Cell-free DNA (cfDNA) is shed into the circulation by both normal and malignant cells, and next-generation sequencing analysis of cfDNA offers minimally invasive genomic profiling of tumor alterations without tumor biopsy. Prior applications of cfDNA have focused on tracking specific mutations 28 - 33 or sequencing targeted panels of cancer-related genes. 22 , 34 - 38 Building on others’ work demonstrating the feasibility of genome-wide copy number analysis from plasma in patients with cancer, 21 , 39 , 40 we developed an algorithm, ichorCNA, 38 to profile SCNAs and quantify tumor fraction (TFx) via low-coverage (0.1×) whole-genome sequencing of cfDNA, without the need for prior knowledge of tumor mutations. Here, we evaluate the association of cfDNA TFx with survival and use cfDNA as a comprehensive biopsy surrogate to study the genomics of a disease infrequently biopsied in clinical practice, identifying key SCNAs that are enriched and prognostic in mTNBC.

Triple-negative breast cancer (TNBC) makes up 10% to 15% of all breast cancers yet accounts for more than one third of breast cancer–related deaths. 1 - 5 TNBC is defined by lack of expression of therapeutic targets human epidermal growth factor receptor 2 (HER2) and estrogen receptor alpha (ERα), and chemotherapy remains the mainstay of treatment. 4 , 6 , 7 Extensive recent efforts have defined clinicopathologic, genomic, and transcriptomic features of primary TNBC (pTNBC). 2 , 5 , 8 - 18 pTNBC is defined by relatively few somatic single-nucleotide variants and indels (approximately one mutation per megabase). 2 , 18 , 19 However, pTNBC demonstrates frequent loss of TP53 and genomic instability with widespread somatic copy number alterations (SCNAs), implicating a critical role of SCNAs in TNBC tumorigenesis. 2 , 9 , 10 Although a few studies have begun to interrogate the genomic features of metastatic TNBC (mTNBC), 20 - 27 there have been no analyses of large cohorts of patients with mTNBC published to date.

All Kaplan-Meier plots were generated using packHV package. 52 For baseline clinicopathologic characteristics, survival was defined as time from metastatic diagnosis and significance evaluated by log-rank test. For cfDNA variables, including line of metastatic therapy at blood draw, first blood draw, and highest TFx blood draw, survival was defined as time from blood draw. Univariate and multivariable Cox proportional hazards models were calculated using the survival package.

For principal component analysis (PCA), gene-level cfDNA GISTIC copy number calls were projected onto the METABRIC TNBC PCA coordinate basis and visualized using ggbiplot. 48 Comparison in frequency of gain/amplification ( v no gain) and loss/deletion ( v no loss) between metastatic and primary samples was calculated using Fisher’s exact test. All frequency calculations of copy number calls across the genome were multiple-testing corrected using Benjamini–Hochberg procedure for false discovery rate. Volcano plots were generated using ggplot2 package, 49 CoMut plots were visualized with GenVisR package, 50 and genome-wide significance plot using qqman package. 51

All statistical analyses and data visualizations were performed in R version 3.3.1. Contrasts in patient and tumor characteristics were evaluated using Pearson’s χ 2 tests. The association of TFx to continuous and categorical clinicopathologic factors was evaluated using Wilcoxon rank-sum and χ 2 test or analysis of variance, respectively. Correlation of cfDNA yield and TFx from independently processed same-day blood draw samples was calculated using interclass correlation coefficient. Performance of cfDNA relative to paired metastatic biopsy—including sensitivity and specificity with biopsy considered truth—was computed across 1-Mb bins. Correlation among bin-level copy number calls for all samples was calculated using Spearman correlation coefficient, and hierarchical clustering was performed using average linkage.

GISTIC2.0 44 , 45 output was used for all gene-level copy number analyses. Segmented data files derived from ichorCNA for mTNBC cfDNA and publicly available segmented data for METABRIC 1 were purity and ploidy corrected, then input into GISTIC2.0 44 , 45 with amplification/deletion threshold log 2 ratio > 0.3, confidence level 0.99, and Q-value threshold 0.05. Genes were defined as gain (GISTIC value 1; corresponds to three copies) or amplification (GISTIC value 2; corresponds to four or more copies) versus diploid (GISTIC value 0). TCGA GISTIC2.0 copy number data were obtained from cBioPortal. 46 , 47

Patients with triple-negative breast cancer were identified in The Cancer Genome Atlas (TCGA 18 ; n = 166) and METABRIC 1 (n = 277) on the basis of study-reported negative for the ER and progesterone receptor via IHC and HER2-receptor copy number diploid (GISTIC2.0 value of 0) or IHC 0 to 1. If ER status was not available, ER status was inferred from RNA expression data. 43

Library construction of cfDNA was performed using the Kapa HyperPrep kit with custom adapters (IDT, Coralville, IA). Three to 20 ng of cfDNA input (median, 5 ng), or approximately 1,000 to 7,000 haploid genome equivalents, was used for ultra-low-pass whole-genome sequencing. Constructed sequencing libraries were pooled (2 μL of each × 96 per pool) and sequenced using 100-bp paired-end runs over 1 × lane on a HiSeq2500 (Illumina, San Diego, CA) to average genome-wide fold coverage of 0.1×. Segment copy number and TFx were derived via ichorCNA. 38 Samples were excluded if the median absolute deviation of copy ratios (2 log2 ratio ) between adjacent bins, genome-wide, was > 0.20, suggesting poor-quality sequence data.

Venous blood samples were collected in EDTA (BD, Franklin Lakes, NJ), CellSave Preservative (Cell Search, Raritan, NJ), or Cell-Free DNA BCT (Streck, Omaha, NE) tubes. 42 Blood processing to component parts within 4 hours of collection, cell-free DNA extraction from plasma, and DNA quantification were performed as described previously. 38 For metastatic biopsy samples, > 50% tumor was confirmed via hematoxylin and eosin staining of fresh frozen samples and then DNA extracted using Qiagen AllPrep DNA kit (Qiagen, Germantown, MD).

Consecutive, nonoverlapping patients with metastatic biopsy-proven TNBC enrolled on ongoing clinical data and biospecimen banking protocols for metastatic breast cancer (DFCI#09-204, n = 97; and DFCI#05-246, n = 10) or collected as part of two clinical trials for patients with metastatic TNBC (DFCI#12-024 [ruxolitinib; ClinicalTrials.gov identifier: NCT01562873], n = 14; and DFCI#12-431 [cabozantinib; ClinicalTrials.gov identifier: NCT01738438 41 ], n = 37) were identified for analyses. TNBC was defined as < 5% staining for estrogen receptor (ER) and progesterone receptor and human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) 0 to 1+ and/or HER2:Cep17 fluorescent in situ hybridization ratio < 2.0. Clinicopathologic data were abstracted from the medical record. Survival events were determined from medical record or Social Security Death Index. BRCA1 or BRCA2 germline mutation status was ascertained by medical record review for the receipt of Clinical Laboratory Improvement Amendments (CLIA)-approved germline testing. All patients received chemotherapy in the adjuvant, neoadjuvant, or metastatic setting before first blood draw used in analyses. Use of patients’ clinicopathologic data were institutional review board approved, and all patients provided written consent.

Allele fraction of tumor mutations detected in cfDNA is suggested to be a prognostic for metastatic breast cancer. 28 We evaluated prognostic association of TFx at a prespecified threshold ≥ 10% on the basis of > 2,400 tumor/normal in silico admixtures of varying sequencing coverage and tumor fractions that demonstrated optimal SCNA prediction performance at a tumor fraction ≥ 10%. 38 In this cohort, TFx ≥ 10% on a patient’s first blood draw was associated with significantly shorter survival, median 6.4 months versus 15.9 months (log-rank P < .001; Fig 4C ). TFx remained an independent prognostic factor in a multivariate Cox proportional hazards model (hazard ratio, 2.14; 95% CI, 1.40 to 3.28; P < .001; Fig 4D ) and also in sensitivity analyses including only patients whose primary tumor was TNBC (n = 121) and with TFx as a continuous variable (Appendix Fig A6C-A6E ).

Tumor fraction measurement using ichorCNA has a broad dynamic range, both within individuals and among distinct patients. Within-patient TFx variability is illustrated by a single patient on a clinical trial of cabozantinib, 41 who demonstrated a TFx nadir of 3.5% while responding to therapy with a maximum TFx of 48.2% at progression ( Fig 4A ). Evaluating a first sentinel blood draw, this cohort demonstrated a diverse range of TFx from < 3% to 77.2% ( Fig 4B ). We hypothesized that metastases to more highly vascular organs could be associated with higher TFx, and indeed the presence of liver metastasis was associated with significantly higher TFx in both the sentinel draw and maximum TFx draw (Appendix Fig A6A-A6B , online only), remaining significant when adjusting for characteristics in a multivariate model (Appendix Fig A6A-A6B ).

Our approach offers a tumor fraction calculation on the basis of SCNAs detected in cfDNA without a priori knowledge of tumor mutation status. 38 To evaluate reproducibility, two plasma samples were drawn in a single venipuncture and fractionated in independent laboratories for 11 patients. Data showed high TFx concordance of paired samples (intraclass correlation coefficient = 0.984) and nearly identical copy number profiles despite variable cfDNA yield (Appendix Fig A5A-A5D , online only).

Little is known regarding genomic determinants of TNBC metastatic survival. Focusing on mTNBC-enriched SCNAs ( Fig 3A ), we calculated the Cox proportional hazard ratio of each gene for metastatic survival ( Fig 3B ; Data Supplement). Only a subset of mTNBC-enriched loci were prognostic in the metastatic setting. Unexpectedly, the loci most strongly associated with poor metastatic survival, 18q11 and 19p13, have never previously associated with TNBC survival. More than half of mTNBCs harbored gain/amplification of 18q11, 19p13, or both, significantly more frequent than in pTNBCs (χ 2 P < .001; Appendix Fig A4A , online only). Gain/amplification of both 18q11 and 19p13 was strongly associated with worse survival in univariate analyses and multivariable Cox proportional hazard models including clinicopathologic factors and TFx (hazard ratio, 3.30; 95% CI, 1.30 to 8.38; P = .012) and was also associated with poor prognosis in pTNBCs (log-rank P = .038; Fig A4B-A4E ).

To evaluate SCNA differences in a large number of primary versus metastatic TNBCs, we identified pTNBCs in publicly available data sets METABRIC 1 and TCGA 18 (total, n = 433) and determined gene-level copy number status in both data sets via GISTIC2.0 to facilitate uniform comparison. Overall, altered regions were remarkably concordant between pTNBC and mTNBC ( Figs 2A and 2B ); however, mTNBCs demonstrated greater SCNA frequency of both commonly altered regions (1q, 7q, 8q) and less commonly altered regions (11q, 18q, 19p; Appendix Fig A3E ). A subset of genes was altered more frequently in mTNBC relative to pTNBC, including high-frequency (> 50% of samples) gains in MYC (8q), AKT3 (1q), GATA3 (10p), NOTCH2 (1p), EZH2 (7q), BRAF (7q), and MET (7q; Fisher’s exact, genome-wide false discovery rate (FDR) correction P < .05; Figs 2A-2C ; Data Supplement). Four genes were enriched in mTNBC relative to pTNBC both in paired samples and across cohorts: gains in GATA3 and drivers NOTCH2 , AKT2 , and AKT3 . Interestingly, the genome-wide percentage of genes altered was not significantly increased in mTNBC relative to pTNBC, although there was greater heterogeneity among primary tumors ( Fig 2D ).

We hypothesized that chemoresistant mTNBCs would be enriched for specific SCNAs relative to chemotherapy-naïve pTNBCs, including alterations potentially involved in drug resistance and/or metastasis. We determined gene-level SCNA status via GISTIC2.0 44 , 45 for the highest TFx (≥ 10%) cfDNA sample per patient with mTNBC (n = 101; Appendix Figs A3A and A3B , online only). We then identified 20 patients with mTNBC with at least one cfDNA sample with TFx ≥ 10% whose primary tumor underwent targeted panel sequencing 56 as part of clinical management. The median time between primary sample and metastatic cfDNA was 26 months (interquartile range, 11 to 38 months) with 18 of 20 primary tumors resected. We compared frequency of gain or loss for 25 cancer-related genes commonly altered in breast cancer between primary tumor panel sequencing and metastatic low-coverage cfDNA sequencing. Four genes demonstrated greater frequency of gain in mTNBC versus pTNBC samples ( NOTCH2 on 1p, AKT3 on 1q, GATA3 on 10p, AKT2 on 19q; Fisher’s exact P < .05), whereas four genes demonstrated single copy loss more frequently in pTNBC than mTNBC ( CDKN2A on 9p, PTEN on 10q, RB1 on 13q, NF1 on 17q; Fisher’s exact P < .05; Appendix Figs A3C and A3D ).

TNBC is a heterogeneous disease comprising distinct subtypes. 8 , 55 To investigate patterns of chromosomal alterations, we compared genome-wide copy number profiles for all cfDNA samples with TFx ≥ 10%. Hierarchical clustering revealed two main copy number clusters, with cluster1 significantly enriched for patients with mTNBC whose primary receptor status was non-TNBC (χ 2 P = .007; Appendix Figs A2A-A2C , online only). We observed that the gene-level copy number profile of cluster2 tumors closely mirrors basal-like IntClust10 pTNBCs in METABRIC 1 (Appendix Figs A2D-A2E ). Principal component analysis of METABRIC gene-level copy number data revealed high concordance of cfDNA cluster2 with basal-like METABRIC IntClust10 and cfDNA cluster1 with non-IntClust10 (nonbasal) pTNBCs (Appendix Fig A2F ), although formal IntClust designation requires concurrent gene expression analysis. 1

We and others have demonstrated robust concordance of copy number and mutation between metastatic biopsy specimens and paired cfDNA. 38 , 53 As confirmation in this data set, we performed low-coverage sequencing of metastatic biopsy samples obtained at disease progression with concurrent plasma (range, 0 to 7 days from biopsy; n = 10 pairs). We compared copy number of 1-megabase segments across the genome using ichorCNA. Altered segments in the tumor biopsy specimen were detected in cfDNA with high sensitivity (0.86) and specificity (0.90), and, as anticipated, overlap was not identical, 22 , 54 with instances of private SCNAs present in cfDNA ( Fig 1B ).

Low-coverage whole-genome sequencing provided evaluable sequencing data for 478 (94.5%) samples that subsequently underwent copy number analysis and TFx determination via ichorCNA. 38 TFx could be determined for 158 of 164 patients (96.3%); 337 of 478 evaluable samples (70.5%) had detectable tumor DNA above the lower limit of detection (TFx ≥ 3%). One hundred one of 158 evaluable patients (63.9%) had at least one sample with TFx ≥ 10%, the prespecified proportion of tumor DNA adequate for high-confidence copy number calls on the basis of extensive prior benchmarking. 38 Patients with maximum TFx ≥ 10% had similar clinicopathologic characteristics relative to patients with maximum TFx < 10% ( Table 1 ).

We identified 506 plasma samples from 164 patients with biopsy-proven mTNBC collected between August 2010 and November 2016 under institutional review board–approved protocols at a single institution and abstracted detailed clinicopathologic information ( Fig 1A ; Table 1 ). All patients received chemotherapy before blood collection, with most patients having received neoadjuvant or adjuvant anthracycline and taxane-based chemotherapy. The median time to follow-up from metastatic diagnosis was 17 months (range, 0 to 82 months). Overall, this cohort reflects similar trends to other analyses of mTNBC, including worse prognosis for patients initially diagnosed with stage III relative to lower stage (I or II) disease and improved prognosis for patients with germline BRCA1 or BRCA2 mutations (Appendix Fig A1 , online only).

DISCUSSION Section: Choose Top of page Abstract INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION << REFERENCES

We present the largest genomic characterization of mTNBC to our knowledge. Using a cfDNA-exclusive approach relevant for most patients with mTNBC, we demonstrate that TFx is a robust, minimally invasive independent prognostic biomarker in mTNBC. pTNBC and mTNBC exhibit remarkably similar copy number profiles, yet we identified known cancer drivers among SCNAs enriched in mTNBC relative to pTNBC.

Allele fraction of known mutations detected in cfDNA is suggested to be prognostic but is dependent on knowledge of existing tumor mutations and has not been evaluated in a large cohort of mTNBCs.28 Our approach evaluates TFx without a priori tumor mutation status and is evaluable in the vast majority of patients with mTNBC. We demonstrate that TFx is a genomic biomarker for mTNBC independent of standard clincopathologic characteristics in a large modern cohort. Patients with higher tumor fraction (TFx ≥ 10%) had significantly inferior survival but showed no significant differences in baseline characteristics relative to patients with lower TFx. Patients with higher TFx were more likely to have documented liver metastases, potentially associated with highly vascular organs or distinct features of TNBC that metastasizes to the liver. In support of further testing of this approach in clinical practice, we will be launching a prospective cohort study to further investigate mTNBC TFx dynamics while on therapy and subsequent association with response to standard or experimental therapies. Future efforts may allow minimally invasive analysis of clinically relevant mutational signatures, such as homologous recombination deficiency or microsatellite instability.

Several cancer types have been shown to evolve with progressive collection of mutations over time and on therapy.2,34,53 It has been hypothesized that primary tumors with genomic instability such as TNBC will collect immense numbers of genomic alterations in the metastatic setting after chemotherapy. Surprisingly, we demonstrate no significant difference in percent genome altered and remarkably similar patterns of chromosomal alterations when comparing more than 100 mTNBCs with more than 400 pTNBCs. This suggests that large-scale chromosomal events are rare in metastatic development and supports prior work demonstrating that most SCNAs occur early in tumorigenesis in TNBCs.57,58

Despite few large-scale SCNA changes between primary and metastatic tumors, we identify certain loci enriched in mTNBCs relative to paired primary and/or large cohorts of pTNBCs. We identify a novel association of 18q11 and 19p13 gains with metastatic survival that is independent of both clinicopathologic factors as well as TFx. Gain or amplification of both regions identifies a subset of TNBC rapid progressors with remarkably poor survival in the metastatic and also the primary setting. Both 18q1159 and 19p1360,61 include known breast cancer risk loci.59,60 19p13 is associated with increased breast cancer risk, specifically among BRCA1 mutation carriers,60 and associated specifically with ER-negative61 and TNBC60 in the general population. An assessment of focal events, recently shown to be a driving force in prostate cancer,54 might lead to identification of additional prognostic SCNAs.

Our study involved a modern cohort representing current standard treatment approaches, with 86% of patients without distant metastasis at diagnosis having received anthracycline and taxane-based (neo)adjuvant chemotherapy. Over the first 20 months from metastatic diagnosis, patients initially diagnosed with stage III disease are more likely to die as a result of their disease relative to patients diagnosed with stage I or II or de novo metastatic disease, supporting prior epidemiologic studies.5 Patients with germline BRCA1/2 mutations have improved prognosis. The patients in our cohort were relatively young, with more than half of the patients’ primary diagnoses before age 50 years, primarily wild-type for germline BRCA1/2 (15% with documented mutation), and most patients were white, an important limitation of this study.

In summary, we illustrate a framework for minimally invasive genomic characterization of metastatic cancer and subsequent integration with clinicopathologic data and patient outcomes. This analysis provides the most comprehensive genomic profile of metastatic TNBC SCNAs to date, to our knowledge, and suggests that determining cfDNA TFx via a blood test provides important prognostic information beyond standard clinicopathologic factors. This approach has the potential to reveal clinically useful biomarkers while identifying unique genomic features of metastatic cancer and may advance our understanding of metastasis, drug resistance, and novel therapeutic targets.