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. Although there have been trials to prove intra-tumoral heterogeneity using single cell sequencing, that of CSCs has not been clearly elucidated. Here, we articulate the presence of heterogeneous subclones within CD133 positive cancer stem cells through single cell sequencing. As a proof of principle, we performed phenotype-based high-throughput laser isolation and single cell sequencing (PHLI-seq) of CD133 positive cells in a frozen tumor tissue obtained from a patient with colorectal cancer. The result proved that CD133 positive cells were shown to be heterogeneous both in copy number and mutational profiles. Single cancer stem cell specific mutations such as RNF144A, PAK2, PARP4, ADAM21, HYDIN, KRT38 and CELSR1 could be also detected in liver metastatic tumor of the same patient. Collectively, these data suggest that single cell analysis used to spot subclones with genetic variation within rare population, will lead to new strategies to tackle colon cancer metastasis.

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. Study 2 recruited a new sample of 485 Americans and Canadians and examined their profiles on measures of social orientation and cognitive style twice, one month apart, to assess the stability of individual profiles using these variables. RESULTS: Despite finding typical cross-cultural differences, Study 1 demonstrated negligible correlations both among measures of social orientation and among measures of cognitive style. Study 2 demonstrated stable intraindividual behavioral profiles across measures capturing idiosyncratic patters of social orientation and cognitive style, despite negligible correlations among the same measures. CONCLUSION: The results provide support for the behavioral profile approach to conceptualizing social orientation and cognitive style, highlighting the need to assess intraindividual stability of psychological constructs in cross-cultural research.

Activation of WNT/ß-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. Out of a panel of 29 CRC cell lines, we identified 7 harboring one or more PIK3CA mutations; of these, 5 and 2 were found to be sensitive and resistant to gedatolisib, respectively. Both of the gedatolisib-resistant cell lines expressed high levels of active glycogen synthase kinase 3-beta (GSK3ß) and harbored the same frameshift mutation (c.465_466insC; H155fs*) in TCF7, which encodes a positive transcriptional regulator of the WNT/ß-catenin signaling pathway. Inhibition of GSK3ß activity in gedatolisib-resistant cells by siRNA-mediated knockdown or treatment with a GSK3ß-specific inhibitor effectively reduced the activity of molecules downstream of mTOR and also decreased signaling through the WNT/ß-catenin pathway. Notably, GSK3ß inhibition rendered the resistant cell lines sensitive to gedatolisib cytotoxicity, both in vitro and in a mouse xenograft model. Taken together, these data demonstrate that aberrant regulation of WNT/ß-catenin signaling and active GSK3ß induced by the TCF7 frameshift mutation cause resistance to the dual PI3K/mTOR inhibitor gedatolisib. Cotreatment with GSK3ß inhibitors may be a strategy to overcome the resistance of PIK3CA- and TCF7-mutant CRC to PI3K/mTOR-targeted therapies.

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 2.33 mutational events per Gb genome, comprising 2.15 indels, 0.17 SVs, and 0.01 CGRs, despite a high level of inter-cellular stochasticity. The mutational burden was dependent on total irradiation dose, regardless of dose rate or cell type. The findings were further validated in IR-induced secondary cancers and single cells without clonalization. Overall, our study highlights a comprehensive and clear picture of IR effects on normal mammalian genomes.

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. Organoids were established from 54 patients with colorectal cancer who (except for one patient) did not receive any form of therapy before, and tumor organoids were assessed through whole-exome sequencing. For comparisons of in vitro drug responses in matched patients, we developed an 'organoid score' based on the variable anticancer treatment responses observed in organoids. Very interestingly, a higher organoid score was significantly correlated with a lower tumor regression rate after the standard-of-care treatment in matched patients. Additionally, we confirmed that patients with a higher organoid score (≥ 2.5) had poorer progression-free survival compared with those with a lower organoid score (< 2.5). Furthermore, to assess potential drug repurposing using an FDA-approved drug library, ten tumor organoids derived from patients with disease progression were applied to a simulation platform. Taken together, organoids and organoid scores can facilitate the prediction of anticancer therapy efficacy, and they can be used as a simulation model to determine the next therapeutic options through drug screening. Organoids will be an attractive platform to enable the implementation of personalized therapy for colorectal cancer patients.

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. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.