Jerry Lee

Phenotypic Heterogeneity of Circulating Tumor Cells Informs Clinical Decisions between AR Signaling Inhibitors and Taxanes in Metastatic Prostate Cancer

The heterogeneity of an individual patients tumor has been linked to treatment resistance, but quantitative biomarkers to rapidly and reproducibly evaluate heterogeneity in a clinical setting are currently lacking. Using established tools available in a College of American Pathologists-accredited and Clinical Laboratory Improvement Amendments-certified clinical laboratory, we quantified digital pathology features on 9,225 individual circulating tumor cells (CTC) from 179 unique metastatic castration-resistant prostate cancer (mCRPC) patients to define phenotypically distinct cell types. Heterogeneity was quantified on the basis of the diversity of cell types in individual patient samples using the Shannon index and associated with overall survival (OS) in the 145 specimens collected prior to initiation of the second or later lines of therapy. Low CTC phenotypic heterogeneity was associated with better OS in patients treated with androgen receptor signaling inhibitors (ARSI), whereas high heterogeneity was associated with better OS in patients treated with taxane chemotherapy. Overall, the results show that quantifying CTC phenotypic heterogeneity can help inform the choice between ARSI and taxanes in mCRPC patients. Cancer Res; 77(20); 5687-98. ©2017 AACR.

Chromosomal Instability Estimation Based on Next Generation Sequencing and Single Cell Genome Wide Copy Number Variation Analysis

Genomic instability is a hallmark of cancer often associated with poor patient outcome and resistance to targeted therapy. Assessment of genomic instability in bulk tumor or biopsy can be complicated due to sample availability, surrounding tissue contamination, or tumor heterogeneity. The Epic Sciences circulating tumor cell (CTC) platform utilizes a non-enrichment based approach for the detection and characterization of rare tumor cells in clinical blood samples. Genomic profiling of individual CTCs could provide a portrait of cancer heterogeneity, identify clonal and sub-clonal drivers, and monitor disease progression. To that end, we developed a single cell Copy Number Variation (CNV) Assay to evaluate genomic instability and CNVs in patient CTCs. For proof of concept, prostate cancer cell lines, LNCaP, PC3 and VCaP, were spiked into healthy donor blood to create mock patient-like samples for downstream single cell genomic analysis. In addition, samples from seven metastatic castration resistant prostate cancer (mCRPC) patients were included to evaluate clinical feasibility. CTCs were enumerated and characterized using the Epic Sciences CTC Platform. Identified single CTCs were recovered, whole genome amplified, and sequenced using an Illumina NextSeq 500. CTCs were then analyzed for genome-wide copy number variations, followed by genomic instability analyses. Large-scale state transitions (LSTs) were measured as surrogates of genomic instability. Genomic instability scores were determined reproducibly for LNCaP, PC3, and VCaP, and were higher than white blood cell (WBC) controls from healthy donors. A wide range of LST scores were observed within and among the seven mCRPC patient samples. On the gene level, loss of the PTEN tumor suppressor was observed in PC3 and 5/7 (71%) patients. Amplification of the androgen receptor (AR) gene was observed in VCaP cells and 5/7 (71%) mCRPC patients. Using an in silico down-sampling approach, we determined that DNA copy number and genomic instability can be detected with as few as 350K sequencing reads. The data shown here demonstrate the feasibility of detecting genomic instabilities at the single cell level using the Epic Sciences CTC Platform. Understanding CTC heterogeneity has great potential for patient stratification prior to treatment with targeted therapies and for monitoring disease evolution during treatment.

Androgen receptor expression on circulating tumor cells in metastatic breast cancer

Purpose Androgen receptor (AR) is frequently detected in breast cancers, and AR-targeted therapies are showing activity in AR-positive (AR+) breast cancer. However, the role of AR in breast cancers is still not fully elucidated and the biology of AR in breast cancer remains incompletely understood. Circulating tumor cells (CTCs) can serve as prognostic and diagnostic tools, prompting us to measure AR protein expression and conduct genomic analyses on CTCs in patients with metastatic breast cancer. Methods Blood samples from patients with metastatic breast cancer were deposited on glass slides, subjected to nuclear staining with DAPI, and reacted with fluorescent-labeled antibodies to detect CD45, cytokeratin (CK), and biomarkers of interest (AR, estrogen receptor [ER], and HER2) on all nucleated cells. The stained slides were scanned and enumerated by non-enrichment-based non-biased approach independent of cell surface epithelial cell adhesion molecule (EpCAM) using the Epic Sciences CTC platform. Data were analyzed using established digital pathology algorithms. Results Of 68 patients, 51 (75%) had at least 1 CTC, and 49 of these 51 (96%) had hormone-receptor-positive (HR+)/HER2-negative primary tumors. AR was expressed in CK+ CTCs in 10 patients. Of these 10 patients, 3 also had ER expression in CK+ CTCs. Single cell genomic analysis of 78 CTCs from 1 of these 3 patients identified three distinct copy number patterns. AR+ cells had a lower frequency of chromosomal changes than ER+ and HER2+ cells. Conclusions CTC enumeration and analysis using no enrichment or selection provides a non-biased approach to detect AR expression and chromosomal aberrations in CTCs in patients with metastatic breast cancer. The heterogeneity of intrapatient AR expression in CTCs leads to the new hypothesis that patients with AR+ CTCs have heterogeneous disease with multiple drivers. Further studies are warranted to investigate the clinical applicability of AR+ CTCs and their heterogeneity.

Abstract A061: Tumor mutation burden (TMB), microsatellite instability (MSI), and chromosomal instability (CIN) analysis using low pass whole genome sequencing of single circulating tumor cell (CTC)

Background: Genomic instability (GI) is a hallmark of cancer often associated with poor patient outcome. TMB, MSI, and CIN represent the majority of GI in metastatic patients. Recent studies show that TMB and MSI are emerging immune checkpoint inhibitor drug sensitivity biomarkers and CIN is a sensitivity marker for PARP inhibitors. Assessment of genomic instability in bulk tumor samples is well explored, but it is limited by sample availability and tumor heterogeneity. Analysis of ctDNA is feasible for TMB and MSI analysis but not CIN, and it also suffers in sensitivity and specificity in patients who harbor subclonal GI, limiting the clinical utility of these assays to detect early clonal alterations. The Epic Sciences CTC platform is a non-enrichment-based approach for the detection and characterization of rare tumor cells in clinical blood samples, and could provide insight into subclonal heterogeneity. Here we present downstream single cell GI assay(s) for the detection of TMB, MSI and CIN from individual CTCs using next-generation sequencing and PCR. Methods: Contrived samples were prepared by spiking three prostate cancer cell lines, LNCaP, PC3, and VCaP, into healthy donor blood. Red blood cells were lysed, all nucleated cells deposited onto slides, slides immunofluorescence stained (DAPI, CK, CD45, and Androgen Receptor), and identified cancer cells individually picked up from the slides. Each recovered cell was lysed, whole genome amplified (WGA), shotgun library prepared, and low pass whole genome sequenced using Illumina NextSeq 500. Data were analyzed for TMB scores (# of INDELs per Mbp) and large-scale transitions (LSTs, a surrogate of CIN). MSI was measured using Qiagen Type-It microsatellite PCR kit for four sites (BAT26, BAT25, D2S123, and D5S346). Samples from metastatic castration resistant prostate cancer (mCRPC) patients were included to evaluate clinical feasibility. Results: TMB scores for LNCaP (average 652, coefficient of variation 15%) were significantly higher than PC3 (558, 0.9%), VCaP (548, 1.1%), and WBC from healthy donor (540, 7.6%) with p Citation Format: Angel Rodriguez, Jerry Lee, Ramsay Sutton, Rhett Jiles, Yipeng Wang, Mark Landers, Ryan Dittamore. Tumor mutation burden (TMB), microsatellite instability (MSI), and chromosomal instability (CIN) analysis using low pass whole genome sequencing of single circulating tumor cell (CTC) [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A061.

Abstract A35: Single cell genomic profiling of circulating tumor cells (CTCs) from metastatic colorectal cancer (mCRC) identify tumor heterogeneity and rare somatic driver alterations

Background: Mostly asymptomatic until late stage, colorectal cancer is driven by the successive accumulation of genetic alterations resulting in genomic instability within subclonal tumor populations. mCRC often progresses as a subclonally diverse multifocal disease due to selective therapeutic pressure, the surrounding tumor microenvironment, and underlying genomic heterogeneity. Targeted therapies against EGFR, VEGF or BRAF have shown increased response in a subset of patients; however, patient stratification using standard population analysis of DNA markers from tumor biopsy, (i.e. chromosomal instability, microsatellite instability, promoter methylation, resistance mutations), is problematic due to tumor heterogeneity. CTCs reflect the active metastatic subclonal populations at any given time, making single cell analysis of DNA markers a more accurate, real-time picture of cumulative metastatic diversity. Using Epic9s enrichment-free CTC analysis platform, we characterized individual CTCs from a mCRC patient to understand the extent of intra-patient genomic heterogeneity, including the presence genomic instability and point mutations. We compared the prevalence of clinically relevant subclonal alterations within patient CTCs to CRC TCGA data, offering insights into identification of therapeutic opportunities and potential mechanisms of resistance. Methods: Blood was collected from a heavily pretreated mCRC patient and was processed for CTC analysis using the Epic Platform. 34 CTCs were individually recovered, lysed, whole genome amplified, constructed into shotgun libraries and target enriched for all coding regions of 500 pan-cancer genes. Enriched libraries were sequenced to an average depth of 697X coverage by 2×150 PE sequencing. Sequences were aligned and somatic mutations were determined using VarScan with the patient9s WBC as germline reference. Variants were filtered for functional gain- or loss-of-function mutations by SIFT/PolyPhen2 and selected based on low frequency in 1000g database. Genomic instability and loss of heterozygosity (LOH) was also assessed. Somatic variants deriving from the patient CTC cohort and TCGA CRC cohort of 302 patients were annotated, analyzed, and compared using GenePool™ software (Station X). Results: MLL3 alterations, frequently observed in primary CRC biopsies (14%), were identified in 70% of all CTCs sequenced. Previously cited somatic variants were detected in minor subclonal populations of CTCs, including APC (12%), BRCA1/2 (8%), KRAS (6%), PI3KCA (6%) and TP53 (6%). A wide range of genomic instabilities and LOH was also observed across CTCs. Conclusions: The Epic CTC platform is suited to identify subclonal population of CTCs harboring clinically relevant genomic alterations on a single cell level, which can inform clonal drift, identify rare clonal populations, and enable patient stratification at higher resolution. Citation Format: Stephanie Greene, Jerry Lee, Mark Landers, Sandeep Sanga, Adam Jendrisak, Ryon Graf, Jessica Louw, Shannon Werner, Yipeng Wang, Ryan Dittamore, Dena Marrinucci. Single cell genomic profiling of circulating tumor cells (CTCs) from metastatic colorectal cancer (mCRC) identify tumor heterogeneity and rare somatic driver alterations. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A35.