Single-Cell Circulating Tumor Cell Analysis Reveals Genomic Instability As a Distinctive Feature of Aggressive Prostate Cancer Paymaneh D

Published OnlineFirst April 27, 2020; DOI: 10.1158/1078-0432.CCR-19-4100

CLINICAL CANCER RESEARCH | TRANSLATIONAL CANCER MECHANISMS AND THERAPY

Single-Cell Circulating Tumor Cell Analysis Reveals Genomic Instability as a Distinctive Feature of Aggressive Prostate Cancer Paymaneh D. Malihi1, Ryon P. Graf2, Angel Rodriguez2, Naveen Ramesh3,4, Jerry Lee2, Ramsay Sutton2, Rhett Jiles2, Carmen Ruiz Velasco1, Emi Sei3, Anand Kolatkar1, Christopher Logothetis5,6, Nicholas E. Navin3,4,6,7, Paul Corn5, Ana M. Aparicio5, Ryan Dittamore2, James Hicks1, Peter Kuhn1, and Amado J. Zurita5

ABSTRACT ◥ Purpose: Aggressive variant prostate cancer (AVPC) represents a Results: A total of 257 individual CTC were sequenced from clinical subset distinguished by therapy resistance and poor prog- 47 patients (1–22 CTC/patient). Twenty patients (42.6%) had nosis, linked to combined losses of the tumor suppressor genes concurrent 2þTSG losses in at least one CTC in association with (TSG) PTEN, RB1, and TP53. Circulating tumor cells (CTC) poor survival and increased genomic instability, inferred by high provide a minimally invasive opportunity for identification and large-scale transitions scores. Higher LST in CTC were indepen- molecular characterization of AVPC. We aimed to evaluate the dent of CTC enumerated, clinically more indicative of aggressive incidence and clinical significance of compound (2þ)TSG losses behavior than co-occurring TSG losses, and molecularly associ- and genomic instability in prostate cancer CTC, and to expand the ated with gains in chromosomal regions including PTK2, Myc, set genomic biomarkers relevant to AVPC. and NCOA2; increased androgen receptor expression; and Experimental Design: Genomic analysis of chromosomal BRCA2 loss. In 57 patients with matched cell-free tumor DNA copy-number alterations (CNA) at single-cell resolution was data, CTC were more frequently detectable and evaluable for performed in CTC from patients with and without AVPC before CNA analysis (in 73.7% vs. 42.1%, respectively). initiating chemotherapy with cabazitaxel or cabazitaxel and Conclusions: Our findings suggest that genomic instability in carboplatin. We evaluated associations between single-CTC CTC is a hallmark of advanced prostate cancer aggressiveness, genomics and clinical features, progression-free survival, and and support single-CTC sequencing as a compelling tool to overall survival. noninvasively characterize cancer heterogeneity.

Introduction Developing a more precise stratification based on biological characteristics has been hindered by the difficulty in reliably accessing Prostate cancer is associated with considerable clinical heteroge- metastatic tumor tissue, as the dominant site of prostate cancer neity and molecular diversity between and within patients (1–8). metastasis is the bone. Tumor burden, anatomical distribution, and line of treatment con- Although metastatic castration-resistant prostate cancer (mCRPC) stitute the foundation of the clinical classification of advanced disease, responds in a majority of cases to novel androgen receptor (AR) but these factors do not fully capture the heterogeneity observed. signaling inhibitors (such as abiraterone and enzalutamide), resistance almost invariably develops over months to years (1, 9, 10). In 20% to 30% of the patients, the disease directly fails to respond. 1USC Michelson Center for Convergent Bioscience, University of Southern AR inhibition–refractory and a number of resistant forms tend California, Los Angeles, California. 2Epic Sciences, Inc, San Diego, California. 3Department of Genetics, University of Texas MD Anderson Cancer Center, to present with atypical clinical characteristics, occasionally with Houston, Texas. 4University of Texas Health Graduate School of Biomedical neuroendocrine features upon pathologic assessment, and to behave Sciences, Houston, Texas. 5Department of Genitourinary Medical Oncology, with increased aggressiveness, resulting in poor prognosis and University of Texas MD Anderson Cancer Center, Houston, Texas. 6David H. increased morbidity (11, 12). Because those patients with aggressive Koch Center for Applied Research of Genitourinary Cancers, University of Texas variant prostate cancer (AVPC) may benefit from intensified treat- MD Anderson Cancer Center, Houston, Texas. 7Department of Bioinformatics ments (13), a number of clinical criteria were proposed to facilitate and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas. AVPC recognition (11). However, the clinical presentations may be difficult to distinguish, and treatment options remain limited and Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). suboptimal, together making molecular characterization and more precise identification priorities for the field (11, 12, 14–16). P.D. Malihi and R.P. Graf contributed equally to this article. Preclinical and clinical studies have established candidate molecular Corresponding Authors: Peter Kuhn, University of Southern California, 1002 markers for the classification of AVPC using prostate cancer cell lines, Childs Way, MCB 351, MC3502, Los Angeles, CA 90089. Phone: 213-821-3980; patient tumor-derived xenografts, and biopsy tissue specimens from Fax: 213-821-7854; E-mail: [email protected]; and Amado J. Zurita, The University primary and metastatic sites (12, 14, 15). Genomic aberrations affect- of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030. PTEN, RB1 TP53 Phone: 713-792-2830; E-mail: [email protected] ing the tumor suppressor genes (TSG) , and are among the most frequently enriched in mCRPC relative to earlier Clin Cancer Res 2020;XX:XX–XX prostate cancer presentations (2–4, 17). TSG loss-of-function has been doi: 10.1158/1078-0432.CCR-19-4100 linked to an adverse prognosis (3, 17–22). In particular, two-hit RB1 2020 American Association for Cancer Research. loss is frequently found in neuroendocrine prostate cancer, often

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TSG losses previously linked to AVPC can be detected in CTC, with Translational Relevance clinical outcomes consistent with previous reports for tissue assess- Early identification and precise characterization of aggressive ments, and to identify relationships between established biology variant prostate cancer (AVPC) is needed to develop better treat- of tumor suppressor losses and emergent phenomena of genomic ment strategies and improve patients’ outcomes. Integrative anal- instability resolvable via clinically friendly blood sampling. Under ysis of morphology, protein expression, and genomics in circulat- the premise that CTC in this clinical setting provides a faithful ing tumor cells (CTC) provides a minimally invasive opportunity representation of the tumor clones driving progression, we next to achieve these goals. We performed single-CTC analysis of used single-CTC resolution to refine the set of genomic biomarkers chromosomal copy-number alterations in blood obtained from associated with aggressive prostate cancer behavior and poor patients on a clinical trial with clinical features of AVPC or prognosis. “conventional” castration-resistant prostate cancer. The strategy proved feasible, more sensitive for detection of compound losses in PTEN, RB1, and TP53 than circulating tumor DNA, and infor- Materials and Methods mative for recognition of other concurrent gene alterations of Patients possible biological significance at the single-cell level. Importantly, Blood samples were collected immediately before starting treatment we identified large-scale transitions, a genome-wide measure of from participants on trial NCT01505868, “Study of cabazitaxel with or DNA scarring that has been linked to homologous recombination without carboplatin in patients with metastatic castration-resistant deficiency, as a novel indicator of genomic instability and aggres- prostate cancer.” This was a prospective, randomized, two-site (MD siveness in prostate cancer CTC. Anderson Cancer Center and Barbara Ann Karmanos Cancer Insti- tute), phase I/II study evaluating the efficacy of cabazitaxel in combination with carboplatin relative to cabazitaxel alone in men with mCRPC (13), including patients with prospectively stratified AVPC together with concurrent alterations in PTEN and/or TP53 (12, 14, 15). (features described in ref. 11). The study was approved by the Indeed, RB1-loss promotes lineage plasticity, anti-androgen blockade corresponding institutional review boards and was conducted in indifference, and a switch to a neuroendocrine transcriptional pro- accordance to ethical principles founded in the Declaration of gram in PTEN-deficient preclinical models in cooperation with TP53 Helsinki. All patients gave written informed consent. No tumor loss (23–26). Moreover, the presence of at least two of those cooper- biopsies were required prior to trial enrollment. Three subcohorts ative TSG alterations may indicate benefit from carboplatin in were evaluated on the basis of the availability of samples for analysis AVPC (13). (Supplementary Fig. S1): (i) CellSearch: samples with matched EsCTC The collective knowledge of cancer genomics has been primarily and CellSearch (clinically measured CTC number) draws (n ¼ 47); built upon tissue sampling of the solid phase of disease: primary (ii) CTC-sequenced: with at least one whole genome-sequenced CTC tumors and metastatic tissue. Comparatively, knowledge about the (n ¼ 47); and (iii) circulating cell-free tumor DNA (ctDNA)-matched genomics of the seeds of metastases in transit, circulating tumor cells (n ¼ 57). We defined high disease volume as >10 focal bone metastases (CTC), is slim. Even slimmer is genomic data about this fluid phase at and/or tumor mass >4 cm at any site, and/or extension to at least three clinically relevant points in disease evolution. Compared with meta- organ sites with one lesion at least 2 cm in diameter. Low disease static tissue sampling, CTC provides a minimally-invasive opportunity volume included 4 bone metastases with or without extension to to serially characterize tumor heterogeneity at single-cell resolution (8). lymph nodes up to 2 cm in diameter. The accelerated rate of pro- The Epic Sciences CTC platform (EsCTC) is based on direct and gression applied to patients with worsening performance status, pain, unbiased identification of all nucleated cells on a slide, inclusive of or other symptoms related to tumor growth in the 6 weeks prior to the heterogeneous CTC populations (27, 28). EsCTC has been used to blood specimen collection, and/or development of >2 new metastatic develop CTC phenotypic heterogeneity indices associated with lesions in a single site or new nonnodal organ site extension occurring increased overall survival (OS) benefit from taxane-based chemother- in the previous 3 months. apy relative to AR-targeted treatments in mCRPC (27), and to portray CTC characteristics in patients with neuroendocrine prostate can- CTC collection and analysis cer (28, 29). In addition to describing morphometric and organiza- Blood (7.5 mL) was collected in Streck tubes at the MD Anderson tional features, EsCTC is amenable to genomic characterization of Cancer Center from each subject before the start of treatment and CTC at the single-cell level via low-pass whole-genome sequencing sent to the Kuhn-Hicks Laboratory at the University of Southern analysis of copy-number alterations (CNA). This enables assessment California, for slide creation and banking within 24 hours, as of focal gains or losses of chromosomal regions as well as genome-wide described previously (27, 32). Sample processing for CTC detection estimations of genomic instability such as large-scale transitions (LST; and characterization was conducted at Epic Sciences. Two slides per ref. 30). LST is defined as chromosomal breakpoints between adjacent patient were processed, corresponding to a median of 0.9 mL blood DNA segments >10 MB in size, and its measurement has been used as a (Table 1). CTC was identified as cells containing an intact nucleus, functional surrogate to identify tumors with homologous recombina- without CD45 expression or leukocyte nuclear morphology, and tion deficiency (HRD), which may be associated with increased with cytokeratin or AR N-terminus expression, under Clinical clinical efficacy from platinum-based chemotherapy and/or PARP Laboratory Improvement Amendments (CLIA)-like conditions inhibitors (30, 31). (Fig. 1A and B). CTC clusters were defined as >1CTCsharinga Here we report on the application of the EsCTC workflow for the boundary. AR expression was calculated from AR-fluorescence analysis of single CTC genomics in blood samples collected from intensity compared with neighboring white blood cells. Califor- patients with mCRPC with clinically definedAVPCaswellasnon- nia-licensed clinical laboratory scientists conducted final QC of AVPC “conventional” mCRPC. Broadly, we sought to evaluate if the CTC classification.

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Table 1. Patient demographic and clinical characteristics of cohorts used in analyses.

ctDNA comparison Sequenced cohort (patients CellSearch-matched cohort with CTC sequenced) cohort

Patients, n 57 47 47 Age, years (median, range) 68 (51–85) 68 (51–85) 66 (51–85) Months to CRPC (median, range) 18 (2–139) 20 (3–139) 17 (2–139) ECOG 0 13 (23%) 9 (20%) 12 (26%) 1 32 (56%) 28 (60%) 26 (55%) 2 12 (21%) 10 (21%) 9 (19%) Gleason score available on diagnosis 6–7 16 (28%) 10 (21%) 11 (23%) 8–9 33 (57%) 32 (68%) 30 (66%) 10 4 (7%) 2 (4%) 3 (6%) Race Caucasian 46 (81%) 40 (85%) 37 (79%) African American 5 (9%) 4 (9%) 4 (9%) Others 6 (11%) 3 (7%) 6 (13%) Prior therapies Abiraterone 29 (51%) 23 (49%) 20 (43%) Enzalutamide 13 (23%) 10 (21%) 10 (21%) Abi and Enz sequentially 9 (16%) 7 (15%) 7 (15%) Abi and Enz in combination 3 (5%) 4 (9%) 4 (9%) Sipuleucel-T 8 (14%) 4 (9%) 6 (13%) Docetaxel 13 (23%) 12 (26%) 11 (23%) Radium 223 2 (4%) 1 (2%) 2 (4%) Other hormonal treatment 37 (65%) 32 (68%) 29 (62%) Clinically-deﬁned AVPC status positive 28 (49%) 21 (45%) 23 (49%) PFS, months (median, 95% CI) 5.9 (4.4 – 7.6) 5.4 (4.3–6.7) 5.2 (4.3–6.9) OS, months (median, 95% CI) 18.2 (16.5–23.8) 18.2 (16.5–23.8) 17.1 (14.4–23.8) Metastatic sites Bone 52 (91%) 42 (89%) 45 (96%) Lymph nodes 33 (58%) 27 (57%) 25 (53%) Prostate bed 24 (42%) 16 (34%) 19 (40%) Lung 7 (12%) 2 (4%) 6 (13%) Liver 5 (9%) 3 (6%) 3 (6%) Others 6 (11%) 4 (8%) 4 (8%) Blood analytes (median, range) PSA (ng/mL) 27.9 (0.1–1,214) 42.2 (0.1–1,214) 30.6 (0.1–1,214) Alkaline phosphatase (IU/L) 113 (45–1,080) 120 (49–1,080) 119.5 (48–1,080) LDH (units/L) 482 (239–2,171) 472.5 (239–2,171) 491 (239–1,589) Hemoglobin (g/dL) 11.9 (8.4–14.2) 11.9 (8.6–14.2) 12.1 (8.4–14.1) Albumin (g/dL) 4.1 (3.5–4.8) 4.1 (3.6–4.8) 4.1 (3.5–4.7) Bone alkaline phosphatase (mg/L) 23 (6.2–285) 31.5 (5.5–285) 30.5 (6.2–260) CEA (mg/L) 2.7 (0.6–391.3) 2.5 (0.7–47.7) 2.7 (0.6–391.3) CTC measures (median, range) EsCTC CTC/mL 3.9 (0–133.5) 5.0 (1.1–133.5) 3.9 (0–133.5) CellSearch CTC/mL 0.93 (0–81.9) Median volume of blood tested 0.9 mL 0.9 mL 7.5 mL Drug after CTC draw Cabazitaxel 23 (40%) 16 (34%) 18 (38%) Cabazitaxel þ Carboplatin 34 (60%) 31 (66%) 29 (62%)

Note: 15 and 13 draws did not have CellSearch values from a total of 62 patients.

CTC isolation, genome amplification, and next-generation hg38 human reference genome from the University of California Santa sequencing Cruz Genome database. BAM files were filtered for MAPQ 30 reads We followed previously described methods for CTC relocation, followed by discrete analyses for genome-wide profiling, individual isolation, and genomic sequencing (27, 30). If <12 CTC were present, gene copy-number changes, and LST. all were picked and processed for sequencing. If 12 were present, For genome-wide profiling, the hg38 human genome was divid- CTC was picked at random within a sample based on sequencing plate ed into 3,000 1Mbp bins and counted across bins for each cell. availability. Genome-wide CNA analysis was performed using the Epic Read counts were normalized against white blood cell controls, and Sciences single-cell CNA analysis pipeline. FASTQ files were aligned to the circular binary segmentation (CBS) algorithm “DNAcopy” was

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Figure 1. Enrichment-free CTC detection and single-cell genomics overview. A, Workflow for enrichment-free CTC isolation and genomics. All nucleated cells from a blood sample are plated onto microscope slides, and CTC are identified by automated fluorescence microscopy, then individually isolated and sequenced. B, Three example images of CTC are shown with all four individual channels shown, plus a merge panel. The top two CTC do not overexpress and the bottom CTC does overexpressthe n-terminus of the AR, a domain shared by full-length AR and all AR splice variants. C, Representative immunofluorescent images of CTC and corresponding genome- wide CNA profiles obtained from patient 180. Disease in this patient is characterized by high clonality, harboring CNA events extensively described in late-stage prostate cancer such as AR gain and PTEN loss. All CTC shown also had AR protein overexpression (not shown in composite). A CNA neutral profile was observed from a patient's WBC and used as a reference control.

used to segment DNA copy-number data (log2 normalized ratio, LST score carries few chromosomal breaks. Gene-based copy-number sample/reference) and identify abnormal copy number. For indi- analysis was performed as described previously, focusing on 578 vidual gene copy-number changes, reads were counted for each cancer genes from the Roche Comprehensive Cancer Design panel gene and each sample and normalized against the total sequencing (Roche Sequencing; ref. 30). reads for the particular sample. Normalized reads were compared with reference leukocytes, and Z scores were calculated for each Circulating tumor DNA extraction and analysis of copy gene. The significant cutoff for calling gene gain or loss was Z-score number > 3orZ-score < 3, respectively. DNA was extracted from plasma (median 3 mL, range 1–4.5 mL) LST was determined as contiguous chromosomal breakpoints that obtained before starting chemotherapy. The peripheral blood mono- were 10Mb in size (30). Each CNA profile was given an LST score based nuclear cell fraction was used to extract genomic DNA as a matched on the number of chromosomal breaks between adjacent regions of at normal sample. The plasma DNA was then size-selected (<1,000 bp) to least 10Mb across the entire genomic landscape (Fig. 1C). A CNA enrich for tumor DNA. QC for ctDNA at 160 bp was then performed, neutral profile carries an LST score close to zero, and CTC with a low quantified on TapeStation (Agilent). Samples with ctDNA >2 ng were

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used for low-input barcoded next-generation sequencing library con- progressed or deceased). Time-dependent ROC analyses were con- struction using highly efficient DNA ligases (KAPA HyperPlus). The ducted with R package “survivalROC.” To normalize for potential barcoded libraries were enriched by PCR and used for both whole- differences in scale, features were normalized to a scale of 0 to 1 prior to genome sparse sequencing (0.1) for copy-number profiling at high adding together for ROC analyses. Dimensionality reduction was (220 kb) resolution. conducted with the R package “umap” without parameter alter- Multiplexed reads sequenced using Illumina's HiSeq4000 (76 bp ation (33). Power estimations for predictive biomarker associations paired-end) were demultiplexed using Illumina's bcltofastq software made use of the method by Peterson and George 1993 for estimating and stored in individual FASTQ files while allowing one barcode the power of a biomarker-treatment interaction with a time-to-failure mismatch. The demultiplexed sequencing data were processed using outcome (34). With the exception of KM curves (R package “survmi- the “variable binning” pipeline. The individual FASTQ files were ner”), data visualizations were created using R package “ggplot2.” For aligned to human genome assembly NCBI Build 37 (hg19/NCBI37) all analyses, P < 0.05 was considered “significant,” without correction using Bowtie2 (2.2.6) alignment software. The aligned reads stored as for multiple comparisons unless noted. All statistical tests were two- SAM files were converted to BAM files and sorted using SAMtools sided unless noted. All statistical analyses were exploratory and not (1.2). PCR duplicates were marked and removed using SAMtools. The prospectively declared under NCT01505868. reads were counted using variable bin sizes at an average genomic resolution of 220 kb. Unique normalized read counts were segmented Data availability using the CBS method from R Bioconductor “DNAcopy” package The datasets generated and analyzed during this study are available followed by MergeLevels to join adjacent segments with nonsignificant from the corresponding author on reasonable request. differences in segmented ratios. The parameters used for CBS segmentation were alpha ¼ 0.0001 and undo.prune ¼ 0.05. Default parameters were used for MergeLevels, which removed erroneous Results chromosome breakpoints. CTC in relation to clinical parameters and CellSearch comparison Statistical analyses Peripheral blood samples were collected from 62 patients with Statistical analysis was done in R v3.4.1. Descriptive statistics were mCRPC prospectively stratified as AVPC (29 patients; ref. 11) or used to evaluate demographic and clinical characteristics at the time of conventional mCRPC (33 patients) before starting chemotherapy on blood draw. Wilcoxon rank-sum tests were utilized to compare trial NCT01505868 (Supplementary Fig. S1). Clinical characteristics differences between patient groups with continuous measures. The are described in Table 1. Using EsCTC, CTC were detectable in coefficient of determination was utilized to compare the fit of linear 49 patients (79.0%). Of those, enumeration, AR expression, and regressions. The probability of survival over time was assessed using genomic sequencing data were available from 47 patients. No asso- Kaplan–Meier (KM) estimation. Differences in time-to-event out- ciation was observed between CTC number (as single cells or CTC comes between groups were measured with the log-rank method, clusters) and site of metastasis, performance status, tumor load, or hazard ratios (HR) were obtained from a univariable Cox model, and clinically defined AVPC characteristics (Supplementary Figs. S2A and the P-value from the log-rank test within the coxph function, all in the S2B). The patients with shorter PFS and OS did however have higher R package “survival.” For comparisons of a continuous variable with a CTC counts (Table 2). CellSearch comparison showing a positive dichotomized time-to-event outcome, a time interval cut point was correlation between the two CTC enumeration methods and higher chosen at a point where censored patients were only in the group after detection sensitivity for EsCTC is available in Table 1 and Supple- the cut point, and all patients before the cut point had an event (i.e., had mentary Figs. S3A and S3B.

Table 2. CTC enumeration and median LST associations with clinical features and outcome.

Median Median Median LST Measure Group Patients CTC/mL P value AR(þ) CTC/mL P value in CTC P value

Clinical AVPC status Yes 21 7.6 0.052 2 0.18 24 0.025 No 26 4.2 0.52 10.25 Molecular AVPC status Yes 20 17.1a 0.0009a 2.3a 0.0041a 28.7 0.0015 No 27 3.9a 0a 9.5 Tumor load High 30 7.2 0.032 2.1 0.025 19.5 0.11 Int/Low 17 3.5 0 12.5 Prior Abiraterone or Enzalutamide Yes 30 4.4 0.39 0.5 0.38 14.5 0.47 No 17 7.6 1.9 18 Progression rate Accelerated 30 7.9 0.017 2.3 0.036 23.25 0.0062 Protracted 17 3.9 0 9.5 PFS <4 months 16 11 0.029 2.1 0.084 24 0.014 >4 months 31 4.1 0 9.5 OS <12 months 11 13.8 0.0101 2.3 0.11 26 0.043 >12 months 36 4.2 0.5 12

Note: The median CTC/mL, median AR(þ) CTC/mL, and median of the median LST across CTC within each patient sample are shown by groups indicated. P values reﬂect Wilcoxon rank-sum tests. aBecause a minimum number of CTC required for sequencing to determine a negative result is not yet formally established, these estimations might be confounded by number of CTC available for sequencing.

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Figure 2. PTEN, RB1, TP53 loss on single CTC. A, n ¼ 47 cohort with CTC detected and sequenced per patient. Progression-free survival is shown alongside subclonal percent of alterations of PTEN, RB1, and TP53, the presence of any two or all three of the alterations present in at least one CTC detected, and LSTs per patient across CTC (median, max, SD). KM curves visualizing (B) PFS and (C) OS of patients by the presence or absence of at least one CTC with loss of two or more of PTEN, RB1, and/or TP53 in the same CTC.

TSG-based signature through single-cell genomics individually was observed in at least one CTC in 21 of 47 patients Genome-wide CNA profiles were used to calculate copy number for (44.7%), concomitant 2þTSG loss in 20 of 47 patients (42.6%), and all- individual gene regions. A candidate molecular signature for AVPC 3TSG loss in 10 of 47 patients (21.3%; Fig. 2A; Supplementary Table S1 defined as loss of at least two of the three (2þ)TSG PTEN, RB1, and shows single-cell data per patient). Loss of 2þTSG on the same CTC TP53 in single CTC was evaluated first. A total of 257 CTC were was associated with shorter median progression-free survival (PFS) individually sequenced across the 47 patients (1–22 CTC/patient; and overall survival (OS; Fig. 2B and C; Supplementary Figs. S4A and ref. Fig. 2A). A patient qualified as TSG-signature positive if at least S4B shows all-3TSG loss). Patients with at least 3 CTC sequenced one CTC had a concurrent loss of 2þTSG. PTEN, RB1, or TP53 loss demonstrated similar results (Supplementary Figs. S4C and S4D).

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Figure 3. Comparison of AVPC molecular signature in CTC and ctDNA. A, Tile plot comparing TSG losses detected in ctDNA and CTC. B, A comparison of CTC enumeration by ctDNA status and the detection of loss of ≥2 of the TSG PTEN, RB1, TP53.

TSG loss signature is more frequently resolvable in CTC than in additive, multifactorial effect of other genes on AR protein expres- circulating tumor DNA sion in individual CTC. We found AR protein expression to be not We sought to compare TSG loss detection in DNA from CTC meaningfully different in the CTC of patients with clinically defined versus ctDNA. In 57 patients with matched samples available for AVPC relative to conventional mCRPC or in CTC with concurrent CTC and ctDNA analysis, 24 (42%) had sufficient ctDNA for loss of RB1 and TP53 relative to all other CTC (Supplementary Figs. CNA assessment, and 44 (77%) had CTC detected and sequenced, S5A and S5B). passing quality control (Fig. 3A).CTC/mLwashigherinpatientswith sufficient versus insufficient ctDNA (median 8.2/mL vs. 1.7/mL; Sup- Genomic instability as measured by LST in single CTC is plementary Table S2). The 2þTSG signature was positive in 12 of 57 associated with aggressive prostate cancer behavior patients (21%) by ctDNA and in 20 of 57 patients (35%) by CTC. For every patient, the median LST across all CTC was determined Concordance between methods was most common in patients with andusedasagenomicinstabilityscore(Fig. 2A; Supplementary relatively high CTC burden (Fig. 3B). The median blood volume assessed TableS1).UsingtheCNAprofiles of white blood cells as a reference, for CTC in this analysis was 0.85 mL, and median plasma assessed for we estimated that approximately 20% of the CTC sequenced in ctDNA was 3 mL. this cohort had flat genomic traces. CTC/mL and genomic instability score per patient did not show a discernable correlation AR protein expression in CTC is multifactorial (Supplementary Figs. S6A and S6B). However, loss of any TSG High-resolution CTC imaging before genomic analysis allowed and, to a lesser degree, median number of AR overexpressing CTC, for comparison of AR protein expression intensity to CNA profiles. were associated with higher genomic instability in single CTC We found that the relationship between AR gene Z-score and AR (Table 2). Higher genomic instability across patient CTC was also protein expression could be patient specific(Fig. 4A), suggesting observed in those with clinical AVPC, accelerated progression that factors other than the AR gene could contribute to AR protein pretherapy, PFS <4months,andOS<12 months. Conversely, those expression. Across all patient CTC in the cohort, there was a with merely higher tumor load did not see an association of relationship between AR gene gain status and AR protein expres- similar magnitude (Table 2). Time-dependent ROC analyses for 12- sion (Fig. 4B). However, the degree of AR protein expression on month survival showed a trend for greater AUC when CTC/mL, individual CTC became more resolved when gains in additional genomic instability score, and TSG presence in CTC were assessed genes, such as MYC and NCOA2 (Fig. 4C), as well as gains in the AR in combination compared with each individually (Supplementary gene enhancer region (Fig. 4D), were factored in, suggesting an Fig. S7).

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Figure 4. The inﬂuence of AR gene gain on AR protein expression in single CTC is multifactorial. The relationship between AR gene Z-score and AR protein (expressed as a signal to noise of surrounding white blood cells on a slide) is shown as a scatter plot, with every CTC colored by the patient of origin for (A) a select few with notable divergent relationships between AR gene and protein in single CTC. B, The relationship between single-CTC AR protein and AR gene gain, as well as (C) the co- occurrence of AR, NCOA2, and MYC gain, and (D) the co-occurrence of AR gene and AR enhancer region gain. The dashed line indicates prespeciﬁed analytical cutoffs for AR gene gain.

High-dimensional gene–gene relationships between CTC and single-cell level (median LST in CTC with vs. without BRCA2 loss: patient prognosis 34 vs. 13.5, respectively; P < 0.0001, Wilcoxon rank sum test). We also For each gene region altered, incidence among all 257 CTC observed that relatively more patients with clinically deﬁned AVPC sequenced and genomic instability score for the CTC carrying the had at least one CTC with BRCA2 and RB1 loss (9/21 patients, 43%) or alteration were calculated (Supplementary Table S3). Two regions on RB1 loss without BRCA2 loss (10/21 patients, 48%), as compared with chromosomes 8q (gain) and 13q (loss) had ≥3 gene alterations those with conventional mCRPC [5/26 (19%) and 7/26 (27%) patients, associated with high genomic instability. Particularly in 13q, we sought respectively]. The most common alteration observed overall (44%) was to evaluate the single-CTC dependency of losses of two closely located a gain corresponding to the 100kb region spanning PTK2, which genes with independent, previously established causal relationships encodes focal adhesion kinase (FAK). with genomic instability: BRCA2 (13q13.1) and RB1 (13q14.1; To more broadly assess the relationship between common CNA, refs. 35, 36; Supplementary Table S4). As expected, BRCA2 loss was genomic instability, prior therapy, and survival, we performed strongly associated with higher genomic instability as per LST on a dimensionality reduction on the Z-scores of the >20% incidence genes

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in all sequenced CTC, projecting these gene–gene relationships into The concordance between AR protein and gene status across CTC may two-dimensional space. These projections of gene–gene relation- have clinical significance and should be further examined (16). ships did not identify trends between CTC from patients previously We sought to expand the landscape of genomic alterations related to treated with abiraterone/enzalutamide versus not (Supplementary aggressive mCRPC behavior that are resolvable through liquid biopsy, Fig. S8A). However, the CTC from patients with >2 years survival with an eye toward disease evolution monitoring. Our analyses led to a clustered in a dense region associated with lower genomic instability broad surrogate of genomic instability: LST, a genome-wide measure and lower incidence of TSG loss, broadly suggesting relationships of DNA scarring that has been linked to HRD, with HRD linked to the between these genomic alterations and prognosis (Supplementary sensitivity of different cancer types to platinum drugs and PARP Figs. S8B and S8D). inhibitors. Clinically, we found higher genomic instability scores as independent of CTC number, more clearly related to measures of aggressiveness than to tumor bulk, and additive to prognostic models. Discussion Molecularly, co-occurring 2þTSG losses were all positively correlated In this study, we extended the classification of patients prospec- with increasing genomic instability but had less prognostic ability than tively identified as either clinical AVPC or conventional mCRPC to the genomic instability score. The chromosomal regions most fre- liquid biopsies through the application of the EsCTC technology for quently altered in CTC with higher genomic instability included those single-CTC genomic analysis. We identified a compound TSG loss- affecting genes linked to HRD [loss in 13q13.1 (BRCA2)], increasing based molecular signature previously related to AVPC (15, 18) and AR activity [gain in 8q12–13 (NCOA2) and loss in 16q23-24 linked it to other specific CNA observed in association with (CBFA2T3)], lineage plasticity [loss in 13q14.2 (RB1)], and cell pro- increased genomic instability in single CTC relative to patients’ liferation and metastasis [gain in 8q24 (Myc, PTK2, EXT1)]. Although outcomes, thus effectively expanding the set of chromosomal genomic instability via LST appears to be a global measure of prostate alterations associated with aggressive prostate cancer behavior. Our cancer aggressiveness in CTC, PTK2 gain, MYC gain, and TP53 loss findings support the feasibility of single CTC genomics to contrib- were the specific gene alterations most strongly associated with poor ute information of biological and clinical relevance beyond CTC prognosis in this cohort (Supplementary Table S6). enumeration, at a resolution that bulk tissue or ctDNA analyses may To more broadly explore the interacting relationships between gene not attain. alterations, TSG losses, and emergent phenomena of genomic insta- In line with detection rates reported in tissue-based stud- bility in the context of patient outcomes, we applied UMAP ies (12, 14, 15, 18, 19), we identified the candidate TSG-based dimensionality reduction to the 32 genes most commonly altered in molecular AVPC signature, defined as concomitant loss of at least the cohort. This analysis revealed gene-gene associations that suggest a two of the three TSG in at least 1 CTC, in 32.2% of the 62 initial continuum of genomic instability related to increasing TSG losses and patients. We next compared TSG status in the CTC versus ctDNA in worse survival. matched blood samples and found a general degree of concordance A shortcoming of our study is the limited sampling of CTC (median that lends credence to both techniques for gene CNA assessment in <1 mL). As such, some patients had very few CTC for low-pass whole- liquid biopsy. Still, only 42% of the patients in the ctDNA cohort had genome sequencing, precluding deeper interrogations. It may well be sufficient ctDNA for CNA analyses, whereas 77% had CTC detected that the number of CTC detected and the number of CTC sequenced and sequenced, demonstrating increased detection sensitivity through could influence the results, such that patients with lower CTC counts CTC (at least with the methods we used). An important caveat is that and fewer CTC sequenced might have lower chances to have a CTC only a median of 3 mL plasma and 0.85 mL blood were respectively with compound TSG losses (but it may not affect the analytically more available and used for ctDNA and CTC analyses, likely underestimat- robust determination of LST/genomic instability, which empirically ing CTC/ctDNA detection rates. within this cohort did not seem to be associated to the number of CTC As our group has reported that the presence of the candidate AVPC detected or sequenced). Although care was taken to accommodate signature in tumor tissue and/or ctDNA may predict improvements in such limitations (we only included patients with CTC detected into our both PFS and OS with the addition of carboplatin to cabazitaxel outcome analyses, and provided extra analyses with the subset of relative to cabazitaxel alone (13), we tried to evaluate whether the patients with at least 3 CTC sequenced in Supplementary Fig. S4C and same relationship could be discerned through CTC. Unfortunately, S4D, showing similar results), studies with larger numbers of patients our cohort represented a subset of the entire cohort and did not have and sample volumes available will enable greater resolution of genomic the requisite sample size power to detect predictive biomarker effects heterogeneity and more refined subclonal assessments of biological less than an exceptionally high treatment-specific interaction hazard and disease classification significance. However, even with our limita- ratio (Supplementary Table S5). Regardless, we found the presence of tions in patient number and CTC sequenced, this study represents the 2þTSG on a single CTC associated with directionally shorter survival most comprehensive single-CTC sequencing dataset completed to outcomes and incremental loss of TSG to be directly proportional to a date in metastatic prostate cancer. poorer prognosis, consistent with established biology of more aggres- Broadly, our single-CTC genomic analysis suggests an increasing sive oncogenic behavior and empirical correlations to poor outcome continuum of gene alterations or altered chromosomal regions asso- seen by other groups (15, 18). ciated with genomic instability towards aggressiveness in mCRPC, and Our ability to simultaneously evaluate AR protein and gene resulted representative of still not well characterized, discrete paths to pro- in the finding of patients with mixed patterns of AR expression and gression that are likely linked to distinct molecularly defined AVPC amplification status in CTC. A probable explanation for some of the categories. TSG seem to be critical components of those paths and AR-expressing mCRPC cases with no or low-level AR amplification based on this study, genomic instability is an additional lens through may have been the presence of amplification in the enhancer region which future interrogations should be viewed. Serial evaluation of upstream of AR (5, 6). Our single-CTC genomic analysis also uncov- liquid biopsies from larger numbers of similarly-treated patients will ered increased AR expression in relation to gains in the chromosomal be needed to untangle clinical and molecular heterogeneity and the regionscontainingMycandNCOA2,anuclearcoactivatorofAR(2,37). biological paths to progression.

AACRJournals.org Clin Cancer Res; 2020 OF9

Malihi et al.

Disclosure of Potential Conflicts of Interest Writing, review, and/or revision of the manuscript: P.D. Malihi, R.P. Graf, R.P Graf is an employee of Epic Sciences. A. Rodriguez holds ownership interest A. Rodriguez, N. Ramesh, R. Sutton, C. Logothetis, P. Corn, A.M. Aparicio, (including patents) in Epic Sciences. R. Sutton is an employee of Epic Sciences. R. Jiles R. Dittamore, J. Hicks, P. Kuhn, A.J. Zurita holds ownership interest (including patents) in Epic Sciences. A. Kolatkar holds Administrative, technical, or material support (i.e., reporting or organizing data, ownership interest (including patents) in Epic Sciences. R. Dittamore is an employee constructing databases): N. Ramesh, R. Sutton, A. Kolatkar, N.E. Navin, of Epic Sciences. J. Hicks holds ownership interest (including patents) in and is an R. Dittamore, A.J. Zurita unpaid consultant/advisory board member for Epic Sciences. P. Kuhn holds own- Study supervision: P. Kuhn, A.J. Zurita ership interest (including patents) in and is an unpaid consultant/advisory board Other (sample processing and data acquisition): E. Sei member for Epic Sciences. A.J. Zurita reports receiving commercial research grants from Infinity Pharmaceuticals; reports receiving speakers bureau honoraria from Acknowledgments Pfizer Oncology; and reports receiving other remuneration from Bayer. No potential We would like to thank the patients participating on trial NCT01505868 and their conflicts of interest were disclosed by the other authors. families for contributing to this clinical research. We would additionally like to thank the laboratory staffs at the Eckstein Tissue Acquisition Laboratory at MD Anderson ’ and at Epic Sciences. This work is funded in whole or in part by the Prostate Cancer Authors Contributions Foundation Award 17CHAL01 (A.M. Aparicio, P. Kuhn, J. Hicks), the Solon Scott III Conception and design: R.P. Graf, C. Logothetis, A.M. Aparicio, R. Dittamore, Prostate Cancer Research Fund (P. Corn), David and Janet Polak Foundation P. Kuhn, A.J. Zurita Fellowship in Convergent Science (P.D. Malihi), and the CPRIT Research Training Development of methodology: P.D. Malihi, R.P. Graf, A. Rodriguez, R. Sutton, Program RP170067 (N. Ramesh). C. Ruiz Velasco, C. Logothetis, J. Hicks, A.J. Zurita Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): P.D. Malihi, A. Rodriguez, N. Ramesh, R. Sutton, R. Jiles, E. Sei, The costs of publication of this article were defrayed in part by the payment of page advertisement C. Logothetis, P. Corn, A.M. Aparicio, A.J. Zurita charges. This article must therefore be hereby marked in accordance Analysis and interpretation of data (e.g., statistical analysis, biostatistics, with 18 U.S.C. Section 1734 solely to indicate this fact. computational analysis): R.P. Graf, A. Rodriguez, N. Ramesh, J. Lee, R. Sutton, A. Kolatkar, C. Logothetis, N.E. Navin, P. Corn, A.M. Aparicio, R. Dittamore, J. Hicks, Received December 16, 2019; revised February 25, 2020; accepted April 22, 2020; A.J. Zurita published first April 27, 2020.

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Genomic Instability as a Novel Biomarker of AVPC in CTC

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AACRJournals.org Clin Cancer Res; 2020 OF11

Single-Cell Circulating Tumor Cell Analysis Reveals Genomic Instability as a Distinctive Feature of Aggressive Prostate Cancer

Paymaneh D. Malihi, Ryon P. Graf, Angel Rodriguez, et al.

Clin Cancer Res Published OnlineFirst April 27, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-19-4100

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