Cell Line Models of Molecular Subtypes of Colorectal Cancer

Gas-Phase Fractionation Data-Independent Acquisition Analysis of 3D Cocultured Spheroid Tumor Model Reveals Altered Translational Processes and Signaling Using Proteomics

Colorectal cancer (CRC) contains considerable heterogeneity; therefore, models of the disease must also reflect the multifarious components. Compared to traditional 2D models, 3D cellular models, such as tumor spheroids, have the utility to determine the drug efficacy of potential therapeutics. Monoculture spheroids are well-known to recapitulate gene expression, cell signaling, and pathophysiological gradients of avascularized tumors. However, they fail to mimic the stromal cell influence present in CRC, which is known to perturb drug efficacy and is associated with metastatic, late-stage colorectal cancer. This study seeks to develop a cocultured spheroid model using carcinoma and noncancerous fibroblast cells. We characterized the proteomic profile of cocultured spheroids in comparison to monocultured spheroids using data-independent acquisition with gas-phase fractionation. Specifically, we determined that proteomic differences related to translation and mTOR signaling are significantly increased in cocultured spheroids compared to monocultured spheroids. Proteins related to fibroblast function, such as exocytosis of coated vesicles and secretion of growth factors, were significantly differentially expressed in the cocultured spheroids. Finally, we compared the proteomic profiles of both the monocultured and cocultured spheroids against a publicly available data set derived from solid CRC tumors. We found that the proteome of the cocultured spheroids more closely resembles that of the patient samples, indicating their potential as tumor mimics.

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care

Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP system uses MRI radio imaging for predicting colorectal cancer. During this process, high- and low-level features are required to examine cancer in an earlier stage. Due to the limitation of the conventional feature extraction process, both features are difficult to extract from cancer suffered locations. Hence, a deep learning system (DLS) is used to examine the entire bowel MRI image to identify the cancer-affected location, feature extraction, and feature training process. Furthermore, the DLS-based IAECP system helps improve the overall colorectal cancer identification accuracy for further process. The derived bowel features are trained by applying the residual convolution network, which minimizes the error between predicted and actual values. Finally, the test query images are compared with the trained image by applying the sum, which is more absolute to the cross-correlation template feature matching (SACC) algorithm. The experimental process is performed using 100,000 histological data sets, which is considered a publicly available data set. Moreover, the introduced method does not use generic features, whereas the deep learning features help improve the overall IAECP prediction rate (99.8%) ratio as predicted at lab-scale analysis.

Focused screening reveals functional effects of microRNAs differentially expressed in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is still a leading cause of death worldwide. Recent studies have pointed to an important role of microRNAs in carcinogenesis. Several microRNAs are described as aberrantly expressed in CRC tissues and in the serum of patients. However, functional outcomes of microRNA aberrant expression still need to be explored at the cellular level. Here, we aimed to investigate the effects of microRNAs aberrantly expressed in CRC samples in the proliferation and cell death of a CRC cell line.

METHODS

We transfected 31 microRNA mimics into HCT116 cells. Total number of live propidium iodide negative (PI-) and dead (PI+) cells were measured 4 days post-transfection by using a high content screening (HCS) approach. HCS was further used to evaluate apoptosis (via Annexin V and PI staining), and to discern between intrinsic and extrinsic apoptotic pathways, by detecting cleaved Caspase 9 and 8, respectively. To reveal mRNA targets and potentially involved mechanisms, we performed microarray gene expression and functional pathway enrichment analysis. Quantitative PCR and western blot were used to validate potential mRNA targets.

RESULTS

Twenty microRNAs altered the proliferation of HCT116 cells in comparison to control. miR-22-3p, miR-24-3p, and miR-101-3p significantly repressed cell proliferation and induced cell death. Interestingly, all anti-proliferative microRNAs in our study had been previously described as poorly expressed in the CRC samples. Predicted miR-101-3p targets that were also downregulated by in our microarray were enriched for genes associated with Wnt and cancer pathways, including MCL-1, a member of the BCL-2 family, involved in apoptosis. Interestingly, miR-101-3p preferentially downregulated the long anti-apoptotic MCL-1 L isoform, and reduced cell survival specifically by activating the intrinsic apoptosis pathway. Moreover, miR-101-3p also downregulated IL6ST, STAT3A/B, and MYC mRNA levels, genes associated with stemness properties of CRC cells.

CONCLUSIONS

microRNAs upregulated in CRC tend to induce proliferation in vitro, whereas microRNAs poorly expressed in CRC halt proliferation and induce cell death. We provide novel evidence linking preferential inhibition of the anti-apoptotic MCL-1 L isoform by miR-101-3p and consequent activation of the intrinsic apoptotic pathway as potential mechanisms for its antitumoral activity, likely due to the inhibition of the IL-6/JAK/STAT signaling pathway.

Faithful preclinical mouse models for better translation to bedside in the field of immuno-oncology

The success of immunotherapy using immune checkpoint inhibitors has changed the practice of cancer treatment tremendously. However, there are still many clinical challenges, such as drug resistance, predictive biomarker development, exploration of combination therapies, and prediction of immune-related adverse events in preclinical settings. To overcome these problems, it is essential to establish faithful preclinical mouse models that recapitulate the clinical features, molecular genetics, biological heterogeneity, and immune microenvironment of human cancers. Here we review the advantages and disadvantages of current preclinical mouse models, including syngeneic murine tumor cell lines, autochthonous tumor models, cancer cell line-derived xenografts, patient-derived-xenografts, and various kinds of immunologically humanized mice. We discuss how these models should be characterized and applied in preclinical settings, and how we should prepare preclinical studies for successful translation from bench to bedside.

CRISPR/Cas9 Knockout Strategies to Ablate CCAT1 lncRNA Gene in Cancer Cells

Background

With the increasing discovery of long noncoding RNAs (lncRNAs), the application of functional techniques that could have very specific, efficient, and robust effects and readouts is necessary. Here, we have applied and analyzed three gene knockout (KO) strategies to ablate the CCAT1 gene in different colorectal adenocarcinoma cell lines. We refer to these strategies as “CRISPR excision”, “CRISPR HDR”, and “CRISPR du-HITI”.

Results

In order to obstruct the transcription of lncRNA or to alter its structure, in these strategies either a significant segment of the gene is removed, or a transcription termination signal is inserted in the target gene. We use RT-qPCR, RNA-seq, MTT, and colony formation assay to confirm the functional effects of CCAT1 gene ablation in knockout colorectal adenocarcinoma cell lines. We applied three different CRISPR/Cas9 mediated knockout strategies to abolish the transcription of CCAT1 lncRNA. CCAT1 knockout cells displayed dysregulation of genes involved in several biological processes, and a significant reduction for anchorage-independent growth. The du-HITI strategy introduced in this study removes a gene segment and inserts a reporter and a transcription termination signal in each of the two target alleles. The preparation of donor vector for this strategy is much easier than that in “CRISPR HDR”, and the selection of cells in this strategy is also much more practical than that in “CRISPR excision”. In addition, use of this technique in the first attempt of transfection, generates single cell knockouts for both alleles.

Conclusions

The strategies applied and introduced in this study can be used for the generation of CCAT1 knockout cell lines and in principle can be applied to the deletion of other lncRNAs for the study of their function.

Electronic supplementary material

The online version of this article (10.1186/s12575-018-0086-5) contains supplementary material, which is available to authorized users.