Take-home drug checking as a novel harm reduction strategy in British Columbia, Canada.

BACKGROUND: Drug checking is a harm reduction strategy used to identify components of illicitly obtained drugs, including adulterants, to prevent overdose. This study evaluated the distribution of take-home fentanyl test strips to people who use drugs (PWUD) in British Columbia, Canada. The primary aim was to assess if the detection of fentanyl in opioid samples was concordant between a take-home model and testing by trained drug checking staff. METHODS: Take-home fentanyl test strips were distributed at ten sites providing drug checking services from April to July 2019. The fentanyl positivity of the aggregate take-home and on-site drug checking groups were compared by class of substance tested. An administered survey assessed acceptability and behaviour change. RESULTS: 1680 take-home results were obtained from 218 unique participants; 68% of samples (n=1142) were identified as opioids and 23% (n=382) were stimulant samples. During this period, 852 samples were tested using on-site drug checking. The fentanyl positivity of opioid samples was 90.0% for take-home samples and 89.1% for on-site samples (Difference 0.8% (95% CI -2.3% to 3.9%)). These results were not affected by previous experience with test strips. Fentanyl positivity of stimulants in the take-home group was higher than on-site (24.7% vs. 3.2%), but the study was underpowered to conduct statistical analysis on this sub-group. When fentanyl was detected, 27% of individuals reported behaviour change that was considered safer/positive. Greater than 95% of participants stated they would use fentanyl test strips again. CONCLUSIONS: Take-home fentanyl test strips used by PWUD on opioid samples can provide similar results to formal drug checking services and are a viable addition to existing overdose prevention strategies. Use of this strategy for detection of fentanyl in stimulant samples requires further evaluation. This intervention was well accepted and in some participants was associated with positive behaviour change.

Identification of relevant genetic alterations in cancer using topological data analysis.

Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12-/- mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.

A Structure-Informed Atlas of Human-Virus Interactions.

While knowledge of protein-protein interactions (PPIs) is critical for understanding virus-host relationships, limitations on the scalability of high-throughput methods have hampered their identification beyond a number of well-studied viruses. Here, we implement an in silico computational framework (pathogen host interactome prediction using structure similarity [P-HIPSTer]) that employs structural information to predict ∼282,000 pan viral-human PPIs with an experimental validation rate of ∼76%. In addition to rediscovering known biology, P-HIPSTer has yielded a series of new findings: the discovery of shared and unique machinery employed across human-infecting viruses, a likely role for ZIKV-ESR1 interactions in modulating viral replication, the identification of PPIs that discriminate between human papilloma viruses (HPVs) with high and low oncogenic potential, and a structure-enabled history of evolutionary selective pressure imposed on the human proteome. Further, P-HIPSTer enables discovery of previously unappreciated cellular circuits that act on human-infecting viruses and provides insight into experimentally intractable viruses.

Author Correction: Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma.

In the version of this article originally published, the graph in Extended Data Fig. 2c was a duplication of Extended Data Fig. 2b. The correct version of Extended Data Fig. 2c is now available online.

Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma.

Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes as well as with differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor's clonal evolution during treatment.

Evolutionary scalpels for dissecting tumor ecosystems.

Amidst the growing literature on cancer genomics and intratumor heterogeneity, essential principles in evolutionary biology recur time and time again. Here we use these principles to guide the reader through major advances in cancer research, highlighting issues of "hit hard, hit early" treatment strategies, drug resistance, and metastasis. We distinguish between two frameworks for understanding heterogeneous tumors, both of which can inform treatment strategies: (1) The tumor as diverse ecosystem, a Darwinian population of sometimes-competing, sometimes-cooperating cells; (2) The tumor as tightly integrated, self-regulating organ, which may hijack developmental signals to restore functional heterogeneity after treatment. While the first framework dominates literature on cancer evolution, the second framework enjoys support as well. Throughout this review, we illustrate how mathematical models inform understanding of tumor progression and treatment outcomes. Connecting models to genomic data faces computational and technical hurdles, but high-throughput single-cell technologies show promise to clear these hurdles. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.