The National Lung Matrix Trial of personalized therapy in lung cancer

The majority of targeted therapies for non-small-cell lung cancer (NSCLC) are directed against oncogenic drivers that are more prevalent in patients with light exposure to tobacco smoke1,2,3. As this group represents around 20% of all patients with lung cancer, the discovery of stratified medicine options for tobacco-associated NSCLC is a high priority. Umbrella trials seek to streamline the investigation of genotype-based treatments by screening tumours for multiple genomic alterations and triaging patients to one of several genotype-matched therapeutic agents. Here we report the current outcomes of 19 drug–biomarker cohorts from the ongoing National Lung Matrix Trial, the largest umbrella trial in NSCLC. We use next-generation sequencing to match patients to appropriate targeted therapies on the basis of their tumour genotype. The Bayesian trial design enables outcome data from open cohorts that are still recruiting to be reported alongside data from closed cohorts. Of the 5,467 patients that were screened, 2,007 were molecularly eligible for entry into the trial, and 302 entered the trial to receive genotype-matched therapy—including 14 that re-registered to the trial for a sequential trial drug. Despite pre-clinical data supporting the drug–biomarker combinations, current evidence shows that a limited number of combinations demonstrate clinically relevant benefits, which remain concentrated in patients with lung cancers that are associated with minimal exposure to tobacco smoke

The Influence of Object Relative Size on Priming and Explicit Memory

We investigated the effects of object relative size on priming and explicit memory for color photos of common objects. Participants were presented with color photos of pairs of objects displayed in either appropriate or inappropriate relative sizes. Implicit memory was assessed by speed of object size ratings whereas explicit memory was assessed by an old/new recognition test. Study-to-test changes in relative size reduced both priming and explicit memory and had large effects for objects displayed in large vs. small size at test. Our findings of substantial size-specific influences on priming with common objects under some but not other conditions are consistent with instance views of object perception and priming but inconsistent with structural description views

Public Policy and Regulatory Challenges of Artificial Intelligence (AI)

Part 1: Artificial Intelligence and Autonomous SystemsInternational audienceArtificial Intelligence (AI) usage is rapidly expanding in our society. Private sector has already taken the leap of faith in using AI for efficiency and for generating better value for the customers and shareholders. The promise of AI is quite alluring for the governments as well. It promises to be the breakthrough technology which can catapult public sector to hitherto unseen efficiency and productivity. It has the potential to truly transform the public service delivery and the way government interfaces with citizens – from a demand driven model to a predictive model of public service delivery. However, there are a large number of pitfalls and blind-spots associated with AI, which make its adoption in government particularly challenging. For successful adoption of AI in public sector, governments must understand these challenges clearly and lay down regulatory public policies to ensure that the possible adverse impacts (such as exclusion, bias etc.) of AI are mitigated. This paper attempts to systematically explore these challenges with a view to enable public policy makers to respond to them