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. 2024 Sep 3;30(17):3798-3811.
doi: 10.1158/1078-0432.CCR-24-0466.

Detecting Small Cell Transformation in Patients with Advanced EGFR Mutant Lung Adenocarcinoma through Epigenomic cfDNA Profiling

Talal El Zarif #  1   2   3 , Catherine B Meador #  4   5 , Xintao Qiu #  3 , Ji-Heui Seo  1   2 , Matthew P Davidsohn  1   2 , Hunter Savignano  1   2 , Gitanjali Lakshminarayanan  1   2 , Heather M McClure  1   2 , John Canniff  1   2 , Brad Fortunato  1   2 , Rong Li  2 , Mandeep K Banwait  4   5 , Karl Semaan  1   2   6 , Marc Eid  1   2 , Henry Long  2 , Yin P Hung  7 , Navin R Mahadevan  1   8 , David A Barbie  1 , Matthew G Oser  1 , Zofia Piotrowska  4   5 , Toni K Choueiri  1 , Sylvan C Baca  1   2   6 , Aaron N Hata  4   5 , Matthew L Freedman #  1   2   6 , Jacob E Berchuck #  1
Affiliations

Detecting Small Cell Transformation in Patients with Advanced EGFR Mutant Lung Adenocarcinoma through Epigenomic cfDNA Profiling

Talal El Zarif et al. Clin Cancer Res. .

Abstract

Purpose: Histologic transformation to small cell lung cancer (SCLC) is a mechanism of treatment resistance in patients with advanced oncogene-driven lung adenocarcinoma (LUAD) that currently requires histologic review for diagnosis. Herein, we sought to develop an epigenomic cell-free DNA (cfDNA)-based approach to noninvasively detect small cell transformation in patients with EGFR mutant (EGFRm) LUAD.

Experimental design: To characterize the epigenomic landscape of transformed (t)SCLC relative to LUAD and de novo SCLC, we performed chromatin immunoprecipitation sequencing (ChIP-seq) to profile the histone modifications H3K27ac, H3K4me3, and H3K27me3; methylated DNA immunoprecipitation sequencing (MeDIP-seq); assay for transposase-accessible chromatin sequencing; and RNA sequencing on 26 lung cancer patient-derived xenograft (PDX) tumors. We then generated and analyzed H3K27ac ChIP-seq, MeDIP-seq, and whole genome sequencing cfDNA data from 1 mL aliquots of plasma from patients with EGFRm LUAD with or without tSCLC.

Results: Analysis of 126 epigenomic libraries from the lung cancer PDXs revealed widespread epigenomic reprogramming between LUAD and tSCLC, with a large number of differential H3K27ac (n = 24,424), DNA methylation (n = 3,298), and chromatin accessibility (n = 16,352) sites between the two histologies. Tumor-informed analysis of each of these three epigenomic features in cfDNA resulted in accurate noninvasive discrimination between patients with EGFRm LUAD versus tSCLC [area under the receiver operating characteristic curve (AUROC) = 0.82-0.87]. A multianalyte cfDNA-based classifier integrating these three epigenomic features discriminated between EGFRm LUAD versus tSCLC with an AUROC of 0.94.

Conclusions: These data demonstrate the feasibility of detecting small cell transformation in patients with EGFRm LUAD through epigenomic cfDNA profiling of 1 mL of patient plasma.

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Conflict of interest statement

Y.P. Hung reports honoraria from Elsevier and American Society of Clinical Pathology on textbook writing and continuing medical education–related activity, both of which are unrelated to this study. N.R. Mahadevan reports stock ownership in AstraZeneca and Roche. D.A. Barbie reports personal fees from Qiagen/N of One and other support from Xsphera Biosciences outside the submitted work. Z. Piotrowska reports grants from NIH during the conduct of the study. Z. Piotrowska also reports personal fees from Eli Lilly, Boehringer Ingelheim, Bayer, Sanofi, C4 Therapeutics, and Taiho Pharmaceuticals; grants, personal fees, and other support from Janssen and AstraZeneca; grants and personal fees from Takeda, Cullinan Oncology, Daiichi Sankyo, and Blueprint Medicines; grants from Novartis, Spectrum Pharmaceuticals, AbbVie, GlaxoSmithKline/Tesaro, and Phanes Therapeutics; and grants and other support from Genentech/Roche outside the submitted work. T.K. Choueiri reports personal fees and other support from Precede Bio during the conduct of the study, as well as grants and other support from Precede Bio outside the submitted work; in addition, T.K. Choueiri has a patent for Precede Bio with royalties paid. T.K. Choueiri also reports institutional and/or personal, paid and/or unpaid support for research, advisory boards, consultancy, and/or honoraria past 5 years, ongoing or not, from Alkermes, Arcus Bio, AstraZeneca, Aravive, Aveo, Bayer, Bristol Myers Squibb, Calithera, Circle Pharma, Deciphera Pharmaceuticals, Eisai, EMD Serono, Exelixis, GlaxoSmithKline, Gilead, HiberCell, IQVA, Infinity, Ipsen, Janssen, Kanaph, Lilly, Merck, Nikang, Neomorph, Nuscan/Precede Bio, Novartis, Oncohost, Pfizer, Roche, Sanofi/Aventis, Scholar Rock, Surface Oncology, Takeda, Tempest, Up-To-Date, CME events (Peerview, OncLive, MJH, CCO and others), outside the submitted work; institutional patents filed on molecular alterations and immunotherapy response/toxicity, and ctDNA; equity from Tempest, Pionyr, Osel, Precede Bio, CureResponse, InnDura Therapeutics, and Primium; committees for NCCN, GU Steering Committee, ASCO (BOD 6-2024-), ESMO, ACCRU, and KidneyCan; medical writing and editorial assistance support may have been funded by communications companies in part; mentored several non-US citizens on research projects with potential funding (in part) from non-US sources/foreign components; and the institution (Dana-Farber Cancer Institute) may have received additional independent funding of drug companies and/or royalties potentially involved in research around the subject matter. T.K. Choueiri is supported in part by the Dana-Farber/Harvard Cancer Center Kidney SPORE (2P50CA101942-16) and Program 5P30CA006516-56, the Kohlberg Chair at Harvard Medical School and the Trust Family, Michael Brigham, Pan Mass Challenge, Hinda and Arthur Marcus Fund, and Loker Pinard Funds for Kidney Cancer Research at DFCI. S.C. Baca reports personal fees and other support from Precede Biosciences outside the submitted work. A.N. Hata reports grants and personal fees from Amgen, Nuvalent, and Pfizer; grants from BridgeBio, Bristol-Myers Squibb, C4 Therapeutics, Eli Lilly, Novartis, and Scorpion Therapeutics; and personal fees from Engine Biosciences, Oncovalent, TigaTx, and Tolremo outside the submitted work. M.L. Freedman reports personal fees and other support from Precede Biosciences outside the submitted work; in addition, M.L. Freedman has a patent for 'Methods, kits and systems for determining the status of lung cancer and methods for treating lung cancer based on same' pending. J.E. Berchuck reports non-financial support and other support from Precede Biosciences during the conduct of the study. J.E. Berchuck also reports grants, personal fees, and non-financial support from Guardant Health; personal fees and other support from Genome Medical; and other support from Oncotect, TracerDx, and Musculo outside the submitted work. In addition, J.E. Berchuck has an institutional patent on methods to detect neuroendocrine prostate cancer through tissue-informed cell-free DNA methylation analysis issued, licensed, and with royalties paid from Precede Biosciences and an institutional patent on methods to detect small cell lung cancer through epigenomic cfDNA analysis pending. No disclosures were reported by the other authors.

Figures

Figure 1.
Figure 1.
Overview of the experimental approach to perform comprehensive epigenomic profiling of lung cancer PDXs and multianalyte epigenomic profiling of cfDNA from 1 mL of patient plasma and noninvasively detect SCLC transformation in patients with EGFRm LUAD. (Created with BioRender.com).
Figure 2.
Figure 2.
Comprehensive epigenomic profiling of LUAD, tSCLC, and de novo SCLC reveals widespread epigenomic reprogramming in small transformation. A, Principal component analysis (PCA) plots of the ATAC, H3K27ac ChIP, H3K4me3 ChIP, H3K27me3 ChIP, MeDIP, and RNA-seq data reveal clustering of tSCLC tumors with de novo SCLC tumors and their distinction from LUAD tumors. B, Representative epigenomic data from an EGFRm tSCLC, EGFRm LUAD, and de novo SCLC PDX, showing gain of signal in markers of active gene transcription (ATAC-seq, H3K27ac ChIP-seq, HK4me3 ChIP-seq, gene body DNA methylation) and loss of signal with repressive marks (H3K27me3 ChIP-seq) at neural lineage-defining genes in tSCLC. Each track depicts signal intensity for the indicated epigenetic mark in the indicated sample.
Figure 3.
Figure 3.
Comparative analysis identifies a robust set of highly differential epigenomic features between LUAD and SCLC. A, Heatmap of normalized H3K27ac tag densities at differential H3K27ac sites between LUAD and SCLC tumors (FDR-adjusted P < 0.001 and log2 fold-change > 2) located ±2 kb from peak center. B, Volcano plot showing overlap of the log2 fold-change differentially expressed genes between LUAD and SCLC PDXs with respective differential H3K27ac peaks enriched in LUAD (blue) and SCLC (red). Two-sided P values were corrected for multiple hypothesis testing (FDR-adjusted P < 0.05).
Figure 4.
Figure 4.
Noninvasive detection of tSCLC via tissue-informed epigenomic cfDNA analysis. Box plots show cfDNA SCLC risk scores for plasma samples from patients with EGFRm LUAD and EGFRm tSCLC based on H3K27ac cfChIP-seq analysis (A), H3K4me3 cfChIP-seq analysis (B), cfDNA methylation analysis (C), or cfDNA chromatin accessibility analysis (D). Box plots show the interquartile range and median value for each dataset with whiskers representing the larger value of 1.5 times the interquartile range or the largest value in the dataset. P values were calculated using Mann–Whitney test. Corresponding ROC curves with the AUROC are included.
Figure 5.
Figure 5.
Integrating multiple epigenomic cfDNA analytes improves noninvasive detection of tSCLC. A, Venn diagram showing the overlap of differential H3K27ac, DNA methylation, and open chromatin sites between SCLC and LUAD PDXs. B, Box plot shows cfDNA SCLC risk scores for plasma samples from patients with EGFRm LUAD and EGFRm tSCLC based on an integrated epigenomic classifier incorporating H3K27ac, DNA methylation, and chromatin accessibility analysis. Box plot shows the interquartile range and median value for each dataset with whiskers representing the larger value of 1.5 times the interquartile range or the largest value in the dataset. P value was calculated using Mann–Whitney test. ROC curve with the AUROC is included. Optimal cut-off was calculated using Youden index. C, Correlation of SCLC risk score with estimated cfDNA tumor fraction in patients with EGFRm tSCLC and EGFRm LUAD.
Figure 6.
Figure 6.
A–B, Patient vignettes highlight the ability to noninvasively detect small cell transformation in patients with EGFRm LUAD through cfDNA epigenomic profiling. Longitudinal assessment of the integrated epigenomic cfDNA SCLC risk score in 2 patients with EGFRm LUAD who experienced biopsy-proven SCLC.
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