Analytical Challenges in Development of Chemoresistance Predictors for Precision Oncology

Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC)

Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify cell contours which are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow is only reliable if cells maintain their positions during the time-lapse measurements. If the cells move, the original cell contours become unsuitable for evaluating intracellular fluorescence and the CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report a CRRC workflow developed to be applicable to motile cells. The new workflow combines fluorescence microscopy with transmitted-light microscopy and utilizes a new automated tool for cell identification and tracking. A transmitted-light image is taken right before every fluorescence image to determine cell contours, and cell contours are tracked through the time-stack of transmitted-light images to account for cell movement. Each unique contour is used to determine fluorescence intensity of cells in the associated fluorescence image. Next, time dependencies of the intracellular fluorescence intensities are used to determine each cell's rate constant and construct a kinetic histogram "number of cells vs rate constant." The new workflow's robustness to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results. Additionally, the workflow could potentially monitor kinetics of varying biological processes at the single-cell level for sizable cell populations. Although our workflow was designed ad hoc for CRRC, this cell-segmentation/cell-tracking strategy also represents an entry-level, user-friendly option for a variety of biological assays (i.e., migration, proliferation assays, etc.). Importantly, no prior knowledge of informatics (i.e., training a model for deep learning) is required.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

α‑hederin overcomes hypoxia‑mediated drug resistance in colorectal cancer by inhibiting the AKT/Bcl2 pathway

Currently, chemoresistance is a major challenge that directly affects the prognosis of patients with colorectal cancer (CRC). In addition, hypoxia is associated with poor prognosis and therapeutic resistance in patients with cancer. Accumulating evidence has shown that α‑hederin has significant antitumour effects and that α‑hederin can inhibit hypoxia‑mediated drug resistance in CRC; however, the underlying mechanism remains unclear. In the present study, viability and proliferation assays were used to evaluate the effect of α‑hederin on the drug resistance of CRC cells under hypoxia. Sequencing analysis and apoptosis assays were used to determine the effect of α‑hederin on apoptosis under hypoxia. Western blot analysis and reverse transcription‑quantitative PCR were used to measure apoptosis‑related protein and mRNA expression levels. Furthermore, different mouse models were established to study the effect of α‑hederin on hypoxia‑mediated CRC drug resistance in vivo. In the present study, the high expression of Bcl2 in hypoxic CRC cells was revealed to be a key factor in their drug resistance, whereas α‑hederin inhibited the expression of Bcl2 by reducing AKT phosphorylation in vitro and in vivo, and promoted the apoptosis of CRC cells under hypoxia. By contrast, overexpression of AKT reversed the effect of α‑hederin on CRC cell apoptosis under hypoxia. Taken together, these results suggested that α‑hederin may overcome hypoxia‑mediated drug resistance in CRC by inhibiting the AKT/Bcl2 pathway. In the future, α‑hederin may be used as a novel adjuvant for reversing drug resistance in CRC.

Small Extracellular Vesicles and Their Involvement in Cancer Resistance: An Up-to-Date Review

In recent years, tremendous progress has been made in understanding the roles of extracellular vesicles (EVs) in cancer. Thanks to advancements in molecular biology, it has been found that the fraction of EVs called exosomes or small EVs (sEVs) modulates the sensitivity of cancer cells to chemotherapeutic agents by delivering molecularly active non-coding RNAs (ncRNAs). An in-depth analysis shows that two main molecular mechanisms are involved in exosomal modified chemoresistance: (1) translational repression of anti-oncogenes by exosomal microRNAs (miRs) and (2) lack of translational repression of oncogenes by sponging of miRs through long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). At the cellular level, these processes increase the proliferation and survival of cancer cells and improve their ability to metastasize and resist apoptosis. In addition, studies in animal models have shown enhancing tumor size under the influence of exosomal ncRNAs. Ultimately, exosomal ncRNAs are responsible for clinically significant chemotherapy failures in patients with different types of cancer. Preliminary data have also revealed that exosomal ncRNAs can overcome chemotherapeutic agent resistance, but the results are thoroughly fragmented. This review presents how exosomes modulate the response of cancer cells to chemotherapeutic agents. Understanding how exosomes interfere with chemoresistance may become a milestone in developing new therapeutic options, but more data are still required.

Necessity and Challenges of Sample Preconcentration in Analysis of Multiple MicroRNAs by Capillary Electrophoresis

Thousands of putative microRNA (miRNA)-based cancer biomarkers have been reported, but none has been validated for approval by the Food and Drug Administration. One of the reasons for this alarming discrepancy is the lack of a method that is sufficiently robust for carrying out validation studies, which may require analysis of samples from hundreds of patients across multiple institutions and pooling the results together. The capillary electrophoresis (CE)-based hybridization assay proved to be more robust than reversed transcription polymerase chain reaction (the current standard), but its limit of quantification (LOQ) exceeds 10 pM while miRNA concentrations in cell lysates are below 1 pM. Thus, CE-based separation must be preceded by on-column sample preconcentration. Here, we explain the challenges of sample preconcentration for CE-based miRNA analyses and introduce a preconcentration method that can suit CE-based miRNA analysis utilizing peptide nucleic acid (PNA) hybridization probes. The method combines field-amplified sample stacking (FASS) with isotachophoresis (ITP). We proved that FASS-ITP could retain and concentrate both near-neutral PNA with highly negatively charged PNA-miRNA hybrids. We demonstrated that preconcentration by FASS-ITP could be combined with the CE-based separation of the unreacted PNA probes from the PNA-miRNA hybrids and facilitate improvement in LOQ by a factor of 140, down to 0.1 pM. Finally, we applied FASS-ITP-CE for the simultaneous detection of two miRNAs in crude cell lysates and proved that the method was robust when used in complex biological matrices. The 140-fold improvement in LOQ and the robustness to biological matrices will significantly expand the applicability of CE-based miRNA analysis, bringing it closer to becoming a practical tool for validation of miRNA biomarkers.