Abstract
The detection of tumor-specific nucleic acids from blood increasingly is being used as a method of liquid biopsy and minimal residual disease detection. However, achieving high sensitivity and high specificity remains a challenge. Here, we perform a direct comparison of two droplet digital PCR (ddPCR)-based detection methods, circulating plasma tumor RNA and circulating plasma tumor DNA (ptDNA), in blood samples from newly diagnosed Ewing sarcoma patients. First, we developed three specific ddPCR-based assays to detect EWS-FLI1 or EWS-ERG fusion transcripts, which naturally showed superior sensitivity to DNA detection on in vitro control samples. Next, we identified the patient-specific EWS-FLI1 or EWS-ERG breakpoint from five patient tumor samples and designed ddPCR-based, patient-specific ptDNA assays for each patient. These patient-specific assays show that although plasma tumor RNA can be detected in select newly diagnosed patients, positive results are low and statistically unreliable compared with ptDNA assays, which reproducibly detect robust positive results across most patients. Furthermore, the unique disease biology of Ewing sarcoma enabled us to show that most cell-free RNA is not tumor-derived, although cell-free-DNA burden is affected strongly by tumor-derived DNA burden. Here, we conclude that, even with optimized highly sensitive and specific assays, tumor DNA detection is superior to RNA detection in Ewing sarcoma patients.
With the introduction of conventional intensified chemotherapy, long-term survival for patients with localized Ewing sarcoma has dramatically improved from the 8% to 20% seen in the prechemotherapy era to close to 70%.1, 2, 3, 4, 5 Although this is one of the exemplary success stories of intensive chemotherapy in solid tumors, as many as 30% of patients who present with localized disease will not be cured despite multimodal treatment. Similarly, although the majority of Ewing sarcoma patients presenting with metastatic disease achieve complete remission, cure rates hover at approximately 20%. Our inability to distinguish patients who are cured at the end of therapy from those who are destined to relapse highlights the limitations of current radiographic techniques to assess disease status and led us to develop a more personalized and sensitive marker of disease status.
Liquid biopsies, generally defined by the detection of cancer-cell–derived components in the circulation, have rapidly gained attention in recent years. As cancer cells grow and progress, cells can intravasate into the circulation as circulating tumor cells,6, 7, 8 shed nucleotides such as DNA and RNA fragments,8, 9, 10 and release membrane-encapsulated particles known as exosomes.11 By extracting these components and investigating them, liquid biopsies present a novel method to noninvasively study metastatic biology, tumor heterogeneity, and genotype tumors for therapeutic targets.9,12 Furthermore, liquid biopsies have been tested to monitor response to therapy,13 to quantify disease burden,10,14 and to detect minimal residual disease.10 Currently, circulating nucleic acid detection is the most widely used method of liquid biopsy, with multiple ongoing studies investigating the clinical applications of these technologies.
Cell-free circulating tumor DNA has been explored as a biomarker in multiple cancer types in both plasma and serum, and most applications of this technology have focused on developing a liquid biopsy, using cell-free circulating tumor DNA to detect de novo mutations in a heterogeneous tumor to direct therapy.15 Although this is an attractive application for mutationally heterogeneous carcinomas, especially to detect actionable rare mutations within a tumor, pediatric sarcomas are mutationally silent tumors driven by a relatively small number of mutations, making liquid biopsies less attractive for clinical use.16, 17, 18 Nevertheless, this approach has been used to detect the development of secondary mutations before clinical progression as well as to monitor metastatic disease burden, and, although the results have been promising, these efforts have been focused on patients with radiographically detectable disease.15,19, 20, 21, 22 With the introduction of digital droplet PCR (ddPCR), the sensitivity of circulating plasma tumor DNA (ptDNA) analysis has improved dramatically,13,23 making detection of very low disease burden a possibility. In fact, theoretically, ddPCR is sensitive enough such that, at the minimal limit of detection, a single copy of interest among a pool of high background material still may be identified. However, it should be noted that clinically differentiating such a single copy of interest is statistically problematic. To capitalize on this high sensitivity, some groups use next-generation sequencing to detect a tumor-specific genetic signature, which then is screened in ptDNA by ddPCR to detect minimal residual disease before relapse.24 Next-generation sequencing, however, is costly, time-consuming, and not yet available for widespread clinical use. In addition, for a tumor driven by a known translocation oncogene (eg, Ewing sarcoma) next-generation sequencing is not necessary, thus yielding the development of a more economical and sensitive platform.
The detection of RNA from patient blood has been attempted repeatedly with mixed success over the past 2 decades across multiple types of cancer.25, 26, 27, 28, 29 Although many groups have reported their successes and failures in this field, there are many variables that exist that may affect assay sensitivity negatively, including sample preservation, RNA extraction methods, and analysis platforms. Regarding sample preservation, peripheral blood is known to contain abundant RNase, cells within the blood sample can release RNA into the plasma, and cross-linking of RNA can affect the reaction efficiency of the analysis platform significantly.30, 31, 32
Ewing sarcoma (ES) is driven by a reciprocal translocation between the EWS gene and an erythroblast transformation specific (ETS) family member (FLI1 in 85% of tumors and ERG in another 10%).33,34 This translocation oncogene is essential for the initiation and maintenance of the transformed phenotype, making this an ideal molecular marker because it will not be down-regulated or lost during tumor progression and is present as the essential driver in every single tumor cell.35,36 There have been multiple attempts over the past decades to capitalize on this unique biology to develop a blood-based biomarker. Because 80% of ES tumors carry either a type 1 (7/6) or type 2 (7/5) EWS-FLI1 fusion, detection of these fusion transcripts in patient blood has been attempted by RT-PCR37, 38, 39, 40 and, more recently, by ddPCR.29 Results from these earlier trials have been disappointing with mixed results, potentially affected by the differences in sample preservation techniques used such as EDTA tubes, prolonged storage, formalin fixation, cold storage, or use of serum—all factors that now are known to decrease the yield of tumor-derived RNA from blood significantly. Recently, ptDNA has been used increasingly as a target for ES liquid biopsies. Although next-generation sequencing–based methods overcome the variability of the patient-specific intronic breakpoint sequence of the EWS-FLI1 fusion gene, they suffer from low sensitivity and high cost.41 In contrast, the use of ddPCR to detect patient-specific intronic breakpoint sequences can produce significant sensitivity at a lower cost, albeit including the need to develop specific assays for each patient.10,42 Despite the increasing use of a variety of circulating nucleic acid detection methods,28 there has never been a direct comparison of plasma tumor RNA (ptRNA) and ptDNA detection in patients. Because of the biology of ES cells, each cell is expected to contain a higher relative abundance of EWS-ETS transcript compared with the single genomic EWS-ETS translocation. Given this high transcript:genomic copy ratio, it was hypothesized that ptRNA detection may be superior to ptDNA detection when using the recent technological advances in liquid biopsies. Here, we have developed a ddPCR-based EWS-ETS fusion transcript assay for ptRNA detection and directly compared its performance with our previously published patient-specific EWS-ETS ptDNA detection system in newly diagnosed Ewing sarcoma patients.10
Materials and Methods
Patients and Sample Collection
Blood specimens were collected under a protocol approved by the University of Colorado Institutional Review Board after obtaining informed consent. Samples were collected at seven time points from five patients who presented to the Children's Hospital Colorado (Aurora, CO) with a diagnosis of Ewing sarcoma (Table 1). Two 10-mL peripheral blood samples were collected per time point, 10 mL in Cell-Free RNA BCT (Streck, Lavista, NE) and 10 mL in Cell-Free DNA BCT (Streck). In addition, 40 mL peripheral blood was collected from a healthy volunteer, with 20 mL in Streck Cell-Free RNA BCT and 20 mL in K2 EDTA tube (BD, Franklin Lakes, NJ). All blood samples were processed within 1 hour of collection. Blood was centrifuged twice, first at 3500 RCF for 10 minutes at 4°C, then the collected plasma was centrifuged at 21,000 relative centrifugal force (RCF) for 10 minutes at 4°C. The resultant cell-free plasma was stored in 1-mL aliquots and frozen at −80°C until use.
Table 1.
Patient Characteristics
| ID | Age, years | Sex | Fusion gene | Fusion structure (last exon on EWS + first exon on ERG/FLI1) | Primary location | Size, mm | Metastasis |
|---|---|---|---|---|---|---|---|
| ST001 | 15 | M | EWS-ERG | 10 + 10 | Fibula | 54 × 62 × 113 | None |
| ST005 | 15 | M | EWS-FLI1 | 7 + 5 | Pelvis | 73 × 107 × 122 | None |
| ST006 | 8 | M | EWS-FLI1 | 7 + 6 | Paraspinous | 37 × 26 × 31 | None |
| ST009 | 15 | F | EWS-FLI1 | 7 + 6 | Sacrum | 89 × 101 × 105 | Bone metastasis to proximal humerus |
| ST010 | 15 | M | EWS-FLI1 | 10 + 10 | Humerus | 47 × 18 × 44 | None |
Five patients were enrolled in this study with varying fusion gene types, primary tumor locations/sizes, and the presence or absence of metastasis.
Identification of the Translocation Breakpoints
Genomic DNA was isolated from freshly frozen tumor samples using the QIAamp DNA Mini Kit (QIAGEN, Hilden, Germany). Long-range PCR was performed on the genomic DNA using the AccuPrime Taq Polymerase System (Invitrogen, Carlsbad, CA) and a modified EWS-FLI1 primer set as previously described,43 with a modified primer panel (Table 2) or with an EWS-ERG–specific primer set (Table 2). PCR products were sequenced by the 3730xl DNA analyzer (Applied Biosystems, Foster City, CA), and the sequence was aligned to the NCBI reference sequences for EWS, FLI1, and ERG, showing the specific translocation breakpoint for each tumor.
Table 2.
EWS, FLI1, and ERG Gene Primers Used for Identification of EWS-ETS Genomic Breakpoints
| Primer name | Sequence | Location |
|---|---|---|
| EWS 1 | 5′-GCCAAGCTCCAAGTCAATATAGCCAACAG-3′ | 24,076–24,104 |
| EWS 2 | 5′-CATCTTTAGGGCATGGCATTCCAGCTAAC-3′ | 24,564–24,592 |
| EWS 3 | 5′-GCAGTTCTTCTGTATGGAGAGAGGTAACCA-3′ | 25,078–25,107 |
| EWS 4 | 5′-CCTCAGGTTCATTCCGACAGGACC-3′ | 25,592–25,615 |
| EWS 5 | 5′-AGGTGAGAAGCCTCTGTGGGTAGTAGAT-3′ | 26,053–26,080 |
| EWS 6 | 5′-CTGGTGTCTGTACTTTGATGAAGGTGAGC-3′ | 26,521–26,549 |
| EWS 7 | 5′-GGATAGACGGGCATTTCCCTCGTTG-3′ | 27,215–27,239 |
| EWS 8 | 5′-CTGGATGCGTCCTCTGATGTGTGTTGTAG-3′ | 28,040–28,068 |
| EWS 9 | 5′-GGAGATGTGTTTAGCCAGTGCCCTT-3′ | 28,447–28,471 |
| EWS 10 | 5′-CTCCATTTTAGCAGTGCGGGTCATTTTG-3′ | 28,985–29,012 |
| EWS 11 | 5′-TTCTCCTGTCTTGTTGTCTCTGAAAGGC-3′ | 29,454–29,481 |
| FLI1 1 | 5′-TCATCGCCTCAAACTCTCCACAAGC-3′ | 94,177–94,201 |
| FLI1 2 | 5′-CATCGTGGTCTATTCACAAAACCTGTCCTC-3′ | 96,937–96,966 |
| FLI1 3 | 5′-AAGCCAGTATCACTCCTTCCTGCTGT-3′ | 99,797–99,822 |
| FLI1 4 | 5′-CGTTCAGACAGATTCAGACCACCTCAC-3′ | 103,077–103,103 |
| FLI1 5 | 5′-GAAATAGGAGACCAGTGCCCACCTT-3′ | 105,974–105,998 |
| FLI1 6 | 5′-GAAAGTGTGTCCTAGTCACTGTGTGAGC-3′ | 109,064–109,091 |
| FLI1 7 | 5′-GGTGAGAAGAGGCGAACTCTGTAGTG-3′ | 112,016–112,041 |
| FLI1 8 | 5′-AAACACTTCCGTCCCTGAGCTGTG-3′ | 115,096–115,119 |
| FLI1 9 | 5′-CAGGGCTTAGGATGTCATCACGAAGGTAT-3′ | 118,109–118,137 |
| FLI1 10 | 5′-GCCTGAACTGTGCTCTCACTGTGAA-3′ | 120,998–121,022 |
| FLI1 11 | 5′-GTGACAGTGACCATTCATTGGTACAGAACC-3′ | 124,159–124,188 |
| FLI1 12 | 5′-ACATGCCTAGAAACCAAGCAGCCTG-3′ | 127,152–127,176 |
| FLI1 13 | 5′-CTGGAGACAAATGTTCCGAAACACATCCC-3′ | 131,524–131,552 |
| ERG R1 | 5′-CCATTCCTCTGGAAAGCATC-3′ | 262,075–262,094 |
| ERG R2 | 5′-GTCCCAACCAACATCCTCAC-3′ | 263,133–263,152 |
| ERG R3 | 5′-AAAAGGTAGGTTGGGGCAGT-3′ | 264,119–264,138 |
| ERG R4 | 5′-AAGAGGAAAGGATGGGGTTG-3′ | 265,144–265,163 |
| ERG R5 | 5′-ATAGGCAGCTTGGGTGAACA-3′ | 266,147–266,166 |
| ERG R6 | 5′-CTGGCCTCAGAATCAGCATT-3′ | 267,118–267,137 |
| ERG R7 | 5′-CAGCAATTTGATGGGTCAAT-3′ | 268,144–268,163 |
| ERG R8 | 5′-TTCTCACCCGTTTGTCTGAA-3′ | 269,129–269,148 |
| ERG R9 | 5′-GGCCAAGATTCAGGAAAACA-3′ | 269,687–269,726 |
| ERG R10 | 5′-GCACTGTGGAAGGAGATGGT-3′ | 270,075–270,094 |
ETS, erythroblast transformation specific.
Cell Lines
The Ewing sarcoma cell lines TC-71 and SK-ES1 were generous gifts from Dr. David Loeb (Montefiore Children's Hospital, Bronx, NY). RNA and DNA were obtained from cell lines using the RNeasy Mini Kit (QIAGEN) and the QIAamp DNA Mini Kit (QIAGEN), respectively.
ddPCR Detection
Extracted plasma cell-free RNA and cell-free DNA were subjected to ddPCR amplification using custom-designed primers and probes listed in Table 3. All custom assays were validated to produce no signal in the absence of their intended target (Supplemental Figures S1 and S2). In addition, all custom assays were validated to produce a robust signal with their respective sample-positive control [excluding ST001 cell-free RNA (cfRNA) and ST010 cfRNA owing to limited sample material] (Supplemental Figures S1 and S2). ddPCR was performed using either the QX200 Evagreen Supermix or Supermix for probes–no dUTP (Bio-Rad Technologies, Hercules, CA) per the manufacturer's protocol using the QX200 Droplet Digital PCR System (Bio-Rad Technologies). Positive droplets were analyzed using the QuantaSoft program (Bio-Rad Technologies) to quantify the detected fusion-specific ptRNA and breakpoint-specific ptDNA.
Table 3.
Patient-Specific Primers Used for ddPCR Detection of EWS-ETS Genomic Breakpoint or Transcript Fusion from ptDNA and ptRNA, Respectively
| ptDNA primer/probe name | Sequence | Patient |
|---|---|---|
| ST001 forward | 5′-GTGCTTCATATCGTGCTTTGT-3′ | ST001 |
| ST001 reverse | 5′-TCCAGTTGCAGGTTCACA-3′ | |
| ST001 probe | 5′-ATCTCCATAAGGCTGCTGAACTCTAC-3′ | |
| ST005 forward | 5′-CCAGTGAAGATTTCAGTGCTTT-3′ | ST005 |
| ST005 reverse | 5′-CTCTTGGTACAAATGACAACAGAA-3′ | |
| ST005 probe | 5′-TTTCCAATGCCACACAGGCAAGG-3′ | |
| ST006 forward | 5′-GGAATCCAGGACACATCTTTAGG-3′ | ST006 |
| ST006 reverse | 5′-TGCTCCTACCCTGACTTCTT-3′ | |
| ST006 probe | 5′-CCATCATGTTAGCTGGAATGCCATGC-3′ | |
| ST009 forward | 5′-TCCCGAGTAGCTGAGATGATA-3′ | ST009 |
| ST009 reverse | 5′-GCTTTCTATATTCTAACGCATCCTAC-3′ | |
| ST009 probe | 5′-CGCCTGCCACTTAACCAAAGCTAGA-3′ | |
| ST010 forward | 5′-CGTAGAGTTCAGCAGCCTTATAG-3′ | ST010 |
| ST010 reverse | 5′-GTGAAGAAAGGAAGGTGGAATTG-3′ | |
| ST010 probe | 5′-AAGGACAGTTTGGGAAATCCTATTCTGC-3′ | |
| ptRNA primer name | ||
| FL11 reverse type 1 | 5′-TTGAGGCCAGAATTCATGTTATTG-3′ | |
| EWS forward exon 7 | 5′-ATATAGCCAACAGAGCAGCAG-3′ | ST005 |
| FLI1 reverse type 2 | 5′-CGGTGTGGGAGGTTGTATTATAG-3′ | |
| EWS forward exon 10 | 5′-GAGCGAGGTGGCTTCAATAA-3′ | ST001 |
| ERG reverse exon 10 | 5′-GGACGCTGGTCTTCAGTTT-3′ | |
| EWS forward exon 10 | 5′-GAGCGAGGTGGCTTCAATAA-3′ | ST010 |
| FLI1 reverse exon 10 | 5′-CCGTTGCTCTGTATTCTTACTGA-3′ | |
ddPCR, droplet digital PCR; ETS, erythroblast transformation specific; ptDNA, plasma tumor DNA; ptRNA, plasma tumor RNA.
Statistical Analysis
To quantify target-of-interest detection, a calculation using the QuantaSoft program (Bio-Rad Technologies, Hercules, CA) was used to calculate the number of DNA molecules as copies per microliter, taking into account a Poisson distribution of occupied to unoccupied droplets. The statistical difference in positive droplets between samples was tested using a two-sided t-test. All statistical analyses were performed using Prism version 9.0.0 software (GraphPad Software, San Diego, CA).
Results
Optimal Blood Storage, Collection, and Nucleic Acid Extraction Can Improve the Yield of Plasma cfRNA Significantly
First, we sought to optimize the blood collection method and RNA extraction method to improve the yield of plasma cfRNA from peripheral blood. To compare the various collection methods available for the extraction of small amounts of RNA, we tested three separate commercially available RNA extraction kits across two blood collection conditions: the QIAamp Circulating Nucleic Acid Kit (QIAGEN, Hilden, Germany), QIAamp UltraSens Virus Kit (QIAGEN), and QIAamp Viral RNA Mini Kit (QIAGEN). These kits were used to extract cfRNA from blood collected in either K2 EDTA tubes (EDTA tubes) or cfRNA Blood Collection Tubes (Streck tubes). The resultant cfRNA was reverse-transcribed, and cDNA was subjected to ddPCR analysis for the presence of ACTB and B2M transcripts. As a result of the cross-linking of RNA in Streck tubes, the Circulating Nucleic Acid kit produced a significantly improved yield for cfRNA than any of the other extraction methods, as predicted. Interestingly, the superiority of the QIAamp Circulating Nucleic Acid Kit extraction was apparent even when blood was collected in EDTA tubes. Furthermore, as predicted, because of the known abundance of RNases in blood, the Streck tubes, which contain RNase inhibitors, performed significantly better than the EDTA tubes (Figure 1). This shows that appropriate RNase inhibition and sample processing can improve the yield of cfRNA from blood by more than 1000-fold.
Figure 1.
Collection methods significantly impact the sensitivity of cell-free RNA detection. The black dots represent individual results Plasma cell-free RNA investigated by droplet digital PCR (ddPCR) for the housekeeping genes ACTB and B2M shows that the use of RNase-containing collection tubes (Streck) significantly increases the yield compared with EDTA tubes. Cell-free RNA extracted by the Circulating Nucleic Acid Kit (CNA) significantly increased positive droplets compared with other extraction methods. ∗P < 0.0001.
In Vitro ddPCR-Based EWS-FLI1 Detection: An Optimized RNA-Based Assay Is Inherently More Sensitive than an Optimized DNA-Based Assay
To test whether RNA-based EWS-FLI1 detection is more sensitive than DNA-based EWS-FLI1 detection in Ewing sarcoma samples, RNA and genomic DNA were extracted10 two separate Ewing sarcoma cell lines that harbor separate EWS-FLI1 fusion gene types: TC-71 (type 1 fusion) and SK-ES1 (type 2 fusion). A custom ddPCR assay was developed for both the type 1 and type 2 fusion transcripts, and a ddPCR assay for the unique genomic EWS-FLI1 breakpoint of the TC-71 cell line was designed as previously described. The custom EWS-FLI1 assays, alongside commercial housekeeping gene assays (ACTB, B2M), were performed to establish a limit of detection. For housekeeping genes, the ddPCR assay performed well as expected, detecting as little as 1 pg of RNA reliably (Figure 2A). For EWS-FLI1 detection, EWS-FLI1 RNA was detected reliably with input RNA as small as 10 pg, while the custom EWS-FLI1 DNA assay for the TC-71–specific EWS-FLI1 breakpoint was not performing reliably at 10 pg of input DNA (Figure 2B).
Figure 2.
Droplet digital PCR (ddPCR) can detect trace amounts of RNA and DNA from Ewing sarcoma cells. The black dots represent individual replicates within the experiment. RNA and genomic DNA were extracted from TC-71 and SKES1 cells and subjected to ddPCR analysis in serial dilution. A: Housekeeping genes ACTB and B2M can be detected using as little as input RNA as 1 pg to 100 fg. B: EWS-FLI1 fusion transcripts can be detected using as little as an input RNA as 10 pg, whereas the TC-71–specific EWS-FLI1 probes fail to produce reliable positive droplets at 50 pg.
In Vivo ddPCR-Based EWS-FLI1 Detection: Breakpoint-Specific ptDNA Assays Are Superior to Fusion Transcript ptRNA Assays in Patients with Ewing Sarcoma
To determine if an optimized cfRNA-based EWS-FLI1 detection method (ptRNA) can perform better than our previously published personalized EWS-FLI1 ptDNA assay,10 we collected blood and flash-frozen tumor from five patients with newly diagnosed Ewing sarcoma to assess for EWS-FLI detection from ptRNA and previously published personalized EWS-FLI1 genomic breakpoint detection from ptDNA. The patient group included a variety of EWS-ERG and EWS-FLI1 fusion structures with a variety of tumor locations, one metastatic patient, and four localized patients (Table 1).
First, we tested our previously published ptDNA detection system on plasma DNA samples collected from these patients.10 Overall cell-free DNA burden regardless of tumor origin was assessed for quality control, and AP3B1 was detected in all samples from all patients (Figure 3A). Next, using optimized, patient-specific custom ptDNA assays, we were able to reliably detect positive ptDNA in four of the patients with newly diagnosed untreated tumors (Figure 3B). For patient ST010, although there were some replicates with a robust positive signal, there were replicates with no detectable droplets, which led to this sample not reaching statistical significance. Interestingly, for patient ST001, ptDNA became undetectable after the neoadjuvant chemotherapy and remained negative after resection, a phenomenon we also observed in our previous study.10 Furthermore, the overall pattern of DNA burden measured by AP3B1 detection was similar to the pattern of n of DNA burden measured by or EWS-ERG DNA detection, although, as expected, the raw droplet counts were significantly higher in AP3B1 (Figure 3A). Next, we proceeded to detection of circulating RNA. First, plasma collected from RNA BCT was analyzed for housekeeping genes ACTB and B2M to validate the preservation quality of the plasma RNA (Figure 3C). Although the total burden of ACTB and B2M transcripts were less than the healthy controls previously tested, all samples nevertheless showed abundant ACTB and B2M detection. Next, the extracted plasma RNA was assessed for detection of the correlating EWS-ERG and EWS-FLI1 transcript identified from the correlating patient tumor sample. Although it is possible to design a single common assay to detect several types of fusion genes, we used separate assays respective to each breakpoint type for maximum sensitivity based on circulating fragment size.37 Here, the ptRNA assay showed a significantly lower positive copy number burden compared with the ptDNA assays despite using the same methodology as our previous experiments that showed high sensitivity in spiked samples. In fact, the small number of positive droplets detected in most replicates were below the lower limit of detection, defined by statistical significance after Poisson distribution correction. The single exception was patient ST005 at baseline (Figure 3D).
Figure 3.
Plasma tumor DNA (ptDNA) assays are significantly more sensitive than plasma tumor RNA (ptRNA) assays in newly diagnosed Ewing sarcoma patients. The black dots represent individual replicates. Circulating cell-free DNA and RNA were extracted from 1 mL plasma from five patients before starting therapy as baseline samples. For patient ST001, samples also were available from before surgery (after completion of neoadjuvant chemotherapy) and after surgery (after complete resection of primary disease). A: Cell-free DNA content was assessed by an AP3B1 assay, showing satisfactory DNA content in droplets from all five patients at diagnosis with very little variability among replicates. B: Custom designed EWS-FLI1 or EWS-ERG breakpoint ptDNA assays detect significant positive droplet events in the baseline samples for all five patients. C: Cell-free RNA content assessed by housekeeping gene (ACTB, B2M) transcript counts show a satisfactory yield of cell-free RNA. D: EWS-FLI1 or EWS-ERG fusion gene detection shows significant variation of positive signal among all replicates, with statistical significance only in patient ST005. ∗P < 0.0001. CTRL, control; Neg, negative.
Discussion
The field of circulating tumor nucleic acid detection as a method of biology discovery or disease biomarker has seen a rapid expansion over the past decade. Considering the unique nature of fusion-gene–driven sarcomas, wherein a single fusion gene functions as the essential driver of oncogenesis and persists over the entire course of disease, multiple attempts have been made to detect such sarcoma transcripts although with mixed, mostly disappointing, results.28,29,37, 38, 39, 40 Many of these historical studies deploy a variety of sample procurement methods with or without RNase inhibitors, a variety of temperature controls, as well as a variety of detection platforms ranging in sensitivities. In this study, we hypothesized that with improved sample collection methods and stringent assay design when using ddPCR, circulating ptRNA can perform comparably with circulating ptDNA as a viable blood-based biomarker for disease detection in cancer. The use of Ewing sarcoma as a disease model to test this hypothesis presented a unique advantage in that, although the tumors have been found to be genetically silent with few mutations, the EWS-ETS fusion gene is the driver of oncogenesis and is present in every tumor cell. This is clearly advantageous in ptRNA and ptDNA detection, where any EWS-ETS fusion transcript or EWS-ETS breakpoint DNA detected in the blood is highly patient-specific.10,42
Given the scale and highly sensitive nature of ddPCR, the resultant assay signal is particularly susceptible to common PCR challenges—namely, off-target interactions and nonspecific amplification. Suboptimal ddPCR assays may suffer from an ambiguous false-positive signal.44 Therefore, it is critical to stringently design and fully optimize ddPCR assays to maximize specificity to the target and ultimately produce an irrefutable partition of true-positive droplets. As previously noted in Supplemental Figures S1 and S2, all custom assays in this study were subjected to quality control against a nonspecific transcriptomic/genomic background and showed no activity in the absence of their intended target.
In our study, despite the EWS-FLI1 ptRNA assays showing high sensitivity in vitro as expected, the ptRNA assays did not perform as well in vivo or as well as our previously reported ptDNA-based assay.10 Even in samples obtained from ES patients before any therapy, the ptRNA assay had minimal to low positive events in only four of five patients, with the low number of copies detected below the statistically significant threshold. Addressing potential technical problems, such as abundant RNase in blood, did not correct this low positivity, and this result could be owing to biological reasons, such as rapid degradation of tumor-derived RNA in the circulation even before collection. Feasibly, circulating exosomal RNA is protected from this degradation. However, our cell-free plasma collection method (which does not nearly approach the extreme conditions of ultracentrifugation required to precipitate exosomes) ensures that cell-free exosomes remain in the purified plasma, and, as a result, are lysed during plasma extraction, adding their RNA contents to the final sample pool of circulating RNA. In addition, ES tumors may express fluctuating EWS-FLI1 dynamically over disease progression, a recent finding shown by Franzetti et al,45 which directly could influence the abundance of EWS-FLI1 fusion transcripts in the circulation. Indeed, following this proposed model, the newly diagnosed baseline time points used in this study may correspond to such posited EWS-FLI1low states.
In all patients, ptDNA assays were highly replicable with little variability, as shown in the error and distribution of results (Figure 3, B and D). Considering that we used a standard of 1 mL plasma for all assays, it certainly is possible that larger amounts of plasma could yield a more reliable positive finding across disease stages for ptRNA assays. However, although this may be suitable as a liquid biopsy to detect the EWS-FLI1 or EWS-ERG fusion transcript to aid in a new diagnosis when tumor tissue is scant or poorly preserved to perform a standard molecular diagnosis, an assay with this degree of variability will be poorly suited for disease status monitoring. Overall, our study shows that, even in the most technologically sensitive assays, the detection of tumor-specific RNA from plasma is quite unreliable, whereas the detection of tumor-derived DNA offers superior replicability as well as sensitivity. These results also did not align with the overall cfRNA burden measured by the abundance of ACTB and B2M transcripts, indicating that the majority of cfRNA is not tumor-derived, or there is a biologic reason for EWS-FLI1 and EWS-ERG transcripts to be released in the plasma at less frequency than ACTB and B2M transcripts. Combined with the recent discovery that EWS-FLI1 expression can be dynamic within the tumor, further studies into how cell-free EWS-FLI1 RNA burden reflect this dynamic regulation is warranted.
There are an abundance of feasibility and pilot studies that report methods of ptDNA detection in patients in various stages of their malignancies, although how we may use this information to translate into clinical practice still is unproven. Currently, there is a multicenter prospective trial that is aiming to test the clinical predictive utility of ddPCR-based ptDNA assays in newly diagnosed pediatric sarcoma patients through the National Pediatric Cancer Foundation. Here, we hypothesize that ongoing detection of ptDNA in patients, with an otherwise radiologic complete response, may be interpreted as a minimal residual disease marker to inform therapy. Furthermore, we hypothesize that the degree of initial decrease of ptDNA burden can predict patients with low-risk disease. A more accurate identification of patients with treatment-resistant, minimal residual disease through a rapid blood test will have profound effects on the way patients with high-grade sarcomas are managed, as well as how we design clinical trials.
Footnotes
Supported by the St. Baldrick’s Foundation Scholar Award, the NIHK12HD068372 Child Health Research Career Development Grant, the Hyundai Hope on Wheels Young Investigator Award, and internal funds from the University of Colorado (M.H.).
Disclosures: None declared.
Supplemental material for this article can be found at http://doi.org/10.1016/j.jmoldx.2021.04.003.
Author Contributions
M.H. and D.L. acquired funding and conceived the study; M.H. designed the study, analyzed the data, and drafted and revised the manuscript; K.C. and A.B. designed the study, performed the experiments, analyzed the data, and drafted the manuscript; J.C., A.T., N.D., C.C., T.G., B.G., S.L.F., and J.S. collected samples and data and reviewed and revised the manuscript. M.H. is the guarantor of this work and, as such, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Supplemental Data
Validation of true-positive ptDNA droplet events. For each ptDNA ddPCR assay, the breakpoint specificity was validated and the threshold for true positivity was established. A: Template for representative ddPCR plots. Positive control gDNA was derived from patient biopsy tissue. TC-71 gDNA represents a whole-genome background and breakpoint-negative control. H2O was used as a no-template control. A single-well replicate from each patient plasma assay was included to compare ptDNA droplet events with controls. B: Representative ddPCR plots: ST001, ST005, ST006, ST009, and ST010.
Validation of true-positive ptRNA droplet events. For each ptRNA ddPCR assay, fusion specificity was validated and the threshold for true positivity was established. A: Template for representative ddPCR plots. Fusion-positive cDNA was derived from patient biopsy tissue. Because of the scarcity of patient material and rarity of fusion type, fusion-positive controls for ST001 and ST010 could not be obtained or substituted. Fusion-negative cDNA was derived from cell lines containing a nonspecific fusion type. H2O was used as a no-template control. A single-well replicate from each patient plasma assay was included to compare ptRNA droplet events with controls. The patient plasma cfRNA yield was confirmed with the separate (nonmultiplex) ACTB assay. B: Representative ddPCR plots: ST001, ST005, ST006, ST009, and ST010.
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Associated Data
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Supplementary Materials
Validation of true-positive ptDNA droplet events. For each ptDNA ddPCR assay, the breakpoint specificity was validated and the threshold for true positivity was established. A: Template for representative ddPCR plots. Positive control gDNA was derived from patient biopsy tissue. TC-71 gDNA represents a whole-genome background and breakpoint-negative control. H2O was used as a no-template control. A single-well replicate from each patient plasma assay was included to compare ptDNA droplet events with controls. B: Representative ddPCR plots: ST001, ST005, ST006, ST009, and ST010.
Validation of true-positive ptRNA droplet events. For each ptRNA ddPCR assay, fusion specificity was validated and the threshold for true positivity was established. A: Template for representative ddPCR plots. Fusion-positive cDNA was derived from patient biopsy tissue. Because of the scarcity of patient material and rarity of fusion type, fusion-positive controls for ST001 and ST010 could not be obtained or substituted. Fusion-negative cDNA was derived from cell lines containing a nonspecific fusion type. H2O was used as a no-template control. A single-well replicate from each patient plasma assay was included to compare ptRNA droplet events with controls. The patient plasma cfRNA yield was confirmed with the separate (nonmultiplex) ACTB assay. B: Representative ddPCR plots: ST001, ST005, ST006, ST009, and ST010.