Inflammatory Stimuli Responsive Non-Faradaic, Ultrasensitive Combinatorial Electrochemical Urine Biosensor.

In this work, we propose a novel diagnostic biosensor that can enable stratification of disease states based on severity and hence allow for clear and actionable diagnoses. The scheme can potentially boost current Point-Of-Care (POC) biosensors for diseases that require time-critical stratification. Here, two key inflammatory biomarkers­Interleukin-8 and Interleukin-6­have been explored as proof of concept, and a four-class stratification of inflammatory disease severity is discussed. Our method is superior to traditional lab techniques as it is faster (<4 minutes turn-around time) and can work with any combination of disease biomarkers to categorize diseases by subtypes and severity. At its core, the biosensor relies on electrochemical impedance spectroscopy to transduce subtle inflammatory stimuli at the input for IL-8 and IL-6 for a limit of detection (LOD) of 1 pg/mL each. The biosensing scheme utilizes a two-stage random forest machine learning model for 4-state output disease classification with a 98.437% accuracy. This scheme can potentially boost the diagnostic power of current electrochemical biosensors for better precision therapy and improved patient outcomes.

Determining cancer risk: the evolutionary multistage model or total stem cell divisions?

A recent hypothesis proposed that the total number of stem cell divisions in a tissue (TSCD model) determine its intrinsic cancer risk; however, a different model-the multistage model-has long been used to understand how cancer originates. Identifying the correct model has important implications for interpreting the frequency of cancers. Using worldwide cancer incidence data, we applied three tests to the TSCD model and an evolutionary multistage model of carcinogenesis (EMMC), a model in which cancer suppression is recognized as an evolving trait, with natural selection acting to suppress cancers causing a significant mean loss of Darwinian fitness. Each test supported the EMMC but contradicted the TSCD model. This outcome undermines results based on the TSCD model quantifying the relative importance of 'bad luck' (the random accumulation of somatic mutations) versus environmental and genetic factors in determining cancer incidence. Our testing supported the EMMC prediction that cancers of large rapidly dividing tissues predominate late in life. Another important prediction is that an indicator of recent oncogenic environmental change is an unusually high mean fitness loss due to cancer, rather than a high lifetime incidence. The evolutionary model also predicts that large and/or long-lived animals have evolved mechanisms of cancer suppression that may be of value in preventing or controlling human cancers.

Sex Differences in Cancer Driver Genes and Biomarkers.

Cancer differs significantly between men and women; even after adjusting for known epidemiologic risk factors, the sexes differ in incidence, outcome, and response to therapy. These differences occur in many but not all tumor types, and their origins remain largely unknown. Here, we compare somatic mutation profiles between tumors arising in men and in women. We discovered large differences in mutation density and sex biases in the frequency of mutation of specific genes; these differences may be associated with sex biases in DNA mismatch repair genes or microsatellite instability. Sex-biased genes include well-known drivers of cancer such as ß-catenin and BAP1 Sex influenced biomarkers of patient outcome, where different genes were associated with tumor aggression in each sex. These data call for increased study and consideration of the molecular role of sex in cancer etiology, progression, treatment, and personalized therapy.Significance: This study provides a comprehensive catalog of sex differences in somatic alterations, including in cancer driver genes, which influence prognostic biomarkers that predict patient outcome after definitive local therapy. Cancer Res; 78(19); 5527-37. ©2018 AACR.

Dicer loss and recovery induce an oncogenic switch driven by transcriptional activation of the oncofetal Imp1-3 family.

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression critical for organismal viability. Changes in miRNA activity are common in cancer, but how these changes relate to subsequent alterations in transcription and the process of tumorigenesis is not well understood. Here, we report a deep transcriptional, oncogenic network regulated by miRNAs. We present analysis of the gene expression and phenotypic changes associated with global miRNA restoration in miRNA-deficient fibroblasts. This analysis uncovers a miRNA-repressed network containing oncofetal genes Imp1, Imp2, and Imp3 (Imp1-3) that is up-regulated primarily transcriptionally >100-fold upon Dicer loss and is resistant to resilencing by complete restoration of miRNA activity. This Dicer-resistant epigenetic switch confers tumorigenicity to these cells. Let-7 targets Imp1-3 are required for this tumorigenicity and feed back to reinforce and sustain expression of the oncogenic network. Together, these Dicer-resistant genes constitute an mRNA expression signature that is present in numerous human cancers and is associated with poor survival.