Citations

Background: Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders. Objective: To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key “technical challenge” in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed. Methods: PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances. Results: First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed. Conclusion: There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics.

Background: The human Monoamine Transporters (hMATs), primarily including hSERT, hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders with more than the availability of 30 approved drugs. Objective: This paper is to review the recent progress in the binding mode and inhibitory mechanism of hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds to hMATs that are evaluated by in vitro and in vivo experiments will be highlighted. Methods: PubMed and Web of Science databases were searched for protein-ligand interaction, novel inhibitors design and synthesis studies related to hMATs. Results: Literature data indicate that since the first crystal structure determinations of the homologous bacterial Leucine Transporter (LeuT) complexed with clomipramine, a sizable database of over 100 experimental structures or computational models has been accumulated that now defines a substantial degree of structural variability hMATs-ligands recognition. In the meanwhile, a number of novel hMATs inhibitors have been discovered by medicinal chemistry with significant help from computational models. Conclusion: The reported new compounds act on hMATs as well as the structures of the transporters complexed with diverse ligands by either experiment or computational modeling have shed light on the poly-pharmacology, multimodal and allosteric regulation of the drugs to transporters. All of the studies will greatly promote the Structure-Based Drug Design (SBDD) of structurally novel scaffolds with high activity and selectivity for hMATs.

Binge drinking is a severe health problem faced by many US colleges and universities. College students often post drinking-related text and images on social media, portraying their alcohol use as socially desirable. In this project, we investigated the feasibility of mining the heterogeneous data (e.g. text, images, and videos) on Facebook to identify drinking-related contents. We manually annotated 4266 posts during 21 October 2011 and 3 November 2014 from “I’m Shmacked” group on Facebook, where 511 posts were drinking-related. Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81. Prediction models built on text data were more reliable compared to those built on image and video data for predicting drinking-related contents. As the first step of our efforts in this direction, this feasibility study showed promise toward unleashing the potential of mining social media to identify students who binge drink.

With recent changes in United Network for Organ Sharing policy, patients in the United States with hepatocellular carcinoma (HCC) are likely to spend more time on the liver transplantation (LT) waiting list. The increasing wait time will allow for an opportunity to assess tumor biology prior to LT. Modified Response Evaluation Criteria in Solid Tumors (mRECIST) paradigm provides such a framework for this assessment, and yet little is understood of its utility as it would apply for patients listed for LT in the United States. Through a collaboration between the University of California, San Francisco, and the Mayo Clinic, Jacksonville, Florida, the experience of 772 patients listed for LT were retrospectively reviewed to study the impact of immediate mRECIST classification following locoregional therapy (LRT) on pre‐ and post‐LT outcomes. Patients who had progression of disease (PD; n = 72), failed to respond to LRT (n = 89) at any time point, or did not achieve radiologic complete response (CR; n = 224) were all at significant risk for wait‐list dropout (odds ratio [OR] = 12.11, 4.81, and 2.48; respectively). CR identified a cohort of patients who were at a reduced risk for wait‐list dropout. However, 24.9% eventually required further intervention while waiting for transplant, and as many as 82.4% were found to have residual HCC on explant pathology. Failure to respond to LRT was associated with increased risk for recurrence (OR = 3.00) more so than PD (OR = 1.36), suggesting that despite PD, patients who eventually can respond to LRT may represent favorable candidates for LT. In conclusion, for patients awaiting LT, the mRECIST assessment provides critical guidance for patient management. Although PD portends a poor prognosis, our findings suggest that further aggressive LRT should be pursued because a response to LRT may yield acceptable results for patients awaiting LT as well as after LT.

BackgroundPreoperative prediction of tumor recurrence is important in the management of patients with hepatocellular carcinoma (HCC).PurposeTo investigate whether tumor stiffness derived by magnetic resonance elastography (MRE) could predict early recurrence of HCC after hepatic resection.Study TypeRetrospective.PopulationIn all, 99 patients with pathologically confirmed HCCs after surgical resection.Field Strength/Sequence3.0T; preoperative MRE with 60‐Hz mechanical vibrations using an active acoustic driver.AssessmentRegions of interest (ROIs) were manually drawn in the tumors to measure mean tumor stiffness. Surgical specimens were reviewed for histological grade, capsule, vascular invasion, and surgical margins. The early recurrence of HCC was defined as that occurring within 2 years after resection.Statistical TestsCox proportional hazard models were used to evaluate risk factors associated with the time to early recurrence.ResultsHCCs with recurrence had higher tumor stiffness, higher rate of advanced T stage, vascular invasion, lower rate of capsule formation, larger tumor size, higher aspartate aminotransferase (AST), and hepatitis B virus (HBV)‐DNA level and aspartate aminotransferase / alanine aminotransferase ratio (P = 0.031, 0.007, 0.01, <0.001, 0.015, 0.034, 0.01, and 0.014, respectively) than HCCs without recurrence. Vascular invasion (hazard ratio [HR] = 2.922; 95% confidence interval [CI]: [1.079, 7.914], P = 0.035) and mean tumor stiffness (HR = 1.163; 95% CI: [1.055, 1.282], P = 0.002) were risk factors associated with early recurrence. Each 1‐kPa increase in tumor stiffness was associated with a 16.3% increase in the risk for tumor recurrence.Data ConclusionThe mean stiffness of HCCs may be a useful, noninvasive, quantitative biomarker for the prediction of early HCC recurrence after hepatic resection.Level of Evidence: 4Technical Efficacy: Stage 5J. Magn. Reson. Imaging 2019;49:719–730.