Exosomal non-coding RNAs: Blueprint in colorectal cancer metastasis and therapeutic targets

Colorectal cancer (CRC) is ranked as the world's third-most prevalent cancer, and metastatic CRC considerably increases cancer-related fatalities globally. A number of complex mechanisms that are strictly controlled at the molecular level are involved in metastasis, which is the primary reason for death in people with CRC. Recently, it has become clear that exosomes, which are small extracellular vesicles released by non-tumorous and tumorigenic cells, play a critical role as communication mediators among tumor microenvironment (TME). To facilitate communication between the TME and cancer cells, non-coding RNAs (ncRNAs) play a crucial role and are recognized as potent regulators of gene expression and cellular processes, such as metastasis and drug resistance. NcRNAs are now recognized as potent regulators of gene expression and many hallmarks of cancer, including metastasis. Exosomal ncRNAs, like miRNAs, circRNAs, and lncRNAs, have been demonstrated to influence a number of cellular mechanisms that contribute to CRC metastasis. However, the molecular mechanisms that link exosomal ncRNAs with CRC metastasis are not well understood. This review highlights the essential roles that exosomal ncRNAs play in the progression of CRC metastatic disease and explores the therapeutic choices that are open to patients who have CRC metastases. However, exosomal ncRNA treatment strategy development is still in its early phases; consequently, additional investigation is required to improve delivery methods and find novel therapeutic targets as well as confirm the effectiveness and safety of these therapies in preclinical and clinical contexts.

Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials

Background

Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering new clinical trials, or to determine optimal treatment for patients in more routine clinical settings. Unlike prognostic signatures however, predictive signatures require training set data from clinical studies with at least two treatment arms. As two-arm studies with gene expression profiling have been rarer than similar one-arm studies, the methodology for constructing and optimizing predictive signatures has been less prominently explored than for prognostic signatures.

Results

Focusing on two “use cases” of two-arm clinical trials, one for metastatic colorectal cancer (CRC) patients treated with the anti-angiogenic molecule aflibercept, and the other for triple negative breast cancer (TNBC) patients treated with the small molecule iniparib, we present derivation steps and quantitative and graphical tools for the construction and optimization of signatures for the prediction of progression-free survival based on cross-validated multivariate Cox models. This general methodology is organized around two more specific approaches which we have called subtype correlation (subC) and mechanism-of-action (MOA) modeling, each of which leverage a priori knowledge of molecular subtypes of tumors or drug MOA for a given indication. The tools and concepts presented here include the so-called differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case for instance, the resulting signature stratifies the patient population into “sensitive” and “relatively-resistant” groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high.

Conclusions

The tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas.