The National Lung Matrix Trial of personalized therapy in lung cancer

Abstract

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 smoke 1-3. 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. Bayesian design for timely data release The trial uses a Bayesian adaptive design 5 to determine whether there is sufficient evidence of activity in any cohort to warrant further

A framework for mutational signature analysis based on DNA shape parameters

“…52,53 This might entail ascertaining existence of certain driver mutations that can be matched to targeted drugs (classically EGFR or BRAF mutations or HER2 amplifications, but many more are investigated). 54 However another genetic marker of rising importance are genomic profiles of mutational signatures. Because these reflect ongoing genomic instability, which is common in tumors, they could help better stratify patients for targeted therapies, as exemplified in signatures of deficiency in DNA mismatch repair (MMR) nominating patients for immunotherapy.…”

“…17,18 In addition, mutational analysis might conceivably predict when resistance to therapy arises; for example, given that the error-prone DNA polymerase eta can overcome treatment by cisplatin, 57 identification of its mutational signatures – previously via DNA sequence features 43 – but possibly also by the affinities of this error-prone DNA polymerase(s) towards DNA shape features – could inform treatment decisions. In summary, there is great promise for clinical use genomic markers in tumors even though many mechanisms remain elusive, 54 and among such genomic markers, the utility of mutational processes in particular may merit more attention.…”

Organotypic Modeling of the Tumor Landscape

“…Even with strong supporting preclinical evidence, many targeted therapies produce modest clinical results, a fact now highlighted by the tremendous National Lung Matrix Trial that assessed personalized medicine in non-small cell lung cancer (NSCLC) (Middleton et al, 2020). The results have been fairly disappointing with a response rate of only 10% with some abandons due to lack of treatment efficacy.…”

Bridging “Big Data” and Mechanistic Insight To Enable Precision Medicine

“…This step is challenging because it can be difficult to establish what part of laboratory data are clinically relevant. In the short term, opportunities exist in on‐going clinical trials that establish molecular correlates with patient response (analogous to Figure 2A) [9a,17] . In the long term, new datasets based on ex vivo technologies, like tumor organoids, can bridge this knowledge gap by making laboratory data maximally relevant to primary tumors.…”