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Background: Lymph node (LN) involvement is a strong indicator of poor prognosis for breast cancer (BC), with adjuvant chemotherapy remaining fundamental to management of these patients. SWOG S8814 was a Phase III randomized trial of postmenopausal patients with pathologic LN-positive BC who were hormone receptor positive (HR+). The objectives of the clinical trial were to compare disease free survival (DFS) and overall survival (OS) of 1) these postoperative patients treated with a combination of cyclophosphamide, doxorubicin, fluorouracil (CAF) plus tamoxifen versus tamoxifen alone; and 2) patients treated with CAF followed by tamoxifen versus CAF plus concurrent tamoxifen. In this study we sought to evaluate the potential of applying computational image analysis on whole slide images (WSI) for predicting DFS and OS in SWOG S8814. Methods: A cohort of 135 patients (N=53 DFS event) diagnosed with HR+ & LN+ BC from clinical trial ECOG 2197 was utilized as training set D1. Validation set D2 comprised 630 patients (N=260 DFS event, N=195 death) with HR+& LN+ BC from SWOG S8814. Three deep learning models were employed to respectively detect nuclei, mitosis, and tubules in WSIs. Subsequently, a total of 1,810 features relating to nuclear morphology (e.g., spatial distribution, shape, texture, orientation), mitotic activity (e.g., mitosis hotspot, mitotic rates) and tubule formation (e.g., tubular nuclei distribution, ratio of tubule to non-tubule area) were extracted from each WSI. A lasso regularized Cox regression model (IbRiS) was trained on D1 to respectively identify four features from each of the feature categories (nuclei morphology, mitotic activity, and tubule formation) most strongly associated with DFS, a continuous risk score based on the selected features was then constructed. An optimal risk threshold was identified on D1 to dichotomize the risk scores into high vs. low risk of recurrence categories. Blinded validation of the machine learning model on SWOG S8814 using Cox regression was performed by SWOG to evaluate its performance in terms of DFS and OS. Results: In D2, patients identified as high risk of recurrence by IbRiS had a significantly worse prognosis in terms of DFS with hazard ratio=1.30 (p=0.039, 95% CI=1.01-1.66). IbRiS was also found to be significantly prognostic of OS with hazard ratio=1.38 (p=0.026, 95% CI=1.04-1.83). IbRiS was however, neither prognostic of DFS (HR = 1.20; 95% CI 0.93-1.54) nor OS (HR = 1.28; 95% CI 0.96-1.71) in multivariable analysis adjusting for treatment, tumor size, and number of positive nodes. IbRiS was also not a significant predictor of chemotherapy benefit (DFS p=0.45; OS p=0.25). Conclusion: We developed a prognostic model (IbRiS) based on the combined features of nuclear morphology, mitosis count, and tubule formation that can help further risk stratify HR+ & LN+ BC patients by only using H&E slides. Citation Format: Yuli Chen, William E. Barlow, Haojia Li, Cheng Lu, Andrew Janowczyk, German Corredor, Shridar Ganesan, Michael Feldman, Pingfu Fu, Hannah Gilmore, Kathy S. Albain, Lajos Pusztai, James Rae, Daniel Hayes, Andrew K. Godwin, Alastair M. Thompson, Anant Madabhushi. Computerized Measurements of Nuclear Morphology Features, Mitosis Rate, and Tubule Formation from H&E Images Predicts Disease-Free Survival in Patients with HR+ & LN+ Invasive Breast Cancer from SWOG S8814 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-11-16.

Oropharyngeal squamous cell carcinoma (OPSCC), largely fueled by the human papillomavirus (HPV), has a complex biological and immunologic phenotype. Although HPV/p16 status can be used to stratify OPSCC patients as a function of survival, it remains unclear what drives an improved treatment response in HPV-associated OPSCC and whether targetable biomarkers exist that can inform a precision oncology approach. We analyzed OPSCC patients treated between 2000 and 2016 and correlated locoregional control (LRC), disease-free survival (DFS) and overall survival (OS) with conventional clinical parameters, risk parameters generated using deep-learning algorithms trained to quantify tumor-infiltrating lymphocytes (TILs) (OP-TIL) and multinucleated tumor cells (MuNI) and targeted transcriptomics. P16 was a dominant determinant of LRC, DFS and OS, but tobacco exposure, OP-TIL and MuNI risk features correlated with clinical outcomes independent of p16 status and the combination of p16, OP-TIL and MuNI generated a better stratification of OPSCC risk compared to individual parameters. Differential gene expression (DEG) analysis demonstrated overlap between MuNI and OP-TIL and identified genes involved in DNA repair, oxidative stress response and tumor immunity as the most prominent correlates with survival. Alteration of inflammatory/immune pathways correlated strongly with all risk features and oncologic outcomes. This suggests that development of OPSCC consists of an intersection between multiple required and permissive oncogenic and immunologic events which may be mechanistically linked. The strong relationship between tumor immunity and oncologic outcomes in OPSCC regardless of HPV status may provide opportunities for further biomarker development and precision oncology approaches incorporating immune checkpoint inhibitors for maximal anti-tumor efficacy.

This paper proposes a novel political pedagogic approach to conducting engaged research. Drawing critically on elements of Participatory Action Research and popular education, this approach - Engaged Pedagogic Research (EPR) - generates processes of collective co-learning and empowerment for local communities and activist researchers. Central to conducting EPR are five processes: generating situated learning practices, recognizing alternative knowledges, engaging in inter-cultural and inter-ethnic dialogue, deconstructing power relations, and promoting empowerment and social action. The experiences of engaging in EPR through developing and running a Diploma in Territorial Planning to support peacebuilding in Colombia are discussed. Different social groups living in conflict areas gained new knowledge and built social relationships that strengthen collaborative action, while activist academics achieved a deeper understanding of how members of these cultural groups manage their territories, relate to each other, and develop visions for collaborative futures in the context of peacebuilding. By developing participatory, decolonized and transformative bridges to connect and engage universities with communities, EPR facilitates learning that is collectively created and has the potential to contribute to more just and egalitarian societies.

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No predictive biomarkers can robustly identify patients with non–small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes (“delta”) in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3. DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22–2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.

BackgroundTumor infiltrating lymphocytes (TILs) reflect adaptive antitumor immune responses in cancer and are generally associated with favorable prognosis. However, the relationships between TILs subsets and their spatial arrangement with clinical benefit from immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) remains less explored.MethodsWe used multiplexed quantitative immunofluorescence panels to determine the association of major TILs subpopulations, CD8+ cytotoxic T cells, CD4+ helper T cells and CD20+ B cells, and T cell exhaustion markers, programmed cell death protein-1 (PD-1),lymphocyte-activation gene 3 (LAG-3) and T cell immunoglobulin mucin-3 (TIM-3) with outcomes in a multi-institutional cohort of baseline tumor samples from 179 patients with NSCLC treated with ICI. The analysis of full-face tumor biopsies including numerous fields of view allowed a detailed spatial analysis and assessment of tumor immune heterogeneity using a multiparametric quadratic entropy metric (Rao’s Q Index (RQI)).ResultsTILs were preferentially located in the stromal tissue areas surrounding tumor-cell nests and CD8+ T cells were the most abundant subset. Higher density of stromal CD8+ cytotoxic T cells was significantly associated with longer survival, and this effect was more prominent in programmed death ligand-1 (PD-L1) positive cases. The role of baseline T cell infiltration to stratify PD-L1 expressing cases was confirmed measuring the T cell receptor-burden in an independent NSCLC cohort studied with whole-exome DNA sequencing. High levels of LAG-3 on T cells or elevated RQI heterogeneity index were associated with worse survival in the cohort.ConclusionBaseline T cell density and T cell exhaustion marker expression can stratify outcomes in PD-L1 positive patients with NSCLC treated with ICI. Spatial immune heterogeneity can be measured using the RQI and is associated with survival in NSCLC.

PURPOSE Allogenic hematopoietic stem-cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Relapse post-HCT is the most common cause of treatment failure and is associated with a poor prognosis. Pathologist-based visual assessment of aspirate images and the manual myeloblast counting have shown to be predictive of relapse post-HCT. However, this approach is time-intensive and subjective. The premise of this study was to explore whether computer-extracted morphology and texture features from myeloblasts' chromatin patterns could help predict relapse and prognosticate relapse-free survival (RFS) after HCT. MATERIALS AND METHODS In this study, Wright-Giemsa–stained post-HCT aspirate images were collected from 92 patients with AML/MDS who were randomly assigned into a training set ( S t = 52) and a validation set ( S v = 40). First, a deep learning–based model was developed to segment myeloblasts. A total of 214 texture and shape descriptors were then extracted from the segmented myeloblasts on aspirate slide images. A risk score on the basis of texture features of myeloblast chromatin patterns was generated by using the least absolute shrinkage and selection operator with a Cox regression model. RESULTS The risk score was associated with RFS in S t (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and S v (hazard ratio = 1.57; 95% CI, 1.01 to 2.45; P = .044). We also demonstrate that this resulting signature was predictive of AML relapse with an area under the receiver operating characteristic curve of 0.71 within S v. All the relevant code is available at GitHub. CONCLUSION The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS.

Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are both diagnostic and prognostic. Yet, previously developed approaches for automated epithelium segmentation in OC-SCC have not been independently tested in a multi-center setting. In this study, we aimed to investigate the effectiveness and applicability of a convolutional neural network (CNN) model to perform epithelial segmentation using digitized HE-stained diagnostic slides from OC-SCC patients in a multi-center setting.A CNN model was developed to segment the epithelial regions of digitized slides (n = 810), retrospectively collected from five different centers. Deep learning models were trained and validated using well-annotated tissue microarray (TMA) images (n = 212) at various magnifications. The best performing model was locked down and used for independent testing with a total of 478 whole-slide images (WSIs). Manually annotated epithelial regions were used as the reference standard for evaluation. We also compared the model generated results with IHC-stained epithelium (n = 120) as the reference.The locked-down CNN model trained on the TMA image training cohorts with 10x magnification achieved the best segmentation performance. The locked-down model performed consistently and yielded Pixel Accuracy, Recall Rate, Precision Rate, and Dice Coefficient that ranged from 95.8% to 96.6%, 79.1% to 93.8%, 85.7% to 89.3%, and 82.3% to 89.0%, respectively for the three independent testing WSI cohorts.The automated model achieved a consistently accurate performance for automated epithelial region segmentation compared to manual annotations. This model could be integrated into a computer-aided diagnosis or prognosis system.

PURPOSE Epidemiological studies suggest that biological sex influences overall survival (OS) in Glioblastoma (GBM), possibly due to immunological variances between the sexes. Further, while prognostic in other cancers, the association of tumor-infiltrating lymphocytes (TILs) to OS in GBM requires additional exploration. We hypothesize that histological features corresponding to spatial organization of TILs from within the GBM microenvironment, when evaluated in a sex-specific manner, will be associated with OS. METHODS Digitized Hematoxylin-and-Eosin (H&E) slides of resected GBM tumors were collected to establish a multi-site cohort of Nf435 patients. The training set included Nf201 patients from eleven contributing sites of the Cancer Genome Atlas (TCGA). The validation set included Nf234 patients consisting of cases from two hold-out TCGA sites and the Clinical Proteomic Tumor Analysis Consortium. Each digitized H&E slide was assessed for quality, followed by employing a pre-trained classifier to label TIL and non-TIL cells. Spatial features related to the connectivity of nuclei centroids and corresponding cell cluster graphs were extracted for TILs, non-TILs, and between-group interactions. These TIL-centric histological features were fed into a Cox Hazard regression model with least absolute shrinkage and selection operator feature selection, to stratify patients as low or high-risk. Survival models were evaluated using Kaplan-Meier estimates, across male, female, and combined cohorts. RESULTS Selected features (i.e., measures of area and proximity across TIL clusters) were significantly associated with OS in the validation cohort (hazard ratio (HR)=1.95, confidence interval (CI)=1.19–3.2, p=0.00278), for the female cohort. For the male cohort, the selected spatial TIL features’ association with OS for the validation cohort yielded HR=1.43, CI=0.986–2.07, p=0.0667. The spatial TIL survival validation model on the combined cohort did not yield significant survival risk stratification. CONCLUSION Our findings suggest that sex-specific TIL organization histological features may be prognostic of OS in GBM tumors, specifically for the female cohort.

The rapid clinical expansion of immune checkpoint blockers (ICB) has transformed the therapeutic arsenal for non-small cell lung cancer (NSCLC). However, only a relatively small fraction of patients benefits from these treatments and those who respond can develop acquired resistance. This underlines the need to identify factors or biomarkers associated with treatment sensitivity and resistance to guide clinical decisions. It has been proposed that elevated levels of tumor infiltrating lymphocytes (TIL) in the tumor microenvironment could be associated with favorable clinical outcomes after treatment with ICB. However, few studies have explored this relationship and they used semi-quantitative scoring methods and/or analyzed small tumor areas. Here, we used multiplexed quantitative immunofluorescence (QIF) panels to study major TIL subsets (DAPI, CK, CD4, CD8 and CD20) and T-cell exhaustion markers (DAPI, CD3, LAG-3, PD-1, TIM-3) in full-face baseline whole-tumor slides from a large multi-institutional patient cohort (n=179). The analysis of numerous fields of view per case allowed us to examine the spatial distribution of immune cells and spatial immune heterogeneity. Our results demonstrate that CD8+ effector T-cells are the largest TIL subpopulation and that they are preferentially located in the stromal compartment. We also show that tertiary lymphoid structures have limited association with survival and their detection depends on the size of the tissue area analyzed. Additionally, higher levels of baseline CD8+ T-cells in the stroma were significantly associated with better survival in PD-L1 positive patients but not in PD-L1 negative. To validate these results, we used whole-exome sequencing to analyze the TCR-burden in an independent cohort of ICI-treated NSCLC patients. Increased expression of the exhaustion markers LAG-3 and TIM-3 in CD3 positive T-cells was associated with reduced survival. Finally, we used a novel multiparametric heterogeneity metric termed Rao’s quadratic entropy that enabled us to measure the spatial immune heterogeneity and was associated with worse survival. In summary, we quantitatively measured the density, functional properties, and spatial distribution of major TIL subsets in a large cohort of NSCLC patients treated with ICB. Our results highlight the prominent role of TILs in NSCLC and their potential role as a biomarker. These results could be easily translated into the clinic and used to guide optimal treatment options. Citation Format: Miguel Lopez de Rodas Gregorio, Venkata Nagineni, Arvind Ravi, Ila J. Datar, Mari Mino-Kenudson, German Corredor, Cristian Barrera, Lindsey Behlman, David L. Rimm, Roy S. Herbst, Anant Madabhushi, Jonathan W. Riess, Vamsidhar Velcheti, Matthew Hellmann, Justin F. Gainor, Kurt A. Schalper. Role of tumor infiltrating lymphocytes and spatial immune heterogeneity in sensitivity to PD-1 axis blockers in non-small cell lung cancer (NSCLC) [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 1722.