Cell-free DNA Methylation as a Predictive Biomarker of Response to Neoadjuvant Chemotherapy for Patients with Muscle-invasive Bladder Cancer in SWOG S1314.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is the standard of care in muscle-invasive bladder cancer (MIBC). However, treatment is intense, and the overall benefit is small, necessitating effective biomarkers to identify patients who will benefit most. OBJECTIVE: To characterize cell-free DNA (cfDNA) methylation in patients receiving NAC in SWOG S1314, a prospective cooperative group trial, and to correlate the methylation signatures with pathologic response at radical cystectomy. DESIGN, SETTING, AND PARTICIPANTS: SWOG S1314 is a prospective cooperative group trial for patients with MIBC (cT2-T4aN0M0, ≥5 mm of viable tumor), with a primary objective of evaluating the coexpression extrapolation (COXEN) gene expression signature as a predictor of NAC response, defined as achieving pT0N0 or ≤pT1N0 at radical cystectomy. For the current exploratory analysis, blood samples were collected prospectively from 72 patients in S1314 before and during NAC, and plasma cfDNA methylation was measured using the Infinium MethylationEPIC BeadChip array. INTERVENTION: No additional interventions besides plasma collection. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Differential methylation between pathologic responders (≤pT1N0) and nonresponders was analyzed, and a classifier predictive of treatment response was generated using the Random Forest machine learning algorithm. RESULTS AND LIMITATIONS: Using prechemotherapy plasma cfDNA, we developed a methylation-based response score (mR-score) predictive of pathologic response. Plasma samples collected after the first cycle of NAC yielded mR-scores with similar predictive ability. Furthermore, we used cfDNA methylation data to calculate the circulating bladder DNA fraction, which had a modest but independent predictive ability for treatment response. In a model combining mR-score and circulating bladder DNA fraction, we correctly predicted pathologic response in 79% of patients based on their plasma collected at baseline and after one cycle of chemotherapy. Limitations of this study included a limited sample size and relatively low circulating bladder DNA levels. CONCLUSIONS: Our study provides the proof of concept that cfDNA methylation can be used to generate classifiers of NAC response in bladder cancer patients. PATIENT SUMMARY: In this exploratory analysis of S1314, we demonstrated that cell-free DNA methylation can be profiled to generate biomarker signatures associated with neoadjuvant chemotherapy response. With validation in additional cohorts, this minimally invasive approach may be used to predict chemotherapy response in locally advanced bladder cancer and perhaps also in metastatic disease.

Cancer transcriptomic profiling from rapidly enriched circulating tumor cells.

Transcriptomic profiling of metastatic cancer can illuminate mechanisms of progression and lead to new therapies, but standard biopsy is invasive and reflects only a single metastatic site. In contrast, circulating tumor cell (CTC) profiling is noninvasive and repeatable, reflecting the dynamic and systemic nature of advanced disease. To date, transcriptomic profiling of CTCs has not delivered on its full potential, because white blood cells (WBCs) vastly outnumber CTCs. Current profiling strategies either lack cancer sensitivity and specificity or require specialized CTC capture protocols that are not readily scalable to large patient cohorts. Here, we describe a new strategy for rapid CTC enrichment and transcriptomic profiling using commercially available WBC depletion, microfluidic enrichment and RNA sequencing. When applied to blood samples from patients with advanced prostate cancer (PC), transcriptomes from enriched samples cluster with cancer positive controls and previously undetectable prostate-specific transcripts become readily measurable. Gene set enrichment analysis reveals multiple significantly enriched signaling pathways associated with PC, as well as novel pathways that merit further study. This accessible and scalable approach yields cancer-specific transcriptomic data and can be applied repeatedly and noninvasively in large cancer patient cohorts to discover new therapeutic targets in advanced disease.