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Abstract Introduction: To counteract the rapidly increasing incidence of human papillomavirus (HPV)-related oropharyngeal cancer (OPC), development of effective biomarkers and other novel screening strategies are necessary to effectively detect early disease in well-defined high-risk populations. The most promising biomarkers are circulating tumor HPV DNA (ctHPV), antibodies (Abs) to HPV16 early (E) antigens, and persistent infection with oral oncogenic HPV. Here, we report the prevalence of ctHPV in a population of middle-aged men, a group with high OPC incidence, and evaluate concordance between the three HPV biomarkers. Materials and Methods: We included participants enrolled in the HPV-related Oropharyngeal and Uncommon Cancers Screening Trial of Men (HOUSTON) study between April 2017 and December 2019. The HOUSTON study was designed to evaluate biomarkers and novel screening strategies for HPV-related cancers among middle-aged men (50-64 years), the group with the highest incidence of HPV-related OPC. We tested plasma for ctHPV using a digital droplet polymerase chain reaction (ddPCR) assay (NavDx, Naveris, Waltham, MA). We previously tested the plasma from these participants for HPV16 E Abs using a novel RAPID ELISA and for prevalent oral HPV16 (oHPV16) infection in oral rinse using the cobas HPV Test (Roche Diagnostics, Indianapolis, IN). We used Fisher’s exact test to determine statistical significance for the association between biomarkers (alpha < 0.05). Results: Of 345 samples tested, 343 were adequate for ctHPV analysis. Of these, 314 were negative (92.4%) and two were positive (0.6%; both for HPV16). Twenty-four had an indeterminate (Ind) result (7.0%), meaning ctHPV levels fell outside the established parameters for a negative or positive results. All three markers were available for 340 samples with the following results: ctHPV+/Ab+/oHPV16+: 1 (0.3%); ctHPV+/Ab-/oHPV16-: 1 (0.3%); ctHPV Ind/Ab-/oHPV16+: 3 (0.9%); ctHPV Ind/Ab-/oHPV16-: 21 (6.2%); ctHPV-/Ab+/oHPV16+: 1 (0.3%); ctHPV-/Ab+/oHPV16-: 3 (0.9%); ctHPV-/Ab-/oHPV16+: 16 (4.7%); ctHPV-/Ab-/oHPV16-: 294 (86.5%); all other combinations had no observations. ctHPV was associated with HPV16 E Abs and oHPV16 status individually and combined (individually, p = 0.032 for both and combined, p = 0.025). A ctHPV-/Ab+/oHPV16-man was diagnosed with an anal low-grade squamous intraepithelial lesion and was persistently high-risk HPV-positive at the right tonsil/base of tongue. One man was positive for all three markers and was subsequently diagnosed with stage II (T1N1) HPV16-positive/Epstein-Barr-negative nasopharyngeal cancer four months following study enrollment. Conclusions: ctHPV was rare in a general population of middle-aged men. Our results suggest that these markers in combination may be able to correctly identify early HPV-related cancers. Larger studies are needed to confirm this finding. The authors accept sole responsibility for the statements in this abstract. Citation Format: Kristina R. Dahlstrom, Karen S. Anderson, Erich M. Sturgis. Biomarkers for early diagnosis of human papillomavirus-related oropharyngeal cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-037. 

CRISPR offers new hope for many patients and promises to transform the way we think of future therapies. Ensuring safety of CRISPR therapeutics is a top priority for clinical translation and specific recommendations have been recently released by the FDA. Rapid progress in the preclinical and clinical development of CRISPR therapeutics leverages years of experience with gene therapy successes and failures. Adverse events due to immunogenicity have been a major setback that has impacted the field of gene therapy. As several in vivo CRISPR clinical trials make progress, the challenge of immunogenicity remains a significant roadblock to the clinical availability and utility of CRISPR therapeutics. In this review, we examine what is currently known about the immunogenicity of CRISPR therapeutics and discuss several considerations to mitigate immunogenicity for the design of safe and clinically translatable CRISPR therapeutics. 

Abstract A major technical hurdle for T cell immune profiling is the time and cost to accurately genotype the Human Leukocyte Antigen (HLA) loci from peripheral blood. Here, we developed a rapid, highly multiplexed approach for HLA typing using RNA from <100,000 peripheral blood mononuclear cells with the Oxford Nanopore Technology (ONT) Minion sequencer. This method uses selective reverse transcription of mRNA of six HLA loci (A,B,C, DRB1, DQB1, DPB1), followed by PCR amplification. The individual amplified HLA cDNA was multiplexed in a single sequencing pool using primers with unique molecular identifiers, designed to permit sequencing errors for enhanced data capture. Pooled HLA amplicons were sequenced using the ONT Minion MK1B and R10.4 flowcells, with sequence Q scores> 20. Total RNA was extracted from PBMC samples from 12 individuals, reverse transcribed and amplified using the designed HLA loci specific primers. The pooled, amplified cDNA was then sequenced for 16 hours on the ONT Minion sequencer. The resulting sequencing data was analyzed and an average depth of coverage of 6000x was observed per sample. An average per loci depth of coverage of 1000x was observed. This method is designed to permit rapid (<24h), low-cost, portable HLA sequencing for T cell immune monitoring and epitope identification for immunologic studies. Arizona Piper Foundation 

Abstract The ability to accurately identify peptide ligands for a given major histocompatibility complex class I (MHC-I) molecule has immense value for targeted anticancer therapeutics. However, the highly polymorphic nature of the MHC-I protein makes universal prediction of peptide ligands challenging due to lack of experimental data describing most MHC-I variants. To address this challenge, we have developed a deep convolutional neural network, HLA-Inception, capable of predicting MHC-I peptide binding motifs using electrostatic properties of the MHC-I binding pocket. By approaching this immunological issue using molecular biophysics, we measure the impact of sidechain arrangement and topology on peptide binding, feature not captured by sequence-based MHC-I prediction methods. Through a combination of molecular modeling and simulation, 5821 MHC-I alleles were modeled, providing extensive coverage across human populations. Predicted peptide binding motifs fell into distinct clusters, each defined with different degrees of submotif heterogeneity. Peptide binding scores generated by HLA-Inception are strongly correlated with quantitative MHC-I binding data, indicating predicted peptides can be ranked, both within and between alleles. HLA-inception also showed high precision when predicting naturally presented peptides and can be used for rapid proteome-scale MHC-I peptide binding predictions. Finally, we show that the binding pocket diversity measured by HLA inception predicts response to checkpoint blockade. Citation Format: Eric A. Wilson, John Kevin Cava, Diego Chowell, Abhishek Singharoy, Karen S. Anderson. Protein structure-based modeling to improve MHC class I epitope predictions. [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 5376. 

Abstract Amplification of chromosome 9p24.1 targeting PD-L1 , PD-L2 , and JAK2 (PDJ amplicon) is present in subsets of triple negative breast cancers (TNBCs) and is associated with poor clinical outcomes. However, the prevalence of PDJ+ TNBCs varies extensively across studies applying different methods for interrogating samples of interest. To rigorously assess the prevalence of PDJ amplicons in TNBC, its prognostic value and whether it is enriched by chemotherapy, we interrogated 360 TNBC samples including 74 surgical resections from patients treated in the neoadjuvant setting, and tissue microarrays (TMAs) with 31 cases from African American women and 255 resected non-metastatic cases, with a 3 color fluorescence in situ hybridization (FISH) assay targeting the 9p24.1 PDJ amplicon, 9q24.3, and 9q34.1. Samples with mean PDJ signal of > 4.5 copies, and ratios of PDJ/9q24 ≥ 2 and/or PDJ/9q34.1 ≥ 2 were called amplified (PDJ+). Correlative analyses included the association of tumor infiltrating lymphocytes (TILs) with PDJ amplicons in TNBCs. In addition, we investigated intratumor copy number of PDJ amplicons in PDJ+ and PDJ− TNBCs. Matched pre- and post-neoadjuvant treatment biopsies were available from patients (n = 6) to evaluate the effects of therapy on PDJ status. Our study provides a rigorous analysis of the prevalence, distribution, and clinical correlatives of the PDJ amplicon in TNBC. 

Drone-based multispectral sensing is a valuable tool for dryland spatial ecology, yet there has been limited investigation of the reproducibility of measurements from drone-mounted multispectral camera array systems or the intercomparison between drone-derived measurements, field spectroscopy, and satellite data. Using radiometrically calibrated data from two multispectral drone sensors (MicaSense RedEdge (MRE) and Parrot Sequoia (PS)) co-located with a transect of hyperspectral measurements (tramway) in the Chihuahuan desert (New Mexico, USA), we found a high degree of correspondence within individual drone data sets, but that reflectance measurements and vegetation indices varied between field, drone, and satellite sensors. In comparison to field spectra, MRE had a negative bias, while PS had a positive bias. In comparison to Sentinel-2, PS showed the best agreement, while MRE had a negative bias for all bands. A variogram analysis of NDVI showed that ecological pattern information was lost at grains coarser than 1.8 m, indicating that drone-based multispectral sensors provide information at an appropriate spatial grain to capture the heterogeneity and spectral variability of this dryland ecosystem in a dry season state. Investigators using similar workflows should understand the need to account for biases between sensors. Modelling spatial and spectral upscaling between drone and satellite data remains an important research priority.