Attractor Landscape Analysis Reveals a Reversion Switch in the Transition of Colorectal Tumorigenesis.

A cell fate change such as tumorigenesis incurs critical transition. It remains a longstanding challenge whether the underlying mechanism can be unraveled and a molecular switch that can reverse such transition is found. Here a systems framework, REVERT, is presented with which can reconstruct the core molecular regulatory network model and a reversion switch based on single-cell transcriptome data over the transition process is identified.

Quantitative and qualitative mutational impact of ionizing radiation on normal cells.

The comprehensive genomic impact of ionizing radiation (IR), a carcinogen, on healthy somatic cells remains unclear. Using large-scale whole-genome sequencing (WGS) of clones expanded from irradiated murine and human single cells, we revealed that IR induces a characteristic spectrum of short insertions or deletions (indels) and structural variations (SVs), including balanced inversions, translocations, composite SVs (deletion-insertion, deletion-inversion, and deletion-translocation composites), and complex genomic rearrangements (CGRs), including chromoplexy, chromothripsis, and SV by breakage-fusion-bridge cycles. Our findings suggest that 1 Gy IR exposure causes an average of 2.

Patient-derived organoids as a preclinical platform for precision medicine in colorectal cancer.

Patient-derived organoids are being considered as models that can help guide personalized therapy through in vitro anticancer drug response evaluation. However, attempts to quantify in vitro drug responses in organoids and compare them with responses in matched patients remain inadequate. In this study, we investigated whether drug responses of organoids correlate with clinical responses of matched patients and disease progression of patients.

Phenotype-based single cell sequencing identifies diverse genetic subclones in CD133 positive cancer stem cells.

Tumor heterogeneity is one of the ongoing huddles in the field of colon cancer therapy. It is evident that there are countless clones which exhibit different phenotypes and therefore, single cell analysis is inevitable. Cancer stem cells (CSCs) are rare cell population within tumor which is known to function in cancer metastasis and recurrence.

Culture and personality revisited: Behavioral profiles and within-person stability in interdependent (vs. independent) social orientation and holistic (vs. analytic) cognitive style.

Objective: We test the proposition that both social orientation and cognitive style are constructs consisting of loosely related attributes. Thus, measures of each construct should weakly correlate among themselves, forming intraindividually stable profiles across measures over time.

Method: Study 1 tested diverse samples of Americans (N = 233) and Japanese (N = 433) with a wide range of measures of social orientation and cognitive style to explore correlations among these measures in a cross-cultural context, using demographically heterogeneous samples.

Activation of WNT/β-catenin signaling results in resistance to a dual PI3K/mTOR inhibitor in colorectal cancer cells harboring PIK3CA mutations.

PIK3CA is a frequently mutated gene in cancer, including about ~15 to 20% of colorectal cancers (CRC). PIK3CA mutations lead to activation of the PI3K/AKT/mTOR signaling pathway, which plays pivotal roles in tumorigenesis. Here, we investigated the mechanism of resistance of PIK3CA-mutant CRC cell lines to gedatolisib, a dual PI3K/mTOR inhibitor.

Deep Learning in Medical Imaging: General Overview.

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network.