Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features.

Colorectal cancer is the leading cause of cancer-associated morbidity and mortality worldwide. One of the causes of developing colorectal cancer is untreated colon adenomatous polyps. Clinically, polyps are detected in colonoscopy and the malignancies are determined according to the biopsy. To provide a quick and objective assessment to gastroenterologists, this study proposed a quantitative polyp classification via various image features in colonoscopy. The collected image database was composed of 1991 images including 1053 hyperplastic polyps and 938 adenomatous polyps and adenocarcinomas. From each image, textural features were extracted and combined in machine learning classifiers and machine-generated features were automatically selected in deep convolutional neural networks (DCNN). The DCNNs included AlexNet, Inception-V3, ResNet-101, and DenseNet-201. AlexNet trained from scratch achieved the best performance of 96.4% accuracy which is better than transfer learning and textural features. Using the prediction models, the malignancy level of polyps can be evaluated during a colonoscopy to provide a rapid treatment plan.

Fluoroquinolones Suppress TGF-ß and PMA-Induced MMP-9 Production in Cancer Cells: Implications in Repurposing Quinolone Antibiotics for Cancer Treatment.

BACKGROUND: Fluoroquinolones (FQs) are potent antimicrobials with multiple effects on host cells and tissues. Although FQs can attenuate cancer invasion and metastasis, the underlying molecular mechanisms remain unclear. Matrix metalloproteinase-9 (MMP-9) has functional roles in tumor angiogenesis, invasion, and metastasis, and is associated with cancer progression and poor prognosis, suggesting that inhibitors of MMP-9 activity and transcription are prime candidates for cancer therapy. Despite numerous preclinical data supporting the use of MMP-9 inhibitors as anticancer drugs, the few available examples are not therapeutically useful due to low specificity and off-target effects. We examined the effects of FQs on MMP-9 production in cancer cells following transforming growth factor beta (TGF-ß) and phorbol 12-myristate 13-acetate (PMA) stimulation. EXPERIMENTAL APPROACHES: Using confluent cultures of HepG2 and A549 cells, the effects of FQs (ciprofloxacin, levofloxacin, clinafloxacin, gatifloxacin, and enrofloxacin) on TGF-ß and PMA-induced MMP-9 mRNA expression and production were studied in RNA extracts and culture supernatants, respectively. FQs specifically abrogated TGF-ß and PMA-induced MMP-9 levels and activity in a concentration and time-dependent manner, without affecting other MMPs or proteins involved in epithelial-mesenchymal transition. Additionally, FQs inhibited TGF-ß and PMA-induced cell migration via p38 and cyclic AMP signaling pathways. CONCLUSIONS AND IMPLICATIONS: Overall, we demonstrated that FQs inhibit cancer cell migration and invasion by downregulating MMP-9 expression and revealed the cellular mechanisms underlying their potential value in cancer treatment.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics.

BACKGROUND: Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks. RESULTS: We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism. CONCLUSIONS: In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.

Epithelial-mesenchymal transition (EMT) beyond EGFR mutations per se is a common mechanism for acquired resistance to EGFR TKI.

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) is a major advance in treating NSCLC with EGFR-activating mutations. However, acquired resistance, due partially to secondary mutations limits their use. Here we report that NSCLC cells with acquired resistance to gefitinib or osimertinib (AZD9291) exhibit EMT features, with a decrease in E-cadherin, and increases in vimentin and stemness, without possessing any EGFR secondary mutations. Knockdown of E-cadherin in parental cells increased gefitinib resistance and stemness, while knockdown of vimentin in resistant cells resulted in opposite effects. Src activation and Hakai upregulation were found in gefitinib-resistant cells. Knockdown of Hakai elevated E-cadherin expression, attenuated stemness, and resensitized the cells to gefitinib. Clinical cancer specimens with acquired gefitinib resistance also showed a decrease in E-cadherin and an increase in Hakai expression. The dual HDAC and HMGR inhibitor JMF3086 inhibited the Src/Hakai and Hakai/E-cadherin interaction to reverse E-cadherin expression, and attenuated vimentin and stemness to restore gefitinib sensitivity. The EMT features of AZD9291-resistant H1975 cells were related to the upregulation of Zeb1. Both gefitinib and AZD9291 sensitivity was restored by JMF3086 through reversing EMT. Our study not only revealed a common mechanism of EMT in both gefitinib and AZD9291 resistance beyond EGFR mutations per se, but also provides a new strategy to overcome it.

Mutation load estimation model as a predictor of the response to cancer immunotherapy.

The determination of the mutation load, a total number of nonsynonymous point mutations, by whole-exome sequencing was shown to be useful in predicting the treatment responses to cancer immunotherapy. However, this technique is expensive and time-consuming, which hampers its application in clinical practice. Therefore, the objective of this study was to construct a mutation load estimation model for lung adenocarcinoma, using a small set of genes, as a predictor of the immunotherapy treatment response. Using the somatic mutation data downloaded from The Cancer Genome Atlas (TCGA) database, a computational framework was developed. The estimation model consisted of only 24 genes, used to estimate the mutation load in the independent validation cohort precisely (R2 = 0.7626). Additionally, the estimated mutation load can be used to identify the patients with durable clinical benefits, with 85% sensitivity, 93% specificity, and 89% accuracy, indicating that the model can serve as a predictive biomarker for cancer immunotherapy treatment response. Furthermore, our analyses demonstrated the necessity of the cancer-specific models by the constructed melanoma and colorectal models. Since most genes in the lung adenocarcinoma model are not currently included in the sequencing panels, a customized targeted sequencing panel can be designed with the selected model genes to assess the mutation load, instead of whole-exome sequencing or the currently used panel-based methods. Consequently, the cost and time required for the assessment of mutation load may be considerably decreased, which indicates that the presented model is a more cost-effective approach to cancer immunotherapy response prediction in clinical practice.

Polymorphisms in metabolic GSTP1 and DNA-repair XRCC1 genes with an increased risk of DNA damage in pesticide-exposed fruit growers.

Pesticide exposure is associated with various neoplastic diseases and congenital malformations. Previous studies have indicated that pesticides may be metabolized by cytochrome P450 3A5 or glutathione S-transferases. DNA-repair genes, including X-ray repair cross-complementing group 1 (XRCC1) and xeroderma pigmentosum group D (XPD), may also be implicated in the process of pesticide-related carcinogenesis. Thus, we investigated whether various metabolic and DNA-repair genotypes increase the risk of DNA damage in pesticide-exposed fruit growers. Using the comet assay, the extent of DNA damage was evaluated in the peripheral blood of 135 pesticide-exposed fruit growers and 106 unexposed controls. The metabolic genotypes CYP3A5 (A(-44)G) and GSTP1 (Ile105Val) and DNA-repair genotypes XRCC1 (Arg399Gln, Arg194Trp, T(-77)C) and XPD (Asp312Asn, Lys751Gln) were identified by polymerase chain reaction. Our multiple regression model for DNA tail moment showed that age, high pesticide exposure, low pesticide exposure, GSTP1 Ile-Ile, and XRCC1 399 Arg-Arg genotype were associated with increased DNA tail moment (DNA damage). Further analysis of interaction between GSTP1 and XRCC1 genes that increase susceptibility revealed a significant difference in DNA tail moment for high pesticide-exposed subjects carrying both GSTP1 Ile-Ile with XRCC1 399 Arg-Arg genotypes (2.49+/-0.09 microm/cell; P=0.004), compared to those carrying GSTP1 Ile-Val/Val-Val with XRCC1 399 Arg-Gln/Gln-Gln genotypes (1.98+/-0.15 microm/cell). These results suggest that individuals with susceptible metabolic GSTP1 and DNA-repair XRCC1 genotypes may be at increased risk of DNA damage due to pesticide exposure.