Dynamics of multiple resistance mechanisms in plasma DNA during EGFR‐targeted therapies in non‐small cell lung cancer

Research Article30 May 2018Open Access Transparent process Dynamics of multiple resistance mechanisms in plasma DNA during EGFR-targeted therapies in non-small cell lung cancer Dana Wai Yi Tsui Dana Wai Yi Tsui orcid.org/0000-0002-0595-6664 Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Muhammed Murtaza Muhammed Murtaza Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Department of Oncology, University of Cambridge, Cambridge, UK Search for more papers by this author Alvin Seng Cheong Wong Alvin Seng Cheong Wong Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Search for more papers by this author Oscar M Rueda Oscar M Rueda Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Christopher G Smith Christopher G Smith Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Dineika Chandrananda Dineika Chandrananda Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Ross A Soo Ross A Soo Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Cancer Science Institute, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore Search for more papers by this author Hong Liang Lim Hong Liang Lim Parkway Cancer Center, Singapore, Singapore Search for more papers by this author Boon Cher Goh Boon Cher Goh Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Cancer Science Institute, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore Search for more papers by this author Carlos Caldas Carlos Caldas Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Department of Oncology, University of Cambridge, Cambridge, UK Department of Oncology, Addenbrooke's Hospital, Cambridge University Health Partners, Cambridge, UK Search for more papers by this author Tim Forshew Tim Forshew Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Davina Gale Davina Gale Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Wei Liu Wei Liu Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author James Morris James Morris Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Francesco Marass Francesco Marass Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Tim Eisen Tim Eisen Department of Oncology, University of Cambridge, Cambridge, UK Department of Oncology, Addenbrooke's Hospital, Cambridge University Health Partners, Cambridge, UK Oncology Early Clinical Development, AstraZeneca, Cambridge, UK Search for more papers by this author Tan Min Chin Corresponding Author Tan Min Chin [email protected] orcid.org/0000-0002-3289-8498 Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Cancer Science Institute, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore Raffles Cancer Centre, Raffles Hospital, Singapore, SingaporeThese authors should be considered as (co-)senior authors and project co-leaders Search for more papers by this author Nitzan Rosenfeld Corresponding Author Nitzan Rosenfeld [email protected] orcid.org/0000-0002-2825-4788 Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UKThese authors should be considered as (co-)senior authors and project co-leaders Search for more papers by this author Dana Wai Yi Tsui Dana Wai Yi Tsui orcid.org/0000-0002-0595-6664 Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Muhammed Murtaza Muhammed Murtaza Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Department of Oncology, University of Cambridge, Cambridge, UK Search for more papers by this author Alvin Seng Cheong Wong Alvin Seng Cheong Wong Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Search for more papers by this author Oscar M Rueda Oscar M Rueda Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Christopher G Smith Christopher G Smith Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Dineika Chandrananda Dineika Chandrananda Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Ross A Soo Ross A Soo Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Cancer Science Institute, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore Search for more papers by this author Hong Liang Lim Hong Liang Lim Parkway Cancer Center, Singapore, Singapore Search for more papers by this author Boon Cher Goh Boon Cher Goh Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Cancer Science Institute, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore Search for more papers by this author Carlos Caldas Carlos Caldas Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Department of Oncology, University of Cambridge, Cambridge, UK Department of Oncology, Addenbrooke's Hospital, Cambridge University Health Partners, Cambridge, UK Search for more papers by this author Tim Forshew Tim Forshew Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Davina Gale Davina Gale Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Wei Liu Wei Liu Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author James Morris James Morris Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Francesco Marass Francesco Marass Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UK Search for more papers by this author Tim Eisen Tim Eisen Department of Oncology, University of Cambridge, Cambridge, UK Department of Oncology, Addenbrooke's Hospital, Cambridge University Health Partners, Cambridge, UK Oncology Early Clinical Development, AstraZeneca, Cambridge, UK Search for more papers by this author Tan Min Chin Corresponding Author Tan Min Chin [email protected] orcid.org/0000-0002-3289-8498 Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore Cancer Science Institute, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore Raffles Cancer Centre, Raffles Hospital, Singapore, SingaporeThese authors should be considered as (co-)senior authors and project co-leaders Search for more papers by this author Nitzan Rosenfeld Corresponding Author Nitzan Rosenfeld [email protected] orcid.org/0000-0002-2825-4788 Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Major Center - Cambridge, Cambridge, UKThese authors should be considered as (co-)senior authors and project co-leaders Search for more papers by this author Author Information Dana Wai Yi Tsui1,2,10,‡, Muhammed Murtaza1,2,3,11,12,‡, Alvin Seng Cheong Wong4, Oscar M Rueda1,2, Christopher G Smith1,2, Dineika Chandrananda1,2, Ross A Soo4,5, Hong Liang Lim6, Boon Cher Goh4,5, Carlos Caldas1,2,3,7, Tim Forshew1,2,13, Davina Gale1,2, Wei Liu1,2,14, James Morris1,2, Francesco Marass1,2,15,16, Tim Eisen3,7,8, Tan Min Chin *,4,5,9 and Nitzan Rosenfeld *,1,2 1Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK 2Cancer Research UK Major Center - Cambridge, Cambridge, UK 3Department of Oncology, University of Cambridge, Cambridge, UK 4Department of Haematology-Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore 5Cancer Science Institute, Centre for Translational Medicine, National University of Singapore, Singapore, Singapore 6Parkway Cancer Center, Singapore, Singapore 7Department of Oncology, Addenbrooke's Hospital, Cambridge University Health Partners, Cambridge, UK 8Oncology Early Clinical Development, AstraZeneca, Cambridge, UK 9Raffles Cancer Centre, Raffles Hospital, Singapore, Singapore 10Present address: Department of Pathology, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA 11Present address: Center for Noninvasive Diagnostics, Translational Genomics Research Institute, Phoenix, AZ, USA 12Present address: Mayo Clinic, Center for Individualized Medicine, Scottsdale, AZ, USA 13Present address: Inivata Ltd., Granta Park, Cambridge, UK 14Present address: University of Glasgow, Glasgow, UK 15Present address: Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland 16Present address: SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ‡These authors contributed equally to this work *Corresponding author. Tel: +65 63112306; E-mail: [email protected] *Corresponding author. Tel: +44 1223 769769; E-mail: [email protected] EMBO Mol Med (2018)10:e7945https://doi.org/10.15252/emmm.201707945 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Tumour heterogeneity leads to the development of multiple resistance mechanisms during targeted therapies. Identifying the dominant driver(s) is critical for treatment decision. We studied the relative dynamics of multiple oncogenic drivers in longitudinal plasma of 50 EGFR-mutant non-small-cell lung cancer patients receiving gefitinib and hydroxychloroquine. We performed digital PCR and targeted sequencing on samples from all patients and shallow whole-genome sequencing on samples from three patients who underwent histological transformation to small-cell lung cancer. In 43 patients with known EGFR mutations from tumour, we identified them accurately in plasma of 41 patients (95%, 41/43). We also found additional mutations, including EGFR T790M (31/50, 62%), TP53 (23/50, 46%), PIK3CA (7/50, 14%) and PTEN (4/50, 8%). Patients with both TP53 and EGFR mutations before treatment had worse overall survival than those with only EGFR. Patients who progressed without T790M had worse PFS during TKI continuation and developed alternative alterations, including small-cell lung cancer-associated copy number changes and TP53 mutations, that tracked subsequent treatment responses. Longitudinal plasma analysis can help identify dominant resistance mechanisms, including non-druggable genetic information that may guide clinical management. Synopsis Identification of molecular targets in oncology is pertinent. Our work shows that cDNA can detect response, progression, and switch of molecular drivers in EGFR mutant lung cancers, allowing a relatively non-invasive, real-time molecular profiling of the cancer, aiding treatment decisions. cDNA of EGFR mutation titres in EGFR mutant lung cancers should be monitored, as they may be predictive and prognostic. Serial monitoring of cDNA correlate well with clinical and radiological response and progression in a subset of patients who shed DNA into the circulation. Other molecular drivers such as p53 may affect the overall prognosis of EGFR mutant patients, and may be useful to monitor. In EGFR mutant transformed to small cell lung cancers, we report a marked change in the copy number changes of cDNA, and possibly a switch of molecular driver(s). Introduction Molecularly targeted therapies offer substantial clinical benefit in a subset of patients whose tumours harbour specific oncogenic drivers. Unfortunately, treatment resistance inevitably develops, partly driven by the evolving genetic landscape of cancer cells. For example, though non-small-cell lung cancer (NSCLC) patients carrying activating mutations in EGFR (epidermal growth factor receptor) initially respond to EGFR-targeted tyrosine kinase inhibitors (EGFR-TKIs; Lynch et al, 2004; Paez et al, 2004), the emergence of mutations that confer resistance to these TKIs or activate alternative drivers (such as EGFR T790M, MET/HER2 amplifications, PIK3CA mutation) leads to eventual drug resistance (Yu et al, 2013; Camidge et al, 2014). Some of these resistance mechanisms are targetable, such as T790M (Janne et al, 2015; Sequist et al, 2015) and MET amplification (Sierra & Tsao, 2011). Apart from such individual genetic changes, a small subset of EGFR-mutant NSCLC patients develop resistance to EGFR-TKI therapy by undergoing histological transformation to small-cell lung cancer (SCLC) and become sensitive to standard SCLC treatment (Sequist et al, 2011; Niederst et al, 2015). Therefore, longitudinal monitoring of the dynamic genetic changes during the course of a patient's treatment has become increasingly important to guide treatment at progression or when resistance occurs. Plasma circulating tumour DNA (ctDNA) is a non-invasive method that has been used to identify EGFR mutations and other genetic drivers in NSCLC and in response to treatment of NSCLC patients with EGFR-TKIs (Yung et al, 2009; Couraud et al, 2014; Douillard et al, 2014; Newman et al, 2014, 2016; Weber et al, 2014; Paweletz et al, 2015; Wan et al, 2017). During treatment of NSCLC patients with first-generation EGFR-TKIs, serial assessment of EGFR mutations in plasma ctDNA has proved successful in allowing early detection of T790M-driven resistance prior to radiographic progression (Oxnard et al, 2014; Mok et al, 2015). However, a subset of the patients develop resistance that is independent of the EGFR pathway, and multiple resistance mechanisms may co-exist because of tumour heterogeneity (Sequist et al, 2015; Abbosh et al, 2017). Here, we performed longitudinal analysis of plasma ctDNA to study the dynamics of co-existing multiple resistance mechanisms during sequential therapy in NSCLC patients. In this study, we analysed a cohort of 392 plasma samples collected longitudinally from 50 Stage IV NSCLC patients. All were treated with the first-generation TKI gefitinib in combination with hydroxychloroquine as part of the "Hydroxychloroquine and Gefitinib to Treat Lung Cancer" trial (NCT00809237). Thirty-four patients were TKI-naïve (i.e. not previously treated with EGFR-TKI), and 16 were TKI-treated (i.e. previously treated with TKI with a 2-week washout period). Eligibility for the trial and patient characteristics are summarized in the Appendix Supplementary Methods. This is a phase II study with a phase I lead in that studies the tolerability, safety profile and efficacy of hydroxychloroquine and gefitinib in advanced non-small-cell lung cancer. Appendix Fig S1 summarizes the number of patients in each arm (Appendix Fig S1). We performed tagged-amplicon deep sequencing (TAm-Seq; Forshew et al, 2012) for de novo identification and quantification of mutations in EGFR exons 18–21, coding regions of TP53 and PTEN, and selected hotspot regions of PIK3CA, KRAS and BRAF; and digital PCR for detection and quantification of hotspot mutations in EGFR. For a subset of patients, we also performed shallow whole-genome sequencing to analyse global copy number changes during treatment (Heitzer et al, 2013). Results Mutational profiling by plasma DNA To determine whether plasma was a good surrogate of EGFR mutation status in the tumour, we compared the EGFR mutation status in plasma samples (as determined by our assays) with the tumour status reported in hospital records. The EGFR status was known in the tumour of 43 of the 50 patients, and we detected the same EGFR mutation in any follow-up plasma samples of 41 of 43 (95%) patients (Fig 1A and Appendix Table S1). In the remaining seven patients, two were found to be EGFR wild-type in both tumour and plasma, and the remaining five have EGFR mutations detected in plasma. In 24 patients who responded to the treatment within the initial 70 days, 19 of them showed a drop in EGFR cfDNA levels within that period (Appendix Fig S2 and Appendix Table S2). In addition to EGFR, somatic mutations in other cancer genes, such as in TP53 or the PI3K/AKT/mTOR pathway (PIK3CA and PTEN), were also identified in the plasma of 29 patients (Fig 1B). Of the identified mutations, 25–43% are likely oncogenic (TP53, 10/23, 43%; PIK3CA, 3/7, 43%; PTEN, 1/4, 25%) according to OncoKB annotation (Chakravarty et al, 2017). To further compare molecular profiles between tumour and plasma, we studied paired tumour and plasma samples in four patients, where tumour samples were available before and after disease progression. The types of EGFR mutations identified in plasma and tumour (EGFR activating, resistance-conferring mutations in EGFR and other mutations) were identical for 11 of 12 (92%) mutations before treatment, and for 9 of 12 (75%) mutations after treatment (Appendix Fig S3 and Appendix Table S3). Plasma captured the same or more mutations than tumour in 23 of 24 cases (96%). These results confirmed that plasma analysis is informative for mutation profiling in NSCLC patients using our assays. Initial changes in EGFR ctDNA levels after start of treatment mirrored in most cases the radiographic assessment of clinical response. Figure 1. Summary of somatic mutations identified in the 50 NSCLC patients Detection of tumour EGFR mutations in plasma. EGFR mutation status in tumour samples was documented in the clinical record for 43 patients (Appendix Table S1), of which 38 had verified hotspot activating mutations (deletion in exon 19 for 23 patients and the L858R mutation for 15 patients), three patients had other mutations in EGFR (one of these patients had two different mutations detected in the tumour sample), and two patients were wild-type for EGFR according to tumour analysis and confirmed by plasma analysis. Summary of the mutations identified in any of the plasma samples during longitudinal follow-up in the 50 patients. TKI-naïve (n = 34) and TKI-treated (n = 16) patients are presented separately. Download figure Download PowerPoint Prognostic value of baseline plasma DNA We studied the relationship between pre-treatment EGFR ctDNA levels and prognosis in 19 TKI-naïve patients (Appendix Table S4), for which at least one plasma sample was collected before initiation of treatment. Patients with low levels of EGFR-activating mutations pre-treatment tended to have better progression-free survival (PFS) and overall survival (OS; Fig 2A and B), though this did not reach statistical significance level of 0.05 (their corresponding Cox P-values were 0.06 for both PFS and OS). Of note, patients with low levels of EGFR-activating mutation allele fractions had reduced tumour burden (median 17 mm) by RECIST measurements, as compared to those with intermediate (median 42 mm) and high (median 80 mm) levels of EGFR-activating mutation (Appendix Table S4). These findings suggest that baseline mutation concentrations in the plasma correlate with tumour burden. In addition, patients with both EGFR and TP53 detected in pre-treatment plasma tended to have worse prognosis (Fig 2C and D, Cox P-value 0.109 for PFS and 0.035 for OS). We repeated the analysis with copies/ml instead of mutant allele fractions, and the conclusions were the same. These data suggest that both the molecular profile of genomic alterations, and the quantification of ctDNA levels in baseline plasma, can have prognostic implications. Figure 2. Prognostic value of qualitative and quantitative assessments of pre-treatment ctDNA A, B. The relationship of pre-treatment EGFR-activating mutation levels (allele fractions) with progression-free survival (PFS) and overall survival (OS) of 19 first-line TKI-treated patients where baseline plasma samples (collected before the start of treatment) were available. Patients were grouped into three groups according to their pre-treatment ctDNA levels, as measured by EGFR-activating mutation allele fractions: low (< 25% quartile), intermediate (25–75% quartile) and high (> 75% quartile) ctDNA levels. Kaplan–Meier survival curves indicated that patients with high baseline pre-treatment EGFR-activating mutant allele fractions were non-significantly associated with unfavourable (A) PFS (log-rank P-value = 0.11) and (B) OS (log-rank P-value = 0.16), Cox P-value of 0.06 for either PFS or OS. C, D. The prognostic value of concurrent TP53 and EGFR mutations in pre-treatment plasma samples before EGFR-TKI therapy. This analysis was performed in 30 first-line EGFR-TKI patients where plasma samples were available within 2 months of start of treatment. The presence of both TP53 and EGFR mutations in plasma was associated with a trend of worse PFS (log-rank P-value = 0.109, hazard ratio and 95% confidence interval: 0.53 [0.24–1.17]) and significantly worse OS (log-rank P-value = 0.035, hazard ratio and 95% confidence interval: 0.43 [0.20–0.97]). Download figure Download PowerPoint Mutation dynamics in plasma DNA reveals heterogeneous resistance mechanisms For 45 of 50 patients, EGFR mutations were detected before treatment in tumour and/or plasma and more than one plasma sample was available from clinical follow-up. Longitudinal analysis of ctDNA in plasma revealed heterogeneity of resistance mechanisms (Fig 3A). During longitudinal follow-up, a large subset of patients retained the sensitizing mutation and developed resistance-conferring EGFR T790M mutation (n = 28/45, 62%, Fig 3B). To estimate the detection lead time (i.e. the interval between detection of the resistance-conferring mutation in plasma and radiographic evidence of disease progression), we focus on 28 patients where T790M was detected in plasma at any time during EGFR-TKI, including detection before disease progression became evident. In patients treated with first-line EGFR-TKI, we found that the median time-to-appearance of T790M in plasma was 4 months from the start of TKI treatment, with a lead time between T790M detection and clinical progression of 6.8 months. Patients with EGFR T790M can now be treated with third-generation, irreversible EGFR-TKIs (Janne et al, 2015; Piotrowska et al, 2015). One patient (220) had a biopsy of the lung tumour after progression, in which both activating EGFR exon 19 and T790M mutations were detected. The same mutations were detected in plasma at the time of progression. This patient was then treated with a third-generation EGFR-TKI (EGF-816, Novartis (NCT02108964)) and demonstrated partial radiological response. Subsequent plasma samples showed no further EGFR mutations (data shown in Dataset EV1). Figure 3. Longitudinal analysis of ctDNA dynamics reveals distinct patterns of resistance mechanisms Longitudinal analysis of ctDNA dynamics in 45 NSCLC patients revealed three main groups of concurrent heterogeneous resistance mechanisms. In the first group (n = 28/45, 62%), patients retained EGFR-sensitizing mutations before and after disease progression, with the development of T790M in their plasma samples, indicating that at least some of the progressing clones developed resistance to TKI by acquiring T790M. In the second group (n = 10/45, 22%), patients retained EGFR-sensitizing mutations but progressed without developing T790M in their plasma samples, suggesting that resistance arose due to other mechanisms which were not analysed in this dataset. In the third group (n = 7/45, 15%), patients progressed without EGFR-sensitizing nor resistance-conferring T790M mutations detected in their plasma samples. Resistance possibly develops through dependence on alternative cancer driver pathways. Data information: For patients where multiple EGFR-activating mutations were identified in plasma, only the most abundant one is shown here (complete data for all patients are shown in Dataset EV1). Clinical progression and CT imaging times are indicated with a dotted line, with RECIST classification: SD, stable disease; PR, partial response; PD, progressive disease. Progressive disease defined by presentation of symptoms on brain or bone scan is indicated by PD**. Download figure Download PowerPoint In a second group of patients (n = 10/45, 22%), the activating EGFR mutation was detected in plasma before and after progression, with an average mutant allele fraction (AF, i.e. the fractional concentration of mutant allele over total DNA) of 7.9%, but not T790M (Fig 3C). The continued presence of activating mutations in plasma suggests possible positive selection of the mutations in the EGFR pathway in the corresponding cancers. In these patients, mutations in other pathways also emerged in plasma, such as TP53 and PIK3CA (Dataset EV1). One possible hypothesis is that tumours of patients in this group may retain partial sensitivity to EGFR-TKI treatment, and may respond clinically if EGFR-TKI is used in combination with treatments targeting additional resistance pathway. The third group of patients (n = 7/45, 15%) did not have EGFR-activating nor known resistance-conferring mutations in EGFR detected in plasma when they progressed. These patients initially had exon 19 deletion detected in the tumour (7/7) and their first plasma sample (6/7). Interestingly, comparing to the other two groups, this group of patients had EGFR-activating mutations present at relatively lower allele fractions in their first plasma samples [groups 1 and 2: median EGFR mutations mutant allele fractions was 3% (range: 0.07–65.7%) versus group 3: median 0.23% (range: 0.06–2.11%)]. We do not rule out the possibility that the tumours of these patients might release less tumour-derived DNA into the circulation. In some of these patients, we detected alternative cancer mutations such as TP53 and PIK3CA in plasma before treatment was initiated, and the levels of these mutations then increased to present the highest allele fractions in ctDNA when disease progressed (Fig 3D). We speculate that one possible explanation for the absence of EGFR mutations in cfDNA at disease progression could be that, EGFR mutations were subclonal in those patients initially, and under the selective pressure of the EGFR-targeting therapy, the EGFR-driven clones shrank below detection limit of the assay, while clones that were driven by alterative drivers (such as TP53 and PIK3CA) and did not carry the EGFR-sensitizing mutations, expanded. Based on our data from cfDNA, these alternative drivers pre-existed even before treatment initiation, but were present in parts of the tumour that were not analysed, or alternatively were present at very low cellularity such that their allele fractions in those samples were below the detection limit by standard clinical tumour sequencing assay. Recent data from tumour sequencing suggested EGFR may be subclonal in a