Early Changes in Tumor-Naive Cell-Free Methylomes and Fragmentomes Predict Outcomes in Pembrolizumab-Treated Solid Tumors

Abstract

Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) in 204 plasma samples from 87 patients before and during treatment with pembrolizumab from a pan-cancer phase II investigator-initiated trial (INSPIRE). We trained a pan-cancer methylation signature using independent methylation array data from The Cancer Genome Atlas to quantify cancer-specific methylation (CSM) and fragment-length score (FLS) for each sample. CSM and FLS are strongly correlated with tumor-informed ctDNA levels. Early kinetics of CSM predict overall survival and progression-free survival, independently of tumor type, PD-L1, and tumor mutation burden. Early kinetics of FLS are associated with overall survival independently of CSM. Our tumor-naïve mutation-agnostic ctDNA approach integrating methylomics and fragmentomics could predict outcomes in patients treated with pembrolizumab.

Significance: Analysis of methylation and fragment length in plasma using cfMeDIP-seq provides a tumor-naive approach to measure ctDNA with results comparable with a tumor-informed bespoke ctDNA. Early kinetics within the first weeks of treatment in methylation and fragment quantity can predict outcomes with pembrolizumab in patients with various advanced solid tumors. This article is featured in Selected Articles from This Issue, p. 897.

Figure 1.

Overview of the study and the analysis of cell-free methylomes and fragmentomes. A, CONSORT diagram for cfMeDIP-seq analysis within the INSPIRE study. B, We performed cfMeDIP-seq in a cohort of patients with various solid tumors treated with pembrolizumab. We computed CSM scores and fragment-length scores through joint analysis of the methylome and fragmentome. C, CSM and (D) FLS were significantly higher in samples from patients with cancer than those from normal controls, by Wilcoxon rank-sum tests. Boxplots show median and quartiles.

Figure 2.

Joint analysis of cell-free methylomes and fragmentomes enables accurate estimation of ctDNA abundance. A, We fit a logistic regression model with cancer vs. noncancer as the response variable using cfMeDIP-seq data from 85 blood samples from patients with baseline advanced cancer and 100 normal controls. CSM and FLS were each independently associated with cancer. B, The predictions of the logistic regression model can be interpreted as a joint cfMeDIP-seq score corresponding to the log odds of a sample arising from a patient with cancer. C, Using this score, we found that a threshold of −1.754 best identified cases which had undetectable ctDNA based on targeted deep sequencing of cancer-specific mutations. D, cfMeDIP-seq scores correlated with tumor-informed CMC determined by tumor-informed bespoke array (SignateraTM) in all cohorts at baseline (SB) and cycle 3 (C3B). E, cfMeDIP-seq scores vary dynamically across time points and decrease more in patients who exhibit PR or CR to pembrolizumab.

Figure 3.

Association of CSM and CMC with survival outcomes in patients with advanced cancer treated with pembrolizumab. Circulating tumor DNA was quantified using a methylation-based approach (CSM) and a bespoke tumor-informed mutation sequencing approach (CMC). A, Methylation probabilities were summed across 200 cancer-specific sites, curated based on independent analysis of methylation array data from The Cancer Genome Atlas. ΔCSM was calculated based on the change in CSM from SB to C3B. Changing regions are shown in the heat map, alongside final ΔCSM values. B, Decrease in CSM and CMC from baseline to cycle 3 are each associated with improved OS and PFS. C, In multivariable Cox analyses, ΔCSM was a significant, independent predictor of PFS and OS, adjusted for cohort, PD-L1 expression, and tumor mutation burden. D, A decrease in either CSM or CMC was associated with improved survival, whereas increase in both metrics identified patients with particularly poor outcome.

Figure 4.

CSM and FLS are jointly predictive of survival outcomes in patients with advanced cancer receiving pembrolizumab. A, We computed FLS based on the relative similarity of fragmentomic profiles to reference cancer and normal fragment-length histograms. A decrease in FLS from baseline to pre-cycle 3 is associated with prolonged OS, with a nonstatistically significant trend for prolonged PFS. B, In a joint model of CSM and FLS, Kaplan–Meier plots show that patients with a decrease in each biomarker from baseline to cycle 3 are associated with improved OS and PFS. C, Multivariable analyses confirm that both CSM and FLS are independently predictive of OS, whereas only CSM achieves statistical significance for PFS.

Figure 5.

CSM and FLS estimate ctDNA clearance status, identifying patients with durable response to pembrolizumab. The log odds of cancer were computed using a logistic regression model with CSM and FLS as independent variables. A threshold of below −1.754 was determined to identify patients with clearance. A, By this criterion, 11 patients cleared ctDNA based on cfMeDIP-seq, of which 9 also had undetectable ctDNA based on CMC from targeted deep sequencing. B, Patients with clearance demonstrated persistently low cfMeDIP-seq scores. The two patients with clearance by CMC but not cfMeDIP-seq (INS-A-019 and INS-D-012) both had low cfMeDIP-seq scores near the clearance threshold. C, The clinical course and radiologic size of index tumors of all 13 patients with clearance by either method are shown. Vertical lines show the median and maximum PFS of patients within each cohort. D, Patients meeting the cfMeDIP-seq clearance criteria demonstrated strikingly favorable PFS and OS.