MicroRNAs (miRNAs) are small RNAs that post-transcriptionally regulate the expression of thousands of genes in a broad range of organisms in both normal physiological contexts and in disease contexts. miRNA expression profiling is gaining popularity because miRNAs, as key regulators in gene expression networks, can influence many biological processes and also show promise as biomarkers for disease. Technological advances have spawned a multitude of platforms for miRNA profiling, and an understanding of the strengths and pitfalls of different approaches can aid in their effective use. Here, we review the major considerations for carrying out and interpreting results of miRNA-profiling studies.

MicroRNA profiling: approaches and considerations.

Epithelial-mesenchymal transition (EMT) is a reversible and dynamic process hypothesized to be co-opted by carcinoma during invasion and metastasis. Yet, there is still no quantitative measure to assess the interplay between EMT and cancer progression. Here, we derived a method for universal EMT scoring from cancer-specific transcriptomic EMT signatures of ovarian, breast, bladder, lung, colorectal and gastric cancers. We show that EMT scoring exhibits good correlation with previously published, cancer-specific EMT signatures. This universal and quantitative EMT scoring was used to establish an EMT spectrum across various cancers, with good correlation noted between cell lines and tumours. We show correlations between EMT and poorer disease-free survival in ovarian and colorectal, but not breast, carcinomas, despite previous notions. Importantly, we found distinct responses between epithelial- and mesenchymal-like ovarian cancers to therapeutic regimes administered with or without paclitaxel in vivo and demonstrated that mesenchymal-like tumours do not always show resistance to chemotherapy. EMT scoring is thus a promising, versatile tool for the objective and systematic investigation of EMT roles and dynamics in cancer progression, treatment response and survival.

https://www.embopress.org/doi/pdf/10.15252/emmm.201404208

Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients.

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

Cancer transcriptome profiling at the juncture of clinical translation

https://www.gynecologiconcology-online.net/article/S0090-8258(15)00004-9/pdf

The passenger strand, miR-21-3p, plays a role in mediating cisplatin resistance in ovarian cancer cells.

When cells are exposed to death ligands such as TRAIL, a fraction undergoes apoptosis and a fraction survives; if surviving cells are re-exposed to TRAIL, fractional killing is once again observed. Therapeutic antibodies directed against TRAIL receptors also cause fractional killing, even at saturating concentrations, limiting their effectiveness. Fractional killing arises from cell-to-cell fluctuations in protein levels (extrinsic noise), but how this results in a clean bifurcation between life and death remains unclear. In this paper, we identify a threshold in the rate and timing of initiator caspase activation that distinguishes cells that live from those that die; by mapping this threshold, we can predict fractional killing of cells exposed to natural and synthetic agonists alone or in combination with sensitizing drugs such as bortezomib. A phenomenological model of the threshold also quantifies the contributions of two resistance genes (c-FLIP and Bcl-2), providing new insight into the control of cell fate by opposing pro-death and pro-survival proteins and suggesting new criteria for evaluating the efficacy of therapeutic TRAIL receptor agonists.

/pdf/fractional-killing-arises-from-cell-to-cell-variability-in-1vsdtomby9.pdf

Fractional killing arises from cell-to-cell variability in overcoming a caspase activity threshold

TNF-related apoptosis inducing ligand (TRAIL) is a promising anticancer agent because of its ability to selectively induce apoptosis in cancer cells but not in most normal cells. However, some cancer cells are resistant to TRAIL cytotoxicity thereby limiting its therapeutic efficacy. Using genome-wide mRNA expression profiles from the NCI60 panel and their differential sensitivities to TRAIL-induced apoptosis, we have identified 71 genes whose expression levels are systemically higher in TRAIL-sensitive cell lines than resistant lines. The elevated expression of the 71 genes was able to accurately predict TRAIL sensitivity in the NCI60 training set and two test sets consisting of a total of 95 human cancer cell lines. Interestingly, the 71-gene signature is dominated by two functionally related gene families-interferon (IFN)-induced genes and the MHC genes. Consistent with this result, treatment with IFN-γ augmented TRAIL-induced apoptosis. The 71-gene signature could be evaluated clinically for predicting tumor response to TRAIL-related therapies.

https://mct.aacrjournals.org/content/molcanther/11/1/34.full.pdf

A 71-Gene Signature of TRAIL Sensitivity in Cancer Cells

Fulvestrant is a selective estrogen receptor antagonist. Based on the measured growth inhibition of 60 human cancer cell lines (NCI60) in the presence of fulvestrant, as well as the baseline gene expression of the 60 cell lines, a gene expression score that predicts response to fulvestrant was developed. The score is based on 414 genes, 103 of which show increased expression in sensitive cell lines, while 311 show increased expression in the non-responding cell lines. The sensitivity genes primarily sense signaling through estrogen receptor alpha, whereas the resistance genes modulate the PI3K signaling pathway. The latter genes suggest that resistance to fulvestrant can be overcome by drugs targeting the PI3K pathway. The level of this gene expression score and its correlation with fulvestrant response was measured in a panel of 20 breast cancer cell lines. The predicted sensitivity matched the measured sensitivity well (CC = −0.63, P = 0.003). The predictor was applied to tumor biopies obtained from a Phase II clinical trial. The sensitivity of each patient to treatment with fulvestrant was predicted based on the RNA profile of the biopsy taken before neoadjuvant treatment and without knowledge of the subsequent response. The prediction was then compared to clinical response to show that the responders had a significantly higher sensitivity prediction than the non-responders (P = 0.01). When clinical covariates, tumor grade and estrogen receptor H-score, were included in the prediction, the difference in predicted senstivity between responders and non-responders improved (P = 0.003). Using a pre-defined cutoff to separate patients into predicted sensitive and predicted resistant yielded a positive predictive value of 88% and a negative predictive value of 100% when compared to clinical data. We conclude that pre-screening patients with the new gene expression predictor has the potential to identify those postmenopausal women with locally advanced, estrogen-receptor-positive breast cancer most likely to respond to fulvestrant.

/pdf/development-and-validation-of-a-gene-expression-score-that-1wmeuqp3zm.pdf

Development and Validation of a Gene Expression Score That Predicts Response to Fulvestrant in Breast Cancer Patients

BACKGROUND Methods using cell line microarray and drug sensitivity data to predict patients' chemotherapy response are appealing, but groups may be reluctant to release details to preserve intellectual property. Here we describe a case study to validate predictions while treating the methods as a "black box." METHODS Medical Prognosis Institute (MPI) constructed cell-line-derived sensitivity scores (SSs) and combined scores (CSs) that incorporate clinical variables. MD Anderson researchers evaluated their predictions. We searched the Gene Expression Omnibus (GEO) to identify validation datasets, and we performed statistical evaluation of the agreement between prediction and clinical observation. RESULTS We identified 3 suitable datasets: GSE16446 (n = 120; binary outcome), GSE17920 (n = 130; binary outcome), and GSE10255 (n = 161; continuous and time-to-event outcomes). The SS was statistically significantly associated with primary treatment responses for all studies (GSE16446: P = .02; GSE17920: P = .02; GSE10255: P = .02). Dichotomized SSs performed no better than chance for GSE16446 and GSE17920, and categorized SSs did not predict disease-free survival (GSE10255). SSs sometimes improved on predictions using clinical variables (GSE16446: P = .05; GSE17920: P = .31; GSE10255: P = .045), but gains were limited (95% confidence intervals for GSE16446 and GSE17920 include 0). The CS did not predict treatment response for GSE16446 (P = .55), but it did for GSE17920 (P < .001). Coefficients of clinical variables provided by MPI for CSs agree with estimates for GSE17920 better than estimates for GSE16446. CONCLUSIONS Model predictions were better than chance in all three datasets. However, these scores added little to existing clinical predictors; statistically significant contributions were likely to be too small to change clinical practice. These findings suggest that discovering better predictors will require both cell line data and a clinical training dataset of patient samples.

/pdf/independent-validation-of-a-model-using-cell-line-5186prn3ui.pdf

Independent Validation of a Model Using Cell Line Chemosensitivity to Predict Response to Therapy

The NCI-60 panel of 60 human cancer cell-lines of nine different tissues of origin has been extensively characterized in biological, molecular and pharmacological studies. Analyses of data from such studies have provided valuable information for understanding cellular processes and developing strategies for the diagnosis and treatment of cancer. Here, Affymetrix® GeneChip™ miRNA version 1 oligonucleotide microarrays were used to quantify 847 microRNAs to generate an expression dataset of 495 (58.4%) microRNAs that were identified as expressed in at least one cell-line of the NCI-60 panel. Accuracy of the microRNA measurements was partly confirmed by reverse transcription and polymerase chain reaction assays. Similar to that seen among the four existing NCI-60 microRNA datasets, the concordance of the new expression dataset with the other four was modest, with mean Pearson correlation coefficients of 0.37–0.54. In spite of this, comparable results with different datasets were noted in clustering of the cell-lines by their microRNA expression, differential expression of microRNAs by the lines’ tissue of origin, and correlation of specific microRNAs with the doubling-time of cells or their radiation sensitivity. Mutation status of the cell-lines for the TP53, PTEN and BRAF but not CDKN2A or KRAS cancer-related genes was found to be associated with changes in expression of specific microRNAs. The microRNA dataset generated here should be valuable to those working in the field of microRNAs as well as in integromic studies of the NCI-60 panel.

/pdf/expression-of-micrornas-in-the-nci-60-cancer-cell-lines-2n0cki0lbq.pdf

Expression of MicroRNAs in the NCI-60 Cancer Cell-Lines

MicroRNAs (miRNA) are a group of short noncoding RNAs that regulate gene expression at the posttranscriptional level. It has been shown that microRNAs are independent predictors of outcome in patients with diffuse large B-cell lymphoma (DLBCL) treated with the drug combination R-CHOP. Based on the measured growth inhibition of 60 human cancer cell lines (NCI60) in the presence of doxorubicine, cyclophosphamide, vincristine and etoposide as well as the baseline microRNA expression of the 60 cell lines, a microRNA based response predictor to CHOP was developed. The response predictor consisting of 20 microRNAs was blindly validated in a cohort of 116 de novo DLBCL patients treated with R-CHOP or R-CHOEP as first line treatment. The predicted sensitivity based on diagnostic FFPE samples matched the clinical response, with decreasing sensitivity in poor responders (P = 0.03). When the International Prognostic Index (IPI) was included in the prediction analysis, the separation between responders and non-responders improved (P = 0.001). Thirteen patients developed relapse, and five patients predicted sensitive to their second and third line treatment survived a median 1194 days, while eight patients predicted not sensitive to their second and third line treatment survived a median 187 days (90% CI: 485 days versus 227 days). Among the latter group it was predicted that four would have been sensitive to another second line treatment than the one they received. The predictions were almost the same when diagnostic biopsies were used as when relapse biopsies were used. These preliminary findings warrant testing in a larger cohort of relapse patients to confirm whether the miRNA based predictor can select the optimal second line treatment and increase survival.

/pdf/development-and-blind-clinical-validation-of-a-microrna-4rrvvrk5n8.pdf

Wiktor Mazin

Papers

A 71-Gene Signature of TRAIL Sensitivity in Cancer Cells

Development and Validation of a Gene Expression Score That Predicts Response to Fulvestrant in Breast Cancer Patients

Independent Validation of a Model Using Cell Line Chemosensitivity to Predict Response to Therapy

Expression of MicroRNAs in the NCI-60 Cancer Cell-Lines

Development and blind clinical validation of a microRNA based predictor of response to treatment with R-CHO(E)P in DLBCL.