Citations

ABSTRACT This case highlights the diagnostic challenges posed by rare oropharyngeal neurofibromas, their potential to mimic other conditions, and the novel application of transoral robotic surgery (TORS) for the management of an oropharyngeal neurofibroma. A 47‐year‐old female presented with a 1‐year history of a left tonsillar mass. Clinical evaluation, imaging, and biopsy results were inconclusive but raised suspicion for a neoplasm. The patient underwent robotic‐assisted tonsillectomy and mass resection. Subsequent histopathological evaluation confirmed a benign spindle cell tumor consistent with neurofibroma. Oropharyngeal neurofibromas may present diagnostic challenges due to their resemblance to common tonsillar pathologies. Imaging and biopsies may not always provide definitive diagnoses, necessitating surgical excision for accurate characterization. This case demonstrates the utility of TORS, offering improved visualization and access while minimizing patient morbidity.

3118 Background: Improved risk stratification is critically needed across multiple cancers to guide treatment decisions. This includes reducing overtreatment in Ductal Carcinoma in Situ (DCIS), addressing late recurrence risk in estrogen receptor-positive/lymph node-negative (ER+/LN-) breast cancer (BC), and refining prognosis in head & neck squamous cell carcinomas (HNSCC), and high-grade serous ovarian carcinoma (HGSOC). Tumor-infiltrating lymphocytes (TILs) reflect anti-tumor immunity, whereas tumor-associated macrophages (TAMs) polarize toward anti-tumor M1-like (CD68+) or immunosuppressive M2-like (CD163+) phenotypes. Integrating TILs with TAM polarization markers may better capture inflammatory balance and improve outcome prediction. We developed a hematoxylin and eosin (H&E)-only AI method (VAIP) to quantify TILs and virtual CD68/CD163 TAMs to develop multi-cancer based potential prognostic models. Methods: TILs were detected on H&E with HoVer-UNet, and virtual CD68 and CD163 macrophages were inferred with VISTA. In cancer regions, we computed cell densities and immune-balance ratios and trained cancer-specific Cox models with compact feature sets (DCIS 6; HGSOC 7; ER+ 4; HNSCC 4). Prognostic stratification used disease-free survival (DFS) for DCIS (N=273), HGSOC (training N=165, independent test N=220), and ER+ BC (training N=144, test N=622), and overall survival (OS) for HNSCC (N=77), reflecting clinical endpoints. Evaluation used cross-validation for cohorts without an external test set (DCIS, HNSCC) and independent training and testing when independent cohort was available (HGSOC, ER+). Results: Risk stratification performance across cancer types is in Table 1. The DCIS model used 6 features, largely TIL-macrophage contrast ratios (TIL/M2, TIL/M1). HGSOC used 7 features with a similar ratio-heavy immune-balance signature, plus macrophage-normalized density and polarization metrics (e.g., M1/N, M/N). In contrast, ER+ BC and HNSCC achieved best performance with 4 features dominated by macrophage measures, particularly M2 density and the M1/M2 ratio. Across sites, CD163-related macrophage metrics and immune-balance ratios were consistently informative. Conclusions: Integrating H&E-visible TILs with virtual CD68/CD163 TAMs enables fully automated, H&E-only prognostic biomarkers that stratify OS and DFS across multiple cancers, demonstrating consistent prognostic value in DCIS, ER+ BC, HGSOC, and HNSCC. These findings support further study of this histomorphometric risk classifier through broader external and prospective validation. Performance across cancer types. Cancer Type Cohort C-index HR [95% CI] p-value DCIS All 0.616 1.76 [1.20-2.59] <0.001 DCIS RT/No-treatment 0.624 1.94 [1.15-3.26] 0.003 HGSOC Test 0.553 1.38 [1.02-1.87] 0.030 ER+ BC Test 0.577 1.51 [1.00-2.28] 0.004 HNSCC All 0.603 1.92 [1.03-3.36] 0.012

Abstract Inflammatory bowel diseases (IBD), principally Crohn’s disease (CD) and ulcerative colitis (UC), are common chronic disorders involving inflammation and often progressive tissue damage. Genome-wide association studies have mapped many risk signals, but the causal variants, effector genes and relevant cellular contexts remain difficult to resolve, limiting mechanistic interpretation and therapeutic translation. Here we performed a multi-ancestry GWAS meta-analysis of 125,992 individuals with IBD and more than 1.2 million controls, identifying 619 independent association signals (374 novel) at 420 IBD regions that account for 77–80% of SNP-based heritability. Fine-mapping resolved 81 high-confidence variants, 41 not previously reported. Although most signals were shared between CD and UC, 39% showed IBD subtype specificity, with UC signals showing stronger enrichment in functional annotations from intestinal epithelial, secretory and enteroendocrine cells, and CD showing stronger genetic correlations with circulating inflammatory biomarkers, including C-reactive protein and glycoprotein acetylation. Latent causal modelling supported a causal effect of decreased high-density lipoprotein on CD risk. By integrating bulk and single-cell eQTL and pQTL resources using colocalisation and Mendelian randomisation, together with coding-variant evidence from exome sequencing, we prioritised 664 candidate effector genes across 341 signals, including 390 newly implicated IBD genes, revealing new biological mechanisms and candidate therapeutic targets supported by human genetics.

Abstract Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation 1,2 . Here we analysed whole-exome and whole-genome sequencing data from 86,213 IBD cases and 478,363 controls of European ancestry. We identified 68 IBD genes directly implicated by conditionally independent protein-coding associations across the allele frequency spectrum. Many newly implicated IBD genes are supported by orthogonal genomic or pleiotropic evidence, pointing to disease-related pathways and nominating targets with therapeutic relevance. We further identified allelic series and non-additive effects at key loci such as NOD2 and TYK2. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.

Current spatial T cell receptor (TCR) profiling approaches lack the resolution needed to link clonal identity, transcriptional state, and spatial positioning of individual T cells in the tumor microenvironment. Here, we introduce a spatial TCR profiling strategy that resolves individual T cell clones together with their transcriptional states at single-cell resolution and applied the method to human head and neck squamous cell carcinoma. Presumed tumor-specific T cells were broadly dispersed throughout the tumor microenvironment, and cells of the same clone occupied distinct transcriptional states in different locations: Immune-rich regions contained more plastic or progenitor cells, whereas tumor-dense regions were enriched for exhausted states. Patients exhibited notably different spatial architectures of antitumor T cell responses, revealing variation that was not captured by high-resolution, spatially agnostic methods such as spectral flow cytometry and single-cell RNA sequencing. These results provide a blueprint for dissecting antigen-specific T cell states in human tumors and reveal how T cell states are spatially coordinated with local cues across the tumor microenvironment.

Abstract Sinonasal squamous cell carcinoma (SNSCC) is an aggressive head and neck cancer of the sinonasal cavity which has not benefitted from therapeutic advances over decades 1 . Though historically attributed to inhaled carcinogens such as hardwood dust and tobacco smoking 2 , SNSCC is incidentally associated with human papillomavirus (HPV) 3,4 . Importantly, HPV is the primary oncogenic driver of >80% of anatomically adjacent oropharyngeal cancers 5 . While viral status drives clinical staging and treatment guidelines in these malignancies 6,7 , the potentially oncogenic consequences and prognostic value of host–virus interactions in SNSCC remain incompletely defined. Here, through paired host and viral whole-genome sequencing (WGS), we map the genomic footprint of HPV in SNSCC. Strikingly, lesser studied strains such as HPV45, 51, and 39 constitute driver infections in this rare but clinically credentialed cancer, where extrachromosomal DNA (ecDNA)-associated viral integration and APOBEC mutagenesis are shown to underpin somatic tumor evolution. Statement of Significance Paired host viral and whole-genome sequencing of SNSCC nominates HPV as a primary oncogenic driver of SNSCC. HPV–human ecDNA amplicons harboring noncanonical strains such as HPV45, 51 mediate viral carcinogenesis. Routine clinical diagnostic HPV panels should be expanded to capture the activity of lesser studied strains.

ABSTRACT Human papillomavirus (HPV) is comprised of >200 genotypes and has an ~8 kb, circular, double-stranded DNA genome. Transmission of HPV occurs through skin-to-skin contact and infection of squamous epithelial cells of cutaneous and mucosal surfaces. HPV genotypes are categorized as low- or high-risk (hrHPV) based on oncogenic potential. There are approximately 14 types of hrHPV that can cause several types of cancer, including HPV-associated oropharyngeal squamous cell carcinoma (HPV(+)OPSCC). Detection of HPV(+)OPSCC is traditionally accomplished using p16 immunohistochemistry (IHC) and HPV-specific testing, either DNA or RNA in situ hybridization (ISH) staining or DNA-based PCR of suspected tumor biopsy tissue. More recently, platelet-poor plasma (PPP) samples from patients with HPV(+)OPSCC have proven useful for detection and quantitation of fragments of HPV circulating tumor DNA (ctDNA). ctDNA has been shown to be useful in determining treatment response and monitoring for disease recurrence. In this study, a novel droplet digital PCR assay (ddPCR) was developed and validated for the detection and quantitation of ctDNA from 5 hrHPV genotypes in PPP. Analytical sensitivity ranged from 7.71 to 19.45 fragments of HPV ctDNA per milliliter of PPP across five hrHPV genotypes. In patients with confirmed primary or recurrent HPV(+)OPSCC or HPV(-)OPSCC, testing of corresponding PPP samples ( n = 32) by ddPCR demonstrated 90.63% (29/32) overall agreement with p16/HPV-ISH biopsy results. Compared with reference ddPCR assays performed at outside laboratories, our ddPCR assay yielded 90% (9/10) overall agreement. This assay may provide clinicians with a tool for monitoring HPV ctDNA prior to, during, and after treatment of an HPV-associated cancer. IMPORTANCE At least 14 genotypes of human papillomavirus (HPV) have been identified to have high oncogenic potential. While molecular diagnostic testing for HPV is widely available for liquid cytologic cervical samples, testing is limited for other sample types, including liquid biopsy samples, such as platelet-poor plasma (PPP). With the rising incidence of HPV-associated oropharyngeal squamous cell carcinoma (HPV(+)OPSCC), laboratory testing is an essential part of patient diagnosis, management, and surveillance. Here, we summarize the development and analytical performance validation of a multiplexed, droplet digital PCR (ddPCR) assay for the detection and quantitation of HPV circulating tumor DNA (ctDNA) in PPP. This assay may provide clinicians with a tool to address minimal residual disease for patients with an HPV-associated cancer.

The head and neck region is a marvel of anatomic complexity and it encompasses a broad array of subsites, each with its own architectural nuances. This histologic richness begets a commensurate diversity of disease. Advances in our understanding of the genetic and epigenetic alterations that underlie many of these tumors have not only refined their classification, but also reshaped our approaches to diagnosis, prognosis, and therapy. This Fascicle reflects these scientific and diagnostic evolutions. Departing from prior editions this volume is organized by anatomic subsite. Each chapter delves into the neoplastic conditions and key differential diagnoses germane to a specific location. This anatomic framework is intended to provide readers with a more practical and intuitive means of navigating the field. This Fascicle serves as a useful companion—equal parts atlas, reference, and diagnostic confidant—for pathologists encountering the intricate, fascinating, and occasionally exasperating world of head and neck pathology.

Inverted papilloma (IP) is a benign neoplasm of the nasal cavity and paranasal sinuses, known for its variable risk of recurrence and potential for developing carcinoma. Emerging evidence has shown high rates of activating EGFR mutation, and a smaller subset is associated with low-risk human papillomavirus (lrHPV). While certain morphologic features, including an inverted growth pattern, are well-established, the presence of condylomatous features, such as large fungating lesions with thick undulating surface epithelium, hyperkeratosis, cytoplasmic clearing, raisinoid nuclei, and binucleation (koilocytic changes) in low-risk HPV-associated IP suggests that these tumors may be a distinct subtype of sinonasal papilloma (SP) with features similar to low-risk HPV-associated anogenital condylomas. This study presents a series of SP with condylomatous morphology and explores the association with lrHPV, the clinicopathologic features, and the rates of carcinoma development. In total, 17 cases of SP exhibiting condylomatous morphology were retrospectively identified. We performed lrHPV and high-risk HPV (hrHPV) RNA in situ hybridization and p16 immunohistochemistry and gathered detailed clinical and pathologic data along with treatment, disease follow-up, and outcomes. These condylomatous papillomas almost all developed in active smokers, were large, were primarily located in the nasal cavity (47%), and showed frequent transformation to invasive squamous cell carcinoma (29%). This malignant transformation rate is much higher than what has been reported for inverted, exophytic, and oncocytic papillomas. The tumors were almost uniformly associated with transcriptionally-active lrHPV (94%) and were consistently negative for p16 and hrHPV. This study shows that a subgroup of IPs with condylomatous morphology have a predilection for the nasal cavity, strong association with lrHPV, and high rates of carcinoma. These findings support the concept that these tumors are a distinct (fourth) type of SP with a higher risk of malignant transformation.

6073 Background: Laryngeal Squamous Cell Carcinoma (LaSCC) has varying outcomes based on the stage of cancer patients present with. Currently, a high proportion of patients are diagnosed with advanced-stage LaSCC complicating the treatment landscape and over-treating low risk patients. Risk stratification of LaSCC can help tailor treatment plans. Numerous studies have identified spatial architecture of tumor-infiltrating lymphocytes (TILs) as a prognostic biomarker in oral cavity and oropharyngeal SCC. In this work, we evaluate the prognostic value of an artificial intelligence (AI)-leveraged approach that characterizes the spatial architecture of TILs on digitized hematoxylin and eosin (H&E)-stained slides from patients with LaSCC. Methods: H&E slides from 192 patients with LaSCC were collected from Baylor Medical Center. This dataset was randomly divided into two equal cohorts, A and B. The slides were digitized as whole slide images at 40x magnification. The nuclei of all cells were automatically segmented using a deep-learning model (Hover-Net). Each nucleus was then classified as TIL or non-TIL based on morphological features. TILs and non-TILs were clustered based on proximity, and features related to the density and spatial distribution were extracted. The top features, determined by the least absolute shrinkage and selection operator, were used to train a Cox Proportional Hazards regression model that assigned a risk score for recurrence of cancer to each patient in cohort A. For validation, the model was applied to patients in cohort B. The 25 th percentile training risk score was used as a cutoff for classifying patients as high or low risk. The performance of the model in prognosticating loco-regional recurrence (LRR) was evaluated using survival analysis. Results: Patients in Cohort B identified as “high risk” by the model based on spatial organization of TILs had a significantly shorter survival time. Univariate survival analysis showed this model was prognostic for DFS with a hazard ratio of 2.57 (95% Confidence Interval: 1.12-5.89, p-value=0.048), meaning that patients classified as “high risk” are approximately 2.5 times more likely to develop LLR. Conclusions: We used computational pathology to characterize the architecture of TILs and develop a model to predict risk of LLR in LaSCC. With additional validation, this approach can be used to assist clinicians with making clinical decisions. Multivariate analysis. Variable Reference vs Comparison Pr(>|z|) HR (95% CI) Race Black vs Caucasian 0.93 0.92 (0.40 - 2.31) N N0 vs N+ 0.67 1.80 (0.12 - 26.06) T 1-2 vs 3-4 0.83 1.28 (0.13 - 12.38) Chemo Yes vs No 0.38 3.49 (0.21 - 56.90) Tobacco Yes vs No 0.26 2.08 (0.58 - 7.47) Alcohol Yes vs No 0.43 1.53 (0.54 - 4.35) TIL Architecture Risk High vs Low 0.03 * 0.26 (0.08 - 0.89)