Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming carcinoma cell clusters via E-cadherin-mediated junctional complexes. However, the factors enabling mesenchymal-like micrometastatic cells to resume growth and reacquire an epithelial phenotype in the target organ microenvironment remain elusive. In this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with controllable stiffness varying from 0.2kPa (soft tissues) to 64kPa (bone tissues). We identified 3 distinct morphological cell types (morphs) varying from compact round-shaped to flattened irregular-shaped cells with lamellipodia, predominantly populating 2-kPa and >16kPa substrates, respectively. These observations were accompanied by significant changes in E-cadherin and vimentin expression. Furthermore, we demonstrate that the bone-mimicking substrate (64kPa) induced multicellular cluster formation accompanied by E-cadherin cell surface localisation. MDA-MB-231 cells responded to different substrate stiffness by morphological adaptation, changes in proliferation rate and cytoskeleton markers, and cluster formation on bone-mimicking substrate. Our results suggest that the stiffest microenvironment can induce MET.

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

The lack of consensus among reported molecular (gene, protein, regulatory marker) signatures (MSs) in the literature is often an initial concern for researchers and subsequently it discourages larger scale prospective studies, prevent the translation of such knowledge into a practical clinical setting and ultimately hindering the progress of the field of biomarker-based disease classification, prognosis and prediction. Understanding the high level of clinical and biological heterogeneity in patients' cohort distribution, (e.g. by diseases subtypes and stages, age, treatment methods etc), limitations and misbalances in the number of samples, and uncertainty in the dimensionality of potential biomarker space, are critical for getting the signature consensus and identification of novel potential biomarkers. Differences in use of technological platforms, as well as variations in experimental protocols in different studies are also often contributing factors in the lack of strong consensus among signatures. In view of these differences, it would be inappropriate to compare MSs in entirety. Here, we investigate each variable in the signature of interest, and attempt to generate computationally “a null frequency distribution” of the expected number of co-occurrences in other MSs, i.e. other published MSs, and identify both novel and common biomarker within the given MS. We demonstrated an application of proposed model to identification of clinically essential genes of our somatically mutated genes in breast cancer.

MOTIVATION: We consider any collection of microarrays that can be ordered to form a progression; for example, as a function of time, severity of disease or dose of a stimulant. By plotting the expression level of each gene as a function of time, or severity, or dose, we form an expression series, or curve, for each gene. While most of these curves will exhibit random fluctuations, some will contain a pattern, and these are the genes that are most likely associated with the quantity used to order them. RESULTS: We introduce a method of identifying the pattern and hence genes in microarray expression curves without knowing what kind of pattern to look for. Key to our approach is the sequence of ups and downs formed by pairs of consecutive data points in each curve. As a benchmark, we blindly identified genes from yeast cell cycles without selecting for periodic or any other anticipated behaviour. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: The complete versions of Table 2 and Figure 4, as well as other material, can be found at http://www.lps.ens.fr/~willbran/up-down/ or http://www.tcm.phy.cam.ac.uk/~tmf20/up-down/

MOTIVATION: Genomic DNA regions are frequently lost or gained during tumor progression. Array Comparative Genomic Hybridization (array CGH) technology makes it possible to assess these changes in DNA in cancers, by comparison with a normal reference. The identification of systematically deleted or amplified genomic regions in a set of tumors enables biologists to identify genes involved in cancer progression because tumor suppressor genes are thought to be located in lost genomic regions and oncogenes, in gained regions. Array CGH profiles should also improve the classification of tumors. The achievement of these goals requires a methodology for detecting the breakpoints delimiting altered regions in genomic patterns and assigning a status (normal, gained or lost) to each chromosomal region. RESULTS: We have developed a methodology for the automatic detection of breakpoints from array CGH profile, and the assignment of a status to each chromosomal region. The breakpoint detection step is based on the Adaptive Weights Smoothing (AWS) procedure and provides highly convincing results: our algorithm detects 97, 100 and 94% of breakpoints in simulated data, karyotyping results and manually analyzed profiles, respectively. The percentage of correctly assigned statuses ranges from 98.9 to 99.8% for simulated data and is 100% for karyotyping results. Our algorithm also outperforms other solutions on a public reference dataset. AVAILABILITY: The R package GLAD (Gain and Loss Analysis of DNA) is available upon request.