Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance

Impact of meta-roles on the evolution of organisational institutions

This paper investigates the impact of changes in agents' beliefs coupled with dynamics in agents' meta-roles on the evolution of institutions. The study embeds agents' meta-roles in the BDI architecture. In this context, the study scrutinises the impact of cognitive dissonance in agents due to unfairness of institutions. To showcase our model, two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company are simulated. Results show how change in roles of agents coupled with specific institutional characteristics leads to changes of the rules in the system.Comment: arXiv admin note: text overlap with arXiv:2004.1185

GeneXpert—A Game-Changer for Tuberculosis Control?

Carlton Evans considers whether the new tuberculosis diagnostic test, GeneXpert, is the solution for TB control that it's said to be

Ambulatory Multi-Drug Resistant Tuberculosis Treatment Outcomes in a Cohort of HIV-Infected Patients in a Slum Setting in Mumbai, India

Background: India carries one quarter of the global burden of multi-drug resistant TB (MDR-TB) and has an estimated 2.5 million people living with HIV. Despite this reality, provision of treatment for MDR-TB is extremely limited, particularly for HIV-infected individuals. Médecins Sans Frontières (MSF) has been treating HIV-infected MDR-TB patients in Mumbai since May 2007. This is the first report of treatment outcomes among HIV-infected MDR-TB patients in India. Methods: HIV-infected patients with suspected MDR-TB were referred to the MSF-clinic by public Antiretroviral Therapy (ART) Centers or by a network of community non-governmental organizations. Patients were initiated on either empiric or individualized second-line TB-treatment as per WHO recommendations. MDR-TB treatment was given on an ambulatory basis and under directly observed therapy using a decentralized network of providers. Patients not already receiving ART were started on treatment within two months of initiating MDR-TB treatment. Results: Between May 2007 and May 2011, 71 HIV-infected patients were suspected to have MDR-TB, and 58 were initiated on treatment. MDR-TB was confirmed in 45 (78%), of which 18 (40%) were resistant to ofloxacin. Final treatment outcomes were available for 23 patients; 11 (48%) were successfully treated, 4 (17%) died, 6 (26%) defaulted, and 2 (9%) failed treatment. Overall, among 58 patients on treatment, 13 (22%) were successfully treated, 13 (22%) died, 7 (12%) defaulted, two (3%) failed treatment, and 23 (40%) were alive and still on treatment at the end of the observation period. Twenty-six patients (45%) experienced moderate to severe adverse events, requiring modification of the regimen in 12 (20%). Overall, 20 (28%) of the 71 patients with MDR-TB died, including 7 not initiated on treatment. Conclusions: Despite high fluoroquinolone resistance and extensive prior second-line treatment, encouraging results are being achieved in an ambulatory MDR-T- program in a slum setting in India. Rapid scale-up of both ART and second-line treatment for MDR-TB is needed to ensure survival of co-infected patients and mitigate this growing epidemic.</br

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

Standardized Treatment of Active Tuberculosis in Patients with Previous Treatment and/or with Mono-resistance to Isoniazid: A Systematic Review and Meta-analysis

Performing a systematic review of studies evaluating retreatment of tuberculosis or treatment of isoniazid mono-resistant infection, Dick Menzies and colleagues find a paucity of evidence to support the WHO-recommended regimen