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1 Supplementary Figure 1 Genomic alterations detectable in cfdna of EGFR-mutant p.t790m-positive and p.t790m-negative patients. (a-b) Lolliplots of gene level alterations in EGFR-mutant p.t790m mutant positive compared to EGFR-mutant p.t790m mutant negative samples. Alterations in AR and PDGFRA in cfdna of EGFR-mutant p.t790m positive (n=440) and EGFR-mutant p.t790m mutant negative (n=682) are indicated. (c) Concurrent genomic alterations detectable in EGFR-mutant NSCLC patients encoding the EGFR p.c797s mutation. Frequency of non-synonymous genomic alterations of known or predicted functional significance: single nucleotide variants (SNV), copy number gains (CNG), insertions or deletions (INDEL), or gene rearrangements (FUSION) in cancer-related genes detectable by next-generation sequencing of circulating tumor DNA are indicated.

2 Supplementary Figure 2 Effects of demographic variables on genomic alterations detectable in cfdna of advanced EGFR-mutant patients with NSCLC. (a) Effect of age on number of alterations detected in circulating cfdna from 137 samples from 97 patients. Mean patient age = 64. Graph shows number of non-synonymous alterations detectable in plasma samples from patient aged less than 65 (n=61) compared to age 65 or greater (n=76). Mean ± S.E.M. indicated. P-value determined by two-tailed, unpaired T-Test with Bonferroni correction (t=1.978, df=135). (b) Number of cfdna alterations detectable in plasma samples by gender. Female (n=87), male (n=50). Mean ± S.E.M. indicated. P-value determined by two-tailed, unpaired T-Test with Bonferroni correction (t=0.7072, df=135). (c) Number of cfdna alterations detectable in plasma samples by smoking status: never (n=99), former (n=31). Mean ± S.E.M. indicated. P-value determined by two-tailed unpaired T-Test with Bonferroni correction (t=0.2455, df=128). (d) Number of non-synonymous and predicted functional genomic alterations detectable in cfdna from 73 patients with known clinical/radiographic response to subsequent EGFR TKI treatment. The number of alterations in patients who subsequently responded (radiographic PR by clinician assessment, see Online Methods) to TKI treatment (n=37) was compared to non-responders (radiographic SD or PD, see methods, n=36). Mean ± S.E.M. indicated, p-value determined by two-tailed, unpaired T-Test with Bonferroni correction (t=5.439, df=71).

3 Supplementary Figure 3 Effects of detectable cfdna alterations on EGFR TKI clinical response. (a-b) Somatic mutations (SNV or Indel) (blue) of known or predicted functional significance (Methods) and copy-number gains (red), or both (pink) detected in patients who responded, n = 37 (a) or did not respond, n = 36 (b) to subsequent EGFR TKI treatment (see Online Methods). (c) Forest plot demonstrating effect of cfdna detectable gene level alterations on EGFR TKI PFS determined by Coxproportional Hazard Ratio (HR) with 95% CI. (d) Kaplan-Meier curves demonstrating difference in median PFS to EGFR TKI treatment (logrank test) in patients with cfdna detectable alterations in CDK4 or CDK6. (e) Pathway level alterations detectable in cfdna of patients who responded to subsequent EGFR TKI treatment (see methods) versus patients who did not respond (see methods). Q- values determined by two-tailed Fisher s Exact test with Benjamini-Hochbeg correction for multiple hypothesis testing. (f-h) Forest plot and Kaplan-Meier curves assessing the effects of indicated cfdna detectable pathway alterations on PFS. See also Supplementary Data Sets 3 and 4.

4 Supplementary Figure 4 Effects of detectable cfdna alterations on overall survival. (a-d) Forest plots assessing the effects of indicated cfdna detectable gene and pathway alterations on OS. (a-b) Effect of gene level (a) and pathway level (b) alterations on OS in response to EGFR TKI treatment. HR with 95% CI and p-values determined by Coxproportional regression test (methods). (c-d) Effect of gene level (c) and pathway level (d) alterations on OS in response to osimertinib treatment. HR with 95% CI and p-values determined by Cox-proportional regression test (Online Methods).

5 Supplementary Figure 5 Patient-level evolution of genomic alterations detected in cfdna of advanced-stage patients with EGFR-mutant NSCLC. (a-g) cfdna alterations detectable by clinical assay in patients with EGFR mutations. Mutant allele frequency of detected cfdna alterations are indicated, as is line of therapy and clinical response pre-and post-assay. Computed-tomography (CT) scans of the chest and abdomen are shown, demonstrating sites of metastases (red arrows). Scale bar = 5 cm, ND (no alteration detected). PR (Partial Response), PD (Progressive Disease), SD (Stable Disease). Plasma copy number gain (CNG) of is reported as + and as ++ (see Online Methods).

6 Supplementary Figure 6 Clinical, radiographic, and histopathologic longitudinal analysis of a patient with EGFR-mutant NSCLC. (a) Radiographic images (computed tomography (CT) scans Chest) and sites of tissue acquisition (tumor site indicated by red arrow) from EGFR-mutant lung cancer patient at the time initial presentation, followed by surgical resection of EGFR-mutant lung adenocarcinoma (right lung upper lobectomy, R1), at the time of development of metastatic disease (mediastinal lymph node metastasis, R2), upon progression to first line treatment with erlotinib (left lung metastases core needle biopsy, R3), and at autopsy after treatment with the 2 nd line EGFR TKI rociletinib followed by PD and death (left lung metastasis, R4; right rib metastasis, R5; right lung metastasis, R6; spine metastasis, R7). Treatment immediately prior to imaging is indicated. Clinician assessment of radiographic response is indicated (PR Partial Response, SD Stable Disease, PD Progressive Disease). Clinical histopathological diagnosis is indicated (LUAD = lung adenocarcinoma). Scale bar = 5 cm. (b) Representative images of hematoxylin and eosin (H&E) stained histopathological specimens obtained from surgery, biopsy, or autopsy as described in (a). Clinical histopathological assessment of all specimens revealed LUAD with no evidence of small cell lung cancer transformation. Scale bar = 50 microns.

7 Supplementary Figure 7 Copy number analysis of EGFR-mutant lung adenocarcinoma tumor specimens from patients. Each panel represents allele specific floating point copy number state profile for an individual sample for all autologous chromosomes. Arm level GISTIC events for lung adenocarcinoma are indicated on relevant chromosomal arms by the background color, red for a significant arm gain, blue for a significant arm loss. Genes of interest are annotated along the bottom of each panel. Regions of chromosomal loss or gain were determined by analysis of whole-exome sequencing (see Online Methods) of the indicated samples as described in Figure 5. Cancer-related genes in regions of copy-number gain of than 1.5X ploidy (Blue) or copy-number loss of 0.5X ploidy (Red) are indicated.

8 Supplementary Figure 8 Schematic of clonal evolution in a patient with EGFR-mutant lung adenocarcinoma. Top panel displays the subclonal phylogeny over the course of the patient s disease with the two posited independent origins of the EGFR p.t790m SNV. Bottom panel shows the region specific subclonal phylogenies also shown in figure 5c. Below each sample phylogeny there is a beehive plot containing 100 hexagons that each represent 1% of the cells present in a sample. These illustrate the relationships between subclonal clusters by displaying their cellular prevalence in that sample. In a subclonal phylogeny each cluster linked to another is present in a subset of the cells containing the previous cluster. Therefore populations of cells will contain the mutations from multiple clusters and these can be considered subclones. The subclones present in a region are indicated to the right of the subclonal phylogeny for a sample and may contain multiple clusters of mutations. The prevalence of a subclone in each sample can be inferred from the proportion of hexagons the clusters that it consists of occupy.

9 Supplementary Figure 9 Relative frequency of co-occurring genomic alterations in early- versus late-stage EGFR-mutant NSCLC. Data available from The Cancer Genome Atlas (TCGA) were analyzed to determine the relative frequency of co-occurring alterations detectable in TP53, SMAD4, CTNNB1, and CDK6 in early (stage I or II) versus late (stage III or IV) EGFRmutant NSCLC. P-values determined by two-tailed Fischer s exact test. DEL (Deletion), INS (Insertion), SNP (Single Nucleotide Polymorphism).

10 Supplementary Figure 10 Prediction of mutational clonality determined by cfdna analysis. (a) Relative mutant allele frequency compared to maximum mutant allele frequency (corrected for copy-number) from the case presented in Fig. 5. The algorithm described in Online Methods was applied to predict clonality of the six mutational alleles. Based on the distribution of percentage of Max-maf (corrected for copy number gain, the cut-off of < 0.2 was defined as subclonal, >=0.2 as clonal. (b) Clonal/subclonal calls based on cfdna analysis were compared to whole exome sequencing of tumor samples (Fig. 5a). This method demonstrated 100% sensitivity and specificity when comparing cfdna clonality assessments to tumor clonality assessments for the 6 mutant alleles analyzed.

11 Supplementary Note Introduction: Specific therapeutically-targetable somatic genetic alterations drive the growth of many cancers, including non-small cell lung cancer (NSCLC), the major subtype of lung cancer, the leading cause of cancer mortality 1-9. An example of the impact of such oncogenic driver alterations is the improved outcomes achieved by treatment of NSCLC patients harboring oncogenic forms of EGFR that encode for constitutively activated mutant proteins (e.g. EGFR p.leu858arg and EGFR p.glu746_ala750del). with a single-agent EGFR tyrosine kinase inhibitor (TKI) such as erlotinib. However, genetically-driven resistance to single-agent targeted therapy limits patient survival (reviewed in 10 ). A common mechanism of targeted cancer therapy resistance is a second-site mutation in the drug target that interferes with the ability of the targeted agent to block the oncogenic driver protein. This second-site mutation typically occurs in the setting of retention of the original oncogenic driver mutation (e.g. EGFR p.leu858arg and EGFR p.glu746_ala750del), creating a compound mutant allele. For instance, acquired resistance to first- and second-generation EGFR TKIs occurs via the additional EGFR p.thr790met resistance mutation in approximately 50-60% of cases (e.g. resulting in EGFR p.leu858arg and p.thr790met ) 11. The third-generation EGFR TKI osimertinib is effective against lung cancers harboring both traditional activating mutations in EGFR (EGFR p.leu858arg and EGFR p.glu746_ala750del) and the additional EGFR p.thr790met mutation 5, and is FDA-approved for the treatment of EGFR p.thr790met-positive patients. However, even in patients whose tumors harbor EGFR p.thr790met, the objective response rate (ORR) to osimertinib is only approximately 60-70% and complete responses are rare 4,5. Further, all patients who respond to osimertinib eventually develop disease progression (median PFS months) 4,5 and succumb to their drug-resistant cancer. While mechanisms of acquired resistance to osimertinib are known, such as the EGFR p.cys797ser mutation 12,13, the basis of non-response or incomplete response to osimertinib therapy is poorly understood. Results: Functional significance of co-occurring genomic alteration. Similar to our larger cohort of EGFRmutant NSCLC patients (Fig. 1-4), this case highlights the co-occurrence of genetic alterations within the WNT (CTNNB1 variant p.ser37phe), PI3K (PIK3CA variant p.gly106val), and cell cycle pathways (CDK6 CNG, and CDKN2A loss). We hypothesized such co-occurring alterations might function non-redundantly to drive tumor metastasis or limit targeted therapy response. We examined the functional role(s) of oncogenic β-catenin p.ser37phe and PIK3CA p.gly106val that co-occurred with oncogenic EGFR in this cancer. β-catenin nuclear expression and the levels of phosphorylated AKT (as a measure of β-catenin and PI3K activation, respectively) confirmed activation of each pathway particularly in specimens with

12 these activating alterations (Fig. 6a). Expression of β-catenin p.ser37phe in EGFR-mutant NSCLC cells decreased EGFR TKI response by suppressing apoptosis, without significantly enhancing growth (Fig. 6bd). Expression of β-catenin p.ser37phe also promoted cellular invasion, but not migration (Fig. 6e-g). A trend towards increased frequency of CTNNB1 alterations in advanced-stage compared to early-stage EGFR-mutant lung adenocarcinomas in the TCGA dataset (Supplementary Fig. 9) is consistent with a role for CTNNB1 alterations in metastatic EGFR-mutant NSCLC progression and preclinical studies 14. Expression of PIK3CA p.gly106val did not limit EGFR TKI response, consistent with recent clinical data 15, but promoted invasion and migration (Fig. 6b-g). The emergence of the PIK3CA variant encoding p.gly106val upon metastasis but before EGFR TKI treatment, coupled with the finding of,pik3ca variant encoding p.his1047arg 16,17, at a metastatic site (R5) lacking the PIK3CA variant p.gly106val (Fig. 5a-c) suggests that tumor metastasis is partially dependent on PI3K pathway activation. Clonality assessments of somatic variants. To estimate the relative proportions of cfdna that would differentiate a mutant allele as clonal or subclonal, as a benchmark we correlated tumor-based clonality analysis with cfdna mutational analysis performed in EGFR-mutant clinical specimens (tumor and plasma cfdna) from the advanced-stage patient profiled above (see Fig. 5a-c). Based on this analysis, we estimated clonal alterations may be detectable as low as 20% of the maximum MAF (corrected for copy number gain, see methods) in cfdna, but likely to be subclonal when detected at <20%. This method demonstrated 100% sensitivity and specificity when comparing cfdna to tumor DNA (Supplementary Fig. 10). EGFR encoding variant p.cys797ser, which causes acquired osimertinib resistance 12, was frequently subclonal (47% (7/15) clonal; P = 0.08 compared to EGFR variant p.thr790met). Co-alterations in CTNNB1 (clonal in 47/63; 95% CI 63.1%-86.1%), TP53 (clonal in 492/752; 95% CI 61.9%-68.9%), and PIK3CA (clonal in 72/108; 95% CI 57.3%-76.0%) were frequently clonal, while AR mutations were more frequently subclonal in EGFR-mutant samples (clonal in 12/21; 95% CI 33.6%~ 80.6%). These data support a model wherein both clonal and subclonal evolution of several co-occurring oncogene and tumor suppressor co-alterations, beyond mutant EGFR alone, can influence advanced-stage EGFR-mutant NSCLC progression and targeted therapy response. Discussion: The data demonstrate a new role for oncogenic β-catenin as a co-driver of tumor progression with mutant EGFR that can also limit EGFR TKI response in lung cancer, extending a prior case report 18. Intriguingly, while WNT pathway alterations did not influence PFS in response to EGFR TKI treatment, there was a trend towards worse OS in patients whose cfdna harbored these alterations, suggesting a potential role in driving tumor metastases and highlighting putative functional specialization among co-occurring genetic alterations.

13 We identify CDK6 alterations as more frequent in EGFR-mutant mutation-positive patients and show that cell cycle gene (e.g. CDK4/6) alterations are a biomarker of non-response to EGFR TKI treatment, broadly as well as to osimertinib treatment specifically in EGFR-mutant lung cancer. This finding provides the rationale for novel clinical trials testing an EGFR TKI plus a CDK4/6 inhibitor to enhance response in EGFR-mutant lung cancer patients with CDK4/6 gene amplification. Preclinical NSCLC models have shown synergy between EGFR and CDK4/6 co-inhibition (with patient-ready drugs including the FDAapproved CDK4/6 inhibitor palbociclib) 19,20. The availability of clinical CDK4/6 inhibitors (e.g. palbociclib, ribociclib) makes these combination therapy trials possible immediately. We report that genomic complexity increases during EGFR TKI treatment, and that multiple subclonal oncogenic events are present at terminal TKI resistance and death. This increased number of cooccurring alterations present in advanced lines of therapy offers new evidence that may help explain the variable and incomplete clinical efficacy of next-generation mutant EGFR-targeted drugs (e.g. osimertinib) as well as the lack of consistent efficacy observed to date in the acquired resistance setting of other targeted agents (e.g. MET kinase inhibitors) against a particular mechanism associated with acquired resistance (e.g. MET gene amplification). Our data strongly suggest that early (pro-active) and potentially dynamic 21 use of one or more co-targeting strategies will be critical to forestall cancer evolution to a more complex drug-resistant state. The data also offer a novel rationale for testing immunotherapy (with anti- PD1 or anti-pdl1 antibodies) in such genomically-complex EGFR TKI-resistant cancers. Current approved immunotherapies are not typically effective in EGFR-mutant NSCLC patients, but our findings identify a sub-population of these patients as potential immunotherapy responders. This new hypothesis arising from our data is reminiscent of findings in other advanced-stage tumor types with high genetic diversity in which immunotherapy is effective, such as smoking-related lung cancers (which are typically EGFR mutation-negative), melanomas, and carcinomas with mismatch-repair deficiency 22,23. These are clinical trials that could initiate immediately and define a new population of cancer patients who may benefit from existing immunotherapy. We link clinical outcomes to genetic interactions in both a large-scale survey of advanced-stage oncogene-driven lung cancer, as well as a deep and comprehensive analysis of 7 tumors collected longitudinally from a typical EGFR-mutant lung cancer patient. Many of the genetic interactions identified were not anticipated by prior literature. For instance, we show how co-occurring oncogenic mutations in EGFR, β-catenin(ctnnb1) and PIK3CA can cooperatively interact within a tumor, via non-redundant functions, to promote metastatic progression or limit EGFR TKI response. Future studies beyond the scope here will be required to assess the roles of additional co-alterations identified and with predicted functional impact, such as mutation of CHD4, an epigenetic regulator 24 and TLR4 an activator of NF-κB 25. It is notable that both epigenetic regulation 26 and NF-κB 27,28 can promote EGFR inhibitor tolerance and resistance. Likewise, our examination of the largest cohort of EGFR-mutant/T790M-positive samples to date shows an abundance of co-occurring functional genetic alterations, offering novel genetic explanations for the typically incomplete and temporary clinical responses to osimertinib and new

14 therapeutic strategies to enhance these responses. While a functional analysis of all potential genetic interactions identified is beyond the scope here, our data open new avenues for studying the cooperative roles of co-occurring alterations driving lung cancer. Supplementary References: 1. Sequist, L.V. et al. First-line gefitinib in patients with advanced non-small-cell lung cancer harboring somatic EGFR mutations. J Clin Oncol 26, (2008). 2. Zhou, C. et al. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, phase 3 study. Lancet Oncol 12, (2011). 3. Sequist, L.V. et al. Rociletinib in EGFR-mutated non-small-cell lung cancer. N Engl J Med 372, (2015). 4. Mok, T.S. et al. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive Lung Cancer. N Engl J Med (2016). 5. Janne, P.A. et al. AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer. N Engl J Med 372, (2015). 6. Shaw, A.T. et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med 368, (2013). 7. Shaw, A.T. & Engelman, J.A. Ceritinib in ALK-rearranged non-small-cell lung cancer. N Engl J Med 370, (2014). 8. Planchard, D. et al. Dabrafenib plus trametinib in patients with previously treated BRAF(V600E)- mutant metastatic non-small cell lung cancer: an open-label, multicentre phase 2 trial. Lancet Oncol 17, (2016). 9. Shaw, A.T. et al. Crizotinib in ROS1-Rearranged Non-Small-Cell Lung Cancer. N Engl J Med (2014). 10. Bivona, T.G. & Doebele, R.C. A framework for understanding and targeting residual disease in oncogene-driven solid cancers. Nat Med 22, (2016). 11. Yu, H. et al. Analysis of Mechanisms of Acquired Resistance to EGFR TKI therapy in 155 patients with EGFR-mutant Lung Cancers. Clin Cancer Res 19, (2013). 12. Thress, K.S. et al. Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M. Nat Med 21, (2015). 13. Piotrowska, Z. et al. Heterogeneity Underlies the Emergence of EGFRT790 Wild-Type Clones Following Treatment of T790M-Positive Cancers with a Third-Generation EGFR Inhibitor. Cancer Discov 5, (2015). 14. Nguyen, D.X. et al. WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis. Cell 138, (2009). 15. Eng, J. et al. Impact of Concurrent PIK3CA Mutations on Response to EGFR Tyrosine Kinase Inhibition in EGFR-Mutant Lung Cancers and on Prognosis in Oncogene-Driven Lung Adenocarcinomas. J Thorac Oncol 10, (2015). 16. Trejo, C.L. et al. Mutationally activated PIK3CA(H1047R) cooperates with BRAF(V600E) to promote lung cancer progression. Cancer Res 73, (2013). 17. Wallin, J.J. et al. Active PI3K pathway causes an invasive phenotype which can be reversed or promoted by blocking the pathway at divergent nodes. PLoS One 7, e36402 (2012). 18. Sequist, L.V. et al. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci Transl Med 3, 75ra26 (2011). 19. Yu, Z. et al. Resistance to an irreversible epidermal growth factor receptor (EGFR) inhibitor in EGFR-mutant lung cancer reveals novel treatment strategies. Cancer Res 67, (2007). 20. Liu, M. et al. PD , a selective cyclin D kinase 4/6 inhibitor, sensitizes lung cancer cells to treatment with epidermal growth factor receptor tyrosine kinase inhibitors. Oncotarget 7, (2016). 21. Jonsson, V.D. et al. Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution. Sci Rep 7, (2017). 22. Topalian, S.L. et al. Safety, activity, and immune correlates of anti-pd-1 antibody in cancer. N Engl J Med 366, (2012).

15 23. Le, D.T. et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med 372, (2015). 24. Lai, A.Y. & Wade, P.A. Cancer biology and NuRD: a multifaceted chromatin remodelling complex. Nat Rev Cancer 11, (2011). 25. Krauthammer, M. et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet 44, (2012). 26. Sharma, S.V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, (2010). 27. Bivona, T.G. et al. FAS and NF-kappaB signalling modulate dependence of lung cancers on mutant EGFR. Nature 471, (2011). 28. Blakely, C.M. et al. NF-kappaB-activating complex engaged in response to EGFR oncogene inhibition drives tumor cell survival and residual disease in lung cancer. Cell Rep 11, (2015).

16 EGFR.mutation2postive EGFR.mutation2negative Total&Samples Total&#&of&patients Date&range March&2015&=&April&2016 Jan&2016&April&2016 Male 346&(34.4%) 457&(45.7%) Female 660&(65.6%) 542&(54.3%) Mean&Age Adenocarcinoma 747&(74.3%) 558&(55.9%) NSCLC=NOS 259&(25.7%) 441&(44.1%) Stage&III/IV Supplementary Table 1. EGFR-mutant positive and EGFR-mutant negative patient characteristics.

17 Gene CCND1 CCND2 CCNE1* CDK4* CDK6* CDKN2A CDKN2B FBXW7 JAK2 JAK3 MPL SRC ATM BRCA1 BRCA2 MLH1 TERT ARID1A EZH2 GATA3 HNF1A MYC* NFE2L2 VHL IDH1 IDH2 ALK NTRK1 RET ROS1 AR* ESR1 NOTCH1 CDH1 SMO SMAD4 NPM1 STK11 TP53 AKT1 PIK3CA* PTEN RHEB ARAF BRAF* GNA11 GNAQ GNAS HRAS KRAS* MAP2K1 MAP2K2 NF1 NRAS* PTPN11 RAF1* RIT1 EGFR* ERBB2* FGFR1* FGFR2* FGFR3 KIT MET* PDGFRA* APC CTNNB1 RHOA Pathway Cell Cycle Cell Cycle Cell Cycle Cell Cycle Cell Cycle Cell Cycle Cell Cycle Cell Cycle Cytokine signaling Cytokine signaling Cytokine signaling Cytokine signaling DNA Repair DNA Repair DNA Repair DNA Repair DNA Repair Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Epigenetic Modification/Transcription Factor Fusion Fusion Fusion Fusion Hormone signaling Hormone signaling Other signaling (SHH, TGF- b, NOTCH, ECM) Other signaling (SHH, TGF- b, NOTCH, ECM) Other signaling (SHH, TGF- b, NOTCH, ECM) Other signaling (SHH, TGF- b, NOTCH, ECM) P53 pathway P53 pathway P53 pathway PI3K/AKT/MTOR PI3K/AKT/MTOR PI3K/AKT/MTOR PI3K/AKT/MTOR RTK RTK RTK RTK RTK RTK RTK RTK WNT- APC- Beta- catenin WNT- APC- Beta- catenin WNT- APC- Beta- catenin Supplementary Table 2. Genes included in clinical cell-free DNA analysis of EGFR-mutant positive and EGFR-mutant negative patients. Targeted exome sequencing was performed on the indicated genes (n=68) to identify somatic variants in cfdna. (*) Indicates genes also assessed for copy-number gain (CNG). Genes were assigned to one of 12 primary cancer-related pathway based on known function.

18 EGFR-mutation EGFR-mutation Statistical comparisons 1 positive negative Total patients Average genetic 2.58±1.70 (N= ±1.99 without P = alterations ± SEM without EGFR) EGFR) (95% CI: ~ 0.119) 2 (beyond EGFR) per d= patient Patients with at least 1 variant of known/likely functional impact (other than EGFR) 92.9% (1043/1122) 96.7% (913/944) P = 2.2E-4 (95% CI: 1.7% ~ 5.7%) 2 4 OR=1.04 Genetic co-alterations with known/likely functional impact 89.8% (3030/3375) 83.9% (2163/2578) P = 3.84E-11 (95%CI: 4.0%~7.6%) 2 4 OR=1.07 Co-alterations classified as likely passenger events CTNNB1 co-alterations 5.3% (60/1122) CDK6 co-alterations 7.0% (79/1122) AR co-alterations 5.1% (57/1122) TP53 co-alterations 54.6% (613/1122) 10.2% (345/3375) 16.1% (415/2578) P = 1.3E-11 (95% CI: -7.7% ~ -4.1%) 2 4 OR= % (17/944) 3.2% (30/944) 2.6% (25/944) 50% (475/944) P = 1.9E-05, q = 1.7E-04 (95%CI: 1.9% ~ 5.2%) 2 4 OR=2.97 P = 1.0E-04, q = 7.6E-04 (95% CI: 1.9%~ 5.8%) 2 4 OR=2.22 P = 4.7E-03, q = 0.02 (95% CI: 0.7% ~ 4.2%) 2 4 OR=1.92 P = 5.1E-02, q = 0.14 (95% CI: -0.1%~ 8.7%) 2 4 OR=1.09 Notes: (1) EGFR-mutation-positive compared to EGFR-mutation-negative cohort, P-values determined by Fisher s test. The Benjamini-Hochberg method was used to correct for multiple hypothesis testing and generate q- values. (2) 95% CI is the range of mean difference between two cohort (3) d represents effect size derived from t-test. (4) OR represents odds ratio derived from proportions test Supplementary Table 3. Statistical analysis of genomic alterations detectable in the cfdna of EGFR-mutation positive compared to EGFR-mutation negative patients.

19 EGFR p.t790m positive EGFR p.t790m negative Statistical comparisons 1 Total patients Average genetic alterations 2.41± ± 1.77 without P =4.5E-04 ±SEM per patient (beyond EGFR) (95% CI: ~ 0.617) 2 EGFR) d=0.22 Patients with at least 1 variant of known/likely functional impact (other than EGFR) Genetic co-mutations with known/likely functional impact 100% (440/440) 89.9%(613/682) P = 1.38E-11 (95% CI: 7.7% ~12.6%) 2 4 OR= /%(1474/1615) 88.4%(1556/1760) P = 7.3E-03 (95% CI: 0.77% ~ 4.95%) 2 4 OR=1.032 Co-mutations classified as likely passenger events 8.7% (141/1615) 11.6%(204/1760) CTNNB1 co-alterations 7.5% (33/440) CDK6 co-alterations 9.8% (43/440) AR co-alterations 6.8% (30/440) KRAS co-alterations 4.8% (21/440) MYC co-alterations 10.7% (47/440) PDGFRA co-alterations 4.8% (21/440) BRCA1 co-alterations 7.0% (31/440) CCNE1 co-alterations 8.9% (39/440) TP53 co-alterations 57% (252/440) 4.0% (27/682) 5.3% (36/682) 3.9% (27/682) 2.5% (17/682) 6.0% (41/682) (1.6%) (11/682) 3.5% (24/682) 5.7% (39/682) 53% (361/682) P = 7.3E-03 (95% CI: ~ -4.95) 2 4 OR=0.75 P = 0.014, q = 0.12 (95% CI: 0.49%~ 6.5% ) 2 4 OR=1.89 P = , q = 0.08 (95% CI: 1.1% ~ 7.9%) 2 4 OR=1.85 P = 0.037, q = 0.23 (95% CI: -0.01% ~ 5.8%) 2 4 OR=1.72 P = 0.04, q = 0.24 (95% CI: -0.22% ~ 4.8%) 2 4 OR=1.92 P = 0.006, q = 0.08 (95% CI: 1.1% ~ 8.3%) 2 4 OR=1.78 P = 0.003, q = 0.06 (95% CI: 0.77% ~ 5.6%) 2 4 OR=2.96 P = 0.01, q = 0.10 (95% CI: 0.6% ~ 6.3%) 2 4 OR=2.00 P = 0.054, q = 0.27 (95% CI: -0.22% ~ 6.5%) 2 4 OR=1.55 P = 0.15, q = 0.62 (95% CI: -0.8%~ 11.3%) 2 4 OR=1.08 Notes: (1) EGFR p.t790m positive compared to EGFR p.t790m negative cohort, P-values determined by Fisher s test. The Benjamini-Hochberg method was used to correct for multiple hypothesis testing and generate q- values. (2) 95% CI is the range of mean difference between two cohort (3) d represents effect size derived from t-test. (4) OR represents odds ratio derived from proportions test Supplementary Table 4. Statistical analysis of genomic alterations detectable in the cfdna of EGFR p.t790m-positive positive compared to EGFR p.t790m -negative patients.

20 Total&#&of&samples 137 Total&#&of&patients 97 Date&range May&2015&;&June&2017 Male 53&(38.6%) Female 84&(61.4%) Median&Age 65.3 Stage&III/IV 137&(100%) Never&smoker 106&(77.4%) Former&smoker 31&(22.6%) Median&No.&alterations 3 No.$of$previous$anti0cancer$therapies 0 21&(15%) 1 65&(47.4%) 2 34&(24.8%) 3 11&(8.0%) >3 6&(4.4%) EGFR$TKI$therapy$prior$to$G360$assay Erlotinib 64&(46.7%) Afatinib 7&(5.1%) Rociletinib 11&(8.0%) Osimertinib 9&(6.6%) Afatinib&+&Cetuximab 6&(4.4%) Supplementary Table 5. Demographic information from 137 samples collected from 97 patients with clinical data available.

21 EGFR%TKI%treated Osimertinib%treated Total&#&of&samples Total&#&of&patients Date&range May&2015&<&March&2017 June&2015&<&Feb&2017 Male 27&(37.0%) 53&(38.6%) Female 46&(63.0%) 84&(61.4%) Median&Age Stage&III/IV 73&(100%) 41&(100%) Never&smoker 59&(80.8%) 31&(75.6%) Former&smoker 14&(19.2%) 10&(24.4%) Median&No.&alterations 3 4 Type%of%EGFR%mutation Exon&19&deletion 44&(60.3%) 23&(56.1%) L858R 23&(31.5%) 16&(39.0%) T790M 39&(53.4%) 35&(85.4%) No.%of%previous%anti<cancer%therapies 0 18&(24.7%) 0&(0%) 1 36&(49.3%) 26&(63.4%) 2 12&(16.4%) 9&(21.9%) 3 5&(6.8%) 4&(9.7%) >3 2&(2.7%) 2&(4.9%) EGFR%TKI%therapy%prior%to%G360%assay Erlotinib 33&(45.2%) 27&(65.9%) Afatinib 5&(6.8%) 3&(7.3%) Rociletinib 5&(6.8%) 4&(9.8%) Osimertinib 1&(1.5%) 0&(0%) Afatinib&+&Cetuximab 4&(5.5%) 3&(7.3%) EGFR%TKI%therapy%after%G360%assay Erlotinib 17(23.3%) 0 Afatinib 2&(2.7%) 0 Rociletinib 3&(4.1%) 0 Osimertinib 41&(56.2%) 41(100%) Afatinib&+&Cetuximab 3(4.1%) 0 Other&combination&TKI&therapy 4(5.5%) 0 Supplementary Table 6. Demographic information from 73 samples collected from 64 patients with EGFR TKI clinical response data available.