Sensitivity Of Mutation Detection Research Articles

Abstract A020: Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection

Abstract Introduction: Extracting cfRNA from biological samples is crucial in liquid biopsy applications, particularly for detecting low-frequency mutations like KRAS G12V, essential for early cancer diagnosis and minimal residual disease (MRD) monitoring. Traditional cfRNA extraction methods involve multiple transfer steps, aliquoting, and pooling, leading to cfRNA loss and affecting mutation detection sensitivity and accuracy. The nRichDX Revolution cfTNA Max 20 kit, with its innovative nRicher cartridge, simplifies the extraction process by eliminating these extra steps, potentially enhancing yield and efficiency. This study evaluates the cfRNA recovery efficiency of the nRichDX kit compared to the Qiagen Circulating Total Nucleic Acid kit. Methods: Plasma samples (5 mL each) were spiked with RNA containing KRAS G12V mutation at concentrations ranging from 100 to 2 million copies, mimicking early-stage cancer detection and MRD monitoring, where low copy numbers represent early disease and high copy numbers reflect advanced cancer stages. cfRNA was extracted using the nRichDX and Qiagen kits, with each condition tested in triplicate. cfRNA recovery efficiency was assessed using quantitative reverse transcription PCR (qRT-PCR) to quantify KRAS G12V RNA copies recovered. High-sensitivity RNA ScreenTape Analysis was also used to evaluate cfRNA profile quality. Results: The nRichDX kit recovered significantly higher cfRNA yields than Qiagen across all concentrations. Recovery rates were consistent and reproducible, with over 70% of input RNA recovered, even at lower inputs (100 to 100,000 copies), where recovery rates were approximately 70-85%. High Sensitivity RNA ScreenTape Analysis showed a linear increase in 18S and 28S ribosomal peaks and high RNA Integrity Number (RIN) scores in nRichDX profiles. The ability to robustly recover low copy numbers is significant for early cancer detection and MRD monitoring. In contrast, the kit’s effectiveness in high copy number samples reflects its ability to monitor advanced cancer stages accurately. Conclusion: The nRichDX cfTNA kit demonstrates exceptional efficiency in cfRNA extraction from plasma samples across a broad dynamic range, from as few as 100 copies to as many as 2 million copies of the KRAS G12V mutation. Its versatility makes it suitable for various cancer detection and monitoring applications. The streamlined process offered by the nRicher cartridge minimizes sample handling and reduces cfRNA loss, enhancing performance. This translates to more accurate and sensitive detection of early-stage and high-burden mutations associated with advanced cancer. The high-quality profiles indicated by RNA ScreenTape Analysis further validate the reliability of the nRichDX kit, making it a valuable tool for liquid biopsy applications, with significant advantages in early cancer detection and monitoring disease progression. Citation Format: Nafiseh Jafari, Mayer Saidian, Jason Saenz, Andrew Dunnigan, Carlos Hernandez, Leila Aghili. Enhanced cell-free RNA (cfRNA) recovery for liquid biopsy applications: Comparative analysis using controlled RNA samples for early and advanced cancer detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A020.

Abstract B057: Base-error model (BEM) for improved detection of ctDNA in lymph samples of HPV-negative head and neck cancer patients

Abstract Introduction: Previously we demonstrated that ctDNA present in lymphatic exudate collected via surgical drains (“lymph”) outperformed plasma for detecting minimal residual disease (MRD) in head and neck squamous cell carcinoma (HNSCC) patients through a targeted sequencing (TS) approach. However, artifacts introduced during library preparation and sequencing remain challenges to sensitive low allelic fraction mutation detection. To enhance the performance of the TS approach, we characterized the background noise in lymph samples and developed a base-error model (BEM) that reduces sequencing artifacts and maximizes ctDNA signal. Methods: Lymph and blood were collected from 44 HPV-negative HNSCC patients postoperatively at 24 hours along with resected tumor. Cell-free DNA was extracted from lymph and sequenced using a custom TS panel with unique molecular identifiers (UMI). Somatic mutations were identified by tumor and blood exome sequencing (200x). Five patients had <2 mutations in tumor and were excluded. Two patients were censored due to lack of clinical data, yielding 17 patients with disease recurrence (REC) and 20 with no evidence of disease (NED) with >1 year of follow-up. Tumor-specific variants were force-called in lymph using a custom pipeline. A BEM of each tumor variant was built using a series of high-quality lymph reference samples to quantify the background noise. BEM was fit by Weibull distribution if more than 5 non-zero non-reference alleles were observed at a tumor variant position across the reference samples. Otherwise, Gaussian distribution was used to fit BEM. Force-called variants were considered artifacts if the variant allele fraction (VAF) was not greater than BEM cutoff controlled by false discovery rate. Patients were considered MRD positive if the mean VAF was greater than the estimated limit of detection. The Kaplan-Meier (KM) estimator with log-rank test and Cox proportional-hazards model were used for survival analyses. Results: Background noise was observed in reference lymph samples across all 12 nucleotide substitution classes. Out of 299 single nucleotide variants (SNVs), 94 were determined as artifacts by BEM. Among the artifacts, 77 were C/G to A/T mutations, indicating that the majority of recurrent background artifact in TS is likely the result of oxidative damage. The artifacts had VAFs ranging from 0.004% to 0.12% with median of 0.02%. Incorporation of BEM in lymph TS showed significantly improved specificity (sensitivity (SN) = 71%, specificity (SP) = 65%; p = 0.027, Hazard ratio (HR) = 3.07) compared to the same cohort without BEM (SN = 76%, SP = 30%, p = 0.60, HR = 1.34). Conclusions: BEM is a sensitive and robust method to improve ctDNA detection accuracy in TS. We demonstrated its utility in improving recurrence prediction in a HNSCC cohort. Given its advantages, we envision that BEM will prove useful as a general strategy for deep sequencing applications requiring precise digital quantification of low frequency alleles in TS. Citation Format: Zhuosheng Gu, Maciej Pacula, Seka Lazare, Noah Earland, Marra S. Francis, Adam Harmon, Megan Long, Aadel A. Chaudhuri, Jose P. Zevallos, Wendy Winckler. Base-error model (BEM) for improved detection of ctDNA in lymph samples of HPV-negative head and neck cancer patients [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B057.

A novel label-free impedance biosensor for KRAS G12C mutations detection based on PET-RAFT and ROP synergistic signal amplification

The KRAS G12C mutations, as crucial biomarkers, are closely associated with non-small cell lung cancer. Here, a novel label-free electrochemical biosensor with synergistic signal amplification of photocell energy transfer-reversible addition fragmentation chain transfer (PET-RAFT) and ring-opening polymerization (ROP) was developed for the first time for sensitive detection of KRAS G12C mutations. Specifically, hairpin DNA (hDNA), which act as biomolecular probe, was self-assembled on Au electrode surface by Au-S bond. 4-cyano-4-[(dodecylsulfanylthiocarbonyl) sulfanyl] pentanoic acid (CDTPA), the chain transfer agent of PET-RAFT reaction, was then attached to hDNA via amide bond. After that, the target DNA (tDNA) was captured on the electrode surface by complementary base pairing with hDNA. Subsequently, large numbers of electro-active monomers N-acryloxysuccinimide (NAS) were successfully grafted to the electrode surface via PET-RAFT reaction, which provided plenty of junction sites for doxorubicin-polycaprolactone (Dox-PCL) synthesized by ROP. Finally, the Dox-PCL was connected to the electrode surface by ester bond, significantly amplifying the electrochemical signal. Under optimized conditions, the biosensor has a wide linear detection range of 0.1 pM to 1 μM, with a detection limit of 86.9 fM. Attribute to its high sensitivity, specificity, reproducibility and stability, this biosensor possesses considerable potential in early diagnosis of disease and biomedical research.

Development and preliminary assessment of the iFIND TBR: all-in- one molecular diagnostic assay for rapid detection of Mycobacterium tuberculosis and rifampicin resistance.

Early and accurate diagnosis of tuberculosis (TB) is crucial for initiating timely treatment and preventing new infections. In this study, we introduced the iFIND TBR assay, an automated all-in-one tuberculosis detection approach that simultaneously detect Mycobacterium tuberculosis (MTB) and rifampicin (RIF) resistance. The limits of detection (LOD), sensitivity, specificity, and RIF-R rpoB mutation detection of the iFIND TBR were tested on Mycobacterium tuberculosis DNA or sputum samples spiked with known numbers of M.tuberculosis H37Rv. Frozen clinical samples from patients suspected of having TB were also tested. The LOD of the iFIND TBR for MTB detection were 13.34 CFU/ml (95% CI, 11.71-16.47), and for RIF resistance was 109.79CFU/mL (95% CI, 95-138.19). The iFIND TBR assay accurately distinguish MTB strains from non-tuberculous mycobacteria (NTM) without any cross reactivity. Testing on 157 clinical sputum samples, compared with the bacteriologically TB standard, the overall sensitivity and specificity of the iFIND TBR was 100% (95%CI, 94.64, 100) and 85.29% (95% CI, 74.61, 92.72), respectively. When assessing RIF susceptibility, the iFIND TBR achieved a sensitivity of 98.15% (95% CI, 90.11-99.95) and a specificity of 85.71% (95% CI, 67.33-95.97), compared with phenotypic drug susceptibility testing. Discordant RIF susceptibility results were more frequently observed in samples exhibiting heteroresistance. These findings demonstrate that iFIND TBR assay performs well in detecting TB and RIF resistance, and shows promise as a point-of-care tool in resource-limited areas.

Effect of sequencing platforms on the sensitivity of chemical mutation detection using Hawk-Seq™

BackgroundError-corrected next-generation sequencing (ecNGS) technologies have enabled the direct evaluation of genome-wide mutations after exposure to mutagens. Previously, we reported an ecNGS methodology, Hawk-Seq™, and demonstrated its utility in evaluating mutagenicity. The evaluation of technical transferability is essential to further evaluate the reliability of ecNGS-based assays. However, cutting-edge sequencing platforms are continually evolving, which can affect the sensitivity of ecNGS. Therefore, the effect of differences in sequencing instruments on mutation data quality should be evaluated.ResultsWe assessed the performance of four sequencing platforms (HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400) with the Hawk-Seq™ protocol for mutagenicity evaluation using DNA samples from mouse bone marrow exposed to benzo[a]pyrene (BP). The overall mutation (OM) frequencies per 106 bp in vehicle-treated samples were 0.22, 0.36, 0.46, and 0.26 for HiSeq2500, NovaSeq6000, NextSeq2000, and DNBSEQ-G400, respectively. The OM frequency of NextSeq2000 was significantly higher than that of HiSeq2500, suggesting the difference to be based on the platform. The relatively higher value in NextSeq2000 was a consequence of the G:C to C:G mutations in NextSeq2000 data (0.67 per 106 G:C bp), which was higher than the mean of the four platforms by a ca. of 0.25 per 106 G:C bp. A clear dose-dependent increase in G:C to T:A mutation frequencies was observed in all four sequencing platforms after BP exposure. The cosine similarity values of the 96-dimensional trinucleotide mutation patterns between HiSeq and the three other platforms were 0.93, 0.95, and 0.92 for NovaSeq, NextSeq, and DNBSeq, respectively. These results suggest that all platforms can provide equivalent data that reflect the characteristics of the mutagens.ConclusionsAll platforms sensitively detected mutagen-induced mutations using the Hawk-Seq™ analysis. The substitution types and frequencies of the background errors differed depending on the platform. The effects of sequencing platforms on mutagenicity evaluation should be assessed before experimentation.

Integrating allele-specific PCR with CRISPR-Cas13a for sensitive KRAS mutation detection in pancreatic cancer

BackgroundThe clustered regulatory interspaced short palindromic repeats (CRISPR)-Cas13a system has strong potential for highly sensitive detection of exogenous sequences. The detection of KRASG12 point mutations with low allele frequencies may prove powerful for the formal diagnosis of pancreatic ductal adenocarcinoma (PDAC).ResultsWe implemented preamplification of KRAS alleles (wild-type and mutant) to reveal the presence of mutant KRAS with CRISPR-Cas13a. The discrimination of KRASG12D from KRASWT was poor for the generic KRAS preamplification templates and depended on the crRNA design, the secondary structure of the target templates, and the nature of the mismatches between the guide and the templates. To improve the specificity, we used an allele-specific PCR preamplification method called CASPER (Cas13a Allele-Specific PCR Enzyme Recognition). CASPER enabled specific and sensitive detection of KRASG12D with low DNA input. CASPER detected KRAS mutations in DNA extracted from patients’ pancreatic ultrasound-guided fine-needle aspiration fluid.ConclusionCASPER is easy to implement and is a versatile and reliable method that is virtually adaptable to any point mutation.

Highly sensitive and accurate detection of ALK-TKI resistance mutations by oligoribonucleotide interference-PCR (ORNi-PCR)-based methods

BackgroundPatients with anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) are treated with ALK tyrosine kinase inhibitors (TKIs). Although most patients benefit from ALK-TKIs, the development of resistance mutations is common and results in NSCLC recurrence. To identify ALK-TKI-resistant NSCLC at the early recurrent phase, highly sensitive and accurate methods for the detection of mutations are essential. ObjectiveThe aim of this study was to establish highly sensitive, accurate, cost-effective, and clinically practical methods for the detection of two frequent ALK-TKI resistance mutations, ALK G1202R and L1196M, by liquid biopsy. MethodsThe efficacy of oligoribonucleotide interference-PCR (ORNi-PCR) was examined by first optimizing experimental conditions to specifically amplify the ALK-TKI resistance mutant DNA corresponding to ALK G1202R and L1196M mutations. ORNi-PCR was then combined with droplet digital PCR (ddPCR) or real-time PCR to detect these mutations in cell-free DNA (cfDNA) extracted from NSCLC patients. ResultsORNi-PCR followed by ddPCR/real-time PCR detected 1–10 copy(s) of G1202R and L1196M DNA in model cfDNA. These mutations in patients’ cfDNA were identified using ORNi-PCR-based methods, whereas conventional ddPCR failed to detect them. ConclusionORNi-PCR followed by ddPCR/real-time PCR enables highly sensitive and accurate detection of ALK mutations by liquid biopsy. Although the clinical data are limited, our results show that these methods are potentially useful for identifying ALK-TKI-resistant NSCLC at the early recurrent phase.

A Novel Next-Generation Sequencing Assay for the Identification of BCR::ABL1 Transcript Type and Accurate and Sensitive Detection of TKI-Resistant Mutations.

The clinical management of chronic myeloid leukemia (CML) patients requires the identification of the type of BCR::ABL1 transcript at diagnosis and the monitoring of its expression and potential tyrosine kinase inhibitor (TKI) resistance mutations during treatment. Detection of resistant mutation requires transcript type-specific amplification of BCR::ABL1 from RNA. In this study, a custom RNA-based next-generation sequencing (NGS) assay (Dup-Seq BCR::ABL1) that enables (a) the identification of BCR::ABL1 transcript type and (b) the detection of resistance mutations from common and atypical BCR::ABL1 transcript types was developed and validated. The assay design covers BCR exon 1 to ABL1 exon 10 and employs duplicate PCR amplification for error correction. The custom data analysis pipeline enables breakpoint determination and overlapped mutation calling from duplicates, which minimizes the low-level mutation artifacts. This study demonstrates that this novel assay achieves high accuracy (positive percent agreement (PPA) for fusion: 98.5%; PPA and negative percent agreement (NPA) for mutation at 97.8% and 100.0%, respectively) and sensitivity (limit of detection (LOD) for mutation detection at 3% from 10 000 copies of BCR::ABL1 input). The Dup-Seq BCR::ABL1 assay not only allows for the identification of BCR::ABL1 typical and atypical transcript types and accurate and sensitive detection of TKI-resistant mutations but also simplifies molecular testing work flow for the clinical management of CML patients.

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.