Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The National Lung Matrix Trial of personalized therapy in lung cancer

A Publisher Correction to this article was published on 05 September 2020

This article has been updated


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.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Scheme of the umbrella phase II NLMT, recruiting patients with advanced NSCLC.
Fig. 2: The progress of patients through SMP2 and NLMT arms A–H.
Fig. 3: Estimates of primary outcome measures for 19 drug–biomarker cohorts in NLMT grouped according to 4 modules of genomic aberrations.
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.

Data availability

For NLMT data, scientifically sound proposals from appropriately qualified Research Groups will be considered for data sharing. Requests should be made by returning a completed Data Sharing Request Form and curriculum vitae of the lead applicant and statistician to The Data Sharing Request Form captures information on the specific requirements of the research, the statistical analysis plan, and the intended publication schedule. The request will be reviewed independently by the Cancer Research UK Clinical Trials Unit (CRCTU) Directors at University of Birmingham in discussion with the Chief Investigator and relevant Trial Management Group and independent Trial Steering Committee. In making their decision the Director’s Committee will consider the scientific validity of the request, the qualifications of the Research Group, the views of the Chief Investigator, Trial Management Group and Trial Steering Committee, consent arrangements, the practicality of anonymizing the requested data and contractual obligations. Where the CRCTU Directors and appropriate Trial Committees are supportive of the request, and where not already obtained, consent for data transfer will be sought from the Sponsor of the trial before notifying the applicant of the outcome of their request. It is anticipated that applicants will be notified of a decision within 3 months of receipt of the original request. The results published here are based in part on data generated by TCGA pilot project established by the NCI and the National Human Genome Research Institute. The data were retrieved through database of Genotypes and Phenotypes (dbGaP) authorization (accession number: phs000178.v10.p8). TRACERx sequencing datasets used in this study are described in Hanjani et al.11 and Abbosh et al.32.

Code availability

NLMT statistical analysis code is available for download from the Github repository (

Change history


  1. 1.

    Lynch, T. J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).

    CAS  Article  Google Scholar 

  2. 2.

    Bergethon, K. et al. ROS1 rearrangements define a unique molecular class of lung cancers. J. Clin. Oncol. 30, 863–870 (2012).

    CAS  Article  Google Scholar 

  3. 3.

    Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

    CAS  Article  Google Scholar 

  4. 4.

    Carr, T. H. et al. Defining actionable mutations for oncology therapeutic development. Nat. Rev. Cancer 16, 319–329 (2016).

    CAS  Article  Google Scholar 

  5. 5.

    Berry, S. M., Carlin, B. P., Lee, J. J. & Muller, P. Bayesian Adaptive Methods for Clinical Trials (CRC, 2010).

  6. 6.

    Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    CAS  Article  Google Scholar 

  7. 7.

    The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    ADS  Article  Google Scholar 

  8. 8.

    The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

    ADS  Article  Google Scholar 

  9. 9.

    Koselugo (selumetinib) approved in US for paediatric patients with neurofibromatosis type 1 plexiform neurofibromas. (AstraZeneca, 2020).

  10. 10.

    Schmid, P. et al. Capivasertib plus paclitaxel versus placebo plus paclitaxel as first-line therapy for metastatic triple-negative breast cancer: the PAKT trial. J. Clin. Oncol. 38, 423–433 (2020).

    CAS  Article  Google Scholar 

  11. 11.

    Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    CAS  Article  Google Scholar 

  12. 12.

    de Bruin, E. C. et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014).

    ADS  Article  Google Scholar 

  13. 13.

    Kim, I. A., Lee, J. S., Kim, H. J., Kim, W. S. & Lee, K. Y. Cumulative smoking dose affects the clinical outcomes of EGFR-mutated lung adenocarcinoma patients treated with EGFR-TKIs: a retrospective study. BMC Cancer 18, 768 (2018).

    Article  Google Scholar 

  14. 14.

    Offin, M. et al. Tumor mutation burden and efficacy of EGFR-tyrosine kinase inhibitors in patients with EGFR-mutant lung cancers. Clin. Cancer Res. 25, 1063–1069 (2019).

    CAS  Article  Google Scholar 

  15. 15.

    McFadden, D. G. et al. Mutational landscape of EGFR-, MYC-, and Kras-driven genetically engineered mouse models of lung adenocarcinoma. Proc. Natl Acad. Sci. USA 113, E6409–E6417 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Westcott, P. M. et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517, 489–492 (2015).

    ADS  CAS  Article  Google Scholar 

  17. 17.

    Liao, R. G. et al. Inhibitor-sensitive FGFR2 and FGFR3 mutations in lung squamous cell carcinoma. Cancer Res. 73, 5195–5205 (2013).

    CAS  Article  Google Scholar 

  18. 18.

    Castiglione, R. et al. Comparison of the genomic background of MET-altered carcinomas of the lung: biological differences and analogies. Mod. Pathol. 32, 627–638 (2019).

    CAS  Article  Google Scholar 

  19. 19.

    Gautschi, O. et al. Targeting RET in patients with RET-rearranged lung cancers: results from the global, multicenter RET registry. J. Clin. Oncol. 35, 1403–1410 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    Subbiah, V. et al. Precision targeted therapy with BLU-667 for RET-driven cancers. Cancer Discov. 8, 836–849 (2018).

    CAS  Article  Google Scholar 

  21. 21.

    Morgan, P. et al. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving phase II survival. Drug Discov. Today 17, 419–424 (2012).

    CAS  Article  Google Scholar 

  22. 22.

    Basu, B. et al. First-in-human pharmacokinetic and pharmacodynamic study of the dual m-TORC 1/2 inhibitor AZD2014. Clin. Cancer Res. 21, 3412–3419 (2015).

    CAS  Article  Google Scholar 

  23. 23.

    Rothwell, D. G. et al. Utility of ctDNA to support patient selection for early phase clinical trials: the TARGET study. Nat. Med. 25, 738–743 (2019).

    CAS  Article  Google Scholar 

  24. 24.

    Pengelly, R. J. et al. A SNP profiling panel for sample tracking in whole-exome sequencing studies. Genome Med. 5, 89 (2013).

    Article  Google Scholar 

  25. 25.

    Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    CAS  Article  Google Scholar 

  26. 26.

    Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).

  27. 27.

    Thall, P. F., Wooten, L. H. & Tannir, N. M. Monitoring event times in early phase clinical trials: some practical issues. Clin. Trials 2, 467–478 (2005).

    Article  Google Scholar 

  28. 28.

    Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).

    ADS  Article  Google Scholar 

  29. 29.

    Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).

    CAS  Article  Google Scholar 

  30. 30.

    Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007).

  31. 31.

    Cheng, J. et al. Single-cell copy number variation detection. Genome Biol. 12, R80 (2011).

  32. 32.

    Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

Download references


The Investigators and Sponsor thank all the patients and their families who participated in this trial, as well as the NHS Trusts and staff and the members of the Trial Steering Committee, chaired by R. Kaplan and including independent members K. Kerr, C. Dick, J. Paul and J. Wason, who supported this trial. We thank our patient and public representatives, M. Baker on the Trial Steering Committee and T. Haswell on the Trial Management Group. We are grateful for the support of the Trial Management Group, including G. Cook, A. Desai, A. Devaraj, D. Fennell, M. Griffiths, S. Janes, S. M. Lee, U. McDermott, A. Nicholson, P. Rehal, S. Subramaniam, P. Taniere, Y. Wallis and D. White and for the support of principal investigators P. Shaw (Velindre Cancer Centre), C. Lewanski and D. Power (Imperial College Healthcare NHS Trust) and S. Danson (Weston Park Cancer Centre). We thank all staff at the Cancer Research UK Clinical Trials Unit for their contributions to the trial, including A. Allen, K. Brock, L. Crack, K. Davies, K. James, S. Mee, I. Nutt and S. Ordish. This is an investigator-initiated and investigator-led trial funded by CRUK grant C11497/A19363 and C11497/A22209. Trial drugs were supplied free of charge by AstraZeneca (AZD4547, vistusertib, selumetinib, capivasertib, osimertinib and durvalumab), Pfizer (palbociclib and crizotinib) and Mirati (sitravatinib), who also supported the programme through the funding of SMP2. The Cancer Research UK Clinical Trials Unit at the University of Birmingham is supported by CRUK grant C22436/A25354. The National Lung Matrix Trial was supported by Experimental Cancer Medicine Centres (ECMC) funding and by the ECMC Network. This trial has been independently peer reviewed and has been adopted by the National Institute for Health Research Clinical Research Network Portfolio.

Author information




G.M., L.B. and P.F. designed the study. All analysis was performed by G.M., P.F. and L.B., with the exception of SMP2 data (Extended Data Fig. 1), which was analysed by T.C.M. and M.A.C., and TCGA and TRACERx data processing (Extended Data Figs. 5, 6), which was performed by E.L. and T.B.K.W. Interpretation of data was performed by G.M., L.B., P.F., J. Savage and C.S. The trial was managed by G.M., J. Savage, D.W., M. Mehmi, R.S., A.F. and S.P. The manuscript was written by G.M., L.B., P.F., J. Savage, T.C.M. and M.A.C. Patient recruitment was carried out by G.M., S.P., Y.S., A.G., D.G., J.C., N.O., A.B., E.T., J. Spicer, P.J., A.D., M. Mackean and M.F. Lead Arm Investigators were G.M., S.P., T.A.Y., Y.S. and J. Spicer. All authors critically reviewed the manuscript.

Corresponding author

Correspondence to Gary Middleton.

Ethics declarations

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.

Additional information

Peer review information Nature thanks Maarten van Smeden and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

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 NLMT were collected for a subset of patients (n = 1,433). 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 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. 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.

Extended Data Table 1 Baseline characteristics for patients recruited to targeted treatment arms in NLMT
Extended Data Table 2 Details of targeted drugs included in the NLMT
Extended Data Table 3 Primary outcome measures for each drug–biomarker cohort in NLMT
Extended Data Table 4 Adverse reactions by treatment arm in NLMT

Supplementary information

Supplementary Information

This file contains pre-clinical rationale for triaging participants to genotype-matched targeted therapies in the NLMT and Protocol version 8.0, in use on 30 November 2019.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Middleton, G., Fletcher, P., Popat, S. et al. The National Lung Matrix Trial of personalized therapy in lung cancer. Nature 583, 807–812 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing