Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy.

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.

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.

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.

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.

The ability to perform laboratory testing near the patient and with smaller blood volumes would benefit patients and physicians alike. We describe our design of a miniaturized clinical laboratory system with three components: a hardware platform (ie, the miniLab) that performs preanalytical and analytical processing steps using miniaturized sample manipulation and detection modules, an assay-configurable cartridge that provides consumable materials and assay reagents, and a server that communicates bidirectionally with the miniLab to manage assay-specific protocols and analyze, store, and report results (i.e., the virtual analyzer). The miniLab can detect analytes in blood using multiple methods, including molecular diagnostics, immunoassays, clinical chemistry, and hematology. Analytical performance results show that our qualitative Zika virus assay has a limit of detection of 55 genomic copies/ml. For our anti-herpes simplex virus type 2 immunoglobulin G, lipid panel, and lymphocyte subset panel assays, the miniLab has low imprecision, and method comparison results agree well with those from the United States Food and Drug Administration-cleared devices. With its small footprint and versatility, the miniLab has the potential to provide testing of a range of analytes in decentralized locations.

A major goal of current signaling research is to develop a quantitative understanding of how receptor activation is coupled to downstream signaling events and to functional cellular responses. Here, we measure how activation of the RET receptor tyrosine kinase on mouse neuroblastoma cells by the neurotrophin artemin (ART) is quantitatively coupled to key downstream effectors. We show that the efficiency of RET coupling to ERK and Akt depends strongly on ART concentration, and it is highest at the low (approximate to 100 pm) ART levels required for neurite outgrowth. Quantitative discrimination between ERK and Akt pathway signaling similarly is highest at this low ART concentration. Stimulation of the cells with 100 pm ART activated RET at the rate of approximate to 10 molecules/cell/min, leading at 5-10 min to a transient peak of approximate to 150 phospho-ERK (pERK) molecules and approximate to 50 pAkt molecules per pRET, after which time the levels of these two signaling effectors fell by 25-50% while the pRET levels continued to slowly rise. Kinetic experiments showed that signaling effectors in different pathways respond to RET activation with different lag times, such that the balance of signal flux among the different pathways evolves over time. Our results illustrate that measurements using high, super-physiological growth factor levels can be misleading about quantitative features of receptor signaling. We propose a quantitative model describing how receptor-effector coupling efficiency links signal amplification to signal sensitization between receptor and effector, thereby providing insight into design principles underlying how receptors and their associated signaling machinery decode an extracellular signal to trigger a functional cellular outcome.

Mycobacterium tuberculosis (Mtb) survives under oxidatively hostile environments encountered inside host phagocytes. To protect itself from oxidative stress, Mtb produces millimolar concentrations of mycothiol (MSH), which functions as a major cytoplasmic redox buffer. Here, we introduce a novel system for real-time imaging of mycothiol redox potential (EMSH) within Mtb cells during infection. We demonstrate that coupling of Mtb MSH-dependent oxidoreductase (mycoredoxin-1; Mrx1) to redox-sensitive GFP (roGFP2; Mrx1-roGFP2) allowed measurement of dynamic changes in intramycobacterial EMSH with unprecedented sensitivity and specificity. Using Mrx1-roGFP2, we report the first quantitative measurements of EMSH in diverse mycobacterial species, genetic mutants, and drug-resistant patient isolates. These cellular studies reveal, for the first time, that the environment inside macrophages and sub-vacuolar compartments induces heterogeneity in EMSH of the Mtb population. Further application of this new biosensor demonstrates that treatment of Mtb infected macrophage with anti-tuberculosis (TB) drugs induces oxidative shift in EMSH, suggesting that the intramacrophage milieu and antibiotics cooperatively disrupt the MSH homeostasis to exert efficient Mtb killing. Lastly, we analyze the membrane integrity of Mtb cells with varied EMSH during infection and show that subpopulation with higher EMSH are susceptible to clinically relevant antibiotics, whereas lower EMSH promotes antibiotic tolerance. Together, these data suggest the importance of MSH redox signaling in modulating mycobacterial survival following treatment with anti-TB drugs. We anticipate that Mrx1-roGFP2 will be a major contributor to our understanding of redox biology of Mtb and will lead to novel strategies to target redox metabolism for controlling Mtb persistence.

Assimilatory sulfate reduction supplies prototrophic organisms with reduced sulfur that is required for the biosynthesis of all sulfur-containing metabolites, including cysteine and methionine. The reduction of sulfate requires its activation via an ATP-dependent activation to form adenosine-5'-phosphosulfate (APS). Depending on the species, APS can be reduced directly to sulfite by APS reductase (APR) or undergo a second phosphorylation to yield 3'-phosphoadenosine-5'-phosphosulfate (PAPS), the substrate for PAPS reductase (PAPR). These essential enzymes have no human homologue, rendering them attractive targets for the development of novel antibacterial drugs. APR and PAPR share sequence and structure homology as well as a common catalytic mechanism, but the enzymes are distinguished by two features, namely, the amino acid sequence of the phosphate-binding loop (P-loop) and an iron-sulfur cofactor in APRs. On the basis of the crystal structures of APR and PAPR, two P-loop residues are proposed to determine substrate specificity; however, this hypothesis has not been tested. In contrast to this prevailing view, we report here that the P-loop motif has a modest effect on substrate discrimination. Instead, by means of metalloprotein engineering, spectroscopic, and kinetic analyses, we demonstrate that the iron-sulfur cluster cofactor enhances APS reduction by nearly 1000-fold, thereby playing a pivotal role in substrate specificity and catalysis. These findings offer new insights into the evolution of this enzyme family and extend the known functions of protein-bound iron-sulfur clusters.

Adenosine-5'-phosphosulfate reductase (APSR) is an iron sulfur protein that catalyzes the reduction of adenosine-5'-phosphosulfate (APS) to sulfite. APSR coordinates to a [4Fe-4S] cluster via a conserved CC-X-similar to 80-CXXC motif; and the cluster is essential for catalysis. Despite extensive functional, structural, and spectroscopic studies, the exact role of the iron sulfur cluster in APS reduction remains unknown. To gain an understanding into the role of the cluster, density functional theory (DFT) analysis and extended X-ray fine structure spectroscopy (EXAFS) have been performed to reveal insights into the coordination, geometry, and electrostatics of the [4Fe-4S] cluster. X-ray absorption near-edge structure (XANES) data confirms that the cluster is in the [4Fe-4S](2+) state in both native and substrate-bound APSR while EXAFS data recorded at similar to 0.1 angstrom resolution indicates that there is no significant change in the structure of the [4Fe-4S] cluster between the native and substrate-bound forms of the protein. On the other hand, DFT calculations provide an insight into the subtle differences between the geometry of the cluster in 1 native and APS-bound forms of APSR A comparison between models with and without the tandem cysteine pair coordination of the cluster suggests a role for the unique coordination in facilitating a compact geometric structure and "fine-tuning" the electronic structure to prevent reduction of the cluster. Further, calculations using models in which residue Lys144 is mutated to Ala confirm the finding that Lys144 serves as a crucial link in the interactions involving the [4Fe-4S] cluster and APS.

Mycobacterium tuberculosis adenosine 5′-phosphosulfate reductase (MtAPR) is an iron-sulfur protein and a validated target to develop new antitubercular agents, particularly for the treatment of latent infection. The enzyme harbors a [4Fe-4S]2+ cluster that is coordinated by four cysteinyl ligands, two of which are adjacent in the amino acid sequence. The iron-sulfur cluster is essential for catalysis; however, the precise role of the [4Fe-4S] cluster in APR remains unknown. Progress in this area has been hampered by the failure to generate a paramagnetic state of the [4Fe-4S] cluster that can be studied by electron paramagnetic resonance spectroscopy. Herein, we overcome this limitation and report the EPR spectra of MtAPR in the [4Fe-4S]+ state. The EPR signal is rhombic and consists of two overlapping S = ½ species. Substrate binding to MtAPR led to a marked increase in the intensity and resolution of the EPR signal and to minor shifts in principle g values that were not observed among a panel of substrate analogs, including adenosine 5′-diphosphate. Using site-directed mutagenesis, in conjunction with kinetic and EPR studies, we have also identified an essential role for the active site residue Lys-144, whose side chain interacts with both the iron-sulfur cluster and the sulfate group of adenosine 5′-phosphosulfate. The implications of these findings are discussed with respect to the role of the iron-sulfur cluster in the catalytic mechanism of APR.

Mycobacterium tuberculosis adenosine-5'-phosphosulfate (APS) reductase is an iron-sulfur protein and a validated target to develop new antitubercular agents, particularly for the treatment of latent infection. To facilitate the development of potent and specific inhibitors of APS reductase, we have probed the molecular determinants that underlie binding and specificity through a series of substrate and product analogues. Our study highlights the importance of specific substitutent groups for substrate binding and provides functional evidence for ligand-specific conformational states. An active site model has been developed for M. tuberculosis APS reductase that is in accord with the results presented here as well as prior structural data reported for Pseudomonas aeruginosa APS reductase and related enzymes. This model illustrates the functional features required for the interaction of APS reductase with a ligand and provides a pharmacological roadmap for the rational design of small molecules as potential inhibitors of APS reductase present in human pathogens, including M. tuberculosis.

The identification of new antibacterial targets is urgently needed to address multidrug resistant and latent tuberculosis infection. Sulfur metabolic pathways are essential for survival and the expression of virulence in many pathogenic bacteria, including Mycobacterium tuberculosis. In addition, microbial sulfur metabolic pathways are largely absent in humans and therefore, represent unique targets for therapeutic intervention. In this review, we summarize our current understanding of the enzymes associated with the production of sulfated and reduced sulfur-containing metabolites in Mycobacteria. Small molecule inhibitors of these catalysts represent valuable chemical tools that can be used to investigate the role of sulfur metabolism throughout the Mycobacterial lifecycle and may also represent new leads for drug development. In this light, we also summarize recent progress in the development of inhibitors of sulfur metabolism enzymes.

The identification of new antibacterial targets is urgently needed to address multidrug resistant and latent tuberculosis infection. Sulfur metabolic pathways are essential for survival and the expression of virulence in many pathogenic bacteria, including Mycobacterium tuberculosis. In addition, microbial sulfur metabolic pathways are largely absent in humans and therefore, represent unique targets for therapeutic intervention. In this review, we summarize our current understanding of the enzymes associated with the production of sulfated and reduced sulfur-containing metabolites in Mycobacteria. Small molecule inhibitors of these catalysts represent valuable chemical tools that can be used to investigate the role of sulfur metabolism throughout the Mycobacterial lifecycle and may also represent new leads for drug development. In this light, we also summarize recent progress in the development of inhibitors of sulfur metabolism enzymes.

Cu(salen) complex 1 was found to be a versatile catalyst for the asymmetric alkylation of a range of enolates derived from α-amino acids, leading to α,α-disubstituted amino acids. The enantioselectivity of the process decreases as the size of the amino acid sidechain increases, but functionalized amino acids such as allylglycine and aspartic acid are substrates for the process. Benzylic bromides are found to be more enantioselective alkylating agents than propargylic bromides. As an example of the utility of this chemistry, an α-propargylic allylglycine derivative is prepared and subjected to ene–yne metathesis using Grubbs' catalyst to give a non-racemic cyclopentenyl amino acid.