Goode, Ellen L.

Abstract Background Multiplexed immunofluorescence (MxIF) enables high-dimensional immune cell phenotyping and detailed characterization of the tumor immune microenvironment (TIME), but complex cyclic staining can introduce signal degradation and uncertainty in cell-level measurements. In routine practice, pathologists rely on hematoxylin and eosin (H&E) staining as the primary reference for tissue morphology and frequently cross-reference H&E to interpret MxIF findings. Methods We present a framework for aligning H&E and MxIF images at the tissue microarray (TMA) core level to support cross-modal analysis. Cell nucleus detections from each modality are used as anchor points, formulating the task as a point-set alignment problem. Coherent Point Drift (CPD) is applied for global alignment, followed by graph-matching–based refinement to improve local consistency. Evaluation on ovarian TMAs demonstrates consistent alignment performance for both restained and serial sections. We further explore the use of restained H&E as a reference for MxIF interpretation and investigate virtual H&E generation from MxIF data as a complementary approach when restained H&E is unavailable. Results The aligned images enable quantitative assessment of cross-modal feature concordance between MxIF-derived measurements and H&E morphology. The generated virtual H&E demonstrated similar cell population compared to the real H&E. Conclusions Cell-centric alignment can facilitate integrative multimodal histopathology analysis for TMA cores in spatial co-localization, cell feature validation, and virtual H&E generation.

Abstract Background: Chronic inflammation is implicated in ovarian carcinogenesis, but how different inflammation-related exposures individually or jointly affect histotype-specific associations remains unclear. Materials and Methods: We pooled data from 16 case-control studies in the Ovarian Cancer Association Consortium to evaluate associations of eight inflammation-related factors (anti-inflammatory: aspirin use, tubal ligation (TL); pro-inflammatory: endometriosis, obesity, lifetime ovulatory cycles (LOC), smoking, pelvic inflammatory disease (PID), polycystic ovary syndrome (PCOS)) with epithelial ovarian cancer (OvC) by histologic subtype. We examined individual associations and clustering of risk factors across histotypes and computed population attributable risk (PAR) for each factor. We assessed additive and multiplicative interactions for exposure combinations. Results: Associations with OvC risk differed by histotype (e.g., high-grade serous: aspirin: OR=0.90; 95%CI 0.82, 0.99; TL: OR=0.80; 95%CI 0.73, 0.88; overall serous: endometriosis: OR=1.17; 95%CI 1.03, 1.31; high LOC: OR=1.42; 95%CI 1.28, 1.58; obesity (low-grade serous): OR=1.50; 95%CI 1.14, 1.98). Clustering analyses showed highly correlated risk profiles in endometrioid and clear cell (r=0.91). High-grade serous and mucinous profiles were moderately correlated with endometrioid and clear cell (r=0.60) tumors. The profile for low-grade serous (r=0.36) tumors was distinct from other histotypes. PAR estimates suggested modifying aspirin use, TL, and LOCs could substantially reduce burdens of endometrioid, clear cell and mucinous tumors. Out of 28 exposure combinations tested in overall OvC and 189 by histotype, we observed 12 interactions. Not using aspirin regularly showed positive additive interactions with obesity and high LOCs, particularly in endometrioid tumors (obesity relative excess risk due to interaction (RERI)=0.74, 95%CI 0.31, 1.18; Pint=0.001 for; LOCs RERI=0.80, 95%CI 0.03, 1.56; Pint=0.04). Not using aspirin regularly also showed a positive additive interaction with endometriosis in clear cell tumors (RERI=1.77, 95%CI 0.03, 3.52; Pint=0.05). Lack of TL showed positive interactions with obesity in endometrioid (RERI=0.86, 95%CI 0.17, 1.53; Pint=0.01) and mucinous (RERI=1.10, 95%CI 0.23, 1.97; Pint=0.01) tumors, while negative additive interactions were observed for smoking and endometriosis in endometrioid tumors (RERI=-1.12, 95%CI -2.19, -0.05; Pint=0.04). A multiplicative interaction was observed between obesity and endometriosis in mucinous tumors (Pint=0.01). Conclusion: The findings suggest ovarian tumorigenesis is strongly shaped by pro- and anti-inflammatory pathways that act largely independently. Further examining these pathways may clarify the origins of histotype heterogeneity and guide prevention strategies. Citation Format: Maxwell Akonde, Britton Trabert, SHELLEY TWOROGER, Allan Jensen, Kathryn L. Terry, Joshua Sampson, Hoda Anton-Culver, David Bowtell, Elisa V. Bandera, Angela Brooks-Wilson, Andrew Berchuck, Daniel William Cramer, Linda S. Cook, Julie M. Cunningham, Jennifer A. Doherty, Ellen L. Goode, Marc T. Goodman, Holly Ruth Harris, Susanne K. Kjaer, Nhu Le, Alice Wen-Ron Lee, Francesmary Modugno, Kirsten B. Moysich, Celeste Pearce, Malcolm C. Pike, Harvey A. Risch, Mary A. Rossing, Joellen M. Schildkrau, Daniel O. Stram, Rebecca Sutphen, David Van Den Berg, Penelope M. Webb, Anna Wu, Argyrios Ziogas, Nicolas A. Wentzensen. Inflammation-related exposures and histotype- specific ovarian cancer risk in the Ovarian Cancer Association Consortium (OCAC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6261.

Abstract Ovarian endometrioid carcinoma (OEC) is the second most common histotype associated with the most favorable prognosis among ovarian carcinomas. Similar to endometrial carcinoma, OEC exhibit significant molecular diversity. We aimed to develop a practical immunohistochemical (IHC) prognosticator by integrating IHC biomarkers with clinical substage, and surgical outcomes. This multi-institutional study included participants with primary invasive OEC from the Alberta Cancer Registry (AOVT), Mayo Clinic, and Disease of the Ovary and their Evaluation Study (DOVE). Pathology review excluded misclassified high-grade serous and mesonephric carcinomas leaving 422 OEC cases (AOVT N=158; DOVE N=143; Mayo N=121) with available tumor biospecimens who did not receive neoadjuvant treatment. Clinical characteristics included age, stage, grade, residual disease and 5-year survival. Clinical risk group was defined by combining stage and residual disease: Low (FIGO stage IA/IB & no macroscopic disease), Intermediate (stage IC-II& no macroscopic disease), High (stage III/IV or macroscopic disease). Tissue microarrays were stained for TP53, PMS2, MSH6, PGR, and CTNNB1 using IHC and were scored by a single pathologist. Tumors were hierarchically categorized as TP53-abnormal (TP53abn)/PGR-loss, mismatch-repair deficient (MMRd, via PMS2 and MSH6), nuclear-CTNNB1 (nCTNNB1), or no-specific-immunohistochemical-profile (NSIP). Overall survival (OS) at 5 years was compared across clinical risk and biomarker groups using Cox regression, adjusted for age and site to generate hazard ratios (HR), and 5-year survival rates (5-YSR). High clinical risk group was associated with worse 5-year OS compared to low & intermediate risk (p<0.0001). Hierarchical IHC biomarker groups were also associated with 5-year OS, adjusted for age and study site (p<0.0001), with consistently worse survival for combined TP53abn/PGR-loss (n=17, HR=6.07, 95% CI 2.78-13.26), PGR-loss only (n=55, HR=4.17, 95% CI 2.19-7.94), TP53abn only (n=29, HR=2.04, 95% CI 0.85-4.90), and better survival for nuclear-CTNNB1 (n=146, HR=0.22, 95% CI 0.07-0.65) compared to NSIP (n=132). Within the low-risk group, MMRd, nCTNNB1, or NSIP had greater than 97% 5-YSR, while OEC with TP53abn/PGR-loss or PR-loss only had less than 75% 5-YSR. Within the intermediate group, only nCTNNB1 exceeded a 5-YSR of greater than 97%. Within the high-risk group, 5YSR for nCTNNB1 OEC was 90.9%, compared to only 16.7% for TP53abn/PGR-loss. This IHC-based algorithm refines prognosis beyond clinical substage. It identifies low-risk patients with unfavorable prognosis (TP53abn/PGRloss, PGR loss only) who may benefit from adjuvant therapy, and also patients with favorable prognosis within the intermediate group for whom adjuvant therapy could be de-escalated. Further patient selection for chemo vs. hormone therapy in the high-risk group may be improved by biomarkers. Citation Format: Gamaliel Taengwa, Hunter J. Atkinson, Bryan M. McCauley, Sebastian M. Armasu, Chen Wang, Jennifer A. Doherty, Holly R. Harris, Ellen L. Goode, Martin Koebel, Stacey J. Winham. IHC-based prognostic sub-stratification of ovarian endometrioid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1184.

This cohort study evaluates the reduction in risk of high-grade serous carcinoma among women undergoing opportunistic bilateral salpingectomy in Canada.

Abstract Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as “black boxes”, lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method on a deep learning model trained for ovarian tumor histologic subtype prediction. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the model’s predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.

Abstract BRCA-associated homologous recombination deficiency (HRD) is present in ~ 50% of high-grade serous carcinomas (HGSC) and predicts sensitivity to platinum-based therapy. However, there is little understanding of why some patients with BRCA-deficient tumors experience unexpectedly poor outcomes. We profiled 154 tumors, enriched for patients with BRCA-deficient tumors that experienced short overall survival (≤ 3 years, n = 42), using whole-genome, transcriptome, and methylation analyses. All but one BRCA-deficient tumor exceeded an accepted HRD genomic scarring threshold. However, patients with BRCA1-deficient HGSC with a more elevated HRD score survived significantly longer. Patients with BRCA2-deficient HGSC and loss of NF1 survived twice as long as those without NF1 loss, whereas PIK3CA or RAD21 amplification defined BRCA2-deficient HGSC with exceptionally short survival. BRCA1-deficient tumors in short survivors had evidence of immunosuppressive c-kit signaling and EMT. In a large HGSC cohort (n = 1,389) including 282 individuals with pathogenic germline BRCA variants (gBRCApv), the location of the mutation within functional domains stratified clinical outcomes. Notably, residual disease after primary surgery had limited prognostic effect in gBRCApv-carriers compared to non-carriers. Our findings indicate that tumor HR proficiency in the context of therapy response and survival is not a binary property, and highlight genomic and immune modifiers of outcomes in BRCA-deficient HGSC.

Abstract Despite homologous recombination (HR) deficiency being an important predictor of survival in high-grade serous ovarian carcinoma (HGSC), its status is rarely considered in large-scale studies of prognostic signatures and biomarkers. We sought to develop a gene expression-based classifier of HR deficient (HRD) HGSCs, suitable for archival FFPE tumor samples, to determine whether established prognostic markers were dependent on HR status. Using 361 HGSCs from The Cancer Genome Atlas (TCGA) where HR status was defined by the genomic gold standard, a HRD score, we trained and tested a ridge penalized logistic regression model. This 218-gene mRNA expression signature was then applied in 6002 HGSCs from the Ovarian Tumor Tissue Analysis (OTTA) consortium to predict the probability of a sample being HRD. In this large cohort of FFPE samples, we sought to understand the relationship between HR and overall survival. Applying Kaplan-Meier analyses and multivariate Cox proportional hazard regression modelling, adjusting for age and stratified by stage and study, we examined the relationship between HR status and the PrOTYPE subtype, SPOT prognostic score and other known prognostic markers in HGSC. Our machine learning model was 84% accurate in a held-out subset of the TCGA (n=73). Of the 6002 OTTA HGSCs, 40.4% were predicted to be HRD. These cases were significantly younger, were enriched for early stage (FIGO I/II) and had improved outcomes when compared to HR proficient (HRP) cases (hazard ratio [HZR]=0.71, 95% confidence interval [CI]=0.66-0.76, p<2x10-16). A linear increase in survival time was observed as the probability of a sample being HRD increased. HGSCs with >75% likelihood of being HRD had a median 5-year survival of 49.3% (95%CI=46.2-52.6%) which was a 17.7% increase from those with <25% likelihood. Both PrOTYPE and SPOT were significantly associated with survival, independent of HR status (p<2x10-16 and p<2x10-16 respectively). Notably the probability of being HRD was negatively correlated with an unfavorable SPOT score (R=-0.32, p<2x10-16), suggesting that SPOT partially captures the survival advantage of HR deficiency. Several individual biomarkers, assessed in 1248 to 2858 cases, showed HR dependent survival associations. HRD HGSCs with high GMNN expression had significantly improved, long-term outcomes (4-10years, HZR=0.39, 95%CI=0.20-0.78, p=0.01), and may be a marker of exceptional survival, while AR negative HRD cases had poorer outcomes, earlier in disease progression (0-4years, HZR=1.43, 95%CI=1.03-1.98, p=0.03). Significantly poorer prognoses were observed in HRP cases expressing PTEN (HZR=1.39, 95%CI=1.04-1.87, p=0.03), and in HRD cases with high expression of MyD88 (HZR=1.21, 95%CI=1.01-1.45, p=0.04). RB1 and FOXJ1 protein expression, and CD8+ T cell abundance were prognostic irrespective of HR status. Our study contributes to understanding the association between HR and HGSC survival in a statistically powered cohort and demonstrates heterogeneity in outcomes through clinically relevant prognostic signatures and biomarkers. Citation Format: Ashley L. Weir, Derek Chiu, Aline Talhouk, Mike S. Anglesio, Dale W. Garsed, Anna DeFazio, Holly H. Harris, Joellen M. Schildkraut, Andrew Berchuck, Ellen L. Goode, Paul D.P. Pharoah, David Huntsman, David Bowtell, Jen A. Doherty, Nadia M. Davidson, Martin Köbel, Susan J. Ramus. Homologous recombination DNA repair dependent survival associations in high-grade serous ovarian carcinoma: an Ovarian Tumor Tissue Analysis consortium study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B046.

Abstract Background: Ovarian high-grade serous carcinomas (HGSC) comprise four distinct molecular subtypes based on mRNA expression patterns, with differential survival. Understanding risk factor associations is important to elucidate the etiology of HGSC. We investigated associations between different epidemiologic risk factors and HGSC molecular subtypes. Methods: We pooled data from 11 case–control studies with epidemiologic and tumor gene expression data from custom NanoString CodeSets developed through a collaboration within the Ovarian Tumor Tissue Analysis consortium. The PrOTYPE-validated NanoString-based 55-gene classifier was used to assign HGSC gene expression subtypes. We examined associations between epidemiologic factors and HGSC subtypes in 2,070 cases and 16,633 controls using multivariable-adjusted polytomous regression models. Results: Among the 2,070 HGSC cases, 556 (27%) were classified as C1.MES, 340 (16%) as C5.PRO, 538 (26%) as C2.IMM, and 636 (31%) as C4.DIF. The key factors, including oral contraceptive use, parity, breastfeeding, and family history of ovarian cancer, were similarly associated with all subtypes. Heterogeneity was observed for several factors. Former smoking [OR = 1.25; 95% confidence interval (CI) = 1.03, 1.51] and genital powder use (OR = 1.42; 95% CI = 1.08, 1.86) were uniquely associated with C2.IMM. History of endometriosis was associated with C5.PRO (OR = 1.46; 95% CI = 0.98, 2.16) and C4.DIF (OR = 1.27; 95% CI = 0.94, 1.71) only. Family history of breast cancer (OR = 1.44; 95% CI = 1.16, 1.78) and current smoking (OR = 1.40; 95% CI = 1.11, 1.76) were associated with C4.DIF only. Conclusions: This study observed heterogeneous associations of epidemiologic and modifiable factors with HGSC molecular subtypes. Impact: The different patterns of associations may provide key information about the etiology of the four subtypes.

Abstract Motivation Multiplex immunofluorescence (MxIF) enables the quantification of multiple protein markers at a single-cell level while preserving spatial information, offering a powerful tool for studying tissue microenvironments. However, the flexibility in MxIF panel design poses challenges in standardizing cell phenotyping. Results We present SpaFlow, an efficient, customizable pipeline for unsupervised clustering and classification of MxIF data, implemented using Nextflow. SpaFlow performs quality control, clustering, and postclustering analysis on segmented and quantified MxIF data, facilitating reproducible and scalable analyses across various computing platforms. The SpaFlow pipeline integrates three clustering and classification packages—Seurat, SCIMAP, and CELESTA—each providing unique methodologies for identifying cell types based on phenotypic markers. A novel “meta-clustering” approach condenses clusters across multiple regions of interest into common meta-clusters, streamlining the cell-type identification process in large datasets. SpaFlow’s robust quality control steps, including signal summation and cell density filtering, mitigate artifacts that may impact clustering accuracy. We demonstrate the utility of SpaFlow in a case study involving 297 ovarian tumor cores, where SpaFlow successfully identified biologically meaningful cell populations, including tumor-infiltrating lymphocytes, efficiently and rapidly. Additionally, SpaFlow’s reproducibility is validated using serial tonsil sections, confirming its capability to consistently identify distinctive cell populations across matched ROIs. Availability and implementation SpaFlow is freely available with detailed documentation and examples at https://github.com/dimi-lab/SpaFlow.

Abstract Multiplexed Immunofluorescence (MxIF) enables detailed immune cell phenotyping, providing critical insights into cell behavior within the tumor immune microenvironment (TIME). However, signal integrity can be compromised due to the complex cyclic staining processes inherent to MxIF. Hematoxylin and Eosin (H&E) staining, on the other hand, offers complementary information through its depiction of cell morphology and texture patterns and is often visually cross-referenced with MxIF in clinical settings. In this study, we proposed a novel framework to align H&E and MxIF images for precise cross-modal cell feature validation. Using cell detection outputs from each modality as anchors, we formulated the multimodal image registration problem as point set alignment. Coherent Point Drift (CPD) is employed for initial alignment, followed by Graph Matching (GM) for refinement. Evaluations on ovarian cancer tissue microarrays (TMAs) demonstrate that our method achieves high alignment accuracy, enabling reliable validation of cell-level features across modalities for both restained and serial sections. Our results indicate that restained H&E enhances confidence in findings derived from MxIF. Additionally, we demonstrated the feasibility of generating high-quality virtual H&E images from MxIF data when restained H&E is unavailable, offering a viable alternative for integrated multimodal analysis.