC-shared and bulk-tissue SNVs. Additionally, the breast-originating identity of the CTC-shared SNVs was verified, and they demonstrated the following CTC behaviours: i) intravasation competency; ii) increased migration or motility; iii) enhanced cell-cell interactions; iv) variation in energy metabolism; v) an activated platelet or coagulation system; and vi) dysfunctional mitosis. These results demonstrated that it is feasible to capture and amplify the genomes of single CTCs using the described pipeline. CTC-shared SNVs are a potential signature for identifying the origin of the primary tumour in a liquid biopsy. Furthermore, CTCs demonstrated some behaviours that are unique from those of bulk tissues. Therefore, therapies to eradicate these precursors of metastasis may differ from the existing traditional regimens.

Photobiomodulation of breast and cervical cancer stem cells using low-intensity laser irradiation

Breast and cervical cancers are dangerous threats with regard to the health of women. The two malignancies have reached the highest record in terms of cancer-related deaths among women worldwide. Despite the use of novel strategies with the aim to treat and cure advanced stages of cancer, post-therapeutic relapse believed to be caused by cancer stem cells is one of the challenges encountered during tumor therapy. Therefore, further attention should be paid to cancer stem cells when developing novel anti-tumor therapeutic approaches. Low-intensity laser irradiation is a form of phototherapy making use of visible light in the wavelength range of 630-905 nm. Low-intensity laser irradiation has shown remarkable results in a wide range of medical applications due to its biphasic dose and wavelength effect at a cellular level. Overall, this article focuses on the cellular responses of healthy and cancer cells after treatment with low-intensity laser irradiation alone or in combination with a photosensitizer as photodynamic therapy and the influence that various wavelengths and fluencies could have on the therapeutic outcome. Attention will be paid to the biomodulative effect of low-intensity laser irradiation on cancer stem cells.

RNA-Seq based genome-wide analysis reveals loss of inter-chromosomal regulation in breast cancer

Breast cancer is a complex heterogeneous disease. Common hallmark features of cancer can be found. Their origin may be traced back to their intricate relationships governing regulatory programs during the development of this disease. To unveil distinctive features of the transcriptional regulation program in breast cancer, a pipeline for RNA-seq analysis in 780 breast cancer and 101 healthy breast samples, at gene expression and network level, was implemented. Inter-chromosomal relationships between genes resulted strikingly scarce in a cancer network, in comparison to its healthy counterpart. We suggest that inter-chromosomal regulation loss may be a novel feature in breast cancer. Additional evidence was obtained by independent validation in microarray and Hi-C data as well as supplementary computational analyses. Functional analysis showed upregulation in processes related to cell cycle and division; while migration, adhesion and cell-to-cell communication, were downregulated. Both the BRCA1 DNA repairing signalling and the Estrogen-mediated G1/S phase entry pathways were found upregulated. In addition, a synergistic underexpression of the γ-protocadherin complex, located at Chr5q31 is also shown. This region has previously been reported to be hypermethylated in breast cancer. These findings altogether provide further evidence for the central role of transcriptional regulatory programs in shaping malignant phenotypes.

GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets

MOTIVATION

The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets.

RESULTS

We developed the GenomeRunner web server to automate systematic statistical analysis of SNP sets within a regulatory context. Besides defining the functional impact of SNP sets, GenomeRunner implements novel regulatory similarity/differential analyses, and cell type-specific regulatory enrichment analysis. Validated against literature- and disease ontology-based approaches, analysis of 39 disease/trait-associated SNP sets demonstrated that the functional impact of SNP sets corresponds to known disease relationships. We identified a group of autoimmune diseases with SNPs distinctly enriched in the enhancers of T helper cell subpopulations, and demonstrated relevant cell type-specificity of the functional impact of other SNP sets. In summary, we show how systematic analysis of genomic data within a regulatory context can help interpreting the functional impact of SNP sets.

AVAILABILITY AND IMPLEMENTATION

GenomeRunner web server is freely available at http://www.integrativegenomics.org/

CONTACT

mikhail.dozmorov@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Measures for the degree of overlap of gene signatures and applications to TCGA

For cancer and many other complex diseases, a large number of gene signatures have been generated. In this study, we use cancer as an example and note that other diseases can be analyzed in a similar manner. For signatures generated in multiple independent studies on the same cancer type and outcome, and for signatures on different cancer types, it is of interest to evaluate their degree of overlap. Many of the existing studies simply count the number (or percentage) of overlapped genes shared by two signatures. Such an approach has serious limitations. In this study, as a demonstrating example, we consider cancer prognosis data under the Cox model. Lasso, which is representative of a large number of regularization methods, is adopted for generating gene signatures. We examine two families of measures for quantifying the degree of overlap. The first family is based on the Cox-Lasso estimates at the optimal tunings, and the second family is based on estimates across the whole solution paths. Within each family, multiple measures, which describe the overlap from different perspectives, are introduced. The analysis of TCGA (The Cancer Genome Atlas) data on five cancer types shows that the degree of overlap varies across measures, cancer types and types of (epi)genetic measurements. More investigations are needed to better describe and understand the overlaps among gene signatures.