Shear wave speeds in Earth's deepest mantle (D") that vary with wave propagation and polarization direction -- a property called seismic anisotropy -- offer insights into mantle convection. To date, global patterns of D" anisotropy have been mostly derived from long wavelength radially anisotropic tomography models, which often disagree except for the large-scale degree-2 pattern. Here, we present 70,000 differential splitting measurements from seismic waves that traverse Earth's mantle and core, sampling nearly 75% of D", including most seismically faster regions. We conduct detailed synthetic tests to demonstrate which splitting measurements indicate the presence of lowermost mantle anisotropy. Evidence for D" anisotropy is found in about two thirds of our sampled area, more than doubling the area in which seismic anisotropy has been detected using shear-wave splitting measurements. Inferred deformation is strong within and around ancient slab remnants, which likely have lower temperatures than the ambient mantle. This is consistent with the crystallographic orientation of postperovskite (pPv) being an important contributor to the new maps of lowermost mantle anisotropy. Our observations suggest a close link between the subduction of tectonic plates and convective flow in the deepest mantle.

Seismic anisotropy can inform us about convective flow in the mantle. Shear waves traveling through azimuthally anisotropic regions split into fast and slow pulses, and measuring the resulting shear-wave splitting provides some of the most direct insights into Earth’s interior dynamics. Shear-wave splitting is a constraint for path-averaged azimuthal anisotropy and is often studied regionally, and global compilations of these measurements exist. Such compilations include measurements obtained using different data processing methodologies (e.g., filtering), which do not necessarily yield identical results, and reproducing a number of studies can be challenging given that not all provide the required information, e.g., about the source location. Here, we automatically determine SKS, SKKS and PKS shear-wave splitting parameters from a global dataset. This dataset includes all earthquakes with magnitudes ≥ 5.9 from 2000 to the present, collected from 24 data centers, totaling over 4,700 events and 16 million three-component seismograms. We obtain approximately 90,000 robust measurements for “fast azimuth”, ϕ, and delay time, δt, and 210,000 robust null measurements. Results generally agree with previous work but our measurements allow us to identify hundreds of “null stations” below which the mantle appears effectively isotropic with respect to azimuthal anisotropy, which are important for some splitting techniques. We make all measurements publicly available as a data product, along with detailed metadata. This serves two purposes: ensuring full reproducibility of results and providing all necessary information for future systematic use of our measurements, in tomography applications or comparisons with geodynamic flow predictions.

Tumor-informed liquid biopsy approaches have proven promising for detecting minimal residual disease (MRD) and recurrence of cancer following surgical resection or other therapy. However, current liquid biopsy MRD assays typically detect ctDNA in a range above 30 to 300 parts per million (PPM), leaving a significant fraction of MRD cases undetected, particularly soon after surgery and in early stage cancers where ctDNA can be at very low levels. To address this, we have developed NeXT Personal™, a tumor-informed liquid biopsy assay that achieves sensitivity down to 1 PPM, therefore enabling earlier detection of MRD and recurrence. NeXT Personal leverages tumor/normal whole genome sequencing to design personalized MRD liquid biopsy panels for each patient. The panel is composed of >1,200 somatic tumor variants enabling higher sensitivity MRD detection in plasma through tracking of larger numbers of high quality and lower noise variants. This allows the platform to achieve high sensitivity across cancer types and stages, including early stage cancers and low mutational burden tumors, utilizing ~4 mL of plasma. Two independent methods were used to establish utility and performance: a proprietary cell-line media system, and well-characterized matched tumor-normal-plasma patient samples. Samples were serially diluted to <1 PPM, with replicates used to confirm performance. Digital droplet polymerase chain reaction (ddPCR) was used to orthogonally validate platform performance to the limit of detection (LOD) of ddPCR. Characterization of MRD LOD in three cell-line media systems, HCC1143, HCC38, and HCC1937, yielded accurate and reproducible detection of signal across a broad range of concentrations, to a lower limit of 1-2 PPM. We then used our platform to characterize MRD LOD in a set of serially diluted patient samples, demonstrating sensitivity down to as low as 1 PPM, with high specificity in normal control samples. Finally, we demonstrated the performance of NeXT Personal with matched tumor-normal-plasma patient samples (8 different cancer types, stages II-IV). In this series, NeXT Personal detected cancers down to 0.8 PPM with high specificity demonstrated across a set of healthy normal donor samples. We estimate that ~50% of the cases in this set of patients would not have been detected by other commercially available liquid biopsy MRD platforms. NeXT Personal achieved highly sensitive and specific MRD detection, reproducibly demonstrating a LOD down to 1 PPM in different cancer types and cell line dilutions, representing approximately 10 to 100 times higher sensitivity than other liquid biopsy MRD approaches. The high sensitivity of NeXT Personal potentially enables MRD detection across a broad variety of cancers and stages, including typically challenging early stage, low mutational burden, and low-shedding cancers. Citation Format: Sean Michael Boyle, Gabor Bartha, John Lyle, Jason Harris, Josette Northcott, Dan Norton, Rachel Marty Pyke, Fabio C. P. Navarro, Alexander Stram, Christian Haudenschild, Rose Santiago, Robin Li, Chris Nelson, Yelia Huo, Manju Chinnappa, Qi Zhang, Lloyd Hsu, John West, Richard O. Chen. A high sensitivity, tumor-informed liquid biopsy platform, designed to detect minimal residual disease at part per million resolution [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 5163.

Circulating tumor cell-free DNA (ctDNA) has become a biomarker for prognosis and disease monitoring. However, studies typically utilize assays limited to a small set of genes that may miss biologically important and clinically actionable mutations. To address this limitation, we have developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy (NeXT LB), that enables sensitive identification of mutations in plasma across ~20,000 genes following interventions such as surgery and treatment therapies. NeXT LB monitors tumor variants and discovers novel mutations in the plasma through analysis of tumor, normal, and plasma samples from the same patient. To enable sensitive detection across the exome in solid tumor and liquid biopsies, we developed an enhanced whole-exome assay and chemistry that augments challenging genomic regions to enable more uniform coverage across the exome. Additionally, we achieve a mean depth of coverage of ~2,000X across the exome, with boosted depth (~5,000X) for 247 clinically relevant oncogenic or tumor suppressor genes to further enhance sensitivity. Finally, we developed computational algorithms to sensitively monitor and discover somatic mutations in liquid biopsies without compromising specificity. In this work, we measure the sensitivity of NeXT LB using SeraCare reference samples with known variants at 0.5%, 1%, and 2% allele fraction (AF). We observe 100% sensitivity at 2% and 1% AF, and >95% sensitivity at 0.5% AF. Additionally, we measure >95% sensitivity for variants with AF >=2% using a proprietary cell-line media system. We generate low-pass Whole Genome Sequencing (lpWGS) data to estimate ctDNA fraction in conjunction with NeXT LB. Considering tumor heterogeneity, NeXT LB is capable of monitoring and discovering somatic variants when lpWGS-reported ctDNA fraction is >=3%, thereby highlighting the performance of the NeXT LB platform. We apply NeXT LB to sequence over 100 matched plasma and normal samples at 250 gigabases (G) and tumors at 50 G. This data demonstrate somatic variation in over 1,000 distinct genes across the cohort, thereby demonstrating the breadth and performance improvements provided by our exome-scale platform in contrast to existing targeted platforms. Additionally, we find that the plasma variants are enriched for higher AFs in solid tumors, thus allowing comprehensive coverage of driver genes and recapitulating hotspots identified in public datasets, including TCGA. We developed an exome-scale NeXT LB technology that enables sensitive monitoring and detection of somatic SNVs and indels from cfDNA. The NeXT LB platform covers a much broader landscape of tumor mutations from the plasma than existing targeted platforms, thereby enabling more comprehensive monitoring and discovery of mutations related to therapies, mechanisms of resistance, intra- and inter-tumor heterogeneity, among others. Citation Format: Fabio C p Navarro, Naveen Ramesh, Josette Northcott, Rui Chen, Lee D. McDaniel, Charles W. Abbott, Dan Norton, Robin Li, John Lyle, Jason Harris, Gabor Bartha, John West, Sean M. Boyle, Richard O. Chen. Applying NeXT Liquid Biopsy™, an exome-scale platform, to monitor and discover somatic variants in a broad set of cancer types [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 6385.

Tumors harbor a complex ecosystem of malignant, immune, and stromal cells. While malignant cells dictate much of the tumor biology, there is evidence that the tumor microenvironment (TME) also plays a major role in disease etiology. Given the complexity and abundance of the TME cellular composition, investigating the role of immune cell types will yield novel biomarkers for tumor progression and response to therapies. The role of B cells as a prognostic biomarker remains elusive. For instance, infiltrating B cells in CRC have both positive and negative prognostic value. Thus, a scalable approach to quantify B cells and the B-cell receptor (BCR) repertoire could yield novel insights into the role of B cells in tumor biology. To address this, we have developed immune cell quantification (InfiltrateID࣪) and immune receptor repertoire profiling (RepertoireID࣪) methods as part of the ImmunoID NeXT Platform®, an augmented, immuno-oncology-optimized exome/transcriptome platform. We estimate B cell abundance and BCR repertoire by profiling FFPE and PBMC samples using ImmunoID NeXT࣪. In expanding upon InfiltrateID to further estimate B cell abundance, here we regress the bulk RNA-seq readout from a reference signature from purified immune cell types. We also generate orthogonal quantifications of B cell abundance by profiling samples with cytometry by time of flight, single-cell RNA-seq, flow cytometry, and immunohistochemistry (IHC). We compare BCR results from ImmunoID NeXT to a standalone sequencing approach to evaluate the concordance of top clones. We then utilize BCR profiling from ImmunoID NeXT to analyze clonality and isotype composition in tumor samples. We first use InfiltrateID to estimate absolute B cell fractions in over 50 samples. Overall, we observe a high correlation between InfiltrateID results and orthogonal data sets in both PBMC and tumor FFPE samples (R2=0.90). When comparing BCR results from RepertoireID to a standalone BCR sequencing method that profiles IgM and IgG, we identify 475 and 387 of the top 500 clones in IgG and IgM, respectively, with highly concordant abundances across all clones (R2>0.72 and R2>0.82 in IgM and IgG, respectively). Next, we use InfiltrateID to estimate absolute B cell fractions in over 650 samples from 14 tumor types. On average, samples display B cell fractions in agreement with the literature and IHC quantifications, with higher B cell fractions in lung, breast, and cervical tumors. We also observe a range of BCR clonality values across tumor types. Finally, we observe differences in B cell composition and repertoire diversity in tumor samples from patients who underwent checkpoint blockade therapy. We show that InfiltrateID and RepertoireID accurately capture the composition and clone diversity of infiltrating B cells in tumor samples. Citation Format: Fabio Navarro, Eric Levy, Pamela Milani, Qiang Li, Shruti Bhide, Upasana Dutta, Charles W. Abbott, Jose Jacob, Rena McClory, John West, John Lyle, Sean Boyle, Richard O. Chen. Accurate quantification of infiltrating B cell composition and clone diversity in tumor samples [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 5021.

Abstract Gastrointestinal stromal tumors (GIST) are lethal tumors characterized by constitutively activating mutations to KIT or PDGFRA. Transient disease control in the first-line setting is achieved via inhibition of tyrosine kinase signaling using the KIT inhibitor imatinib. As patients progress through subsequent lines of therapy a molecularly heterogeneous disease evolves, characterized by distinct subtypes and shifting repertoires of exon-specific KIT variants which directly impact treatment outcomes. Here, we use tumor-informed exome-scale liquid biopsy to identify and track the evolution of multiple resistance mechanisms in patients receiving tyrosine kinase inhibitors (TKIs) to address the unmet need of comprehensive understanding of GIST evolution in response to therapy. Matched tumor, normal and serial plasma samples were obtained from 15 heavily pretreated metastatic GIST patients. Following baseline sample collection, all patients received systemic TKI therapy, and were monitored until disease progression. Exome-scale detection of somatic variants in cfDNA from longitudinal matched plasma samples was achieved using the NeXT Liquid BiopsyTM platform. The ImmunoID NeXT PlatformⓇ, an augmented exome/transcriptome platform and analysis pipeline which generates comprehensive tumor and immune data was used to profile paired tumor and normal samples. Longitudinal whole exome sequencing of plasma identified dynamic shifts in existing clones harboring exon-specific KIT mutations, and evolution of new KIT mutations arising prior to identification of tumor progression using standard imaging techniques. We detected a correlation between the number of damaging mutations detected in baseline ctDNA and tumor exon 11 KIT mutation status, suggesting that plasma mutation profiles may be KIT-variant dependent. ctDNA from patients with shorter overall survival (OS) was enriched for variants in the PI3K-AKT and MAPK pathway, potentially contributing to immune evasion observed in those patients. Additional associations were observed between gene copy-number changes and OS (P = .0097). Previous studies have demonstrated that immune infiltration and activity may be KIT variant specific, here we broaden those findings, identifying a significant correlation between TCRɑ clonality and variants detected only in plasma (P = .04), as well as a significant association between TCRβ diversity and OS (HR = 2.55, log rank P = .04). Comprehensive profiling of paired tumor tissue (WES and RNA-Seq) and WES of serially collected ctDNA sensitively and repeatedly identified evolving KIT mutations and other molecular alterations prior to radiologically confirmed disease progression. These findings suggest plasma-based monitoring of late-stage GIST malignancies may be useful for non-invasive disease tracking, providing treatment guidance prior to traditional approaches. Citation Format: Charles W. Abbott, Niamh Coleman, Jing Wang, Josette Northcott, Jason Pugh, Dan Norton, Fábio C. Navarro, Lee D. McDaniel, Eric Levy, Rachel Marty Pyke, John Lyle, Jason Harris, Gabor Bartha, Filip Janku, John West, Richard O. Chen, Sean Boyle. Exome-scale longitudinal tracking of emerging therapeutic resistance in GIST via analysis of circulating tumor DNA [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 5161.

Background Tumors harbor a complex and dynamic ecosystem of malignant, immune, and stromal cells. While malignant cells dictate much of the tumor biology, there is evidence that the tumor microenvironment (TME) also plays a significant role in disease progression and response to therapy. The role of the immune cells is particularly relevant in immunotherapy, and multiple transcriptome-based biomarkers have shown utility in predicting the efficacy of immune checkpoint blockade. However, little is known about the benefits of enhancing the depth and uniformity of transcriptome sequencing coverage for quantifying the TME cell type composition. Methods We have developed the ImmunoID NeXT Platform®, which combines high-quality exome and transcriptome sequencing with advanced informatics designed for immune-oncology to comprehensively characterize the tumor and TME from a single FFPE tumor sample. Proprietary augmentation technology was applied to bolster sequencing depth in regions of low coverage across approximately 20,000 genes, enhancing transcriptome coverage uniformity. We processed and sequenced 32 PBMC samples, in-vitro cell mixtures (CD8, CD4, Tregs, B-cells), and over 100 purified cell types to assess the biases and performance of gene expression quantification using the augmented transcriptome. Immune cell composition was validated using flow cytometry. Using purified cell types, we applied differential expression analysis to identify genes preferentially expressed in target cell types. Finally, we confirmed the augmented transcriptome identifies well-established cell-type marker genes and novel cell-type enrichment genes fit for deconvolution. Results We observed that the ImmunoID NeXT Platform benefits read coverage and uniformity for the majority of genes as compared to both PBMC (PolyA+) and tumor samples (rRNA-depletion). We identified genes preferentially expressed in immune, stromal and granulocyte cell types, showing high overlap with previous literature, and we describe over 1,000 new potential markers fit to assess cell type enrichment in reference samples. To demonstrate that coverage augmentation did not introduce biases disrupting the collinearity between cell fractions and gene expression, we profiled in-vitro cell mixtures. We found that marker genes for Tregs, CD4, CD8, and B-cells are linearly correlated with the fraction of cells mixed and verified by flow cytometry. For instance, well-established CD8 markers show a strong correlation between cell fraction and expression (CD8A corr=0.947 p-value=2.55e-12). Conclusions We show that ImmunoID NeXT® accurately captures and augments the transcriptome of PBMC and FFPE samples. Applying augmented transcriptome coverage to the assessment of the TME benefited the identification of marker genes for cell type enrichment analysis without introducing bias.

Neoantigen-based biomarkers are a promising approach for stratifying patient response to immunotherapy; however, current neoantigen prediction methods are not accurate enough to optimize these biomarkers. Sequence variability in the major histocompatibility complex (MHC) leads to the presentation of diverse neoantigens to T cells, and accurately representing this diversity in neoantigen prediction is critical for improvement. Previously, we published data from 25 mono-allelic cell lines and built an associated MHC class I, pan-allelic neoantigen prediction algorithm (SHERPATM). Here, we profile an additional 84 MHC alleles including 37 that have never previously been profiled with mono-allelic immunopeptidomics, explore the impact of MHC variability on peptide binding and improve neoantigen prediction of the SHERPA algorithm. To generate the data, we stably and transiently transfected 109 different MHC alleles (43 HLA-A, 56 -B and 10 -C alleles) into independent K562 HLA-null cell lines, immunoprecipitated intact MHC complexes using a W6/32 antibody and profiled the bound peptides using LC/MS-MS. We recovered a median of 1430 peptides per allele, with yields from the transient transfections being consistently higher than the stable transfections. Nearly all alleles have a strong anchor residue in the ninth position, but the positions of the secondary anchor residue vary by gene. HLA-B showed a stronger preference for the second position while HLA-A exhibited more variability across the first, second and third positions. In addition to the 109 mono-allelic cell lines, SHERPA increases generalizability by systematically integrating an additional 104 mono-allelic and 384 multi-allelic samples with publicly available immunopeptidomics data. The 186 alleles in the resulting training dataset have an average allelic coverage of 98% across 18 different US-based ethnicities. We evaluated our updated performance on 10% held-out mono-allelic test data from multiple cell line sources. The positive predictive value (PPV) of SHERPA was markedly higher than either NetMHCPan 4.1 or MHCFlurry-2.0 (1.45 and 1.58-fold increase, respectively), with further gains when only the 37 previously unprofiled alleles were considered (1.51 and 1.79-fold increase, respectively). Furthermore, the SHERPA model was able to detect 1.38-fold more immunogenic epitopes than either other method. Finally, we performed predictions with SHERPA across millions of synthetic binding pockets and peptides to elucidate the impact of MHC variability on peptide diversity. We found a strong correlation between binding pocket positions that highly influence peptide binding and those that are evolutionarily divergent. In conclusion, we profiled 109 mono-allelic cell lines, showed key trends in MHC-associated peptides and improved the SHERPA neoantigen prediction model. Citation Format: Rachel Marty Pyke, Steven Dea, Hima Anbunathan, Charles W. Abbott, Neeraja Ravi, Jason Harris, Gabor Bartha, Sejal Desai, Rena McClory, John West, Michael P. Snyder, Richard O. Chen, Sean Michael Boyle. Mono-allelic immunopeptidomics data from 109 MHC-I alleles reveals variability in binding preferences and improves neoantigen prediction algorithm [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 5640.

HLA loss of heterozygosity (LOH) is increasingly being recognized as an important immune escape mechanism in response to checkpoint inhibitor therapy. HLA LOH reduces the repertoire of neoantigens displayed on the cell surface of cancer cells, limiting the efficacy of the immune system to detect and eliminate them. Though highly accurate HLA LOH detection algorithms are needed to allow clinical utility, the field lacks robust, allele-specific validation approaches. Moreover, algorithms of unknown sensitivity and specificity have led to significant discrepancies in the estimated occurrence of HLA LOH as an immune escape mechanism across tumor types. To address these challenges, we have developed a machine learning algorithm to detect HLA LOH (DASH - Deletion of Allele-Specific HLAs), established the accuracy of the algorithm with an allele-specific PCR validation strategy, investigated the frequencies of HLA LOH across 14 tumor types in a cohort of over 800 patients and observed allele-specific neoantigen expansion in response to immunotherapy. To build DASH, we profiled 279 patients on the ImmunoID NeXT Platform to create a training dataset. Our novel features, which account for allele-specific differences in exome probe capture and capitalize on our whole exome platform by including information about copy number alterations in the regions flanking the HLA genes, were used to train an XGBoost model. Orthogonal, allele-specific validation was required to accurately assess sensitivity and specificity for clinical utility. Thus, we profiled over 30 paired tumor-normal cell lines on the ImmunoID NeXT Platform® and identified cell lines with HLA LOH. Using in silico mixtures, we found 100% sensitivity and specificity for tumors with at least 36% tumor purity. Next, we designed a digital PCR (dPCR) assay using patient-specific, allele-specific primers that target a single HLA allele while avoiding all other HLA alleles and tested the limit of detection of the assay in the same cell lines. Then, we performed dPCR with patient-specific primers on 20 tumor and normal sample pairs and found 94% sensitivity. After establishing the high sensitivity and specificity of DASH, we profiled over 800 patients spanning 14 tumor types on the ImmunoID NeXT Platform. We found that over 25% of patients in the majority of tumor types had at least one HLA LOH event. Further, we observed that novel neoantigens that arose during checkpoint treatment were significantly more likely to bind to deleted HLA alleles as compared to the remaining HLA alleles in a head and neck carcinoma cohort treated with anti-PD-1 therapy, shedding light on the mechanism of immune escape in response to checkpoint inhibitors. In summary, we introduced an HLA LOH detection method, performed allele-specific validation, exposed widespread HLA across tumor types and observed the mechanism of immune escape in response to immunotherapy. Citation Format: Rachel Marty Pyke, Datta Mellacheruvu, Charles Abbott, Steven Dea, Eric Levy, Simo V. Zhang, Nikita Bedi, A. Dimitrios Colevas, Devayani Bhave, Manju Chinnappa, Gabor Bartha, John Lyle, John West, Michael Snyder, John Sunwoo, Richard Chen, Sean Michael Boyle. Pan-cancer survey of HLA loss of heterozygosity using a robustly validated NGS-based machine learning algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 399.

Abstract Introduction: Tumor circulating cell-free DNA (ctDNA) comprises DNA molecules cast from tumors that reach and survive in a patient's bloodstream. Given its non-invasive nature, the liquid biopsy of ctDNA has remarkable potential for diagnosis, prognosis, disease progression tracking, and treatment monitoring. Nonetheless, little is known about which tumor features yield higher representation of ctDNA in blood or even which regions in the tumor genome are more inclined to shed and thus be observed as ctDNA. Typically, studies of ctDNA have focused on a limited and well-established set of genes and recurrent variants. However, these limited gene panels may not capture the breadth of genetic alterations that reflect tumor biology. Their limited footprint hinders a comprehensive understanding of tumor heterogeneity, mechanisms of resistance, and DNA shedding patterns. To address these limitations, we developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy™, that enables sensitive detection and tracking of mutations in over twenty thousand genes from plasma samples. Results: Here we profile over 50 tumor, normal, and plasma matched samples using NeXT Liquid Biopsy to investigate pan-cancer patterns of DNA shedding. We observe varying levels of ctDNA shedding in plasma, suggesting that tracking tumors with an exome-scale set of variants, as opposed to with a targeted panel, can benefit greatly from the higher sensitivity and granularity of our enhanced exome sequencing. Next, we sought to investigate the patterns of somatic variants representation in plasma. For this, we integrate shedding ratios with transcriptome and epigenome data from healthy paired tissues and examine variant shedding biases associated with transcription rate and histone modifications in associated nucleosomes. Conclusion: We sequenced and analyzed one of the largest tumor, normal, and plasma cohorts to date with comprehensive coverage of somatic variants across all human genes. Our results suggest that tracking exome-scale somatic variants adds invaluable information to understand a tumor's biology. Moreover, our comprehensive coverage of the tumor genome can be used to unveil biases of tumor genome shedding patterns. We are able to investigate the shedding ratio across distinct genomic features deriving, to our knowledge, the first exome scale pan-cancer shedding resource of the human genome. Citation Format: Fabio C. P. Navarro, Simo Zhang, Mengyao Tan, Charles Abbott, Josette Northcott, John Lyle, Gabor Bartha, Jason Harris, John West, Richard Chen, Sean Michael Boyle. Pan-cancer shedding patterns of tumor circulating cell free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2227.

Abstract Neoantigens, which are antigens specific to cancer cells, can be harnessed to develop precision immunotherapies, such as personalized cancer vaccines, and prognostic biomarkers for checkpoint blockade inhibition. Next generation sequencing technologies have enabled comprehensive profiling of putative neoantigens by interrogating the tumor exome and transcriptome, but accurate prediction of peptides presented by MHC complexes remains a significant challenge. We present here Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™) that addresses this critical need. SHERPA comprises highly sensitive, accurate and pan-allelic MHC-peptide (MHCp) binding and presentation prediction models, that were built using a multi-pronged strategy. First, we generated a large-scale, high-quality HLA ligandome using approximately 75 stably transfected mono-allelic K562 cell lines. Intact MHCp complexes were immunoprecipitated using W6/32 antibody and profiled using LC/MS-MS. Second, we trained models that predict both MHCp binding and presentation. Briefly, MHCp binding was modeled using the amino acid sequences of the ligand and the binding pocket of the cognate allele. MHCp presentation, which encompasses in vivo antigen processing, was modeled using multiple features including the expression level of the source protein, proteasomal cleavage, and two novel features representing presentation propensities of genes and regions within gene bodies. Third, we expanded the scale and scope of our in-house dataset using a large curated repository of publicly available mono- and multi-allelic datasets resulting in > 160 alleles and > 1.6 million peptides. Integrating data from diverse cell line and tissue types improved the generalizability of our models, a critically important aspect when applying our models to patient samples. Finally, we implemented a model-based deconvolution of multi-allelic datasets to generate pseudo mono-allelic data, and developed an integrative machine learning architecture to model our expanded HLA-ligandome. We evaluated the performance of our binding and prediction models on 10% held-out mono-allelic test data from multiple cell line sources. The precision at various recall values of both binding and prediction models was markedly higher than NetMHCPan 4.0, and the positive predictive values were 0.59 and 0.73 respectively, significantly higher compared to NetMHCpan 4.0 (PPV = 0.38). Additionally, a strong concordance of raw and predicted motifs for alleles excluded from training data indicated a robust pan-allelic performance. When evaluated on 12 tissue samples profiled in-house, the SHERPA presentation model had a consistently high recall (90%) compared to NetMHCpan 4.0 (63%). This trend holds true on external immunopeptidomics datasets from tumor samples. In summary, SHERPA enables precision neoantigen discovery. Citation Format: Rachel Marty Pyke, Dattatreya Mellacheruvu, Steven Dea, Charles Abbott, Nick Phillips, Sejal Desai, Rena McClory, Steven Ketelaars, Pia Kvistborg, John West, Richard Chen, Sean Michael Boyle. Accurate modeling of antigen processing and MHC peptide presentation using large-scale immunopeptidomes and a novel machine learning framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1898.

Background Human leukocyte antigen (HLA) genes facilitate communication between tumor cells and the immune system through the cell surface presentation of a diverse set of peptides. HLA loss of heterozygosity (LOH) has been associated with reduced immune pressure on neoantigens and impaired response to checkpoint blockade immunotherapy. Although HLA LOH is emerging as a key biomarker for response to immunotherapy, few tools exist to detect HLA LOH. Moreover, the accuracy of these tools is not well understood due to lack of orthogonal validation approaches. Here, we briefly describe DASH (Deletion of Allele-Specific HLAs), an algorithm to detect HLA LOH from exome sequencing data, and present a three-pronged validation approach to assess its performance. Methods In-silico evaluation of the limit of detection (LOD) of DASH was performed by deeply sequencing a tumor-normal paired cell line with HLA LOH and mixing reads at different proportions to simulate variable tumor purity and clonality. Direct genomic validation was performed using digital PCR (dPCR) with allele-specific primers targeting both predicted kept and lost alleles in ten patient samples and one cell line. Quantitative immunopeptidomics was performed to compare peptides presented by HLA alleles in tumor cells and adjacent normal cells. The relative increase or decrease of peptide presentation per allele was estimated by predicting the binding of each peptide to the patient-specific alleles. Results DASH is a machine learning model built upon the HLA-enhanced ImmunoID NeXT Platform®. We validated the performance of DASH using three orthogonal approaches to better understand the factors driving sensitivity and specificity of the algorithm. Evaluation using cell line mixtures that simulate LOH at various dilutions helped establish the LOD of DASH. For fully clonal tumors, DASH had 100% sensitivity at all tumor purity levels above 8% and 100% specificity at tumor purity levels higher than 24%. Patient-specific and allele-specific dPCR assays provided sensitive, direct evidence of HLA LOH. All samples predicted to have HLA LOH by DASH with high confidence were confirmed by dPCR. Finally, a quantitative immunopeptidomics experiment in one patient with HLA LOH revealed a large decrease in the peptides presented by deleted alleles, revealing the functional implications of HLA LOH. Conclusions HLA LOH detection methods need to be rigorously validated in order to be used as a clinical biomarker. Here, we introduced three methods to assess performance, demonstrated the strong predictive power of DASH, and highlighted the need to consider tumor purity in such assessments.

Precision immuno-oncology is increasingly relevant to cancer therapy given the ascendance of immunotherapy. While next-generation sequencing (NGS) based algorithms may elucidate immunotherapeutic response, many such algorithms require highly accurate Class I HLA typing. One major challenge of HLA type derivation resides in highly polymorphic HLA allelic diversity, which conventional exome sequencing technologies poorly capture. Further, accurate HLA typing requires definitive distinction between thousands of potential HLA alleles. These challenges may cause widely used NGS HLA typing tools, such as Polysolver and Optitype, to perform inaccurate HLA typing. Poor HLA coverage poses the risk of silently mistyping HLA alleles, yielding inaccurate downstream HLA loss of heterozygosity (LOH) detection and neoepitope predictions.We designed the ImmunoID NeXT Platform® to more comprehensively profile the HLA region. To evaluate the accuracy of conventional NGS-based Class I HLA typing, a widely used dbGaP project (phs000452, n=160) of melanoma NGS data was evaluated alongside a set of over 500 solid tumor cancer patient samples sequenced on the ImmunoID NeXT Platform. Read coverage was derived from both GRCh38 and HLA allele database alignments. To test whether Polysolver over represents specific HLA alleles under reduced read conditions, a Monte Carlo bootstrap approach predicted theoretical allele frequency ranges.Below 20x read coverage, nearly 50% of Polysolver HLA calls (phs000452) are homozygous, representing a divergence from typical HLA homozygous rates of between 10–20%, with p<10-15 (Fisher’s Exact) compared to reference 1000 Genomes homozygous rates. Polysolver’s homozygous, heterozygous, and no-calls demonstrated a statistically significant difference in coverage (p<10-6, Kruskal-Wallis) across all Class I HLA genes per Polysolver and public exome data (phs000452). The Personalis ImmunoID NeXT™ cohort did not demonstrate such a trend despite a similar exome-wide sequencing depth. Further, sixteen rare HLA alleles were identified with sample frequencies greater than expected from the dbGaP data set, with no such alleles identified from the Personalis ImmunoID NeXT data set.HLA typing may silently fail in the context of reduced read coverage without HLA-specific platform augmentation. This silent failure can have large implications for accurate neoantigen prediction and HLA LOH detection, both of which are becoming increasingly important for immuno-oncology treatment modalities such as personalized cancer vaccines, adoptive cell therapies, and blockade therapy response biomarkers. Studies utilizing neoepitope and HLA LOH prediction require careful validation for HLA calls, including assessments of coverage and homozygous rates, and may benefit from increased HLA locus coverage.

Abstract Background: A better understanding of the characteristics of cancer across different indications is required to drive the development of personalized treatments, inform therapy decisions, and improve outcomes. Integrating data from the tumor and the immune system can enable the identification of comprehensive biological signatures and composite biomarkers for the improved stratification of responders/progressors. Here, we describe a pan cancer study, including an enhanced whole-exome and transcriptome sequencing approach, across over 500 samples representing 13 tumor types, analyzed at high depth using the ImmunoID NeXT platform. Methods: We sequenced paired tumor-normal samples on the ImmunoID NeXT platform, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune microenvironment from a single FFPE sample, across all of the approximately 20,000 genes. For each sample, we analyzed a broad set of features focused on both the tumor and immune system. From DNA, we profiled small variants, CNAs, MSI status, oncoviruses, HLA LOH, and neoantigens. From RNA, we profiled gene expression, small variants, fusions, TILs, TCR, BCR, and immune signatures. Integrated analyses assessing the impact of each feature, both within and across tumor types, were performed across the cohort. Results: Through immunogenomic analysis we identified striking differences in both tumor and TME profiles across cancer types. In addition to mutation and neoantigen burden, by. we also computed a composite neoantigen score for each sample, which we have shown in a separate melanoma study can be a stronger predictor of response to immunotherapy. The composite neoantigen score integrates neoantigen prediction with mechanisms of tumor escape that can affect neoantigen presentation, providing a more accurate model of the antigen presentation biology. We also looked at the distribution of HLA LOH using our DASH algorithm and found differences in LOH frequency between tumor types. For example, we found HLA LOH to be five timesmore common in lung cancer than breast cancer. Further, we profiled immune gene signatures, including Gejewski and Ribas signatures, highlighting varied immune activation across cancer types. Analysis of somatic alterations in pathways controlling cell growth, PI3K/AKT signaling, apoptosis, and other canonical pathways revealed malignancy-specific alteration frequencies. The varying frequency, and combination of these alterations is indicative of a complex hierarchy of cross-talk between pathways, which operates in a cancer specific manner. Conclusions: We performed a broad integrated analysis of the tumor and immune microenvironment for over 500 samples across 13 different tumor types using the ImmunoID NeXT platform. This comprehensive profiling revealed significant differences between cancer types beyond mutational burden, including neoantigen burden, immune microenvironment differences, and incidence of putative tumor escape mechanisms Citation Format: Sean Michael Boyle, Charles Abbott, Eric Levy, Rachel Marty Pyke, Dattatreya Mellacheruvu, Simo Zhang, Mengyao Tan, Rena McClory, John West, Richard Chen. Pan-cancer characterization of the tumor and immune microenvironment facilitates identification of cancer-specific biological signatures [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2512.

An increasing number of studies have demonstrated the potential use of circulating cell-free DNA (cfDNA) for diagnosis, prognosis, and disease progression monitoring. However, many of these studies utilize assays covering a limited set of genes, typically tens to a few hundred genes, and therefore can miss biologically and clinically important genetic alterations such as DNA repair pathways, immuno-modulatory pathways, mechanisms of resistance and changes in neoantigen status. To address this, we have developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy, that enables sensitive detection and tracking of mutations in approximately 20000 genes. To enable sensitive detection across the exome, we developed an enhanced exome assay and chemistry that augments hard to sequence genomic regions such as regions of high GC content, to enable more uniform coverage across the exome. Additionally, we achieve a high average depth of approximately 2000X for the entire exome, with additional boosted depth for 248 clinically relevant oncogenic and tumor suppressor genes to further enhance sensitivity. For analysis, we developed a computational pipeline for our NeXT Liquid Biopsy assay optimized to lower the noise floor for variant detection, enabling sensitive monitoring and de novo detection of variants over multiple time points. We have evaluated the sensitivity of our NeXT Liquid Biopsy platform using three approaches. First, we evaluated the sensitivity using both Horizon and Seracare reference materials at multiple allele frequency (AF) dilutions. Our platform identified all 25 Seracare SNV events at 1% AF, all 25 events at 2% AF, and 24 out of 25 events at 0.5% AF. We detected no variants from the negative control. Next, we expanded our sensitivity evaluation using a much larger reference panel of 555 SNVs from the Acrometric Oncology HotspotControl. Similarly, we were able to achieve high sensitivity at our detection limit. Further, to enable sensitivity analysis at the whole exome scale, we developed a cell culture media system that models the shed and degraded tumor DNA fragments seen in human plasma samples. Finally, we demonstrated our ability to capture mutations across the exome using a head and neck cancer cohort on checkpoint therapy, observing a large fraction of mutations in genes not covered by commercially available targeted panels. In conclusion, we have developed a whole-exome scale NeXT Liquid Biopsy platform that enables sensitive monitoring and detection of somatic SNVs from cfDNA across approximately 20000 genes. The NeXT Liquid Biopsy platform generates a much broader view of the tumor mutational landscape from the plasma than typical liquid biopsy platforms that are focused on a much smaller set of genes. The platform enables broader monitoring of changes in response to cancer therapy, acquired mechanisms of resistance, mechanisms of drug resistance, and intra- and inter-tumor heterogeneity. Citation Format: Simo V. Zhang, Mengyao Tan, Josette M. Northcott, Shuyuan Ma, Christopher S. Nelson, L. Gordon Bentley, Manju Chinnappa, Devayani P. Bhave, Dan Norton, Jason Harris, Sean M. Boyle, John West, Richard Chen. Enhanced whole exome profiling of tumor circulating cell-free DNA enables sensitive assessment of tumor mutations [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1989.

Loss of heterozygosity (LOH) in the HLA locus is increasingly being recognized as an important mechanism of immune escape and a proposed biomarker for immunotherapy response. Neoantigens that bind to a deleted HLA allele will no longer be presented to the immune system, potentially allowing subclones with these deletions to escape immune surveillance. Despite interest in the field, few methods exist to detect HLA LOH, and their sensitivity is not well understood. Moreover, the mechanistic impact of HLA LOH in response to immune checkpoint inhibitors (ICI) remains unexplored. Here, using a novel tool to detect HLA LOH, DASH (Deletion of Allele-Specific HLAs), we reveal allele-specific neoantigen expansion in response to ICIs in a head and neck carcinoma cohort and the widespread occurrence of HLA LOH across several tumor types. We performed exome sequencing with ImmunoID NeXT, which enhances coverage of the HLA locus, on tumor and normal samples from 260 patients to create a training dataset for our model. For each patient, we mapped reads to each of their allele-specific HLAs and manually annotated LOH. Then, using purity, ploidy and two novel features (normalized b-allele frequency and allele-specific coverage ratios), we trained an XGBoost model. To evaluate our tool, we compared DASH predictions on held out tumor samples to deletion calls from a standard copy number tool in the regions flanking each HLA gene (91% concordance, 0.73 F1-score), ascertained our sensitivity by diluting cell line data with known HLA LOH to imitate variable purity (100% accuracy in samples above 17% purity) and confirmed the functional impact of HLA LOH using immunopeptidomics data of tumor samples (average of 47% fewer unique peptides binding to lost alleles than kept). To explore the mechanistic impact of HLA LOH in response to ICIs, we studied a cohort of nine head and neck carcinoma patients who received a single dose of nivolumab, and sequenced pre- and posttreatment tumor biopsies for each patient. With DASH, we detected HLA LOH in four of the patients, pretreatment. For these patients, we found a significant posttreatment expansion of neoantigens predicted to bind to the deleted HLA alleles in comparison to the pretreatment biopsy (p=0.046, Wilcoxon signed-rank), revealing the evolutionary force of HLA LOH as a resistance mechanism during ICI therapy. To assess the pervasiveness of HLA LOH across tumor types, we applied DASH to over 500 pretreatment tumors across 13 tumor types and found highly variable frequencies of HLA LOH across the tumor types. In summary, we developed a sensitive method to detect HLA LOH and exposed neoantigen expansion to deleted HLA alleles in response to ICI therapy, emphasizing the limitations of deleted alleles to ignite an immune response. Moreover, we found widespread occurrences of HLA LOH across tumor types, highlighting the importance of accurate HLA LOH detection as a pan-cancer biomarker. Citation Format: Rachel Marty Pyke, Charles Abbott, Dattatreya Mellacheruvu, Simo V. Zhang, Nikita Bedi, A. Dimitrios Colevas, John Sunwoo, John West, Richard Chen, Sean Michael Boyle. Sensitive HLA loss of heterozygosity detection reveals allele-specific neoantigen expansion as resistance mechanism to anti-PD-1 therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6678.

Abstract Loss of human leukocyte antigen (HLA) is of increasing interest as a mechanism of cancer immune evasion and biomarker for cancer immunotherapy response. Each cancer patient has six class I HLA alleles that are capable of presenting a set of tumor-specific neoantigens. However, HLA alleles are often deleted in tumors, resulting in a loss of heterozygosity (LOH). When LOH occurs, neoantigen presentation is significantly impaired, potentially facilitating tumor immune evasion. Given the biologic impact of HLA LOH, there is a need for robust algorithms that can detect allele-specific HLA LOH in tumor samples. Here, we describe a novel computational approach to detect HLA LOH from exome sequencing, demonstrate the robustness of the method, and apply the method to calculate the frequencies of HLA LOH in key cancer types. We performed exome sequencing with augmented HLA region capture on the ImmunoID NeXT platform for tumor and normal samples of 184 patients across several cancer types and identified 430 nonhomozygous HLA genes. Next, we extracted the reads mapping to a custom HLA database and mapped them on to the patient-specific HLA alleles. For each allele, we calculated two key features: 1) the tumor b-allele frequency normalized by the native b-allele frequency and 2) the allele-specific tumor to normal coverage ratio. Using these two features, along with tumor purity and ploidy values, we trained a random forest model on a subset of the HLA genes (n=300). While standard copy-number variant (CNV) tools are unable to detect LOH in the polymorphic HLA genes, they can accurately measure deletions in their flanking regions, which we used to validate the accuracy of our allele-specific HLA LOH algorithm. In our test set (n=130), we found a high concordance between our allele-specific deletion calls and the generic deletion calls (94% accuracy, 0.85 F1 Score). When we constrained our test set to samples with high tumor content (>50%, n=40), we saw even stronger concordance (98% accuracy, 0.95 F1 Score). The only discordant call was a focal deletion within an HLA gene that was detected by our algorithm but missed by the CNV tool. Next, we ran our algorithm on patient samples of different cancer indications. For non-small cell lung cancer, we found a high frequency of patients affected by LOH (35%, 9 of 26), which is similar to frequencies previously reported in the literature. Furthermore, we found a lower frequency of melanoma tumors with LOH (15%, 7 of 48). In conclusion, we developed a novel algorithm to call allele-specific HLA LOH on the ImmunoID NeXT exome sequencing platform that augments coverage in the polymorphic HLA locus and demonstrated overall robust performance. The relatively high frequency of LOH events we detected in the melanoma and lung cancer samples suggests the importance of LOH analysis to inform cancer immunotherapy biomarker studies and personalized cancer therapies that depend on neoantigen presentation. Citation Format: Rachel Marty Pyke, Charles Abbott, Simo V. Zhang, Datta Mellacheruvu, John West, Richard Chen, Sean Michael Boyle. HLA allele-specific loss of heterozygosity detection using augmented exome capture approach [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A19.

There is an increasing need for more advanced, composite biomarkers that can model the complex systems biology driving response and resistance to cancer therapy. However, many cancer diagnostic platforms to date, with their focus on mutational changes in a relatively small panel of genes, provide limited data to support integrative, multidimensional biomarkers that can better predict immunotherapy response. To enable the identification of composite biomarkers that combine tumor- and immune-related information from both DNA and RNA, we have developed ImmunoID NeXT, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune system from a single FFPE sample, across all of the approximately 20,000 genes. By co-optimizing assay and analytics design, we enable sensitive evaluation of clinically-relevant cancer biomarkers from >=25ng of co-extracted DNA/RNA, while also providing a broader evaluation of neoantigens, HLA typing and LOH, antigen processing machinery (APM), TCR/BCR repertoire, immune expression signatures, tumor-infiltrating lymphocytes (TILs), oncoviruses, and germline variants. Leveraging this expansive feature set, we developed methods that combine individual analytes to construct composite biomarker scores that correlate with immunotherapy response. Validation of ImmunoID NeXT demonstrated high sensitivity and specificity to somatic and structural variants across ~20,000 genes at allelic fractions as low as 5%, with clinical diagnostic reporting on actionable mutations (SNVs, indels, CNAs, fusions) in 248 cancer-driver genes that have been boosted further for higher sensitivity, as well as reporting on TMB and MSI status. For neoantigen prediction, immuno-peptidomic data from monoallelic HLA-transfected cell lines were used to train neural networks to predict pMHC binding with higher precision than public tools. For TCRα/β analysis in FFPE tumor samples, strong correlation with targeted TCR kit results was shown (R^2>0.9 and >0.94). For TILs, we developed signatures for eight immune cell types, demonstrating concordance with orthogonal immunofluorescence methods. We achieved genotyping accuracy of 99.1% for HLA Class I, and 95% for HLA Class II, and have developed and verified the performance of a tool for HLA LOH detection. In a cohort of 55 late-stage melanoma patients, the integration of neoantigen burden, HLA LOH, and APM mutational data formed a composite neoantigen score that more accurately predicted response to checkpoint blockade than other markers such as TMB. With ImmunoID NeXT, we have developed a broad diagnostic platform that can be leveraged for the development of advanced composite biomarkers (and novel resistance mechanisms) that combine both tumor and immune features from DNA and RNA; enabling more accurate stratification of patient response to immunotherapy. The platform has been validated and optimized for use with limited FFPE tissue samples, making it ideal for both research and clinical applications. Citation Format: Robert Peter Power, Gabor Bartha, Jason Harris, Sean M. Boyle, Eric Levy, Pamela Milani, Prateek Tandon, Paul McNitt, Mandy Lee, Massimo Morra, Sejal Desai, Sebastian Salvidar, Michael J. Clark, Christian Haudenschild, Sekwon Jang, John West, Richard Chen. A diagnostic platform for precision cancer therapy enabling composite biomarkers by combining tumor and immune features from an enhanced exome and transcriptome [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1334.

An increasing number of studies have demonstrated the potential use of circulating cell-free DNA (cfDNA) for diagnosis, prognosis, disease progression, and treatment monitoring. However, many of these studies use assays covering a limited set of genes and therefore miss biologically and clinically relevant genetic alterations involving immuno-modulatory pathways which confer treatment resistance, and leading to changes in neoantigen status. To address this, we developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy™, that enables sensitive detection and tracking of mutations in approximately 20000 genes.To enable sensitive detection across the exome, our enhanced exome assay and chemistry augments hard-to-sequence genomic regions, such as regions of high GC content, to enable more uniform coverage across the exome. We achieved a high mean sequencing depth of approximately 2000X exome-wide, with additionally boosted depth for 248 clinically relevant oncogenic and tumor suppressor genes to further enhance sensitivity. We developed a computational pipeline for our NeXT Liquid Biopsy assay optimized to lower the noise floor for variant detection, providing sensitive monitoring and de novo detection of variants over multiple time points.We evaluated the sensitivity of our NeXT Liquid Biopsy platform in three ways. First, we evaluated the sensitivity within the coverage boosted regions using the SeraCare reference materials at multiple allele frequency (AF) dilutions. Our platform identified all 8 and 25 Horizon and SeraCare SNV events at 1% AF and above, respectively, and detected 24 out of 25 events at 0.5% for the SeraCare samples. Additionally, to enable sensitivity analysis at the whole-exome scale, we then developed a cell culture media system that models the shedding of tumor DNA fragments seen in human plasma samples and created tumor/normal dilution series in vitro. We achieved >95% sensitivity for variants with AF≥2%, and between 85% to 92% for mutations with AF of 1%-2%. Second, we evaluated false-positive rates on 12 cancer patients using digital droplet PCR. Third, we demonstrated our ability to longitudinally monitor treatment response using a clinical cancer cohort on checkpoint therapy, profiling putative tumor evolution while on therapy.In conclusion, we have developed a whole-exome scale liquid biopsy platform, NeXT Liquid Biopsy, that enables sensitive monitoring and detection of somatic SNVs from cfDNA across ~20000 genes. The platform enables broader monitoring of changes in response to cancer therapy, acquired mechanisms of resistance, and intra- and inter-tumor heterogeneity.