Genomic characterization of human brain metastases identifies drivers of metastatic lung adenocarcinoma

Abstract

Brain metastases from lung adenocarcinoma (BM-LUAD) frequently cause patient mortality. To identify genomic alterations that promote brain metastases, we performed whole-exome sequencing of 73 BM-LUAD cases. Using case-control analyses, we discovered candidate drivers of brain metastasis by identifying genes with more frequent copy-number aberrations in BM-LUAD compared to 503 primary LUADs. We identified three regions with significantly higher amplification frequencies in BM-LUAD, including MYC (12 versus 6%), YAP1 (7 versus 0.8%) and MMP13 (10 versus 0.6%), and significantly more frequent deletions in CDKN2A/B (27 versus 13%). We confirmed that the amplification frequencies of MYC, YAP1 and MMP13 were elevated in an independent cohort of 105 patients with BM-LUAD. Functional assessment in patient-derived xenograft mouse models validated the notion that MYC, YAP1 or MMP13 overexpression increased the incidence of brain metastasis. These results demonstrate that somatic alterations contribute to brain metastases and that genomic sequencing of a sufficient number of metastatic tumors can reveal previously unknown metastatic drivers.

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Fig. 1: Newly identified candidate brain metastatic drivers targeted by amplifications or deletions.
Fig. 2: Co-mutation plot from the WES of brain metastasis patients.
Fig. 3: Phylogenetic analysis of copy-number drivers in brain metastasis and matched primary tumors.
Fig. 4: Functional validation of brain metastatic drivers in a PDX model.

Data availability

Sequencing data are deposited in the database of Genotypes and Phenotypes with accession nos. phs000730.v1.p1 and phs001920.v1.p1. This study makes use of data generated by The Cancer Genome Atlas project.

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Acknowledgements

This work was supported by National Cancer Institute grant no. 1R01CA227156-01 (to P.K.B. and S.L.C.); the Damon Runyon Cancer Research Foundation (to P.K.B.); the Conquer Cancer Foundation (to P.K.B.); the Breast Cancer Research Foundation (to P.K.B.); the Brain Science Foundation (to P.K.B.); the American Brain Tumor Association (to P.K.B.); the Wong Family Awards in Translational Oncology (to S.L.C.); and the LUNGstrong Fund (to S.L.C.). P.K.B. is also supported by Susan G. Komen for the Cure and received institutional support from Massachusetts General Hospital. D.J.H.S. was supported by the Canadian Institutes of Health Research Fellowship. S.L.C. received institutional support from the Dana-Farber Cancer Institute. We thank the patients for providing tissue samples; L. Brown, J. Kim and W. Richards for assisting with sample collection; and G. Parmigiani, J. Miller, M. Yajima, C. Musco, C. Stewart, L. Lichtenstein, S. Lee, M. Babadi, D. Benjamin, B. Reardon and K. Korthauer for fruitful discussions.

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Authors

Contributions

S.L.C. and P.K.B. conceived and supervised the study. M.P.F., S.S. and M.M.-L. confirmed the histological diagnoses and selected sections for the case cohort. P.K.B., D.P.C., I.B., N.N., C.M.G., S. H. Paek, M.S., M.D.W., E.D.B., M.P., A.S.B., E.R.G., B.E.J. T.T.B. and S. H. Park provided or gathered the case-cohort clinical and biological materials. P.K.B., D.J.H.S., N.N., I.D.-J., C.M.G., M.B., A. Kaplan, U.C. and C.A.-B. curated the clinical annotations. S.L.C. and D.J.H.S. designed and performed the genomic and statistical analyses with help from N.D.M., M.L., D.M. and B. Kaufman. D.J.H.S. and P.M. prepared the lentiviral constructs. A.J.I. and D.R.B. supervised the FISH experiments, and R.P.F. and M.M.-L. imaged and scored the FISH slides. N.N., B.A.C., I.B., E.A., J.C.M.G., F.M.I., M.R.S. K.H. and A. Kaneb performed the functional validation experiments. I.B., M.R.D., B. Kuter, D.N. and M.R.S. performed the immunohistochemistry experiments. A.S.B. and M.P. collected the biological materials and clinical annotations for the validation cohort. S.L.C. and P.K.B. prepared the initial draft of the manuscript with D.J.H.S. and N.N. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Scott L. Carter or Priscilla K. Brastianos.

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Competing interests

I.D.-J. has received honoraria from Foundation Medicine, consulting fees from Boehringer Ingelheim, travel support from Pfizer and Array, and research support from Pfizer, Genentech, Array and Novartis. A.S.B. has research support from Daiichi Sankyo (≤10,000€), Roche (>10,000€) and honoraria for lectures, consultation or advisory board participation from Roche, Bristol-Meyers Squibb, Merck, Daiichi Sankyo (all <5,000€) as well as travel support from Roche, Amgen and AbbVie. B.E.J. has received postmarketing royalties from the Dana-Farber Cancer Institute for EGFR mutation testing, has ownership interest (including patents) in the KEW Group and is a consultant/advisory board member for the same. T.T.B. reports receiving a commercial research grant from Pfizer, has received speakers’ bureau honoraria from Research To Practice, Imedex and Oakstone, and is a consultant/advisory board member for Proximagen, Merck, Foundation Medicine, UpToDate and Champions Biotechnology. S.S. consults for RareCyte. M.P. has received honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Bayer; Bristol-Myers Squibb; Novartis; Gerson Lehrman Group; CMC Contrast; GlaxoSmithKline; Mundipharma; Roche; BMJ Journals; MedMedia; Astra Zeneca; AbbVie; Eli Lilly and Company; MEDahead; Daiichi Sankyo; Sanofi; Merck Sharp & Dohme and Tocagen. The following for-profit companies have supported clinical trials and contracted research conducted by M.P. with payments made to his institution: Böhringer-Ingelheim; Bristol-Myers Squibb; Roche; Daiichi Sankyo; Merck Sharp & Dohme; Novocure; GlaxoSmithKline; and AbbVie. P.K.B. has consulted for Eli Lilly and Company, Tesaro, ElevateBio, Genentech-Roche and AngioChem, has received honoraria from Merck and Genentech and research funding (to Massachusetts General Hospital) from Merck, Pfizer, Eli Lilly and Company and BMS. No potential competing interests were disclosed by the other authors.

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Extended data

Extended Data Fig. 1 Power analysis and statistical simulation of case-control study.

a, Estimated effect of increasing fraction of brain metastasis patients in TCGA-LUAD on statistical power to detect metastatic drivers at different mutation frequency levels in BM-LUAD. The driver mutation frequency is assumed to be 1% among TCGA-LUAD patients who do not develop brain metastasis (true controls). Power is calculated for testing an increase in driver mutation frequency among cases compared to controls at a significance level of 0.05. Observations are assumed to be independent and identically distributed. b, Simulated effect of increasing fraction of brain metastasis patients in TCGA-LUAD on false positive rate for detecting metastatic drivers at different mutation frequency levels. Each data point represents a simulation of 100 experiments under the null hypothesis (that is, the mutation frequency among patients who never develop brain metastasis is equal to the mutation frequency among brain metastasis patients). Significance level is set to 0.05. Vertical line represents the estimated fraction of brain metastasis patients in TCGA-LUAD, and shaded region represents the 95% confidence interval, as determined using a mixed effect meta-analysis binomial regression accounting for immunohistological subtype, TNM stage, EGFR mutation status, race, smoking status, gender, and age under an errors-in-variables model to allow for missing or uncertain data.

Extended Data Fig. 2 Covariate balance between TCGA-LUAD and BM-LUAD.

a, Proposed causal model for brain metastasis. Red arrow denotes main causal relationship of interest; black arrows, well-supported relationships; gray arrows, uncertain relationships. Relationship between TNM stage and brain metastasis is bidirectional: brain metastasis at diagnosis is defined as stage IV, and node involvement contributes to metastasis. b, Coarsened exacting matching weights, determined based on biological sex, genetic ancestry, and smoking exposure. c, Distributions of confounding covariates before exact matching. d, Distributions of confounding covariates after exact matching. e, Distributions of TNM stage and age at primary diagnosis before exact matching and f, after. TNM stage and age were not included in exact matching, and their distributions remain similar after exact matching. AFR, African or African American. EAS, East Asian. NFE, Non-Finnish European. SAS, South Asian. AMR, Latino. FIN, Finnish. OTH, Other.

Extended Data Fig. 3 Cancer drivers targeted by SNVs or indels.

Single nucleotide variants (SNVs) and short insertions/deletions (indels) in BM-LUAD were analyzed by MutSig2CV and dNdScv to identify driver genes under positive selection. Identified drivers are statistically significant by both MutSig2CV and dNdScv at 1% false discovery rate, except for EGFR, which harbors recurrent indels that are considered only by MutSig2CV. The mutation frequencies of the identified drivers are shown for BM-LUAD and TCGA-LUAD after matching adjustment by coarsened exact matching, and statistical significances of differences in mutation frequency were assessed by weighted logistic regression using the matching weights. None of the identified drivers were statistically significantly different between BM-LUAD and TCGA-LUAD at 0.05 significance level with Benjamini-Hochberg multiple hypothesis correction.

Extended Data Fig. 4 Genome-wide copy-number profiles of TCGA-LUAD and BM-LUAD.

a, Heatmap of copy-number profiles for samples from TCGA-LUAD (top) and BM-LUAD (bottom). Each row represents the copy-number profile of a tumor sample across chromosomes 1 to 22 and X. Red indicates copy-number gain; blue, loss. b, Frequencies of genome doubling events in TCGA-LUAD and BM-LUAD.

Extended Data Fig. 5 Identification of candidate brain-metastatic drivers targeted by CNAs.

Somatic copy-number profiles in case cohort (BM-LUAD) and weight-matched control cohort (TCGA-LUAD) were analyzed by GISTIC. Copy-number profiles of control samples were multiplied by matching weights, which were defined to balance covariate distributions between case and control cohorts using the coarsened exact matching method. G-score profiles for amplifications and deletions were independently analyzed by a Gaussian process latent difference model to identify significantly enriched regions. Candidate drivers were identified by logistic regression comparing aberration frequencies between case and weighted controls; the candidates were further validated in an independent cohort by fluorescence in situ hybridization.

Extended Data Fig. 6 Correlations between tumor purity and detection of CNAs.

Dot plot of frequencies of copy-number events and tumor purity in BM-LUAD (a) and TCGA-LUAD (b). Correlations are measured by Kendall rank correlation coefficient. Blue curves represent LOESS regressions. High-level amplification, > 8 total copy-number; Deep deletion, < 0.5 total copy-number; Gain, > 3/2 normalized copy-ratio; Loss, < 1/2 normalized copy-ratio. Normalized copy-ratio is total copy-number scaled to tumor ploidy.

Extended Data Fig. 7 Case-control analyses after adjustment for tumor purity and stage.

a, Proposed causal model for sample-level covariates involving tumor purity. Red arrow denotes main causal relationship of interest; black arrows, well-supported relationships; gray arrows, uncertain relationships. “Somatic alteration” (shown in gray) is not directly observable. In contrast, “detected somatic alterations” is directly observable. Observing “detected somatic alterations” (which is a collider) introduces a backdoor path from “somatic alteration” to “brain metastasis”, and this path may be closed by controlling for tumor purity. b, Distributions of tumor purity in TCGA-LUAD and BM-LUAD before and after exact matching on biological sex, genetic ancestry, smoking exposure, and tumor purity. c, Proposed causal model for patient-level covariates including stage. Stage III is a likely mediator variable that may be controlled in order to assess the direct effects of somatic alterations on incidence of brain metastasis. d, Differentially amplified or deleted regions in BM-LUAD compared to TCGA-LUAD after additionally matching on tumor purity. Differential regions of interest are labeled. e, Differentially amplified or deleted regions in BM-LUAD compared to stage III samples in TCGA-LUAD.

Extended Data Fig. 8 Power comparison of matched-pairs analysis vs. case-control analysis.

a, Estimated powers to detect metastatic driver under a matched-pairs primary-metastasis comparison study. Levels of driver alteration frequency among cases are shown in different line colors. Various probabilities of driver alteration occurring late during metastatic progression (see Fig. 3) are considered in separate subplots. Power is calculated for Poisson regression comparing absolute frequencies of late driver alterations against frequencies of late background alterations (which was estimated to be 1.0 from recurrently altered genes). Observations are assumed to be independent and identically distributed. Each case patient requires the processing of 3 samples (brain metastasis, matched primary tumor, and matched germline). b, Estimated powers to detect metastatic driver under a case-control study. Levels of driver alteration frequency among cases are shown in different line colors. The driver alteration frequency is assumed to be 1% among TCGA-LUAD patients who do not develop brain metastasis (true controls). Power analysis corrects for the estimated 30% incidence of brain metastasis among TCGA-LUAD patients (cases-in-controls contamination). Each case patient requires the analysis of 2 samples (brain metastasis and germline). Significance level is set to 0.05. Vertical line represents the realized sample size.

Extended Data Fig. 9 Metastatic spread of intracardiac-injected patient-derived xenograft.

Representative in vivo and ex vivo brain bioluminescence images taken 12 days after intracardiac injections with tumor cells overexpressing lacZ, MYC, MMP13, or YAP1.

Extended Data Fig. 10 Intracranial injections of patient-derived xenograft overexpressing candidate drivers.

a, Representative in vivo bioluminescence images of xenograft mouse model 14 days post intracranial injections of 1 x 104 tumor cells overexpressing lacZ, MYC, MMP13, or YAP1. b, Overall mouse survival following intracranial injections of tumor cells. Median survival of the lacZ control group (29.5 days; n = 8) was compared against those of the other groups by the log-rank test: MYC (22 days; n = 8, p = 0.0004), MMP13 (29 days; n = 8, not significant), or YAP1 (33.5 days; n = 8, not significant).

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Shih, D.J.H., Nayyar, N., Bihun, I. et al. Genomic characterization of human brain metastases identifies drivers of metastatic lung adenocarcinoma. Nat Genet 52, 371–377 (2020). https://doi.org/10.1038/s41588-020-0592-7

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