Background: Liquid biopsy offers a rapid and non-invasive alternative to tissue biopsy for identifying biomarkers. More recently, its application has broadened to include assessment of early response to therapy (i.e. molecular response) and in the early-stage settings, detection of minimal residual disease (MRD) and early disease recurrence1. While circulating tumor fraction (cTF) estimated by somatic mutations is well associated with the tumor progression and prognosis, interference can occur from clonal hematopoiesis of indeterminate potential (CHIP), and for cell-free DNA (cfDNA) samples that lack detectable somatic mutations, somatic tumor fraction cannot be estimated. In this analysis, we demonstrate that epigenomic signatures accurately measure cTF using orthogonal analytes to somatic mutations and enable cTF estimation even in cases without detectable tumor driver variants. Methods: To capture tumor-associated methylated cfDNA, we designed a custom assay of a broad genomic panel (15.2 Mb) targeting unmethylated regions in plasma cfDNA from healthy individuals. We profiled plasma samples from cancer patients with this panel, and utilized machine learning to integrate methylation signals into an estimate of cTF. We benchmarked the accuracy of methylation cTFs on real plasma samples, as well as in-vitro and in-silico titration datasets. Both titration data sets were generated by mixing cfDNA from patients with colorectal cancer (CRC) into the plasma from cancer-free donors, either via titration of CRC cfDNA into cfDNA from cancer-free donors for the in-vitro data, or via computationally mixing reads from CRC patients with those from cancer-free donors for the in-silico data. Results: Our methylation cTF quantified a similar cTF to those derived from well-calibrated genomic tumor driver mutations; across the 670 stage I-IV CRC samples, a strong correlation (Pearson r=0.85) was observed between methylation logit(cTF) and genomic logit(cTF). The methylation cTF was capable of quantifying low cTFs: it quantified a cTF over 0.1% in >99% of the 270 in-vitro and 1,000 in-silico titration samples with true cTFs >0.1%. In contrast, when applied to 2,037 cancer-free samples, less than 5% of the samples resulted in estimated cTFs of >0.1%. Our methylation cTF was more robust than genomic cTF on the 62 in vitro titration samples with true cTFs between 0.3-1%, with a five fold lower coefficient of variation across methylation cTFs compared to genomic cTFs. Conclusions: cTFs from methylated cfDNA may overcome the current limitations of somatic mutation based methods. Our methylation approach is capable of accurately detecting cTFs in tumor-driver positive and negative cases. As we estimate tumor-negative cases to be 30-50% of patients with stage I-III cancer and 15-20% of patients with stage IV cancer, our methylation approach may hold promise for providing better evaluation for patient care and management. Citation Format: William W. Greenwald, Yupeng He, Sai Chen, Tingting Jiang, Anton Valouev, Jun Min, Catalin Barbacioru, Daniel P. Gaile, Dustin Ma, Yvonne Kim, Giao Tran, Indira Wu, Ariel Jaimovich, Victoria Raymond, Rebecca J. Nagy, Han-Yu Chuang. Accurate epigenomic estimates of circulating tumor fraction in large-scale clinical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3758.
Background: Circulating tumor DNA (ctDNA) level and the change in level at a subsequent time point (e.g. on-treatment change from baseline or postoperative changes through time) are promising tools for predicting patient prognosis and response to therapy. Existing methods use somatic variant allele frequencies to quantify circulating tumor fractions (cTF). Their performance can be limited by the number of detectable somatic alterations and the associated limit of detection (LoD), as well as interference from copy number variation and non-tumor alterations, such as clonal hematopoiesis. Here, we describe the LoD, precision and limit of quantitation (LoQ) of cTF level and change using GuardantINFINITY, a next generation sequencing panel covering over 800 genes with genome-wide methylation detection. Method: The cTF of a single sample is estimated from methylation signals across targeted regions of the GuardantINFINITY methylation panel, calibrated using internal training data. cTF change compares two or more samples from the same patient to identify patient-specific methylated regions, and compare the methylation signals of the paired regions. LoQ of cTF level and change were assessed in experimental titrations of advanced colorectal, breast, and lung cancer patient samples and cell line samples into cancer-free backgrounds at different target levels between 0.1%-0.5% cTF. LoD is defined as the lowest cTF level where >95% replicates were detected to have tumor-derived methylation signals. LoQ of cTF level or change is defined as the lowest cTF where the coefficient of variation (CV) across replicates is less than 30%. Accuracy of methylation based cTF compared to cTFs calculated from maximum VAF of somatic mutations was assessed on 1,400 clinical samples of colorectal and lung cancer patients (N=189, 372, 252 and 463 for stage I to IV). Results: Experimental titrations of cancer samples demonstrated a single-sample LoD of 0.05% cTF (lowest dilution level) and quantitative precision down to a LoQ of below 0.1%, compared to the LoQ of 0.3% estimated by somatic mutations. In paired clinical titration samples, the LoQ of methylation ctDNA level change was also below 0.1%, compared to the LoQ of ctDNA level change estimated by somatic mutations at 0.3-0.5%. In the 1,400 clinical samples, 64% had at least one somatic mutation detected, 90% had ct-DNA detected with methylation and 96% of these ct-DNA detected samples had cTF above the defined methylation LoQ. Among patients with both methylation and genomic signals identified, the methylation method quantified a similar cTF to those that were calculated using maximum somatic driver mutations (Pearson r=0.83). Conclusion: Methylome sequencing using GuardantINFINITY enables accurate and precise quantification of ctDNA level and change with a liquid-only approach, offering longitudinal ctDNA monitoring for more patients than previous methods. Citation Format: Sai Chen, Katie Quinn, Che-Yu Lee, Jun Zhao, Kyle Chang, Tingting Jiang, Shile Zhang, Carin Espenschied, Sara Wienke, Thereasa Rich, Indira Wu, Yvonne Kim, Xianxian Liu, Nageswara Alla, Dustin Ma, Giao Tran, Han-Yu Chuang. A method for quantifying circulating tumor DNA level and molecular response using methylome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3123.
Background: HLA and KIR genotypes show great promise as emerging biomarkers for immune checkpoint inhibitors (ICIs) and understanding patient prognosis. Multiple studies have shown that HLA-I heterozygosity and high sequence divergence across alleles positively correlates with response to ICIs. However the high degree of polymorphism and allele sequence similarities in HLA and KIR present a challenge to accurate allele calling. To address these difficulties we developed kmerizer, a novel allele caller optimized for short fragments, such as reads from a cfDNA assay. Methods: We tested kmerizer on MHC class1 and class2 genes, and on KIR genes, on both simulated datasets and real samples (cell lines and plasma samples). To assess the capability of the algorithm to distinguish highly homologous allele pairs, we simulated cfDNA-like fragments with errors on randomly selected allele pairs, and on allele pairs with a high level of homology. Twelve plasma samples and 19 reference cell lines fragmented to cfDNA size were analyzed using a NGS cfDNA assay. For plasma samples, paired buffy coats were sent to an external vendor for HLA typing using multiplex PCR-based amplicon and sequenced by 300bp paired-end reads. Results: Of the 19 cell lines and 12 plasma samples for HLA typing (A, B, C, and DQB1 loci), kmerizer delivered 100% sensitivity with 98% specificity. On the simulated dataset, kmerizer achieved 99% sensitivity and specificity on all the MHC class1 and class 2 loci, and 90% sensitivity and specificity on all KIR loci, for both homozygous and heterozygous pairs. The novel allele caller kmerizer also demonstrated a lighter footprint on computational resource need: one deep-sequencing plasma sample on average can be processed in less than 2 minutes which is about 15 times faster than the most commonly used HLA typing tool HISAT21, which does not support KIR typing. Conclusions: As utilization of ICIs increases, the use of genetic and genomic information to accurately identify patients more likely to respond to ICIs will be critical. kmerizer is a fast and highly sensitive and specific allele caller, and it can effectively call alleles on both HLA and KIR. References: [1] Kim, D., Paggi, J. M., Park, C., Bennett, C., & Salzberg, S. L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature biotechnology, 37(8), 907-915. Citation Format: Sante Gnerre, Brian Yik Tak Tsui, Tingting Jiang, Yvonne Kim, Dustin Ma, Indira Wu, Rebecca Nagy, Han-Yu Chuang. Accurately genotyping HLA and KIR alleles using cfDNA assay and k-mer based algorithm for immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1220.
Background: Despite its revolutionary impact, cancer genomics alone provides little information on tumor phenotype or functional state, which are governed by epigenetic mechanisms, notably methylation of regulatory regions. Tumor and host epigenetic methylation signatures reflect not only tumor phenotype, such as histology, prognosis, protein expression, and functional sub-type, but also that of the tumor microenvironment and the patient, including immune status, therapy-related adverse events, comorbidities, and disease location. Epigenetic markers also provide more sensitive and precise measures of tumor burden, opening up applications for longitudinal therapy response and monitoring. Here we report the initial validation of GuardantINFINITY, a liquid biopsy assay combining genomic information from >800 genes with characterization of the blood-quiet regulatory methylome, both at single-molecule sensitivity from a single tube of peripheral blood. Methods: Analytical performance was assessed using 594 cancer patient cfDNA, cell line, and cancer-free donor samples at 5-30ng cfDNA input. Results: Reportable ranges established for SNVs were ≥0.04% variant allele fraction (VAF), ≥0.04% for indels, ≥0.06% for fusions, ≥2.12 copies for amplifications (CNAs), <1.7 copies for copy loss. Observed 95% limits of detection (LoD) were 0.282% for SNVs across all genes (0.2% for oncogenic hotspots), 0.397% for non-homopolymeric indels, 0.05% for fusions, 2.5 copies for CNAs, 16.3% VAF or 1.84 copies for gene deletions, 7.3 copies for viral (HPV, EBV) detection, and 0.06% for MSI-H. For promoter and sample-level methylation, LoDs were 0.06% and 0.05% tumor fraction, respectively. cfDNA cancer samples demonstrated 100% accuracy for SNVs and Indels above 0.5% VAF and 100% for CNAs and fusions across the reportable range. The analytical false positive rate per base was 6.84e-6 for SNVs, 3.42e-6 for indels, and 0 for CNAs and fusions, with positive predictive values of 97.5% for SNVs, 98% for indels, and 100% for CNAs above 2.5 copies and all tested fusions. Conclusions: GuardantINFINITY is a patient-care-ready liquid biopsy capable of integrated genomic and epigenomic analysis of all solid tumors at single-molecule sensitivity. In addition to traditional genotyping compatible with Guardant360 for more content, the technology’s demonstrated LoD showed the potential for ultra-sensitive ctDNA detection for MRD and recurrence surveillance, tumor fraction quantitation for therapy monitoring, oncogenic virus detection, immunogenotyping, epigenotyping, and tumor phenotype characterization, representing a new standard in biomarker discovery. Citation Format: Tingting Jiang, Indira Wu, Yvonne Kim, Nageswara Alla, Giao Tran, Dustin Ma, Forum Shah, Jun Zhao, Sai Chen, Sante Gnerre, Melis Hazar, Hao Wang, Catalin Barbacioru, Karen Ryall, Ankit Jambusaria, Anupam Chakravarthy, Anthony Zunino, Theresa Pham, Farsheed Ghadiri, Evan Diehl, Benjamin Morck, Arancha Sanchez, Rochelle Dayan, XianXian Liu, Jeffrey Werbin, Jill Lai, Brett Kennedy, Ross Eppler, Justin Odegaard, Han-Yu Chuang, Helmy Eltoukhy. Analytical validation of a robust integrated genomic and epigenomic liquid biopsy for biomarker discovery, therapy selection, and response monitoring [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6601.
Background: HLA germline genotypes and somatic mutations show great promise as emerging biomarkers for immune checkpoint inhibitors (ICIs) and understanding patient prognosis. Multiple studies have shown that HLA homozygosity or loss-of-function somatic mutations negatively correlates ICI response rate. Here we present additional data on the algorithm Kmerizer, designed to perform HLA germline typing and somatic mutation detection from cfDNA input material, and we show how we use these in neoantigen prioritization for patient outcome prediction. Methods: Kmerizer first leverages the high depth coverage of targeted sequencing to rapidly identify germline alleles by matching k-mers from the input reads to the k-mers of known HLA alleles. Careful realignment of reads ontothe called germlines is followed by proprietary somatic variant calling. MHC class1 germline allele calls are combined with patient mutation data to generate in silico TCR binding affinity predictions using net MHC-4.0. These predictions are compared across cohorts to assess how cancer type, TMB, and ICI response vary with the predicted neoantigens and TCR binding affinity. Results: Of nineteen cell lines, twelve plasma samples and eight gDNA samples with confirmed HLA typing information, Kmerizer delivered 100% sensitivity on both MHC-I and II genes, with 99.5% and 98.7% specificity, respectively, based on GuardantINFINITY cfDNA sequencing data. For homozygous/heterozygous status, accuracy of 99.1% on class I and 97.7% on class II genes is achieved. HLA allele prevalence among our development samples is consistent with reference cohorts of similar geographic origin in MHC class I genes. The HLA somatic caller achieves >99.99% specificity per base as computed on 48 normal samples, while achieves >91% sensitivity for somatic events with expected allele frequency (AF) ~ 0.15% (AF range[0.08%,0.26%] for detected events) as evaluated through simulations. Additionally, we generated a total of 2,767 immunogenic (ic50<500nM) class-I somatic neoantigens predictions across 112 samples from cancer patients with germline HLA typing results. We found average patient neoantigen TCR binding affinity was significantly associated with cancer type (χ2=86.08,p<0.0001). Top predicted neoantigen binding affinity across patient HLA types were strongly inversely correlated with patient bTMB(rhospearman=-0.25, p<0.0001). Conclusions: The integration of Kmerizer into GuardantINFINITY enables accurate HLA germline and somatic detection along with neoantigen prediction, offering an enhanced and comprehensive biomarker profiling for ICI outcome prediction. Citation Format: Sante Gnerre, Jun Zhao, Adrian Bubie, Yvonne Kim, Dustin Ma, Indira Wu, Bojan Losic, Tingting Jiang, Han-Yu Chuang. Using Kmerizer, a germline and somatic genotyper for immune associated complex alleles in GuardantINFINITY, for immunotherapy response prediction using cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3128.
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