Sensitivity Of Mutation Detection Research Articles

Performance of a Rapid Digital PCR Test for the Detection of Non-Small Cell Lung Cancer (NSCLC) Variants.

Next-generation sequencing is widely used for comprehensive molecular profiling for many cancers including lung cancer. However, the complex workflows and long turnaround times limit its access and utility. ChromaCode's High Definition PCR Non-Small Cell Lung Cancer Panel (HDPCR™ NSCLC Panel) is a low-cost, rapid turnaround, digital polymerase chain reaction assay that is designed to detect variants in nine NSCLC genes listed in National Comprehensive Cancer Network guidelines. This assay uses TaqMan® probe limiting chemistry and proprietary analysis software to enable multi-target detection within a single-color channel. We compared the performance of the HDPCR™ NSCLC Panel against an in-house, laboratory-developed, targeted next-generation sequencing panel used in the Molecular Diagnostics Laboratory at the University of Minnesota Medical Center to detect biomarkers for NSCLC. The overall accuracy of the HDPCR panel was 99.48% (95% confidence interval 99.01-99.76) with a sensitivity of 95.35% (95% confidence interval 88.52-98.72) and a specificity of 99.69% (95% confidence interval 99.29-99.90). The HDPCR wet lab workflow was 4 h, and the time to generate variant calls from raw data using the ChromaCode Cloud was 2 minutes. We demonstrated that the HDPCR™ NSCLC Panel provides timely, comprehensive, and sensitive mutation detection in NSCLC samples with results in less than 24 h.

IMPI: An Interface for Low-Frequency Point Mutation Identification Exemplified on Resistance Mutations in Chronic Myeloid Leukemia

Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs.

Retraction: Sensitive Detection of Pre-Existing BCR-ABL Kinase Domain Mutations in CD34+ Cells of Newly Diagnosed Chronic-Phase Chronic Myeloid Leukemia Patients Is Associated with Imatinib Resistance: Implications in the Post-Imatinib Era.

Retraction: Sensitive Detection of Pre-Existing BCR-ABL Kinase Domain Mutations in CD34+ Cells of Newly Diagnosed Chronic-Phase Chronic Myeloid Leukemia Patients Is Associated with Imatinib Resistance: Implications in the Post-Imatinib Era.

Ultrasensitive sequencing of STR markers utilizing unique molecular identifiers and the SiMSen-Seq method

Massively parallel sequencing (MPS) is increasingly applied in forensic short tandem repeat (STR) analysis. The presence of stutter artefacts and other PCR or sequencing errors in the MPS-STR data partly limits the detection of low DNA amounts, e.g., in complex mixtures. Unique molecular identifiers (UMIs) have been applied in several scientific fields to reduce noise in sequencing. UMIs consist of a stretch of random nucleotides, a unique barcode for each starting DNA molecule, that is incorporated in the DNA template using either ligation or PCR. The barcode is used to generate consensus reads, thus removing errors. The SiMSen-Seq (Simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing) method relies on PCR-based introduction of UMIs and includes a sophisticated hairpin design to reduce unspecific primer binding as well as PCR protocol adjustments to further optimize the reaction. In this study, SiMSen-Seq is applied to develop a proof-of-concept seven STR multiplex for MPS library preparation and an associated bioinformatics pipeline. Additionally, machine learning (ML) models were evaluated to further improve UMI allele calling. Overall, the seven STR multiplex resulted in complete detection and concordant alleles for 47 single-source samples at 1 ng input DNA as well as for low-template samples at 62.5 pg input DNA. For twelve challenging mixtures with minor contributions of 10 pg to 150 pg and ratios of 1–15% relative to the major donor, 99.2% of the expected alleles were detected by applying the UMIs in combination with an ML filter. The main impact of UMIs was a substantially lowered number of artefacts as well as reduced stutter ratios, which were generally below 5% of the parental allele. In conclusion, UMI-based STR sequencing opens new means for improved analysis of challenging crime scene samples including complex mixtures.

Abstract 3695: Monitoring response to immunotherapy using cell free DNA fragmentomes

Abstract BACKGROUND: The rapid detection of disease progression in patients receiving immune checkpoint inhibitors (ICIs) is challenging since there are no reliable biomarkers of clinical response. Liquid biopsy assays may address this unmet need by using cell-free DNA (cfDNA) to monitor treatment responses. Current targeted next-generation sequencing cfDNA assays are costly and have limited assay sensitivity for detection of cancer-specific mutations at low mutant allele frequency (MAF). Here, we demonstrate the utility of DELFI Tumor Fraction (DELFI-TF), a tumor- and mutation-independent cfDNA fragmentome approach to monitor treatment response in patients with metastatic non-small cell lung cancer (mNSCLC). METHODS: Overall, 422 longitudinal blood samples were collected from 141 mNSCLC patients of which 109 were treated with immunotherapy and 32 with targeted therapy (TT). Plasma-derived cfDNA was processed with whole genome sequencing at low coverage (~4x). Circulating tumor burden was quantified as the maximum MAF (maxMAF) of tumor-derived variants detected using a 500+gene panel. Matched white blood cells were used to filter out germline and clonal hematopoietic variants. A random forest regression model was trained with maxMAF as the response using fragmentation-derived features. The accuracy of DELFI-TF was assessed using leave one patient out cross-validation in the ICI cohort and holdout validation in the cohort of patients receiving TT. For survival analyses, we used the median baseline scores to define low/high DELFI-TF and maxMAF. RESULTS: In the ICI cohort, DELFI-TF scores were strongly correlated with the maxMAF (r=0.94, p<0.001, Pearson). At baseline, DELFI-TF and maxMAF both differentiate responding from non-responding tumors (p = 0.01 and p = 0.001 for DELFI-TF and maxMAF respectively; Wilcoxon). Patients with high DELFI-TF or maxMAF had significantly shorter progression-free survival (PFS) compared to low DELFI-TF or maxMAF (17.0 vs 4.7 months, p < 0.001; 17.0 vs 4.6 months, p < 0.001; Log-Rank respectively) and similar overall survival (OS) (DELFI-TF high 11.4 vs low 34.1 months, p<0.001; maxMAF high 10.9 vs low 34.1 months, p < 0.001, Log-Rank). Changes in DELFI-TF and maxMAF at the earliest timepoint were highly correlated (n=109; r=0.90; Pearson). In the validation cohort of TT patients, DELFI-TF at baseline outperformed maxMAF in distinguishing patients with PFS and OS benefits (DELFI-TF PFS 36.4 vs 11.5 months, p = 0.03; maxMAF PFS 21.9 vs 15.9 months, p=0.6; Log-Rank) (DELFI-TF OS high 37 months vs low not reached (NR), p < 0.05; Max MAF OS high 37 vs NR, p = 0.1, Log-Rank). The majority of TT patients (81%) had consistent changes with DELFI-TF and maxMAF from baseline to the earliest timepoint. CONCLUSIONS: DELFI-TF is a tumor and mutation independent approach that is cost-efficient with high performance comparable to current ctDNA assays for estimating tumor burden and monitoring response in cancer patients. Citation Format: Bahar Alipanahi, Lavanya Sivapalan, Jamie Medina, Zachary L. Skidmore, Paola Ghanem, Erica Peters, Gavin Pereira, Nisha Rao, Kavya Velliangiri, Alissa Konicki, Stephen Cristiano, Laurel Millberg, Jacob Carey, Keith Lumbard, Noushin Niknafs, Bryan Chesnick, Jennifer Tom, Alessandro Leal, Benjamin Levy, Patrick Forde, Peter Bach, Nicholas C. Dracopoli, Robert B. Scharpf, Victor Velculescu, Lorenzo Rinaldi, Valsamo Anagnostou. Monitoring response to immunotherapy using cell free DNA fragmentomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3695.

Abstract 909: Enhancing genomic analysis in cancer diagnostics: A machine learning approach for removing artifacts in FFPE specimens

Abstract Formalin-Fixed Paraffin-Embedded (FFPE) specimens, widely utilized in clinical cancer diagnostics, present significant challenges by introducing artifacts into genomic data. This study aimed to profile these FFPE-induced genomic alterations, with a particular focus on single-nucleotide variants (SNVs), small insertions and deletions (indels), and copy-number variations (CNVs), and to develop computational methods for filtering out such artifacts. Our primary focus was twofold: first, to comprehensively characterize the unique error profile of FFPE specimens observed through whole-genome sequencing (WGS), and second, to construct artifact classifiers and noise filters for SNV/indels and CNVs. We utilized machine learning (ML) and signal-processing techniques on a dataset of FFPE and matched fresh-frozen (FF) samples. The dataset of 52 FFPE-FF pairs were obtained from four different medical institutes and from various cancers including liver, breast, colon, stomach, and lung cancer, with varied FFPE archiving times. We also analyzed additional FFPE-only samples to refine our methods. Our methodology incorporated advanced computational approaches, including stacking ensemble, transfer learning, and wavelet transform, to enhance robustness and accuracy. The method's design was centered around the notion of not only achieving high performance in distinguishing true signals from FFPE-induced artifacts but also addressing the real-world challenges posed by the varying quality and conditions of clinical FFPE samples. In the analysis, we found peculiar patterns of FFPE-specific error profile, including well-known cytosine deamination and novel mutational signatures. The classifier, building on our findings, effectively differentiated true genomic variants from FFPE-induced artifacts for both SNVs and indels, demonstrating a sensitivity of 0.97, specificity of 0.87, and an F1 score of 0.94 for SNVs. For indels, it achieved a sensitivity of 0.91, specificity of 0.91, and an F1 score of 0.91. The CNV filter notably enhanced the signal-to-noise ratio (SNR) of CNV depth profiles, increasing it from 13dB to 17.5dB on average. Furthermore, we conducted evaluations on two critical measures in cancer and clinical genomics: homologous recombination deficiency (HRD) and tumor mutational burden (TMB), achieving post-filtering concordance rates of 0.99 for HRD—correctly identifying all 8 HRD-positive patients in our dataset—and 0.96 for SNV-based TMB and 0.87 for indel-based TMB. Additionally, a post hoc procedure for sensitive detection of cancer driver mutations resulted in concordance rates of 0.94 for SNVs, 0.91 for indels, and 0.95 for oncogene amplification. Taken together, our study advances FFPE WGS analysis in cancer diagnostics by effectively filtering artifacts and addressing challenges with older, degraded samples, enhancing clinical applicability. Citation Format: Joonoh Lim, Seongyeol Park, Won-Chul Lee, Ryul Kim, Sangmoon Lee, Jeong Seok Lee, Brian Baek-Lok Oh, Young Seok Ju. Enhancing genomic analysis in cancer diagnostics: A machine learning approach for removing artifacts in FFPE specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 909.

Abstract 889: Machine learning-enhanced targeted versus whole-exome sequencing as a guide to cancer care

Abstract Targeted DNA sequencing of cancer-associated genes has been widely adopted clinically. In addition, the increasing availability of targeted and whole-exome/genome sequencing (WES/WGS)-derived big data suggests avenues of computationally enhancing power of targeted assays via machine learning. We directly compared targeted (MSK-IMPACT) and WES in a cohort of 1,483 cancer patients using the same DNA libraries. We found high sensitivity and specificity of mutation detection by WES for overlapping genes. Comparatively higher estimates of tumor mutation burden by MSK-IMPACT arose from enrichment of oncogenic mutations in the targeted gene set. WES had minimal value in identifying actionable alterations beyond those from MSK-IMPACT, whereas RNA-sequencing identified additional targetable fusions that led to durable responses. We developed a deep learning-based algorithm (DeepSig) that enhances power of mutational signature detection, enabling detection of a select group of signatures with mutation counts of ~5 or less. Hypermutation-associated signatures, including POLE, temozolomide, and mismatch repair deficiency could be detected robustly using targeted sequencing data. In summary, WES had limited clinical value over targeted sequencing panels and future clinical diagnostic development should focus on transcriptome and WGS that can detect additional signatures and gene fusions. Citation Format: Hyung Jun Woo, Shweta S. Chavan, Aphrothiti J. Hanrahan, Allison L. Richards, Anne Marie Noronha, Ahmet Zehir, Alexander Drilon, Michael F. Berger, David B. Solit, Mark T. Donoghue. Machine learning-enhanced targeted versus whole-exome sequencing as a guide to cancer care [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 889.

Abstract 7312: Construction of a gene panel for liquid biopsy-based diagnostics of gynecologic cancer

Abstract Background: Ovarian carcinoma (OC) is the leading cause of death from gynecologic cancers mainly due to late detection, where most patients are diagnosed at advanced stages (51% in stage III; 29% in stage IV). Despite an overall more favorable prognosis, endometrial carcinoma (EC) is the most common gynecologic cancer, with an increasing incidence and where some subtypes are associated with a poor prognosis. Identification of DNA mutations from precancerous lesions or early-stage OC and EC would allow for early intervention. Liquid biopsies through routine sampling such as endocervical sampling, or blood collection for isolation and analysis of ctDNA have previously been assessed for their use in detection of OC and EC. These studies have provided evidence that tumor-derived mutations can be detected in endocervical samples, supporting endocervical liquid biopsies as a potential source of ctDNA. Herein, we aimed to construct a diagnostic gene panel corresponding to common mutation profiles of OC and EC to detect early-stage malignancy through a non-invasive approach. Methods: Using the NGS-method, Simple multiplexed PCR-based barcoding of DNA for sensitive mutation detection using sequencing (SiMSen-Seq), a gene panel was constructed targeting hotspot mutations in known OC- and EC-associated genes. Hotspots were identified using the Catalogue of Somatic Mutations in Cancer (COSMIC) database or retrieved from the literature and assays were designed for amplification of short DNA fragments. Precise assay development was carried out including multiple steps of careful validation to ensure high specificity and efficient amplification. Individual assays were validated in multiple steps using qPCR and fragment analysis. Results: The gene panel was designed as two non-overlapping subunits for comprehensive diagnosis of OC and EC. The two constructed multiplexes, together composed of 125 single assays target genomic regions in 34 EC and OC-associated genes e.g., PTEN, TP53, PIK3CA and CTNNB1. Included assays had a median amplicon size of 92 bp (range 57-111 bp). Both multiplexes displayed good performance with distinguishable libraries at low DNA input (10 ng). Validation of individual assays showed high specificity and efficient amplification where >80% of assays displayed a sequencing coverage >500 UMI counts at a consensus depth of 3. Patient coverage calculations of COSMIC data suggested comparable coverage as similar published approaches. Conclusion: The results suggest that the constructed gene panel has great potential to detect mutations in liquid biopsies collected from the gynecologic tract, despite low tumor DNA levels. Validation of the two multiplexes by fragment analysis and sequencing showed high performance, hence, prospective for the approaching sequencing of liquid biopsies. Citation Format: Sara Schumacher, Therese Carlsson, Jacob Malchau Lauesgaard, Anna Linder, Amanda Olsson Widjaja, Karin Sundfeldt. Construction of a gene panel for liquid biopsy-based diagnostics of gynecologic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7312.

Abstract 7287: High-grade serous ovarian carcinoma detected with TP53 mutation panel and SiMSen-Seq

Abstract Background: Ovarian carcinoma (OC) has the highest mortality among gynecologic malignancies due to absent or unspecific symptoms. The high-grade serous ovarian carcinoma (HGSC) accounts for over 60% of the OC associated deaths. The most frequent genetic aberrations in HGSC are mutations in the TP53 gene, detected in approximately 96% of the tumors. Previous studies have proposed potential diagnostic utility of detecting mutations in circulating tumor DNA (ctDNA) from OC patients. However, the need for diagnostic methods capable of detecting asymptomatic HGSC is unmet. Objectives: The primary aims of this study were to design a screening tool for identification of HGSC and to evaluate the suitability of various clinical specimen types for diagnostic applications. Methods: In this study, we designed a highly sensitive and robust screening tool using the simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing (SiMSen-Seq) technique for identification of HGSC (n=11) by TP53 mutation profile in various clinical specimen for diagnostic applications. Specimens sampled included primary tumors, ascites, plasma, vaginal samples, and both liquid and solid samples from cyst-, endocervical-, and endometrial origin (n=94). Results: The in-house designed panel amplified 17 regions, encompassing 619 nucleotide positions within the TP53 gene. Pathogenic variants were identified in 10/11 of the primary tumors, with a VAF ≥ 9% (range: 9-91). Preliminary data indicated that all (100%) paired samples obtained corresponding somatic mutations between two or more of the different compartments from the same patient. In total 10/10 ascites-, 16/16 cyst-, 8/8 plasma-, 5/8 vaginal-, 17/22 endocervical-, and 14/19 endometrial samples showed somatic variants at a consensus read depth of 3 and a minimum VAF ≥ 0.1%. Conclusion: The present study suggests that the TP53 mutation panel can identify somatic variants in a variety of non-invasive liquid biopsies. Additionally, the assessment of the TP53 mutation panel exhibits potential for forthcoming clinical utilization as a diagnostic tool. Citation Format: Amanda Olsson Widjaja, Peter Micallef, Benjamin Ulfenborg, Maria Lycke, Ulf Gyllensten, Therese Carlsson, Tobias Österlund, Anders Ståhlberg, Anna Linder, Karin Sundfeldt. High-grade serous ovarian carcinoma detected with TP53 mutation panel and SiMSen-Seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7287.

Abstract 4626: A kit targeting detection of ESR1 mutations from circulating exosomal RNA and cell-free DNA supports longitudinal studies into endocrine therapy resistance in a broadly accessible RT-qPCR format

Abstract Introduction: The most prevalent subtype of breast cancer is HR+/HER2- (positive for hormone receptor, negative for human epidermal growth factor receptor 2). For metastatic breast cancer (mBC), a frequent occurrence is resistance to the aromatase inhibitors used in endocrine therapy (ET). Resistance is mediated through mutations in the ligand binding domain of estrogen receptor 1 (ESR1). Approval of elacestrant, a second-line agent for HR+/HER2-/ESR1 mutated mBC , underscores the need for early detection of ESR1 resistance mutations. We have developed sensitive RT-qPCR reagents for targeted longitudinal studies into the emergence of ESR1 resistance mutations in plasma. This all-inclusive kit analyzes circulating exosomal RNA (exoRNA) and cell-free DNA (cfDNA) to accurately identify low copy number mutations with a rapid turnaround time. Methods: Challenge panels were constructed using blends of synthetic nucleic acids of known mutational status in a background of partially fragmented wild-type DNA from cell lines. Additionally, plasma samples collected from subjects with stage IV mBC (HR+/HER2-) on active aromatase inhibitor therapy, +/- CDK 4/6 inhibitor for a minimum of one year, were subjected to an in-house method optimized to co-enrich exoRNA and cfDNA. Multiplex RT-qPCR-based target enrichment performed on 11 ESR1 mutations and an internal control were evaluated on widely used thermal cycler and qPCR instruments. Results: We developed technology that interrogates 11 key ESR1 mutations associated with ET resistance in HR+/HER2- mBC cases. Earlier studies performed in simplex analyzed plasmid-based ESR1 mutations with detection of 10 or fewer mutant copies, estimating analytical sensitivity of at least 0.1% (5 mutant copies in a background of 5,000 WT copies). Herein, we describe the subsequent work to complete the methodology employed in the kit, including the incorporation of an endogenous control, external batch run control materials (positive, negative), deeply optimized reagents (RT, pre-amplification, qPCR), and automated analysis. We report on the results of studies into the kit’s performance characteristics against their targets: analytical sensitivity of the equivalent of 5 copies of mutation per mL plasma, high precision across replicates at 0.1% allele fraction, analytical specificity of ≥90% negative results in mutation-negative replicates, as well as workflow attributes (reduced pipetting steps and turnaround time). Conclusion: Our solution to detect ESR1 resistance mutations facilitates the generation of nucleic acid sample to results within one day using liquid biopsy samples and a multiplexed RT-qPCR. Enhancing mutation detection sensitivity by complementing cfDNA with exoRNA will facilitate future research into breast cancer treatment options. Citation Format: Justin T. Brown, Julie R. Thibert, Liangjing Chen, Melissa Church, Holli Dale, Jamie Myers, Elliot Hallmark, Megan Yociss, J Aquiles Sanchez, Kurt Franzen, Johan Skog, Gary Latham, Brian Haynes, Sarah Statt. A kit targeting detection of ESR1 mutations from circulating exosomal RNA and cell-free DNA supports longitudinal studies into endocrine therapy resistance in a broadly accessible RT-qPCR format [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4626.

Improved detection of colibactin-induced mutations by genotoxic E. coli in organoids and colorectal cancer

Co-culture of intestinal organoids with a colibactin-producing pks+E.coli strain (EcC) revealed mutational signatures also found in colorectal cancer (CRC). E.coli Nissle 1917 (EcN) remains a commonly used probiotic, despite harboring the pks operon and inducing double strand DNA breaks. We determine the mutagenicity of EcN and three CRC-derived pks+E.coli strains with an analytical framework based on sequence characteristic of colibactin-induced mutations. All strains, including EcN, display varying levels of mutagenic activity. Furthermore, a machine learning approach attributing individual mutations to colibactin reveals that patients with colibactin-induced mutations are diagnosed at a younger age and that colibactin can induce a specific APC mutation. These approaches allow the sensitive detection of colibactin-induced mutations in ∼12% of CRC genomes and even in whole exome sequencing data, representing a crucial step toward pinpointing the mutagenic activity of distinct pks+E.coli strains.

Rapid Tetra-Primer Amplification Refractory Mutation System-Polymerase Chain Reaction Protocol for Detection of Y132F Mutation in Fluconazole Resistant Candida parapsilosis.

There is an emerging fluconazole resistance in Candida parapsilosis in recent years. The leading mechanism causing azole resistance in C. parapsilosis is the Y132F codon alteration in the ERG11 gene which encodes the target enzyme of azole drugs. In this study, we evaluated the sensitivity, compatibility, and specificity of a novel tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS-PCR) method for rapid detection of the Y132F mutation in fluconazole nonsusceptible C. parapsilosis. Antifungal susceptibility tests for detection of fluconazole resistance were performed by broth microdilution according to the CLSI guidelines. All susceptible and nonsusceptible C. parapsilosis isolates were analyzed for ERG11 mutations with Sanger sequencing. T-ARMS-PCR was fully concordant with the Sanger sequencing (100% of sensitivity and specificity) for detection of Y132F mutations. T-ARMS-PCR method could be a rapid, simple, accurate, and economical assay in the early detection of the most common cause of fluconazole resistance in C. parapsilosis isolates. In routine laboratories with high C. parapsilosis isolation rates, performing the T-ARMS-PCR for early detection of the most common reason of fluconazole resistance in C. parapsilosis, could be a life-saving approach for directing antifungal therapy before obtaining the definitive antifungal susceptibility tests results.

Rare subclonal sequencing of breast cancers indicates putative metastatic driver mutations are predominately acquired after dissemination

BackgroundEvolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear.MethodsTo ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 “metastasis-specific” mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition.ResultsThe ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable.ConclusionsOur results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.

Phosphoramidate Azole Oligonucleotides for Single Nucleotide Polymorphism Detection by PCR.

Detection of the Kirsten rat sarcoma gene (KRAS) mutational status is an important factor for the treatment of various malignancies. The most common KRAS-activating mutations are caused by single-nucleotide mutations, which are usually determined by using PCR, using allele-specific DNA primers. Oligonucleotide primers with uncharged or partially charged internucleotide phosphate modification have proved their ability to increase the sensitivity and specificity of various single nucleotide mutation detection. To enhance the specificity of single nucleotide mutation detection, the novel oligonucleotides with four types of uncharged and partially charged internucleotide phosphates modification, phosphoramide benzoazole (PABA) oligonucleotides (PABAO), was used to prove the concept on the KRAS mutation model. The molecular effects of different types of site-specific PABA modification in a primer or a template on a synthesis of full-length elongation product and PCR efficiency were evaluated. The allele-specific PCR (AS-PCR) on plasmid templates showed a significant increase in analysis specificity without changes in Cq values compared with unmodified primer. PABA modification is a universal mismatch-like disturbance, which can be used for single nucleotide polymorphism discrimination for various applications. The molecular insights of the PABA site-specific modification in a primer and a template affect PCR, structural features of four types of PABAO in connection with AS-PCR results, and improvements of AS-PCR specificity support the further design of novel PCR platforms for various biological targets testing.

Development of a molecular barcode detection system for pancreaticobiliary malignancies and comparison with next-generation sequencing

BackgroundObtaining sufficient tumor tissue for genomic profiling is challenging in pancreaticobiliary cancer (PBCA). We determined the utility of molecular barcoding (MB) of liquid biopsies (bile, duodenal fluid, and plasma) for highly sensitive genomic diagnosis and detection of druggable mutations for PBCA. MethodsTwo in-house panels of 60 genes (non-MB panel) and 21 genes using MB (MB panel) were used for the genomic analysis of 112 DNA samples from 20 PBCA patients. We measured the yield of DNA and compared the genomic profiles of liquid samples obtained using the non-MB panel and the MB panel. The utility of the panels in detecting druggable mutations was investigated. ResultsA significantly greater amount of DNA was obtained from bile supernatants and precipitates compared to tumor samples (P < 0.001 and P = 0.001, respectively). The number of mutations per patient was significantly higher using the MB panel than using the non-MB panel (2.8 vs. 1.3, P = 0.002). Tumor-derived mutations were detected more frequently using the MB panel than the non-MB panel (P = 0.023). Five drug-matched mutations were detected in liquid samples. ConclusionsLiquid biopsy with MB may have utility in providing genomic information for the prognosis of patients with PBCA.

BIOM-46. STANDARDIZATION OF LIQUID BIOPSY FOR PEDIATRIC DIFFUSE MIDLINE GLIOMA USING DDPCR

Abstract BACKGROUND Diffuse midline glioma (DMG) is a highly morbid pediatric brain tumor. Up to 80% of DMGs harbor mutations in histone H3-encoding genes, associated with poor prognosis. We previously showed the feasibility of detecting H3 mutations in circulating tumor DNA (ctDNA) in the liquid biome of children diagnosed with DMG. However, detection of low levels of ctDNA is highly dependent on platform sensitivity and sample type. To address this, we optimized ctDNA detection sensitivity and specificity across two commonly used digital droplet PCR (ddPCR) platforms (RainDance and BioRad), and validated methods for detecting H3F3A c.83A &amp;gt;T (H3.3K27M) mutations in DMG CSF, plasma, and primary tumor specimens across three different institutions. METHODS DNA was extracted from H3.3K27M mutant and H3 wildtype (H3WT) specimens, including H3.3K27M tumor tissue (n = 4), CSF (n = 6), plasma (n = 4), and human primary pediatric glioma cells (H3.3K27M, n = 2; H3WT, n = 1). ctDNA detection was enhanced via PCR pre-amplification and use of distinct custom primers and fluorescent LNA probes for c.83 A &amp;gt;T H3F3A mutation detection. Mutation allelic frequency (MAF) was determined and validated through parallel analysis of matched H3.3K27M tissue specimens (n = 3). RESULTS We determined technical nuances between ddPCR instruments, and optimized sample preparation and sequencing protocols for H3.3K27M mutation detection and quantification. We observed 100% sensitivity and specificity for mutation detection in matched DMG tissue and CSF across assays, platforms and institutions. CONCLUSION ctDNA is reliably and reproducibly detected in the liquid biome using ddPCR, representing a clinically feasible, reproducible, and minimally invasive approach for DMG diagnosis, molecular subtyping and therapeutic monitoring.

Analytical Validation of a Duplex Sequencing AML Assay with a Limit of Detection Below 0.01% VAF for SNVs and Indels

Sensitive, specific, precise, and accurate detection of low frequency mutations in acute myeloid leukemia (AML) is critical to assess measurable residual disease (MRD). Next generation sequencing (NGS) assays are powerful tools for this, permitting interrogation of thousands of base positions across genomic regions of interest. However, PCR and sequencing errors in these assays limit accurate mutation detection at variant allele frequencies (VAF) below approximately 1%. Duplex sequencing (DS), an error-corrected NGS (ecNGS) method, greatly reduces such errors by relating original top and bottom DNA strands to make double stranded consensus sequences, thus enabling sensitive, specific, accurate, and precise detection of variants below 0.01% VAF. Here, we report analytical validation of an updated DuplexSeq™ AML assay, a 36-gene panel informed by 2022 European LeukemiaNet (ELN) recommendations combined with kitted library preparation reagents and a cloud-based analysis pipeline. The assay uniformly targets regions that harbor AML-associated mutations: 82% of targeted regions exhibit sequencing depth &amp;gt;80% of the panel-wide mean and &amp;gt;99.5% of targeted regions exhibit depth &amp;gt;20% of the panel-wide mean. These analytical studies also validate an improved “v2 chemistry” version of a DS library preparation kit. This improved chemistry provides an enzymatic fragmentation step that replaces time-consuming mechanical fragmentation methods while increasing the yield of duplex data for a given input mass of DNA sample and eliminating damage that results from mechanical fragmentation. A contrived human genomic DNA sample carrying 26 variants targeted by the panel, DNA extracted from AML-positive peripheral blood (PB) and bone marrow aspirate (BMA) specimens, and DNA extracted from healthy normal PB specimens were used as test samples to execute limit of detection (LoD), limit of blank (LoB), accuracy, linearity, and repeatability &amp; reproducibility studies. In this assay a duplex depth threshold of ≥20,000x is applied; ≥91.8% of the 42,605 genomic base positions targeted by the panel are consistently (in 95% of analyzed libraries) above this threshold. The LoD study assayed samples carrying variants from 0.37% to 0.00055% VAF. The assay demonstrated excellent sensitivity, with variants down to 0.00055% VAF detected. A probit model was used to estimate the VAF at which 95% of variants would be detected, establishing an LoD of 0.008% VAF for single-nucleotide variants (SNVs) and insertions and deletions (indels) (see Figure 1). The LoB study assessed the background detection rate in DNA samples derived from healthy normal specimens. In &amp;gt;95% of sites surveyed, no variants were observed, establishing an assay LoB of 0 (0% VAF). Accuracy of the assay was determined by interrogating PB and BMA samples from AML-positive and -negative donors along with contrived test samples. Results were compared to data acquired via orthogonal methods. For variants above the LoD of 0.008% VAF, accuracy was high, with overall percent agreement (OPA) of 98.3%, positive percent agreement (PPA) of 100.0%, and negative percent agreement (NPA) of 98.3%. Data were linear from 0.0016% VAF to 96.1% VAF with R 2 = 0.95. All 6 expected FLT3-ITD variants, and all 5 expected NPM1 insertions were detected by the assay. Precision study data showed that the assay was highly repeatable, with OPA of 97.5%, PPA of 98.0%, and NPA of 97.3%. Reproducibility across 2 operators, 2 lots of reagents, and 3 independent runs of library preparation was also very high, with OPA of 97.5%, PPA of 98.8%, and NPA of 96.9%. Current ELN guidelines provisionally define NGS-MRD positivity as ≥0.1% VAF, but recent evidence suggests lower frequency variants may be informative. With an LoD of 0.008% VAF for SNVs and indels, and an LoB of 0, this novel AML assay comprising an updated gene panel coupled with improved duplex sequencing chemistry and software represents a highly sensitive, specific, accurate, and precise ecNGS assay for detecting variants associated with AML MRD.

Fully Automated Cell-Free DNA Extraction From Up to 8 ml Plasma Enables Sensitive Mutation Detection With Digital PCR and NGS

Fully Automated Cell-Free DNA Extraction From Up to 8 ml Plasma Enables Sensitive Mutation Detection With Digital PCR and NGS

Combining E-ice-COLD-PCR and Pyrosequencing with Di-Base Addition (PDBA) Enables Sensitive Detection of Low-Abundance Mutations.

Detecting low-abundance mutations is of particular interest in the fields of biology and medical science. However, most currently available molecular assays have limited sensitivity for the detection of low-abundance mutations. Here, we established a platform for detecting low-level DNA mutations with high sensitivity and accuracy by combining enhanced-ice-COLD-PCR (E-ice-COLD-PCR) and pyrosequencing with di-base addition (PDBA). The PDBA assay was performed by selectively adding one di-base (AG, CT, AC, GT, AT, or GC) instead of one base (A, T, C, or G) into the reaction at a time during sequencing primer extension and thus enabling to increase the sequencing intensity. A specific E-ice-COLD-PCR/PDBA assay was developed for the detection of the most frequent BRAF V600E mutation to verify the feasibility of our method. E-ice-COLD-PCR/PDBA assay permitted the reliable detection of down to 0.007% of mutant alleles in a wild-type background. Furthermore, it required only a small amount of starting material (20pg) to sensitively detect and identify low-abundance mutations, thus increasing the screening capabilities in limited DNA material. The E-ice-COLD-PCR/PDBA assay was applied in the current study to clinical formalin-fixed paraffin-embedded (FFPE) and plasma samples, and it enabled the detection of BRAF V600E mutations in samples that appeared as a wild type using PCR/conventional pyrosequencing (CP) and E-ice-COLD-PCR/CP. E-ice-COLD-PCR/PDBA assay is a rapid, cost-effective, and highly sensitive method that could improve the detection of low-abundance mutations in routine clinical use.

Recombinase polymerase amplification in combination with electrochemical readout for sensitive and specific detection of PIK3CA point mutations

As part of the ongoing evolution towards personalized anticancer therapy, mutation screening is becoming increasingly important and, therefore, also alternative detection strategies that allow for fast genetic diagnostics at the point of care. In the case of breast cancer, detecting cancer-associated point mutations in the PIK3CA gene is of particular importance for treatment decisions. We developed a recombinase polymerase amplification assay combined with an enzyme-linked electrochemical assay on multi-channel screen-printed gold sensors for specific and highly sensitive detection of three PIK3CA point mutations (H1047R, E545K, and E542K). Recombinase polymerase amplification (RPA) of the target sequences was optimized and characterized with a real-time RPA assay. Comparison with real-time PCR reveals that RPA is slightly inferior in terms of efficiency and sensitivity. However, the desired target DNA is successfully amplified at initial concentrations down to 100 copies μL−1. For electrochemical readout, biotinylated dCTP is used to label the target DNA during RPA. Single-stranded target DNA is produced with either asymmetric RPA or symmetric RPA followed by lambda exonuclease digestion. Characterization of the two different approaches in terms of sensitivity results in comparable detection limits (229 copies μL−1 and 224 copies μL−1, respectively), though RPA followed by lambda exonuclease digestion yields significantly higher currents. Finally, this method, together with a designed wild-type blocking oligo that inhibits binding of the wild-type target DNA during probe-target hybridization, allows for detecting the PIK3CA point mutations H1047R, E545K, and E542K in the presence of wild-type target DNA when the proportion of mutant target DNA is >20%.