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Article

Analytical Performance and Concordance with Next-Generation Sequencing of a Rapid, Multiplexed dPCR Panel for the Detection of DNA and RNA Biomarkers in Non-Small-Cell Lung Cancer

ChromaCode Inc., 2330 Faraday Ave., Carlsbad, CA 92008, USA
*
Author to whom correspondence should be addressed.
Current address: GENECE, 12250 El Camino Real, Suite 160, Del Mar, CA 92130, USA.
Diagnostics 2023, 13(21), 3299; https://doi.org/10.3390/diagnostics13213299
Submission received: 11 September 2023 / Revised: 18 October 2023 / Accepted: 20 October 2023 / Published: 25 October 2023
(This article belongs to the Special Issue Diagnosis and Management of Lung Cancer)

Abstract

:
FDA approval of targeted therapies for lung cancer has significantly improved patient survival rates. Despite these improvements, barriers to timely access to biomarker information, such as nucleic acid input, still exist. Here, we report the analytical performance and concordance with next-generation sequencing (NGS) of a highly multiplexed research-use-only (RUO) panel using digital PCR (dPCR). The panel’s analytical sensitivity and reactivity were determined using contrived DNA and RNA mixes. The limit of blank was established by testing FFPE curls classified as negative by pathology. Concordance was established on 77 FFPE samples previously characterized using the Oncomine Precision Assay®, and any discordant results were resolved with Archer Fusionplex® and Variantplex® panels. The analytical sensitivity, reported as the estimated mutant allele fraction (MAF), for DNA targets ranged from 0.1 to 0.9%. For RNA targets (ALK, RET, ROS, NTRK 1/2/3 Fusions, and MET Exon 14 skipping alteration), the analytical sensitivity ranged from 23 to 101 detected counts with 5 ng of total RNA input. The population prevalence-based coverage ranged from 89.2% to 100.0% across targets and exceeded 99.0% in aggregate. The assay demonstrated >97% concordance with respect to the comparator method.

1. Introduction

An estimated 236,740 lung and bronchus cancer cases were reported in the United States in 2022, with a five-year relative survival rate of approximately 23% [1]. In the last five years, targeted therapies have increased three-year survival rates by 2.3–13.7% [2,3,4]. As of the publication of this article, with 32 FDA-approved targeted therapies, non-small-cell lung cancer is the solid tumor type with the most approved therapies targeting driver mutations [5,6,7,8,9]. Despite these advances, recent studies indicate that for individuals with NSCLC, equity and accessibility are the main limitations of the use of targeted therapies [2,6,7,10]. A study leveraging a database with commercial and Medicare claims from over 500,000 NSCLC patients in the United States reported that approximately 50% of patients do not obtain complete biomarker testing [6,7]. Of the patients that did receive biomarker test results, 29% did not get the appropriate targeted therapy [6,7]. The top reasons cited by oncologists for not testing all lung cancer patients include tissue or sample limitations, cost of and access to testing, long turnarounds for sequential gene testing, or having to send out for testing [5,11,12,13]. One way to approach this problem would be to increase the timely availability of rapid biomarker testing performed locally in the hospital setting.
Two common molecular biomarker testing modalities include next-generation sequencing (NGS) and sequential single-gene PCR. While NGS provides sequence information of entire genes and regions of the genome, enabling comprehensive detection of multiple variants when present, there are also several critical challenges with sequencing-based approaches. One key challenge is time from sample collection to reported result; 13.1% of NGS testing had a turnaround time (TAT) greater than 14 days, which exceeds the TAT guideline established by the College of American Pathologists, International Association for the Study of Lung Cancer (IASLC), and the Association for Molecular Pathology [7]. Extensive turnaround times additionally have an outsized impact on late-stage patients who have more aggressive cancers and speed to result for biomarker testing should be addressed [3]. In addition to lengthy TAT, approximately 22% of patients do not receive NGS results because of an insufficient quantity of sample or poor sample quality [7,14]. NGS assays feature a high-complexity workflow and analysis that return many variants, some of which are not clinically actionable. In sharp contrast, single-gene PCR tests are manageable in terms of complexity, cost, actionable marker detection, and turnaround time. However, sample sufficiency remains a hurdle due to the need to split the sample across many tests or wells to get a complete result [15,16].
This article describes a highly multiplexed digital PCR (dPCR) assay that reports results for 15 relevant NSCLC variants in nine genes using amplitude modulation and multi-spectral encoding [17]. The panel is designed to detect clinically actionable variants in NSCLC, is compatible with formalin-fixed paraffin-embedded (FFPE) tissue specimens and utilizes a low mass input that could rescue many samples that NGS workflows designate as quantity not sufficient (QNS) [18,19,20,21,22,23,24]. Using dPCR for the panel reduces the complexity of the workflow and TAT by decreasing the number of user manipulations compared to NGS-based workflows. The HDPCR NSCLC panel uses a simplified workflow involving only two touch points post extraction, thus enabling a TAT of less than four hours, excluding extraction time. Cloud-based analysis simplifies and accelerates results interpretation, allowing for results generation in less than 24 h. Additionally, testing for multiple actionable biomarkers in a single test potentially improves tissue requirements compared to single-gene testing. The panel performance reported here includes analytical sensitivity, analytical reactivity (inclusivity), and method correlation with currently accepted amplicon based NGS methodologies.

2. Materials and Methods

2.1. Materials

Plasmids containing the target sequences were sourced from IDT (San Diego, CA, USA) and are detailed in Supplementary Table S1. De-identified, remnant human biological FFPE specimens from NSCLC patients were obtained from Precision for Medicine (Frederick, MD, USA), BioChain (Newark, CA, USA), or CHTN (Durham, NC, USA). Specimen details are listed in Supplementary Table S2. Oncomine Precision Assay testing was performed by Precision for Medicine, and results were reported for all positive specimens. Negative specimens were also tested and comprised of non-tumor adjacent tissue. The mean age of the individual at time of sample acquisition was 63.5 years (standard deviation of 11.1).

2.2. HDPCR NSCLC

High Definition (HDPCR) NSCLC panel utilizes dPCR, where endpoint fluorescent intensities are modulated so that each unique target produces a unique endpoint intensity [17,25,26]. The HDPCR NSCLC Panel (ChromaCode, Carlsbad, CA, USA) comprises three wells: two wells to detect DNA targets, and one to detect RNA fusions. All runs were performed on QIAcuity (Qiagen, Hilden, Germany) using the QIAcuity Nanoplate 26K 24-well plate. The mastermix for DNA wells was formulated by combining 10.5 µL of QIAcuity Probe Master Mix, 8.4 µL of HDPCR Mix, and 2.1 µL of molecular grade water per reaction. The master mix for each RNA well was formulated by combining 10.5 µL of QIAcuity OneStep Advance Probe Master Mix (Qiagen, Germany), 0.45 µL of OneStep RT Mix (Qiagen, Germany), 8.4 µL of HDPCR Mix, and 1.65 µL of molecular grade water per reaction. After the preparation of each mastermix, 21 µL of the sample was combined with 21 µL of the appropriate mastermix and mixed thoroughly. From this reaction mixture, 39 µL was loaded into a well on the QIAcuity Nanoplate. The plate then underwent thermocycling and signal detection on the QIAcuity according to the instructions for use, as follows: (1) reverse transcription at 50 °C for 40 min, (2) preheat to 95 °C for two minutes, (3) 35 cycles consisting of denaturing (95 °C, 30 s) followed by annealing/extension (58 °C for one minute). Terminal fluorescence intensity data were collected in all five available color channels. Analysis was performed using ChromaCode Cloud, which reports detected targets, estimated MAF for DNA targets and positive partition count for RNA targets. The estimated MAF is calculated as (target counts/IC counts) × 100.

2.3. Analytical Sensitivity

Negative FFPE background was prepared by extracting DNA and RNA from pathology-negative FFPE samples using the Maxwell HT FFPE DNA Isolation System according to the recommended protocol for DNA and RNA extraction from a single FFPE sample. The limit of detection for DNA targets was established by spiking plasmids containing the target sequences into negative FFPE background at various MAF concentrations. The RNA fusion targets were transcribed from plasmids using the HiScribe T7 High Yield RNA Synthesis Kit (NEB, Ipswich, MA, USA), isolated using the Monarch Kit (NEB, Ipswich, MA, USA) and spiked into negative FFPE RNA background.
The limit of detection for DNA targets was established at two input amounts: 20 ng and 7.5 ng of DNA per well (40 ng and 15 ng in total for both DNA wells). Mutant allele fractions (MAF) of 0.5%, 1%, 2%, 4%, and 10% were tested at both the 20 ng and 7.5 ng total input amounts. The RNA fusion targets were tested at 5 ng total RNA input. Range-finding was conducted by testing 25 total replicates in a series of five decreasing dilutions. For each target, the lowest concentration at which all five replicates were positive during range-finding was further evaluated with 20 replicates. The limit of detection is reported at the lowest concentration, where greater than 18/20 replicates were detected.

2.4. MAF Correlation Study

Negative FFPE background (screened with HDPCR NSCLC assay and confirmed negative for relevant panel targets) and EGFR G719S positive samples were prepared by extracting DNA from Pan-Cancer 6-Fusion Panel FFPE (Horizon Discovery, Cambridge, UK) and 50% EGFR G719S FFPE DNA (Horizon Discovery, Cambridge, UK) using the Maxwell HT FFPE DNA Isolation System (Promega, Madison, WI). Samples of varying mutant allele fractions (2%, 5%, 10%, 20%, and 40%) were prepared by spiking in 50% EGFR G719S FFPE into negative FFPE background at various MAF concentrations. Samples were analyzed with the HDPCR NSCLC panel at 5 ng, 10 ng, 20 ng, and 40 ng input.

2.5. Analytical Inclusivity

In silico analysis of designs was performed using the COSMIC Mutation Database v97 [27]. The prevalence of each required target assay within the total population of somatic variants at each locus in non-small-cell lung carcinomas (NSCLC) was determined. The parameters utilized in filtering the data are recorded in Table S3. Post filtering, prevalence was calculated based on the count number of distinct entries in the “sample name field”. In silico analysis was completed by confirming the % homology with primer and probe sequences and/or evaluating the binding kinetics of the assay to variant or wild-type sequences. From the in silico results, variants of indeterminant result or variants of high prevalence were then empirically tested.
Analytical inclusivity of the HDPCR NSCLC panel was established by spiking quantified plasmids containing the different target sequences into the appropriate negative FFPE background (RNA or DNA, depending on the well). Each plasmid was tested at 3–5 times the established limit of detection for that target, in two replicates. If a replicate was not detected, the plasmid was tested at a concentration 10 times higher. If no replicate was detected at this higher concentration, the assay was determined to be non-inclusive for that specific sequence. Prevalence was estimated from the reported occurrences of unique sample IDs for each COSMIC ID (LEGACY_MUTATION_ID) associated with a reportable in the filtered COSMIC Mutation Database v97 downloaded on 08FEB2023, as outlined in Table S3.

2.6. Limit of Blank

Sixty unique FFPE curls from 29 unique blocks classified as negative by pathology were tested to evaluate limit of blank. DNA and RNA were extracted from a single 10 µm curl using the Maxwell HT FFPE DNA Isolation System (Promega, Madison, WI, USA) on a KingFisher™ Flex instrument (Thermofisher, Carlsbad, CA, USA). Subsequent to extraction, eluates were quantified using either the Qubit dsDNA BR Assay Kit (Invitrogen, Waltham MA, USA) or the Qubit RNA BR Assay Kit (Invitrogen, Waltham, MA, USA). Samples were evaluated with the HDPCR NSCLC panel in accordance with the previously mentioned methodologies. If a target was detected with the HDPCR NSCLC panel, the sample in question was sent for discordant resolution with the Variant Plex solid tumor focus (ArcherDx, Boulder, CO, USA) or the Fusion Plex Lung (ArcherDx, Boulder, CO, USA).

2.7. Concordance Study

A total of 77 unique FFPE blocks (77 positive samples, as determined by Oncomine Precision Assay) from lung tissue were enrolled in the concordance study. DNA and RNA were extracted from a single 10 µm curl using the Maxwell HT FFPE DNA Isolation System (Promega, Madison, WI, USA) on the KingFisher™ Flex instrument (Thermofisher, Carlsbad, CA). Following extraction, eluates were quantified using either the Qubit dsDNA BR Assay Kit (Invitrogen, Waltham, MA, USA) or the Qubit RNA BR Assay Kit (Invitrogen, Waltham, MA, USA). Samples were evaluated with the HDPCR NSCLC panel according to the above-mentioned methodologies. Results from the Oncomine Precision Assay (from separate sections of the same block) and the HDPCR NSCLC panel were compared, and any discordant samples (same section as evaluated by HDPCR) were sent for discordant resolution using the Variant Plex solid tumor focus (ArcherDx, Boulder, CO, USA) or the Fusion Plex Lung (ArcherDx, Boulder, CO, USA).

3. Results

3.1. Analytical Sensitivity, Limit of Detection (LOD)

Analytical sensitivity was reported in estimated mutant allele fractions (MAF) for DNA targets and in positive partition counts for RNA targets. Each assay featured an internal control (IC) to determine if sufficient amplifiable nucleic acid had been loaded into the well. The estimated MAF for DNA targets was highly correlated (r2 > 0.8) but was affected by the ng input of (Figures S1 and S2). At 20 ng input (1854 average IC Counts) of DNA per well, the LOD ranged from 0.8% to 4.9% estimated MAF (Table 1). When the total DNA input was decreased to 7.5 ng per well (476 average IC Counts), the LOD ranged from 2.4% to 10.9% MAF (Table 1). With an input of 5 ng (97 average IC counts), the LOD for RNA targets ranged from 23 to 101 counts (Table 2). These results indicate that even with minimal inputs of DNA and RNA, the HDPCR NSCLC panel is sensitive to all targets.

3.2. Analytical Inclusivity

Analytical inclusivity was evaluated both in silico for DNA targets and empirically for DNA and RNA fusion targets, by testing plasmids spiked in an FFPE negative matrix. In silico analysis identified 31 DNA targets for subsequent empirical evaluation, while all RNA fusion targets were evaluated empirically. The population prevalence-based coverage for EGFR Exon 20 Insertions, EGFR Exon 20 deletions, ERBB2 Exon 20 Insertions, and EGFR G719X variants was determined to be 89.2%, 99.5%, 99.4%, and 100%, respectively. These results are detailed in Table 3 and Table S4. All RNA fusion targets were evaluated empirically, using 96 different variations. The population prevalence-based coverage for ALK, ROS1, RET and NTRK fusions was determined to be 99.0%, 100%, 98.7%, and 95.1%, respectively. The results are detailed in Table 4 and Table S4.

3.3. Limit of Blank (LoB)

The HDPCR panel was used to evaluate 60 unique FFPE curls from 29 unique blocks, all of which were classified as negative by pathology. Of the 60 samples run, all passed QC metrics and gave valid results, resulting in a 100.0% (94.0–100.0%, 60/60) sample validity rate. Fifty-eight DNA samples were accurately identified as true negatives (TNs), while two DNA samples were called false positives (FPs). The DNA samples had a total NPA of 96.7% (88.6–99.1%). Fifty-nine RNA samples were called true negatives (TNs), one sample was called false positive (FP). The RNA samples had a total NPA of 98.3% (88.6–99.1%).

3.4. Concordance Study

The HDPCR NSCLC panel was used to evaluate 77 unique clinical FFPE samples obtained from Precision for Medicine (Frederick, MD, USA), BioChain (Newark, CA, USA), or CHTN (Durham, NC, USA), which were known to harbor clinically relevant mutations that can be detected with the HDPCR assay. This study functioned as a clinical validation of our method in comparison to orthogonal testing strategies (NGS). The internal control (IC) in DNA wells failed in seventeen of the seventy-seven samples, while the RNA IC failed in six of the seventy-seven samples. These IC failures were found to correlate with the source vendors of the samples, showing variability ranging from 0% to 63%, depending on the specific vendor (Table S5).
In samples that passed IC quality criteria (QC), the concordance of the HDPCR NSCLC panel with the comparator method was 97.3%. The positive percent agreement (PPA) for individual targets ranged from 60.0–100.0% while the positive predictive value (PPV) ranged from 62.5% to 100.0%, prior to discordant resolution. Low PPA/PPV values are driven by the minimal number of positives samples available for certain targets and the high level of discordant results. The negative percent agreement (NPA) ranged from 95.0 to 100.0% and the negative predicted value (NPV) ranged from 94.4 to 100.0% (Table 5). Discordant samples were evaluated from the same extracted material whenever possible and with either the VariantPlex solid tumor focus panel for DNA targets or the FusionPlex Lung for RNA targets. After discordant resolution, each target’s PPA ranged from 71.4 to 100.0% and PPV ranged from 88.9 to 100.0%. The NPA ranged from 98.1 to 100.0% and the NPV ranged from 97.0 to 100.0% for individual targets after discordant resolution.
In total, there were 27 discordant results with the comparator method. Discordant resolution aligned with the HDPCR NSCLC panel in 77.8% (21/27) of discordant samples and aligned with the comparator in 22.2% (6/27) of samples. Of the six discordant results that aligned with the comparator method, 50.0% (3/6) of the results were novel fusions that are outside the inclusivity of the HDPCR NSCLC Panel, leaving 3/6 incongruous samples remaining. A comprehensive breakdown of all discordant analysis outcomes is detailed in Table 6 and Table S6.

4. Discussion

The HDPCR NSCLC panel uses a simplified workflow involving only two touch points post extraction on dPCR, allowing a TAT of less than 24 h. The results from the studies presented here illustrate that the HDPCR NSCLC panel, utilizing dPCR, achieved a sensitivity down to 0.8% estimated MAF for DNA targets and down to 23 positive partition counts for RNA targets, and an accuracy greater than 99% with comparator results after discordant resolution.
Here, we demonstrate an estimated MAF limit of detection between 2.4 and 10.9% when utilizing as little as 15 ng of DNA input split across two wells (7.5 ng per well). At a 40 ng (20 ng per well) DNA input amount, we report an estimated MAF limit of detection between 0.8 and 4.9%. In the future, the HDPCR NSCLC panel can be evaluated with cfDNA and cfRNA to potentially provide a more sensitive and straightforward workflow for liquid biopsy applications.
One disadvantage of traditional PCR approaches in detecting variants is poor sequence coverage or inclusivity [28]. The HDPCR NSCLC panel was designed for high inclusivity of highly variable targets like EGFR Exon 20 insertions (89%), EGFR Exon 19 deletions (99%), and RNA fusions (95–100%). The high inclusivity provides increased confidence in negative results.
The HDPCR NSCLC panel demonstrated high concordance (>97%) with the comparator methods before discordant resolution, and 77.8% (21/27) of discordant results were resolved in favor of HDPCR. Taken together, these results demonstrate how the HDPCR NSCLC panel can test for actionable variants with low nucleic acid input using a simple PCR-based workflow. For the RNA fusion targets, the primary source of false negatives, five of nine, was the detection of novel fusions that were not within the scope of the HDPCR panel.
The ability to provide faster, more accurate results for actionable biomarkers is a crucial step toward democratizing testing. Here, we present a dPCR panel that provides a coverage of actionable biomarkers with high concordance with NGS. The simplified workflow and analysis make it a potential solution for improving accessibility to biomarker testing.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics13213299/s1, Table S1. Plasmid Insert Sequences. Table S2. FFPE Sample Details. Table S3: Parameters for In Silico Bioinformatic Analysis. Table S4: Results of Empirical Analytical Inclusivity Testing by COSMIC ID for DNA and RNA targets. Table S5: Internal Control performance in clinical samples by sample source vendor. Table S6: Fusion partners for discordant resolution. Table S7. Institutional Review Board Statement. Figure S1: Known MAF vs estimated MAF at varying input. EGFR G719S was spiked in at 2, 5, 10, 20, and 40% known MAF (True_MAF) and plotted against the estimated MAF reported out by ChromaCode Cloud (EGFR G719_maf) at 5 ng, 10 ng, 20 ng, and 40 ng total DNA input. Input amount is defined in ng for each well as measured by Qubit. (n = 4 for all MAF/input combinations except 40% known MAF at 40 ng which n = 3). Figure S2: Estimated MAF vs input (ng) at varying known MAF. ChromaCode Cloud estimated MAF (EGFR G719_maf) was plotted against input (ng) for each known MAF. Input amount is defined in ng for each well as measured by Qubit. (n = 4 for all MAF/input combinations except 40% known MAF at 40 ng which n = 3). Figure S3. (A). T790M positive partitions (green), Internal Control (hgDNA) positive partitions (blue), along with calibrator and negative partitions. (B) ROS1 positive partitions (green), Internal Control (hgRNA) positive partitions (blue), along with calibrator and negative partitions.

Author Contributions

Conceptualization, K.C., J.G., B.L., M.S., M.J.S., L.H. and B.A.B.; Methodology, K.C., J.G., B.L., M.S., L.H., L.J. and B.A.B.; Project Administration, K.C., J.G., B.L., M.J.S. and B.A.B.; Formal Analysis, K.C., J.G., B.L., M.S., M.J.S., L.H. and B.A.B.; Writing—Original Draft Preparation, K.C., L.H. and B.A.B.; Writing—Review & Editing, K.C., J.G., B.L., M.S., M.J.S., L.H. and B.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of BioChain Institute Inc. (protocol IRB00008283, date of approval 6/9/21), the Institutional Review Board of Vanderbilt University (protocol IRB010294, date of approval 10/15/22) and Advarra Institutional Review Board (protocol CR00425931 date of approval 3/6/23, and protocol Pro00051469 date of approval 3/6/23).

Informed Consent Statement

All samples were collected under an IRB-approved informed consent or waiver of consent.

Data Availability Statement

The raw digital PCR data that support the findings of this study are available from the corresponding author [[email protected] (accessed on 18 August 2023)] upon reasonable request.

Acknowledgments

For their significant contributions to this work, we thank Megan Allen, Andrew Richards, Heather Carolan, Isabel Hellmers, Juan Enciso, Orlando Perrera, Paige Berroteran, Tiffany Martinez, Janine Wiese, Gladys Ornelas, Aaron Aguiar, Clement Lee, Paul Belitz, and Jerrod Schwartz.

Conflicts of Interest

K.C., J.G., B.L., M.S., M.J.S., L.H., L.J. and B.A.B. are employees and equity holders of ChromaCode Inc.

References

  1. American Cancer Society. Cancer Facts & Figures 2022; American Cancer Society: Atlanta, GA, USA, 2022. [Google Scholar]
  2. Toogood, S. Targeted Therapy, Immunotherapy Propel 10 Years of Progress in NSCLC. Target. Ther. Oncol. 2022, 11, 47. [Google Scholar]
  3. National Cancer Institute. List of Targeted Therapy Drugs Approved for Specific Types of Cancer. Available online: https://www.cancer.gov/about-cancer/treatment/types/targeted-therapies/approved-drug-list#targeted-therapy-approved-for-lung-cancer (accessed on 13 March 2023).
  4. Chi, S.A.; Yu, H.; Choi, Y.-L.; Park, S.; Sun, J.-M.; Lee, S.-H.; Ahn, J.S.; Ahn, M.-J.; Choi, D.-H.; Kim, K.; et al. Trends in Survival Rates of Non–Small Cell Lung Cancer With Use of Molecular Testing and Targeted Therapy in Korea, 2010–2020. JAMA Netw. Open 2023, 6, e232002. [Google Scholar] [CrossRef] [PubMed]
  5. Winkelman, D.F.; Varkhedkar, V.M.; Stellander-Amato, H.; Bailey, L.; Kachadourian, A. Why Are Oncologists Not Testing All NSCLC Patients for Biomarkers in the U.S. Market? J. Clin. Oncol. 2021, 39, e21003. [Google Scholar] [CrossRef]
  6. Kerr, K.; Bibeau, F.; Thunnissen, E.; Botling, J.; Ryška, A.; Wolf, J.; Öhrling, K.; Burdon, P.; Malapelle, U.; Büttner, R. The Evolving Landscape of Biomarker Testing for Non-Small Cell Lung Cancer in Europe. Lung Cancer 2021, 154, 161–175. [Google Scholar] [CrossRef] [PubMed]
  7. Sadik, H.; Pritchard, D.; Keeling, D.-M.; Policht, F.; Riccelli, P.; Stone, G.; Finkel, K.; Schreier, J.; Munksted, S. Impact of Clinical Practice Gaps on the Implementation of Personalized Medicine in Advanced Non–Small-Cell Lung Cancer. JCO Precis. Oncol. 2022, 6, e2200246. [Google Scholar] [CrossRef]
  8. Mileham, K.F.; Schenkel, C.; Bruinooge, S.S.; Freeman-Daily, J.; Basu Roy, U.; Moore, A.; Smith, R.A.; Garrett-Mayer, E.; Rosenthal, L.; Garon, E.B.; et al. Defining Comprehensive Biomarker-Related Testing and Treatment Practices for Advanced Non-Small-Cell Lung Cancer: Results of a Survey of U.S. Oncologists. Cancer Med. 2022, 11, 530–538. [Google Scholar] [CrossRef]
  9. Massafra, M.; Passalacqua, M.; Gebbia, V.; Macrì, P.; Lazzari, C.; Gregorc, V.; Buda, C.; Altavilla, G.; Santarpia, M. Immunotherapeutic Advances for NSCLC. Biologics 2021, 15, 399–417. [Google Scholar] [CrossRef]
  10. National Cancer Institute. Cancer Stat Facts: Lung and Bronchus Cancer. Available online: https://seer.cancer.gov/statfacts/html/lungb.html (accessed on 13 March 2023).
  11. Pennell, N.; Arcila, M.; Gandara, D.; West, H. Biomarker Testing for Patients with Advanced Non-Small Cell Lung Cancer: Real-World Issues and Tough Choices. Am. Soc. Clin. Oncol. Educ. Book 2019, 39, 531–542. [Google Scholar] [CrossRef]
  12. Cooper, K.; Abdallah, K.; Angove, R.; Gallagher, K.; Bonham, V. Navigating Access to Cancer Care: Identifying Barriers to Precision Cancer Medicine. Ethn. Dis. 2022, 32, 39–48. [Google Scholar] [CrossRef]
  13. Roberts, T.J.; Kesselheim, A.S.; Avorn, J. Variation in Use of Lung Cancer Targeted Therapies Across State Medicaid Programs, 2020–2021. JAMA Netw Open 2023, 6, e2252562. [Google Scholar] [CrossRef]
  14. Al-Kateb, H.; Nguyen, T.; Steger-May, K.; Pfeifer, J. Identification of Major Factors Associated with Failed Clinical Molecular Oncology Testing Performed by Next Generation Sequencing (NGS). Mol. Oncol. 2015, 9, 1737–1743. [Google Scholar] [CrossRef] [PubMed]
  15. Shen, C.-I.; Chiang, C.-L.; Shiao, T.-H.; Luo, Y.-H.; Chao, H.-S.; Huang, H.-C.; Chiu, C.-H. Real-World Evidence of the Intrinsic Limitations of PCR-Based EGFR Mutation Assay in Non-Small Cell Lung Cancer. Sci. Rep. 2022, 12, 13566. [Google Scholar] [CrossRef] [PubMed]
  16. O’Donnell, P.; Ferguson, J.; Shyu, J.; Current, R.; Rehage, T.; Tsai, J.; Christensen, M.; Tran, H.B.; Chien, S.S.-C.; Shieh, F.; et al. Analytic Performance Studies and Clinical Reproducibility of a Real-Time PCRassay for the Detection of Epidermal Growth Factor Receptor Gene Mutations Informalin-Fixed Paraffin-Embedded Tissue Specimens of Non-Small Cell Lungcancer. BMC Cancer 2013, 13, 210. [Google Scholar] [CrossRef] [PubMed]
  17. Rajagopal, A.; Yurk, D.; Shin, C.; Menge, K.; Jacky, L.; Fraser, S.; Tombrello, T.A.; Tsongalis, G.J. Significant Expansion of Real-Time PCR Multiplexing with Traditional Chemistries Using Amplitude Modulation. Sci. Rep. 2019, 9, 1053. [Google Scholar] [CrossRef]
  18. Liu, S.; Huang, T.; Liu, M.; He, W.; Zhao, Y.; Yang, L.; Long, Y.; Zong, D.; Zeng, H.; Liu, Y.; et al. The Genomic Characteristics of ALK Fusion Positive Tumors in Chinese NSCLC Patients. Front. Oncol. 2020, 10, 726. [Google Scholar] [CrossRef]
  19. Drusbosky, L.M.; Rodriguez, E.; Dawar, R.; Ikpeazu, C. V Therapeutic Strategies in RET Gene Rearranged Non-Small Cell Lung Cancer. J. Hematol. Oncol. 2021, 14, 50. [Google Scholar] [CrossRef]
  20. Farago, A.F.; Taylor, M.S.; Doebele, R.C.; Zhu, V.W.; Kummar, S.; Spira, A.I.; Boyle, T.A.; Haura, E.B.; Arcila, M.E.; Benayed, R.; et al. Clinicopathologic Features of Non-Small-Cell Lung Cancer Harboring an NTRK Gene Fusion. JCO Precis. Oncol. 2018, 2, 1–12. [Google Scholar] [CrossRef]
  21. Socinski, M.A.; Pennell, N.A.; Davies, K.D. MET Exon 14 Skipping Mutations in Non–Small-Cell Lung Cancer: An Overview of Biology, Clinical Outcomes, and Testing Considerations. JCO Precis. Oncol. 2021, 5, 653–663. [Google Scholar] [CrossRef]
  22. Ettinger, D.S.; Wood, D.E.; Aisner, D.L.; Akerley, W.; Bauman, J.R.; Bharat, A.; Bruno, D.S.; Chang, J.Y.; Chirieac, L.R.; D’Amico, T.A.; et al. Non–Small Cell Lung Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Canc. Netw. 2022, 20, 497–530. [Google Scholar] [CrossRef]
  23. Zhu, G.; Ye, X.; Dong, Z.; Lu, Y.C.; Sun, Y.; Liu, Y.; McCormack, R.; Gu, Y.; Liu, X. Highly Sensitive Droplet Digital PCR Method for Detection of EGFR-Activating Mutations in Plasma Cell–Free DNA from Patients with Advanced Non-Small Cell Lung Cancer. J. Mol. Diagn. 2015, 17, 265–272. [Google Scholar] [CrossRef]
  24. Wang, X.; Li, X.; Guo, H.; Zhu, L.; Peng, Z.; Wang, J.; Yang, F.; Guo, Y. Highly Sensitive Droplet Digital PCR Method for Detection of de Novo EGFR T790M Mutation in Patients with Non-Small Cell Lung Cancer. OncoTargets Ther. 2020, 13, 10621–10630. [Google Scholar] [CrossRef] [PubMed]
  25. Jacky, L.; Yurk, D.; Alvarado, J.; Belitz, P.; Fathe, K.; MacDonald, C.; Fraser, S.; Rajagopal, A. Robust Multichannel Encoding for Highly Multiplexed Quantitative PCR. Anal. Chem. 2021, 93, 4208–4216. [Google Scholar] [CrossRef] [PubMed]
  26. Leatham, B.; McNall, K.; Subramanian, H.K.K.; Jacky, L.; Alvarado, J.; Yurk, D.; Wang, M.; Green, D.C.; Tsongalis, G.J.; Rajagopal, A.; et al. A rapid, multiplex digital PCR assay to detect gene variants and fusions in non-small cell lung cancer. Mol. Oncol. 2023, 1, e13523. [Google Scholar] [CrossRef]
  27. Tate, J.G.; Bamford, S.; Jubb, H.C.; Sondka, Z.; Beare, D.M.; Bindal, N.; Boutselakis, H.; Cole, C.G.; Creatore, C.; Dawson, E.; et al. Cosmic: The catalogue of somatic mutations in cancer. Nucleic Acids Res. 2019, 47, D941–D947. [Google Scholar] [CrossRef]
  28. Vanderpoel, J.; Stevens, A.L.; Emond, B.; Lafeuille, M.-H.; Hilts, A.; Lefebvre, P.; Morrison, L. Total Cost of Testing for Genomic Alterations Associated with Next-Generation Sequencing versus Polymerase Chain Reaction Testing Strategies among Patients with Metastatic Non-Small Cell Lung Cancer. J. Med. Econ. 2022, 25, 457–468. [Google Scholar] [CrossRef]
Table 1. Limit of detection for DNA targets. The limit of detection reported in estimated MAF as determined by ChromaCode Cloud 5.0 software. Input amount is defined in ng for each well as measured by Qubit. Mean counts represent the average number of positive partitions at the reported limit of detection. The detection rate is the positive results out of 20 total replicates.
Table 1. Limit of detection for DNA targets. The limit of detection reported in estimated MAF as determined by ChromaCode Cloud 5.0 software. Input amount is defined in ng for each well as measured by Qubit. Mean counts represent the average number of positive partitions at the reported limit of detection. The detection rate is the positive results out of 20 total replicates.
7.5 ng Input20 ng Input
TargetMean CountsDetection RateEstimated MAFMean CountsDetection RateEstimated MAF
EGFR Exon 19 deletions76.720/2010.9%30.220/201.2%
EGFR G719X40.920/206.3%33.820/202.7%
EGFR S768I45.520/209.6%63.920/203.2%
ERBB2 insertions41.620/207.4%32.220/202.4%
EGFR L858R70.820/208.8%58.419/202.6%
EGFR Exon 20 insertions60.420/2010.1%58.820/204.9%
BRAF V600E21.020/202.4%44.320/202.1%
KRAS G12C23.120/203.6%34.120/202.3%
EGFR T790M37.220/209.5%19.320/200.8%
EGFR L861Q40.420/208.3%39.920/203.4%
Table 2. Limit of detection for RNA Targets. The limit of detection reported mean counts by ChromaCode Cloud 5.0 software. Input amount is defined in ng for each well as measured by Qubit. Mean counts represent the average number of positive partitions at the reported limit of detection. The detection rate is the positive results out of 20 total replicates.
Table 2. Limit of detection for RNA Targets. The limit of detection reported mean counts by ChromaCode Cloud 5.0 software. Input amount is defined in ng for each well as measured by Qubit. Mean counts represent the average number of positive partitions at the reported limit of detection. The detection rate is the positive results out of 20 total replicates.
5 ng Input
TargetMean CountsDetection Rate
ALK fusions2321/21 1
RET fusions2319/19 2
ROS1 fusions7219/20
MET Exon 14 skipping mutations10120/20
NTRK 1/2/3 fusions8520/20
1 21 total replicates were run for ALK fusions due to operator error, all replicates gave valid results. 2 20 total replicates were run for RET fusions. One replicate failed because of low IC and was excluded from the final analysis.
Table 3. Inclusivity results for DNA targets. In silico and empirical (bench testing) results for inclusivity by target. Unique variants listed in the COSMIC Mutation Database for each target were tested via in silico analysis. Any variants of high prevalence or those with indeterminant binding kinetics during in silico analysis were also evaluated empirically. Overall Inclusivity refers to the percent of variants reported in the COSMIC Mutation Database detected by the NSCLC HDPCR panel. Positive Patients per 1000 is the number of samples predicted to be positive for this target based on the reported prevalence in the COSMIC database per 1000 NSCLC samples. Missed Calls due to Inclusivity details the number of results impacted per 1000, due to variants outside the scope of the NSCLC panel.
Table 3. Inclusivity results for DNA targets. In silico and empirical (bench testing) results for inclusivity by target. Unique variants listed in the COSMIC Mutation Database for each target were tested via in silico analysis. Any variants of high prevalence or those with indeterminant binding kinetics during in silico analysis were also evaluated empirically. Overall Inclusivity refers to the percent of variants reported in the COSMIC Mutation Database detected by the NSCLC HDPCR panel. Positive Patients per 1000 is the number of samples predicted to be positive for this target based on the reported prevalence in the COSMIC database per 1000 NSCLC samples. Missed Calls due to Inclusivity details the number of results impacted per 1000, due to variants outside the scope of the NSCLC panel.
TargetOverall Inclusivity
(Prevalence Weighted)
Positive Samples per 1000 NSCLC SamplesMissed Calls per 1000 NSCLC Samples
EGFR Exon 20 Insertions89.2%91
EGFR Exon 19 Deletions99.5%1310.7
EGFR G719X100%100
ERBB299.4%9<0.1
Table 4. Inclusivity results for RNA Targets. Empirical (bench testing) results for inclusivity by target. Overall Inclusivity refers to the percentage of variants reported in the COSMIC Mutation Database detected by the NSCLC HDPCR panel. Positive Patients per 1000 is the number of patients predicted to be positive for this target based on the reported prevalence in the COSMIC database per 1000 NSCLC samples. Missed Calls due to Inclusivity details the number of patients impacted per 1000 due to variants outside the scope of the NSCLC panel.
Table 4. Inclusivity results for RNA Targets. Empirical (bench testing) results for inclusivity by target. Overall Inclusivity refers to the percentage of variants reported in the COSMIC Mutation Database detected by the NSCLC HDPCR panel. Positive Patients per 1000 is the number of patients predicted to be positive for this target based on the reported prevalence in the COSMIC database per 1000 NSCLC samples. Missed Calls due to Inclusivity details the number of patients impacted per 1000 due to variants outside the scope of the NSCLC panel.
TargetOverall Inclusivity
(Prevalence Weighted)
Positive Patients Per 1000 NSCLC SamplesMissed Calls Per 1000 NSCLC Samples
ALK99%27<0.3
ROS1100%40
RET98.7%4<0.1
NTRK95.1%00
Table 5. Concordance data reported by target for Oncomine Precision Assay and HDPCR NSCLC panel. True positive (TP), true negative (TN), false positive (FP), positive percent agreement (PPA), positive predictive value (PPV), negative predictive value (NPV) and negative percent agreement (NPA). Adjusted values after discordant resolution are placed in parenthesis.
Table 5. Concordance data reported by target for Oncomine Precision Assay and HDPCR NSCLC panel. True positive (TP), true negative (TN), false positive (FP), positive percent agreement (PPA), positive predictive value (PPV), negative predictive value (NPV) and negative percent agreement (NPA). Adjusted values after discordant resolution are placed in parenthesis.
TargetTPTNFPFNPPA (PPA Post Discordant)NPA (NPA Post Discordant)PPV (PPV Post Discordant)NPV (NPV Post Discordant)
BRAF V600E 18561280.0%
(100.0%)
98.2%
(100.0%)
88.9%
(100.0%)
96.6%
(100.0%)
EGFR Exon 19 deletion 23621175.0%
(100.0%)
98.4%
(100.0%)
75.0%
(100.0%)
98.4%
(100.0%)
EGFR L858R 35573271.4%
(100.0%)
95.0%
(100.0%)
62.5%
(100.0%)
96.6%
(100.0%)
EGFR S768I36400100.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
EGFR T790M 43620260.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
96.9%
(100.0%)
EGFR G719X25800100.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
EGFR Exon 20 5 insertion85110100.0%
(100.0%)
98.1%
(98.1%)
88.9%
(88.9%)
100.0%
(100.0%)
EGFR L861Q 605910N/A
(100.0%)
98.3%
(100.0%)
N/A
(100.0%)
100.0%
(100.0%)
ERBB2 insertions 785110100.0%
(100.0%)
98.1%
(100.0%)
88.9%
(100.0%)
100.0%
(100.0%)
KRAS G12C35700100.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
ALK fusions 85630362.5%
(71.4%)
100.0%
(100.0%)
100.0%
(100.0%)
95.5%
(97.2%)
MET Exon 14 skipping 93660260.0%
(100.0%)
100.0%
(98.9%)
100.0%
(80.0%)
97.1%
(100.0%)
NTRK1/2/3 fusions 1006704N/A
(N/A)
100.0%
(100.0%)
N/A
(N/A)
94.4%
(97.2%)
RET fusions 113660260.0%
(100.0%)
100.0%
(100.0%)
100.0%
(100.0%)
97.1%
(100.0%)
ROS1 fusions 1246610100.0%
(100.0%)
98.5%
(100.0%)
80.0%
(100.0%)
100.0%
(100.0%)
1 Two BRAF false negatives were not detected by discordant resolution. One BRAF false positive was detected with discordant resolution. 2 One EGFR Exon 19 deletion false negative was not detected in discordant resolution; the QC score was below threshold for discordant resolution. One EGFR Exon 19 deletion false positive was detected by discordant resolution. 3 Two EGFR L858R false negatives were not detected by discordant resolution. Three EGFR L858R false positives were detected by discordant resolution. 4 Two EGFR T790M false negatives were not detected by discordant resolution. 5 One EGFR Exon 20 insertion false positive was not detected by discordant resolution. 6 One EGFR L861Q false positive was detected by discordant resolution. 7 One ERBB false positive was detected by discordant resolution. 8 Two ALK false negatives were not detected by discordant resolution. One detected NTRK, novel fusion, but failed to pass the QC threshold. One false negative was detected by discordant resolution and reported as a novel fusion. 9 One MET false negative was not detected by discordant resolution. One MET false negative was not sent for discordant resolution. 10 Two NTRK false negatives were not detected by discordant resolution. Two NTRK false negatives were detected by discordant resolution but were reported as a novel fusion. 11 Two RET false negatives were not detected by discordant resolution. 12 One ROS1 false positive was detected by discordant resolution.
Table 6. Results of discordant analysis. Results for all discordant samples with comparator and discordant test results. MAFs are reported for all samples where a DNA target was detected. RNA counts, number of positive partitions, are reported for all RNA detections with the ChromaCode panel. MAF (%) and RNA counts are listed as N/A when there was no target detected by the assay. Fusion partners for discordant resolution are reported in Supplementary Table S3.
Table 6. Results of discordant analysis. Results for all discordant samples with comparator and discordant test results. MAFs are reported for all samples where a DNA target was detected. RNA counts, number of positive partitions, are reported for all RNA detections with the ChromaCode panel. MAF (%) and RNA counts are listed as N/A when there was no target detected by the assay. Fusion partners for discordant resolution are reported in Supplementary Table S3.
IDComparator ResultOncomine MAF (%)ChromaCode ResultChromacode Estimated MAF (%) (Counts for RNA)Discordant ResultDiscordant MAF (%) or Reads for RNA
1101ALKN/ANo Target DetectedN/AALK, Novel Isoform8, 5
1114NTRKN/ANo Target DetectedN/ANTRK, Novel Isoform29, 12, 12
2031T790M91%L858R, T790M>80%, 30%L858R, T790M69%, 37%
2429METN/ANot DetectedN/ANo ResultN/A
2720T790M0.11%EGFR L858R20%EGFR L858R90%
2727EGFR Ex19 Deletion, T790M14%, 12%EGFR Ex19 Deletion44%EGFR Ex19 Deletion31%
2733METN/ANot DetectedN/ANovel MET Exon fusion (not 14 skipping)29
3837No Target DetectedN/AROS157ROS19
4553BRAF V600EN/AL858R>80%L858R42% 1
4570NTRKN/AEGFR Ex19 Deletion63%EGFR Ex19 Deletion,49%
4590No Target DetectedN/ABRAF V600E42%BRAF V600E26%
4595NTRKN/ANo Target DetectedN/ANTRK, Novel Isoform17
5016NTRKN/ANo Target DetectedN/ANo Target Detected 1N/A
5143EGFR EX20 Insertion0.25%EGFR EX20 Insertion, L861Q26%, 7%EGFR EX20 Insertion, L861Q22%, 5%
5166ALKN/ANot Detected 2N/AALK22
5386BRAF V600E0.26%EGFR EX20 InsertionN/ANo Target DetectedN/A
5737L858R0.8%No Target DetectedN/ANo Target DetectedN/A
5739ALKN/ANo Target DetectedN/ANovel NTRK detected 15
5745L858R0.85%No Target DetectedN/ANo Target DetectedN/A
5894No Target DetectedN/AERBB25%ERBB22%
5918RETN/ANo Target DetectedN/ANo Target DetectedN/A
7071ALK, RETN/AALK21ALK35
7334EGFR Ex19 Deletion0.2%No Target DetectedN/ANo Target Detected 1N/A
1 Failed sequencing QC. 2 Initial run detected ALK; was excluded due to positive plate control failure.
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Cabrera, K.; Gole, J.; Leatham, B.; Springer, M.J.; Smith, M.; Herdt, L.; Jacky, L.; Brown, B.A. Analytical Performance and Concordance with Next-Generation Sequencing of a Rapid, Multiplexed dPCR Panel for the Detection of DNA and RNA Biomarkers in Non-Small-Cell Lung Cancer. Diagnostics 2023, 13, 3299. https://doi.org/10.3390/diagnostics13213299

AMA Style

Cabrera K, Gole J, Leatham B, Springer MJ, Smith M, Herdt L, Jacky L, Brown BA. Analytical Performance and Concordance with Next-Generation Sequencing of a Rapid, Multiplexed dPCR Panel for the Detection of DNA and RNA Biomarkers in Non-Small-Cell Lung Cancer. Diagnostics. 2023; 13(21):3299. https://doi.org/10.3390/diagnostics13213299

Chicago/Turabian Style

Cabrera, Kerri, Jeffrey Gole, Bryan Leatham, Matthew J. Springer, Molly Smith, Leah Herdt, Lucien Jacky, and Bradley A. Brown. 2023. "Analytical Performance and Concordance with Next-Generation Sequencing of a Rapid, Multiplexed dPCR Panel for the Detection of DNA and RNA Biomarkers in Non-Small-Cell Lung Cancer" Diagnostics 13, no. 21: 3299. https://doi.org/10.3390/diagnostics13213299

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