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Remote inflammation monitoring with digital health technologies (DHTs) would provide valuable information for both clinical research and care. Controlled perturbations of the immune system may reveal physiological signatures which could be used to develop a digital biomarker of inflammatory state. In this study, molecular and physiological profiling was performed following an in vivo lipopolysaccharide (LPS) challenge to develop a digital biomarker of inflammation. Ten healthy volunteers received an intravenous LPS challenge and were monitored for 24 h using the VitalConnect VitalPatch (VitalPatch). VitalPatch measurements included heart rate (HR), heart rate variability (HRV), respiratory rate (RR), and skin temperature (TEMP). Conventional episodic inpatient vital signs and serum proteins were measured pre‐ and post‐LPS challenge. The VitalPatch provided vital signs that were comparable to conventional methods for assessing HR, RR, and TEMP. A pronounced increase was observed in HR, RR, and TEMP as well as a decrease in HRV 1–4 h post‐LPS challenge. The ordering of participants by magnitude of inflammatory cytokine response 2 h post‐LPS challenge was consistent with ordering of participants by change from baseline in vital signs when measured by VitalPatch (r = 0.73) but not when measured by conventional methods (r = −0.04). A machine learning model trained on VitalPatch data predicted change from baseline in inflammatory protein response (R2 = 0.67). DHTs, such as VitalPatch, can improve upon existing episodic measurements of vital signs by enabling continuous sensing and have the potential for future use as tools to remotely monitor inflammation. [ABSTRACT FROM AUTHOR]

Inhibitors of double minute 2 protein (MDM2)-tumor protein 53 (TP53) interaction are predicted to be effective in tumors in which the TP53 gene is wild type, by preventing TP53 protein degradation. One such setting is represented by the frequent CDKN2A deletion in human cancer that, through inactivation of p14ARF, activates MDM2 protein, which in turn degrades TP53 tumor suppressor. Here we used piggyBac (PB) transposon insertional mutagenesis to anticipate resistance mechanisms occurring during treatment with the MDM2-TP53 inhibitor HDM201. Constitutive PB mutagenesis in Arf-/- mice provided a collection of spontaneous tumors with characterized insertional genetic landscapes. Tumors were allografted in large cohorts of mice to assess the pharmacologic effects of HDM201. Sixteen out of 21 allograft models were sensitive to HDM201 but ultimately relapsed under treatment. A comparison of tumors with acquired resistance to HDM201 and untreated tumors identified 87 genes that were differentially and significantly targeted by the PB transposon. Resistant tumors displayed a complex clonality pattern suggesting the emergence of several resistant subclones. Among the most frequent alterations conferring resistance, we observed somatic and insertional loss-of-function mutations in transformation-related protein 53 (Trp53) in 54% of tumors and transposonmediated gain-of-function alterations in B-cell lymphoma-extra large (Bcl-xL), Mdm4, and two TP53 family members, resulting in expression of the TP53 dominant negative truncations △NTrp63 and △NTrp73. Enhanced BCL-xL and MDM4 protein expression was confirmed in resistant tumors, as well as in HDM201-resistant patient-derived tumor xenografts. Interestingly, concomitant inhibition of MDM2 and BCL-xL demonstrated significant synergy in p53 wild-type cell lines in vitro. Collectively, our findings identify several potential mechanisms by which TP53 wild-type tumors may escape MDM2-targeted therapy. [ABSTRACT FROM AUTHOR]

Motivation Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. Results We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. Availability and implementation Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. Supplementary information Supplementary material is available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Activation of the phosphoinositide 3-kinase (PI3K) pathway occurs frequently in breast cancer. However, clinical results of single-agent PI3K inhibitors have been modest to date. A combinatorial drug screen on multiple PIK3CA mutant cancers with decreased sensitivity to PI3K inhibitors revealed that combined CDK 4/6-PI3K inhibition synergistically reduces cell viability. Laboratory studies revealed that sensitive cancers suppress RB phosphorylation upon treatment with single-agent PI3K inhibitors but cancers with reduced sensitivity fail to do so. Similarly, patients' tumors that responded to the PI3K inhibitor BYL719 demonstrated suppression of pRB, while nonresponding tumors showed sustained or increased levels of pRB. Importantly, the combination of PI3K and CDK 4/6 inhibitors overcomes intrinsic and adaptive resistance leading to tumor regressions in PIK3CA mutant xenografts. [ABSTRACT FROM AUTHOR]