Daniel, J.

AbstractBackgroundHead and neck cancers (HNC) present diagnostic challenges due to multifocal disease manifestations, posing difficulties in distinguishing between metastatic disease and second primary malignancies (SPM). This complexity underscores the need for advanced diagnostic approaches. Emerging technologies, such as next‐generation sequencing (NGS) and molecular classifier assays, show promise in providing precise insights into the diverse etiologies of HNC.MethodIn this article, we employed NGS and molecular classifier assays to delve into three distinct clinical cases. The objective was to showcase the instrumental role of these technologies in facilitating accurate diagnoses and differentiating between metastatic disease and SPM in HNC cases.ResultsThe results of this series highlight the effectiveness of NGS and molecular classifier assays in enhancing diagnostic accuracy for HNC and contributing to the precise differentiation of disease etiologies. The utilization of these advanced technologies proved instrumental in avoiding unnecessary interventions and paved the way for more targeted and effective treatment strategies.ConclusionOur findings underscore the necessity of incorporating advanced molecular testing technologies into the diagnostic and therapeutic approaches for HNC, thereby championing a more nuanced and effective approach to managing these complex cases.

Abstract Background: Analyzing interactions and the microanatomical distribution of the tumor immune microenvironment (TIME) is essential for a comprehensive understanding of tumor progression. Imaging Mass Cytometry (IMC) is a high-dimensional tissue imaging system that enables the deeper and multiparametric in situ exploration of tumor microenvironments at a single-cell level. This study describes a pipeline for analyzing 39-plex IMC images of human HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) tumor sections, elucidating immune cells' nature, functions, and interactions with tumor cells. The IMC data analysis provides valuable information for clinical studies that could be used for the identification of prognostic biomarkers and mechanisms of resistance to current immunotherapies. Methods: A retrospective study was conducted on (N=20) patients (10 progressors and 10 non-progressors) with HPV(+) OPSCC. The dataset comprises paired hematoxylin and eosin (H&E) images with IMC data from multiple Regions of Interest (ROIs) (N=82), enabling the in-situ profiling of 39 proteins in primary tumor tissue. Cell segmentation was followed by employing original images and their segmentation masks for cell phenotype identification using a probabilistic neural network method to spatially profile cancer cells, macrophages, NK-M cells, B-cells, and distinct T-cell populations. We identified protein expression at single-cell resolution and investigated the impact of multicellular spatial organization on the disease progression. The spatial neighborhood interaction score of tumor cells with various other cells was computed for all the ROIs across the two groups. Results: The markers showed varying expression levels of intensity distribution, likely due to differences in tissue sample composition. Specifically, CD3, CD4, CD8, CD19, CD20, and CD68 showed significantly higher intensity in non-progressors. Interestingly, FoxP3 also had a significantly higher intensity level in non-progressors. Cell phenotyping emphasized that non-progressors generally have increased levels of NK-M cell markers. Spatial analysis revealed a significant increase in interaction between CD3 cells and tumor cells in non-progressors (p-value=0.07, Mann-Whitney U test). Furthermore, a significant rise in the interaction between B-cells and FoxP3+ cells was observed in non-progressors (p-value<0.05, Mann-Whitney U test) suggesting a coordinated suppression of tumor growth. Conclusion: We developed a deep learning-based pipeline to analyze spatial relationships in the TIME of IMC data, demonstrating its clinical relevance in predicting disease progression. Our findings emphasize the importance of spatial relationships in the TME for response and suggest that proximity between either CD3+ T-cells to cancer cells or B-cells to FoxP3+ cells are candidate biomarkers. Citation Format: Sumanth Reddy Nakkireddy, Routman David, Kathleen Bartemes, Daniel Ma, Tae Hyun Hwang, Kathryn Van Abel, Chadi Nimeh Abdel-Halim. AI-enhanced spatial proteomics reveals B-cells & FoxP3+ cells interactions in HPV(+) oropharyngeal squamous cell carcinoma Progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5488.

Abstract Objective . An algorithm was developed for automated positioning of lattice points within volumetric modulated arc lattice radiation therapy (VMAT LRT) planning. These points are strategically placed within the gross tumor volume (GTV) to receive high doses, adhering to specific separation rules from adjacent organs at risk (OARs). The study goals included enhancing planning safety, consistency, and efficiency while emulating human performance. Approach . A Monte Carlo-based algorithm was designed to optimize the number and arrangement of lattice points within the GTV while considering placement constraints and objectives. These constraints encompassed minimum spacing between points, distance from OARs, and longitudinal separation along the z -axis. Additionally, the algorithm included an objective to permit, at the user’s discretion, solutions with more centrally placed lattice points within the GTV. To validate its effectiveness, the automated approach was compared with manually planned treatments for 24 previous patients. Prior to clinical implementation, a failure mode and effects analysis (FMEA) was conducted to identify potential shortcomings. Main results. The automated program successfully met all placement constraints with an average execution time (over 24 plans) of 0.29 ±0.07 min per lattice point. The average lattice point density (# points per 100 c.c. of GTV) was similar for automated (0.725) compared to manual placement (0.704). The dosimetric differences between the automated and manual plans were minimal, with statistically significant differences in certain metrics like minimum dose (1.9% versus 1.4%), D5% (52.8% versus 49.4%), D95% (7.1% versus 6.2%), and Body-GTV V30% (20.7 c.c. versus 19.7 c.c.). Significance. This study underscores the feasibility of employing a straightforward Monte Carlo-based algorithm to automate the creation of spherical target structures for VMAT LRT planning. The automated method yields similar dose metrics, enhances inter-planner consistency for larger targets, and requires fewer resources and less time compared to manual placement. This approach holds promise for standardizing treatment planning in prospective patient trials and facilitating its adoption across centers seeking to implement VMAT LRT techniques.

Background: Approximately 75% of all head and neck cancer patients are treated with radiotherapy (RT). RT to the oral cavity results in acute and late adverse events which can be severe and detrimental to a patient’s quality of life and function. The purpose of this study was to explore associations between RT dose to a defined oral cavity organ-at-risk (OAR) avoidance structure, provider- and patient-reported outcomes (PROs), opioid use, and hospitalization. Methods: This was a retrospective analysis of prospectively obtained outcomes using multivariable modeling. The study included 196 patients treated with RT involving the oral cavity for a head and neck tumor. A defined oral cavity OAR avoidance structure was used in all patients for RT treatment planning. Validated PROs were collected prospectively. Opioid use and hospitalization were abstracted electronically from medical records. Results: Multivariable modeling revealed the mean dose to the oral cavity OAR was significantly associated with opioid use (p = 0.0082) and hospitalization (p = 0.0356) during and within 30 days of completing RT. Conclusions: The findings of this study may be valuable in RT treatment planning for patients with tumors of the head and neck region to reduce the need for opioid use and hospitalization during treatment.

Abstract Although the majority of HPV (+) oropharyngeal squamous cell carcinoma (OPSCC) patients exhibit a favorable prognosis, approximately 20% face recurrent or metastatic disease, posing management challenges. Therefore, the identification of robust prognostic markers for risk stratification is essential. Our study focused on the comprehensive characterization of intratumor heterogeneity within the tumor immune microenvironment (TME) in both primary tumors and metastatic lymph nodes. Utilizing computational approaches, we integrated hematoxylin and eosin (H&E) and 11 adjacent immunohistochemistry (IHC)-stained slides to investigate the cellular composition and functional characteristics across different regions within the TME. Based on these detailed immune characteristics, we classified patients into specific immune subtypes. Our integrative TME analysis indicated that patients with high tumor-infiltrating lymphocytes (TIL), increased CD8 + levels, and reduced CD163 + cell counts within their primary tumors were likely to have a more favorable progression-free survival outcome. The integration of H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability and improves patient stratification in HPV(+) OPSCC.

Abstract Background: Recent studies support that high levels of tumor-infiltrating lymphocytes (TILs) were associated with better prognosis in HPV(+) oropharyngeal squamous cell carcinoma (OPSCC). While these studies provide a potential role for TILs as a prognostic biomarker, the analyses were relied on manual quantification performed by pathologists, resulting in inter-observer variability. Deep Learning (DL)-based whole slide hematoxylin and eosin (H&E) image analyses may overcome these challenges. However, this analysis only provides the presence of cell types without consideration for the functional roles of each cell within the tumor immune microenvironment (TME). In this work, we develop a computational pipeline to integrate paired H&E and immunohistochemistry (IHC) images to functionally characterize TILs and investigate their prognostic utility. Methods: We analyzed 88 patients: 42 stage I, 39 stage II, and 7 stage III. Our data contain both H&E and IHCs examining FoxP3, CD3, PD-L1, CD20, etc. on serial sections of the tissue. In-house DL-based H&E analysis used to identify TILs, tumors, and stroma in each tumor, then performed registration between adjacent H&E and IHC images from the same tissue. The patients were then classified into three basic immune phenotypes: immune inflamed (IN; high TILs in the tumor region), immune excluded (EX; TILs are mostly localized in stroma), and immune desert (ID; few/no TILs) based on TIL enrichment in the TME. To functionally characterize TILs, we quantified protein expression from the adjacent IHCs. For example, we further classified each patient into different subtypes based on enriched protein expression (e.g., FoxP3 high IN, FoxP3 low IN). We used the Kaplan-Meier method and multivariate Cox analysis to evaluate the prognostic value of different subtypes enriched with different proteins to predict disease-free survival (DFS). Results: The IN group with 43 patients was significantly associated with good prognosis. Interestingly, further stratification of the IN subgroup based on Foxp3 quantification on TIL regions (i.e., high FoxP3 IN and low FoxP3 IN) showed that high protein expression of FoxP3 in TILs in the IN subgroup is significantly associated with a better prognosis compared to other immune subgroups (HR, 0.16; p-value, 0.003). Multivariate analysis, including other clinical covariates showed that the immune subtypes associated with high FoxP3 are independently associated with DFS. These results demonstrate that DL-based integrative IHC and H&E image analysis could be used to identify subgroups with distinct clinical outcomes. Furthermore, our results reveal unknown roles for Foxp3 expression in the TILs in HPV(+) OPSCC as a prognostic biomarker, a finding which should be evaluated in a larger cohort. Citation Format: Sumanth Reddy Nakkireddy, Inyeop Jang, Minji Kim, Linda X. Yin, Michael Rivera, Joaquin J. Garcia MD, Kathleen R. Bartemes, David M. Routman, Eric J. Moore, Daniel J. Ma, Kathryn M. Van Abel, Tae Hyun Hwang. Integrative spatial analysis of paired IHC and H&E images identifies Foxp3 enriched tumor-infiltrating lymphocytes associated with disease-free survival in human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma. [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 4638.

AbstractObjectiveWe aim to explore the prognostic value of tumor‐infiltrating lymphocytes (TILs) in the primary tumor and metastatic lymph nodes of patients with HPV(+)OPSCC. We hypothesize that TILS density at both sites is associated with disease‐free survival in HPV(+)OPSCC.Study DesignMatched case‐control study among HPV(+)OPSCC patients who underwent intent‐to‐cure surgery. Cases developed locoregional or distant recurrence. Controls were matched based on age, sex, pathologic T, N, and overall stage, year of surgery, type of adjuvant treatment received, and the Adult Comorbidity Evaluation‐27 (ACE‐27) score.SettingSingle tertiary care center, May 2007 to December 2016.MethodsTumoral TILs (tTILs) density was defined as % TILs; stromal TILs (sTILs) density was defined as absent/sparse or moderate/dense crowding. Associations between TILs and time to disease progression were assessed using Cox regression models.ResultsForty‐four case‐control pairs (N = 88) were included: 42 (48%) AJCC pStage I, 39 (44%) pStage II, and 7 (8%) pStage III. tTILs density ≥10% (hazard ratio [HR] 0.41, 95% confidence interval [CI] 0.17‐0.99, p = .048) and a moderate/dense sTILs density (HR 0.21, 95% CI 0.06‐0.75, p = .016) in the primary tumor were significantly associated with decreased risk of progression. TILs density in the lymph node was associated with decreased risk of progression but did not reach statistical significance. The tTILs and sTILs density correlated strongly between the primary tumor and lymph node. Concordance between the pathologists' was moderate (60%‐70%).ConclusionsIn HPV(+)OPSCC, a higher density of tumoral and stromal TILs in the primary tumor and possibly the lymph node may predict a lower risk of disease progression.

AbstractBackgroundWe seek to inform radiotherapy (RT) delivery for adenoid cystic carcinoma of the head and neck (ACC) by evaluating RT techniques and recurrence patterns.MethodsWe identified patients with ACC treated with curative‐intent RT from 2005 to 2021. Imaging was reviewed to determine local recurrence (LR).ResultsNinety‐one patients were included. The 5‐year LR risk was 12.2% (6.6–22.7). One patient each experienced a marginal and out‐of‐field recurrence. Patients receiving >60 Gy postoperatively had a 5‐year LR risk of 0% compared to 10.7% (4.2–27.2) with ≤60 Gy. Those receiving 70 and <70 Gy definitively had a 5‐year LR risk of 15.2% (2.5–91.6) and 33.3% (6.7–100.0), respectively. No patients had regional nodal failure.ConclusionsModern, conformal RT for ACC results in low rates of LR. Doses >60 and 70 Gy may improve control in the postoperative and definitive settings, respectively. Elective nodal treatment can be omitted in well‐selected patients.

AbstractBackgroundWe aim to explore the prognostic role of absolute lymphocyte count (ALC) before, during, and after treatment on oncologic outcomes in human papillomavirus associated oropharyngeal cancer (HPV(+)OPSCC).MethodsRetrospective cohort at a tertiary center, 2006–2018. Multivariable Cox regressions were used to determine the effect of ALC on risk of progression. Univariate linear regression was performed to determine clinical factors associated with lower ALC.ResultsAll 197 patients underwent primary surgery. Mean (SD) ALC nadirs (×109 cells/L) were: baseline (N = 149): 1.69 (0.56); postoperative (N = 126): 1.58 (0.59); post‐RT (N = 141): 0.68 (0.35) and long‐term (N = 105): 0.88 (0.37). Lower baseline ALC nadir was associated with worse overall survival (HR 3.85, 95%CI: 1.03–14.29, p = 0.04). Lower postoperative ALC nadir was associated with higher risk of progression (HR 2.63, 95%CI: 1.04–6.67, p = 0.04).ConclusionsLower baseline ALC is associated with worse survival, whereas lower postoperative ALC is associated with increased risk of progression in surgically treated HPV(+)OPSCC.Level of Evidence3