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Evolution and clinical impact of co-occurring genetic alterations in advanced-stage EGFR-mutant lung cancers

Abstract

A widespread approach to modern cancer therapy is to identify a single oncogenic driver gene and target its mutant-protein product (for example, EGFR-inhibitor treatment in EGFR-mutant lung cancers). However, genetically driven resistance to targeted therapy limits patient survival. Through genomic analysis of 1,122 EGFR-mutant lung cancer cell-free DNA samples and whole-exome analysis of seven longitudinally collected tumor samples from a patient with EGFR-mutant lung cancer, we identified critical co-occurring oncogenic events present in most advanced-stage EGFR-mutant lung cancers. We defined new pathways limiting EGFR-inhibitor response, including WNT/β-catenin alterations and cell-cycle-gene (CDK4 and CDK6) mutations. Tumor genomic complexity increases with EGFR-inhibitor treatment, and co-occurring alterations in CTNNB1 and PIK3CA exhibit nonredundant functions that cooperatively promote tumor metastasis or limit EGFR-inhibitor response. This study calls for revisiting the prevailing single-gene driver-oncogene view and links clinical outcomes to co-occurring genetic alterations in patients with advanced-stage EGFR-mutant lung cancer.

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Figure 1: Co-occurring genomic alterations detectable in cfDNA of patients with advanced-stage EGFR-mutant-positive compared with EGFR-mutant-negative NSCLC.
Figure 2: Co-occurring genomic alterations detected in cfDNA of 440 patients with advanced-stage EGFR p.Thr790Met–positive NSCLC compared with 682 patients with advanced-stage EGFR p.Thr790Met–negative NSCLC.
Figure 3: Therapy-induced evolution of genomic co-alterations detected in cfDNA of patients with advanced-stage EGFR-mutant NSCLC.
Figure 4: Effects of cfDNA detectable co-occurring genetic alterations on osimertinib clinical response in patients with advanced-stage EGFR-mutant lung cancer.
Figure 5: Longitudinal genomic analysis of tumor and cfDNA in a patient with EGFR-mutant lung cancer from diagnosis to death.
Figure 6: Functional analysis of CTNNB1 and PIK3CA comutations detected in EGFR-mutant lung adenocarcinoma.
Figure 7: Clonality analysis of co-occurring genetic alterations detectable in the cfDNA of patients with advanced-stage NSCLC.
Figure 8: Evolution of the understanding of the genetic pathogenesis of oncogene-positive (EGFR-mutant) lung cancer.

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Acknowledgements

The authors acknowledge funding support from the NIH (NCI-R01CA169338, NIH Director's New Innovator Award NCI-DP2CA174497), the Pew Charitable Trust, Stewart Foundation, and Searle Foundation (to T.G.B.), and the AACR and Lung Cancer Research Foundation (C.M.B.). The authors thank J. Blakely for artwork and A. Sabnis, R. Okimoto, A. Tulpule, and M. Hutchinson for critical review and input on the manuscript. The authors acknowledge the following researchers for providing plasmids through Addgene: E. Campeau (University of Massachusetts Medical School); H. Land and J. Morgenstern (Imperial Cancer Research Fund); B. Weinberg (Whitehead Institute for Biomedical Research); J. Zhao (Dana-Farber Cancer Institute, Harvard Medical School); and E. Fearon (University of Michigan School of Medicine).

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Authors

Contributions

C.M.B., T.B.K.W., C.S. and T.G.B. designed the study. C.M.B. performed medical-record review, analyzed data and prepared tables and figures. T.B.K.W. performed WES and clonality analysis and prepared tables and figures with assistance from N.M., G.A.W., and N.J.B. W.W. performed analysis of cfDNA-sequencing data on patient cohorts and prepared tables and figures. B.G. performed cell-line experiments and prepared figures with assistance from A.M. J.J.C. and M.D. performed cancer personalized profiling by deep sequencing (CAPP-seq) analysis. V.R.O. and J.R. performed immunohistochemistry analysis. C.E.M., M.A.G., V.W., A.D.S., P.C.M., D.R.G., H.H., R.C.D., and J.W.R. performed medical-record review and provided clinical data. K.C.B. and R.B.L. compiled and annotated cfDNA data from 1,150 patients with EGFR-mutant-positive NSCLC and 1,008 patients with EGFR-mutant-negative NSCLC. A.R.C. extracted DNA and prepared exome libraries from patient tumor samples. A.F.C. and J.S.J. performed exome sequencing alignment and quality analysis. P.G. harvested autopsy tissue and performed pathological assessments. C.M.B. and T.G.B. wrote the manuscript, to which all authors contributed.

Corresponding authors

Correspondence to Charles Swanton or Trever G Bivona.

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Competing interests

K.C.B. and R.B.L. are employees of Guardant Health Inc.; A.F.C., J.S.J., A.R.C., and P.G. are employees of Driver Inc.; A.D.S. is an employee of Clovis Oncology Inc. T.G.B. is an advisor to Novartis, Astrazeneca, Takeda, Array Biopharma, and Revolution Medicines, and has received research funding from Ignyta and Revolution Medicines.

Integrated supplementary information

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.

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).

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 Cox-proportional 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.

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 Cox-proportional 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).

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 2.0-2.49 is reported as + and 2.5-4.0 as ++ (see Online Methods).

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 2nd 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.

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.

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.

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) EGFR-mutant NSCLC. P-values determined by two-tailed Fischer's exact test. DEL (Deletion), INS (Insertion), SNP (Single Nucleotide Polymorphism).

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.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10, Supplementary Tables 1–6 and Supplementary Note (PDF 2973 kb)

Life Sciences Reporting Summary (PDF 204 kb)

Supplementary Data Set 1

cfDNA alterations in EGFR-mutant positive lung cancer patients (XLSX 475 kb)

Somatic variants, copy number gains and clonality of alterations detected in cfDNA of EGFR-mutant positive patients.

Supplementary Data Set 2

cfDNA alterations in EGFR-mutant negative lung cancer patients (XLSX 291 kb)

Somatic variants, copy number gains and clonality of alterations detected in cfDNA of EGFR-mutant negative patients.

Supplementary Data Set 3

Demographic information and genomic alterations identified in cfDNA of EGFR-mutant lung cancer patients. (XLSX 52 kb)

Age, gender, smoking history, prior treatment and treatment outcomes for 137 samples from 97 patients with known clinical course.

Supplementary Data Set 4

Demographic information, clinical response data, and genomic alterations identified in cfDNA of patients treated with an EGFR-TKI. (XLSX 96 kb)

Age, gender, smoking history, prior treatment and treatment outcomes for 73 patients treated with an EGFR TKI including 41 patients treated with osimertinib.

Supplementary Data Set 5

Somatic mutations and clonality analysis of tumor samples described in Figure 5. (XLSX 199 kb)

Somatic variants identified in 7 tumor samples from patient with EGFR-mutant lung cancer throughout the course of her disease with clonality assessment for each variant determined by PyClone.

Supplementary Data Set 6

Copy number alterations in tumor samples described in Figure 5 and Supplementary Figure 7. (XLSX 50 kb)

Chromosomal regions of copy number gain and loss identified in in 7 tumor samples from patient with EGFR-mutant lung cancer throughout the course of her disease.

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Blakely, C., Watkins, T., Wu, W. et al. Evolution and clinical impact of co-occurring genetic alterations in advanced-stage EGFR-mutant lung cancers. Nat Genet 49, 1693–1704 (2017). https://doi.org/10.1038/ng.3990

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