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 smoke1-3. 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.

Conflict of interest statement

Competing interests

G.M. reported receiving research funding from Bristol-Myers Squibb, Kael-Gemvax, Merck Sharp & Dome and Plexxikon, and personal fees from BioLineRx, Boehringer Ingelheim, Bristol-Myers Squibb, Merck Sharp & Dome and Roche. S.P. reported receiving research funding from Boehringer Ingelheim, Epizyme, Bristol-Myers Squibb, Clovis Oncology, Roche, Eli Lilly and Takeda, and personal fees from Boehringer Ingelheim, AstraZeneca, Roche, Takeda, Chugai Pharma, Merck Sharp & Dohme, Bristol-Myers Squibb, EMD Serono, Abbvie, Guardant Health, Pfizer and Novartis. J. Savage reported receiving personal fees from Eli Lilly. Y.S. reported personal fees from Roche, AstraZeneca, Takeda, Pfizer and Merck Sharp & Dohme. A.G. reported personal fees from Pfizer, Boehringer Ingelheim, Merck Sharp & Dohme, Novartis, AstraZeneca, Bristol-Myers Squibb, Takeda, Abbvie, Roche and Foundation Medicine. D.G. reported receiving personal fees from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Genesis Care UK, Pfizer, Roche, Takeda and Merck Sharp & Dohme and non-financial competing interests with Roy Castle Lung Cancer Foundation. E.T. reported receiving personal fees from Roche, Pfizer, Boehringer Ingelheim and Merck Sharp & Dohme. J. Spicer reported receiving research funding from Starpharma, Taiho Pharmaceutical, Bristol-Myers Squibb, Roche, BerGenBio, Genmab and Curis, personal fees from Bristol-Myers Squibb, Lytix Biopharma and IObiotech and owning stocks and shares in IGEA. P.J. reported receiving personal fees from AstraZeneca, Boehringer Ingelheim, Eli Lilly and Pfizer. T.Y. reported research funding from AstraZeneca, Vertex, Pfizer, Bayer, Tesaro, Jounce Therapeutics, Eli Lilly, Seattle Genetics, Kyowa Hakko Kirin, Constellation Pharmaceuticals and personal fees from AstraZeneca, Pfizer, Tesaro, EMD Serono, Vertex, Seattle Genetics, Roche, Janssen, Clovis Oncology, Ignyta, Atrin Pharmaceuticals, Aduro Biotech, Merck Sharp & Dohme, Almac Group, Bayer, Bristol-Myers Squibb, Calithera Biosciences and Cybrexa Therapeutics. C.S. reports receiving research funding from Boehringer Ingelheim and personal fees from Genentech, Roche, Sarah Cannon Research Institute, Boehringer Ingelheim, GlaxoSmithKline, Eli Lilly, Celgene, Ono Pharmaceutical, SERVIER, Pfizer, Bristol-Myers Squibb, Novartis, AstraZeneca and Ilumina and owning stocks and shares in Epic Sciences, Apogen Biotechnologies, GRAIL and Achilles Therapeutics. J.C. reported receiving personal fees from Novartis, Eli Lilly and Boehringer Ingelheim. A.F. reported receiving research funding from Ethicon (Johnson and Johnson). R.S., T.C.M. and M.C. were employed by Cancer Research UK who fund the study. L.B. reports receiving personal fees from AstraZeneca, Novartis and Springer. The other authors declare no competing interests.

Figures

Extended Data Fig. 1. Reasons for attrition and median testing turnaround time in the SMP2 study.

a, Reasons why patients enrolled in SMP2 did not enter the NLMT were collected for a subset of patients (N=1433). PD, progressive disease; 1L, first-line treatment; 2L, second-line treatment; 3L, third-line treatment. b, Median turnaround time (TAT) of SMP2 testing. Turnaround time was measured in days from the 18 SMP2 clinical sites that recruited patients. This consists of the median time from when informed consent was received from the patient to enter SMP2 to the tissue sample being sent for testing (grey bars) and from receipt of the tissue sample at the SMP2 technical hubs to the release of the SMP2 screening report (orange bars).

Extended Data Fig. 2. Heat map of all 28 genes for patients registered to the 19 reported cohorts in NLMT.

Detailed 28-gene NGS panel results were available for 283 patients included in the reported analysis, organized by molecular cohort and drug treatment. Green elements indicate wild-type or tier 3 aberration, red indicates a tier 1 or tier 2 aberration, and black a fail.

Extended Data Fig. 3. Posterior probability distribution plots by cohort for median PFS in months, DCB rate and OR rate.

Plots show the posterior probability distribution for true values of the relevant outcome measure, given the prior probability distribution and the observed data. The blue dotted line indicates the median of the posterior distribution. Given the prior and the observed data, there is an equal probability that the true value is greater than or less than the median value. Median PFS uses an inverse-gamma distribution with a prior of IG(0.001, 0.001), which provides minimal information and hence the posterior is dominated by the observed data in the trial. DCB and OR rate use a beta distribution, with a prior of Beta(1,1), which attributes equal probability to all possible rates of response from 0%–100%, and contributes data to the posterior equivalent to two trial patients. This will therefore be more influential at early stages of recruitment, but as more patients contribute their results the posterior will be dominated by the trial data. Bayesian estimates and 95% credible intervals for the true median PFS, DCB rate and OR rate are generated from these posterior probability distributions, together with PP and PPoS.

Extended Data Fig. 4. Waterfall plots for 19 drug–biomarker cohorts from NLMT.

Plots are grouped according to 4 genomic modules of genomic aberrations showing, for each patient, the best percentage change in sum of target lesion diameters according to RECIST.

Extended Data Fig. 5. PIK3CA amplifications in the TCGA LUAD and LUSC cohort.

a, Bar plot of oncogenes that are in close genomic proximity to PIK3CA and are gained or amplified (co-amplified) on the same SCNA segment with PIK3CA. The height of the bars represents the number of tumours that have the particular oncogene co-amplified with PIK3CA. b, Heat map indicating whether oncogenes are co-amplified on the same SCNA segment with PIK3CA. In a, b, genes are ordered on the basis of genomic location. Dark pink shading indicates that the corresponding SCNA segment was amplified, whereas green shading indicates that the corresponding SCNA segment was gained. c, Density plot indicating the frequency of the sizes of SCNA segments harbouring an amplification or gain involving PIK3CA. The distribution representing LUAD cases is indicated in red, while the distribution representing LUSC cases is indicated in blue. d, Bar plot indicating the sizes of the SCNA segments harbouring the PIK3CA amplification/gain for each TCGA case. Bars are coloured according to cancer type (red, LUAD; blue, LUSC). SCNA segments in b, d are in the same order. Only TCGA cases with a PIK3CA gain or amplification (n = 524 of 1,010) are included in this plot.

Extended Data Fig. 6. PIK3CA amplifications in the TRACERx 100 cohort.

a, Bar plot of oncogenes which are in close genomic proximity to PIK3CA and are gained or amplified (co-amplified) on the same SCNA segment with PIK3CA. The height of the bars represented the number of tumours that have the particular oncogene co-amplified with PIK3CA. b, Heat map indicating whether oncogenes are co-amplified on the same SCNA segment with PIK3CA. In a, b, genes are ordered on the basis of genomic location. The shading in the heat map indicates the type of SCNA affecting the genomic segment encompassing PIK3CA. As TRACERx data are multi-regional, some segments are assigned two different SCNAs (for example, “gain_neutral” indicates that this case harboured a subclonal gain in PIK3CA, where the gain was observed in some regions of that tumour, whereas other regions of that tumour were copy-number neutral at the same locus). c, Density plot indicating the frequency of the sizes of SCNA segments harbouring an amplification or gain involving PIK3CA. The distribution representing LUAD cases is indicated in red, the distribution representing LUSC cases is indicated in blue, and the distribution representing other NSCLC is indicated in green. d, Bar plot indicating the sizes of the SCNA segments harbouring the PIK3CA gain or amplification for each TRACERx case. Bars are coloured according to cancer type (red, LUAD; blue, LUSC; green, other NSCLC). SCNA segments in b, d are in the same order. Only TRACERx cases with a PIK3CA gain or amplification (n = 45 of 100) are included in this plot.

Fig. 1. Scheme of the umbrella phase II NLMT, recruiting patients with advanced NSCLC.

Patients are stratified using the 28-gene NGS panel test results from SMP2, and the trial is currently testing 8 different targeted drugs (A–H) in 22 different actionable biomarker cohorts. The trial also includes a cohort of patients with no actionable aberrations (NA).

Fig. 2. Flow diagram showing the progress of patients through SMP2 and NLMT arms A–H as of 30 November 2019.

Fig. 3. Estimates of primary outcome measures for 19 drug-biomarker cohorts in NLMT grouped according to 4 modules of genomic aberrations.

Forest plots show Bayesian estimates and 95% credible intervals for true values of median PFS, DCB rate and OR rate. Purple is used to highlight estimates for which PFS is the primary outcome measure, with vertical lines showing a pre-specified clinically relevant target of a median PFS of 3 months. Green or blue is used to highlight estimates for which DCB and OR rates are co-primary outcome measures, with vertical lines showing pre-specified clinically relevant target rates of 40% or 30% respectively. Cohorts that are closed to recruitment are represented by solid lines and those still open are represented by dashed lines. Bayesian estimates are the medians of the posterior probability distributions derived from the current data and minimally informative priors. Because the trial is ongoing and the follow-up is not complete (including in some closed cohorts), these estimates are subject to change as the trial continues.

Fig. 4. OR rates and best percentage change in sum of target lesion diameters across 19 reported cohorts in NLMT according to smoking history and histology.

a, Top, waterfall plot shows, for each patient, the best percentage change in sum of target lesion diameters according to RECIST v.1.1. Bars are coloured according to the patient’s smoking history. Patients who discontinued before assessment, or had a value greater than 100% were capped at 100%. Bottom, forest plots show Bayesian estimates (with 95% credible intervals) of OR rates, grouped according to the smoking history of the patients. b, As for a, but coloured and grouped according to the histology of the tumour.