Activity

An amendment to this paper has been published and can be accessed via a link at the top of the paper.Therapeutic strategies are being clinically tested either to eradicate latent HIV reservoirs or to achieve virologic control in the absence of antiretroviral therapy. Attaining this goal will require a consensus on how best to measure the numbers of persistently infected cells with the potential to cause viral rebound after antiretroviral-therapy cessation in assessing the results of cure-directed strategies in vivo. Current measurements assess various aspects of the HIV provirus and its functionality and produce divergent results. Here, we provide recommendations from the BEAT-HIV Martin Delaney Collaboratory on which viral measurements should be prioritized in HIV-cure-directed clinical trials.Recent studies show that aneuploidy and driver gene mutations precede cancer diagnosis by many years1-4. We assess whether these genomic signals can be used for early detection and pre-emptive cancer treatment using the neoplastic precursor lesion Barrett’s esophagus as an exemplar5. Shallow whole-genome sequencing of 777 biopsies, sampled from 88 patients in Barrett’s esophagus surveillance over a period of up to 15 years, shows that genomic signals can distinguish progressive from stable disease even 10 years before histopathological transformation. These findings are validated on two independent cohorts of 76 and 248 patients. These methods are low-cost and applicable to standard clinical biopsy samples. Compared with current management guidelines based on histopathology and clinical presentation, genomic classification enables earlier treatment for high-risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.We report on molecular analyses of baseline tumor samples from the phase 3 JAVELIN Renal 101 trial (n = 886; NCT02684006 ), which demonstrated significantly prolonged progression-free survival (PFS) with first-line avelumab + axitinib versus sunitinib in advanced renal cell carcinoma (aRCC). We found that neither expression of the commonly assessed biomarker programmed cell death ligand 1 (PD-L1) nor tumor mutational burden differentiated PFS in either study arm. Similarly, the presence of FcɣR single nucleotide polymorphisms was unimpactful. We identified important biological features associated with differential PFS between the treatment arms, including new immunomodulatory and angiogenesis gene expression signatures (GESs), previously undescribed mutational profiles and their corresponding GESs, and several HLA types. Crenolanib These findings provide insight into the determinants of response to combined PD-1/PD-L1 and angiogenic pathway inhibition and may aid in the development of strategies for improved patient care in aRCC.Drug-induced liver injury (DILI) is a leading cause of termination in drug development programs and removal of drugs from the market; this is partially due to the inability to identify patients who are at risk1. In this study, we developed a polygenic risk score (PRS) for DILI by aggregating effects of numerous genome-wide loci identified from previous large-scale genome-wide association studies2. The PRS predicted the susceptibility to DILI in patients treated with fasiglifam, amoxicillin-clavulanate or flucloxacillin and in primary hepatocytes and stem cell-derived organoids from multiple donors treated with over ten different drugs. Pathway analysis highlighted processes previously implicated in DILI, including unfolded protein responses and oxidative stress. In silico screening identified compounds that elicit transcriptomic signatures present in hepatocytes from individuals with elevated PRS, supporting mechanistic links and suggesting a novel screen for safety of new drug candidates. This genetic-, cellular-, organoid- and human-scale evidence underscored the polygenic architecture underlying DILI vulnerability at the level of hepatocytes, thus facilitating future mechanistic studies. Moreover, the proposed ‘polygenicity-in-a-dish’ strategy might potentially inform designs of safer, more efficient and robust clinical trials.Intestinal failure, following extensive anatomical or functional loss of small intestine, has debilitating long-term consequences for children1. The priority of patient care is to increase the length of functional intestine, particularly the jejunum, to promote nutritional independence2. Here we construct autologous jejunal mucosal grafts using biomaterials from pediatric patients and show that patient-derived organoids can be expanded efficiently in vitro. In parallel, we generate decellularized human intestinal matrix with intact nanotopography, which forms biological scaffolds. Proteomic and Raman spectroscopy analyses reveal highly analogous biochemical profiles of human small intestine and colon scaffolds, indicating that they can be used interchangeably as platforms for intestinal engineering. Indeed, seeding of jejunal organoids onto either type of scaffold reliably reconstructs grafts that exhibit several aspects of physiological jejunal function and that survive to form luminal structures after transplantation into the kidney capsule or subcutaneous pockets of mice for up to 2 weeks. Our findings provide proof-of-concept data for engineering patient-specific jejunal grafts for children with intestinal failure, ultimately aiding in the restoration of nutritional autonomy.Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been an interest in estimating them algorithmically from multicell activity recordings. We show that even sophisticated methods, applied to unlimited data from every cell in the circuit, are biased toward inferring connections between unconnected but highly correlated neurons. This failure to ‘explain away’ connections occurs when there is a mismatch between the true network dynamics and the model used for inference, which is inevitable when modeling the real world. Thus, causal inference suffers when variables are highly correlated, and activity-based estimates of connectivity should be treated with special caution in strongly connected networks. Finally, performing inference on the activity of circuits pushed far out of equilibrium by a simple low-dimensional suppressive drive might ameliorate inference bias.