The Spatial Context of Tumor-Infiltrating Immune Cells Associates with Improved Ovarian Cancer Survival Free

Ovarian cancer is the most lethal gynecologic malignancy, with over 295,400 new diagnoses and over 184,000 deaths per year, globally (1). While most patients with high-grade serous ovarian cancer (HGSOC) will respond to first-line treatment, a combination of cytoreductive surgery and platinum/taxane-based chemotherapy, over 75% will recur and develop therapy-resistant disease (2). More recently, advanced stage disease (stage IV) is treated with neoadjuvant chemotherapy (NACT) prior to cytoreductive surgery, to facilitate optimal tumor resection. The 5-year overall survival rate of patients with advanced stage HGSOC is lower than 30% (3). The ability of the immune system to mount an antitumor response directly correlates to patient outcomes (4). However, current strategies to induce antitumor immunity in ovarian cancer (e.g., immune checkpoint blockade) have only had a minimal impact in HGSOC management (5). Like most tumors, the microenvironment of HGSOC tumors is heterogeneous and complex, and the biological significance of spatial relationships between cell types is not well understood.

Tumor-associated macrophages (TAM) can convey an inflammatory microenvironment that leads to tumor progression and are the most prevalent immune cells in HGSOC tumors (6). Elevated tumor infiltration of macrophages is a poor prognostic indicator in HGSOC (7, 8). TAMs secrete the cytokine IL6, which binds to the IL6 receptor to initiate The JAK/STAT pathway–mediated activation of the STAT3 transcription factor. IL6 expression can lead to both inflammatory and anti-inflammatory responses (9). Increased IL6 expression and STAT3 activation in tumor cells have both been attributed to HGSOC disease progression and therapy resistance (10, 11). Currently, there is a significant research gap in understanding the tumor-promoting properties of TAMs and defining strategies for TAM depletion in the HGSOC tumor microenvironment (TME).

In contrast to TAMs, the role of B cells in HGSOC tumor biology is less understood. As such, there are several research articles with contradictory conclusions about the prognostic value of tumor-infiltrating B cells (12). For example, Yang and colleagues have reported a median 113.3-month overall survival benefit in HGSOC tumors with low B-cell (CD19⁺) infiltration (13). In contrast, Milne and colleagues found that B-cell (CD20⁺) tumor infiltration promoted an approximately 60-month overall survival benefit (14). The discrepancies are likely due to different subsets or developmental stages of B cells within the tumor, but additional research is needed. The primary function of B cells is antibody production, but B cells also function to secrete cytokines and initiate paracrine signaling with neighboring immune cells, such as macrophages. Interactions between B cells and macrophages are proposed to be both tumor promoting and tumor suppressive. Mechanisms of tumor-promoting B cell–macrophage associations are more established. For instance, secretion of IL10 by B cells was shown to induce polarize macrophages to an M2-like, pro-tumor phenotype, and subsequently drive tumor progression (15). In contrast, the tumor-suppressive B cell–macrophage associations are not well understood. For example, depletion of macrophages blunts the antitumor response of anti-CD20 therapy in a lymphoma model (16). With respect to HGSOC, in a mouse model B-cell depletion led to a more aggressive progression, highlighting the antitumor properties of B cells (17). Thus, to improve therapeutic response and patient outcome, in HGSOC tumors, there is a significant need to dissect B cell–mediated and TAM-mediated tumor effects.

The spatial relationships between tumor cells and tumor-associated immune cells are not well understood, but multispectral IHC has provided a unique opportunity to assess the complex spatial organization within a single tumor section. In this study, we utilized a tissue microarray (TMA) of HGSOC tumors and correlated the tumor composition (TAM, B cell, T cell) to clinical attributes. Specifically, in the cohort of HGSOC tumors, while TAM infiltration did not correlate with survival, the distance of TAMs to B cells or CD4 T cells did significantly correlate to improved survival. We also observed that B-cell and CD4 T-cell tumor infiltration was a positive predictor of survival.

The TMA was constructed with Institutional Review Board approval from the University of Colorado (Aurora, CO; COMIRB numbers 17-7788). A board-certified gynecologic pathologist (M.D. Post) selected the tumor sections included on the array. Patient and clinical attributes of the tissues included on the TMA were published previously (18, 19).

Tissue samples from tumor regions of 128 patients with ovarian cancer were selected by a board-certified gynecologic pathologist (M.D. Post). Primary tumor tissue samples from 103 patients were used for subsequent analyses. Following IHC analysis, one patient's tumor was removed because of lack of an exact date for the “known alive as of” clinical variable. For Cox proportional analyses, one additional sample was removed because of lack of debulking data. All remaining samples are from patients with HGSOC (n = 101).

The TMA containing the HGSOC tumors was previously analyzed via Vectra Automated Quantitative Pathology Systems (Akoya Biosciences; ref. 18). Sequential 5-μm slides from the TMA were stained with antibodies specific for CD8 (T cells, C8/144B, Agilent Technologies), CD68 (macrophages, KP1, Agilent Technologies), cytokeratin (CK, tumor cells, AE1/AE3, Agilent Technologies), CD3 (T cells, LN10, Leica), and CD19 (B cells, BT51E, Leica). The entire TMA core was imaged using the 20× objective on the Vectra 3.0 microscope (Akoya Biosciences) and image analysis was performed using inForm software version 2.3 (Akoya Biosciences), including tissue segmentation to define cells within the tumor regions (tumor-associated cells), cell segmentation to define cellular borders, and cell phenotyping.

All statistical analyses were conducted in R Studio (version 1.3.1073; ref. 20). TMA slides were imaged, and cells classified by their phenotypes using Inform Software from Akoya Biosciences. Cells were classified using the following marker designations: B cell (CD19⁺), macrophage (CD68⁺), CD8 T cell (CD3⁺ CD8⁺), CD4 T cell (CD3⁺ CD8⁻), and tumor cell (CK⁺). Nearest neighbor distances were calculated using the “Phenoptr” package, which is a software provided by Akoya Biosciences to analyze images from their Vectra Polaris Imaging System (21) downstream of cell segmentation and phenotyping. Kaplan–Meier curves and Cox proportional hazards models were used to obtain P values and HRs. A P value of less than 0.05 was considered significant. Because of the exploratory nature of this analysis, P values were not adjusted for multiple comparisons (22–24).

An interaction variable was derived for each pair of immune cell types. Using the B cell-to-macrophage interaction for illustration, this variable was calculated as follows. Within each image, the total count of B cells that had at least one macrophage within 25 μm was counted toward the B cell–macrophage interaction variable. To avoid noise from B cell or macrophage cell presence alone, count was then divided by the total number of B cells and macrophages within the tumor region for a given image and multiplied by 100 (Fig. 1C for representation of interaction variable between B cells and macrophages). To verify the robustness of our interaction variable, we performed a sensitivity analysis with interaction cutoffs at 20 and 30 μm. Results of this sensitivity analysis reflect those at a 25-μm interaction cutoff and are provided in the Supplementary Materials and Methods. For Kaplan–Meier curves, patients were classified as having a high interaction sample (Fig. 1A) between two immune cells if they expressed above the median value of the interaction variable and a low interaction sample (Fig. 1B), if below the median.

To construct Kaplan–Meier curves for high and low infiltration of immune cells, groups were split at the median percentage of a given immune cell in tumor regions within the TME. Tumor-associated immune cell percentages were calculated as total immune cells divided by total number of cells within tumor regions for a given sample and given immune cell type. For example, a patient was classified as having a high macrophage (CD68⁺) presence if their percentage of macrophage cells in tumor regions was above the median for all samples of 2.69% (Table 1A).

To further the study of the TME and assist in reproducibility, data and code are provided in a public GitHub repository at the following link: https://github.com/benjamin643/MCR_paper.

Our models examine how spatial interactions among different immune cells in the ovarian cancer TME are associated with overall survival. We analyzed the immune TME of over 100 HGSOC tumors using the Vectra-Polaris multi-spectral IHC platform.

Immune cell composition across patients for B cells (CD19⁺), macrophages (CD68⁺), CD4 T cells (CD3⁺ CD8⁻), and CD8 T cells (CD3⁺ CD8⁺) is summarized in Table 1A. Average B-cell presence across patients was only 0.286% of cells in tumor regions. Macrophages were more prevalent with a mean of 3.82%. Of the T cells, CD4 T cells and CD8 T cells made up, on average, 0.822% and 2.68%, respectively. Most of the cells in the tumor regions were CK⁺ cells with a mean of 81.9%.

Kaplan–Meier curves were constructed to assess the relationship between different immune cell types and overall survival for the 102 patients. Patients with high B-cell infiltration in tumor regions expressed higher survival probability (P = 0.005), with median survival for the high B-cell presence group of 2,608 days [95% confidence interval (CI): 1,795–3,075] compared with 1,559 days (95% CI: 1,157–1,698) for the low group (Fig. 2A). In addition, patients with higher CD4 T-cell infiltration expressed higher survival probability (P = 0.047), with median survival for the high CD4 T-cell presence group of 2,112 days (95% CI: 1,658–2,738) compared with 1,587 days (95% CI: 1,157–1,804) for the low group (Fig. 2C). The median subjects with high infiltration of macrophages and CD8 T cells in tumor regions did not differ in survival probability from low infiltration groups (P = 0.78 and 0.14, respectively; Fig. 2E; Supplementary Fig. S3A). These analyses were also performed using both chemonaïve and post-NACT samples and the results followed the same direction as the chemonaïve only samples (Fig. 2B, D, and F). These findings indicate that B-cell and CD4 T-cell tumor infiltration positively correlate with improved survival.

We examined the distance relationships between the different immune cell types and correlated these spatial parameters with overall patient survival. Spatial relationships between cell types were represented through our derived cell-type interaction variables (see Fig. 1C for pictorial representation). Kaplan–Meier curves and unadjusted Cox proportional hazards models were used to assess overall survival and show that the “high” and “low” interaction groups did not differ in survival probability for the CD8 to CD68 cell relationship (Supplementary Fig. S4A; P = 0.63), the CD4 to CD68 relationship (Fig. 3C; P = 0.27), or the CD4 to CD8 relationship (Supplementary Fig. S4C; P = 0.84). Kaplan–Meier curves were also constructed to assess the relationship between disease-free survival and B-cell infiltrates (Supplementary Fig. S1). Subjects with high B-cell infiltration and high B cell–macrophage interaction expressed higher median disease-free survival, though results were not statistically significant (Supplementary Table S1).

Cox proportional hazards models similarly found that most cell-type interactions failed to predict overall survival. Specifically, no statistically significant relationships with overall survival were found for the CD8 to CD4 T-cell interaction (P = 0.23) or the CD8 T cell to macrophage interaction (0.25) variables. B-cell interaction with CD4 and CD8 T cells was low across samples, with over 50% of samples not containing any B cells within 25 μm of a CD4 or CD8 T cell. Cox regression models were still implemented and found to be not statistically significant with P values of 0.39 and 0.08, respectively.

Given the discrepant literature on the role of B cells within the HGSOC TME, we hypothesized that B-cell interaction with different immune cells, rather than B-cell presence alone, is an important indicator of overall survival. Subjects with high B cell-to-macrophage interactions (median survival of 2,301 days; 95% CI: 1,884–2,738) exhibited a higher survival probability compared with subjects with low B cell-to-macrophage interactions (median survival of 1,559 days; 95% CI: 1,075–1,698; Fig. 3A). The unadjusted hazard ratio for overall survival, comparing the high B cell-to-macrophage interaction group with the low high B cell-to-macrophage interaction group is 0.54 (95% CI: 0.33–0.88). This analysis was also performed using both chemonaïve and post-NACT samples and the results were statistically significant and followed the same direction as the chemonaïve only samples (Fig. 3B).

Cox proportional hazards models, adjusted for clinical covariates presented in Table 1A, were used to compare B-cell presence alone versus B cell-to-macrophage interaction as predictors of overall survival. In both the unadjusted model and the covariate adjusted model, the total B-cell percentage variable was not a significant predictor of overall survival (P = 0.31 and 0.28, respectively). The B cell-to-macrophage interaction variable was a significant predictor of overall survival in both the unadjusted model and model adjusted for clinical attributes (P = 0.004 and 0.003, respectively). The adjusted HR for the B cell-to-macrophage interaction variable is 0.84 (95% CI: 0.73–0.97), suggesting a decreased hazard of death for increased B cell-to-macrophage interaction. HRs, CIs, and P values for all covariates in the adjusted B cell-to-macrophage interaction model are presented in Table 1B.

In addition to the relationship between B Cells and macrophages, we also found a meaningful spatial relationship between CD4 T cells and macrophages. Splitting subjects into binary groups at the median interaction level, results were not statistically significant (P = 0.15). Subjects with high CD4 T cell-to-macrophage interactions (median survival of 2,072 days; 95% CI: 1,644–2,609) exhibited a higher survival probability, compared with subjects with low CD4 T cell-to-macrophage interactions (median survival of 1,587 days; 95% CI: 1,130–2,491; Fig. 3C). When the analysis was performed using both chemonaïve and post-NACT samples, the effect was in the same direction and not statistically significant (Fig. 3D). However, when analyzed using a Cox proportional hazards model adjusting for the clinical covariates in Table 1A, the interaction between CD4 T cells and macrophages was found to be statistically significant. The CD4 T cell-to-macrophage interaction variable was a significant predictor of overall survival in both the unadjusted model and model adjusted for clinical attributes (P = 0.0459 and 0.0314, respectively). The adjusted HR for the CD4 T cell-to-macrophage interaction variable is 0.9549 (95% CI: 0.92–0.99), suggesting a decreased hazard of death for increased CD4 T cell-to-macrophage interaction. HRs, CIs, and P values for all covariates in the adjusted CD4 T cell-to-macrophage interaction model are presented in Table 1C.

The cellular composition of HGSOC tumors provides critical insight into how patients may respond to primary chemotherapy. Each of the 127 tumor samples in the TMA were obtained from patients who received the standard of care: a combination of surgical debulking and chemotherapy (platinum/taxane-based treatment). Thus, the overall survival benefits and positive immune cell correlations observed in our study may be a direct reflection as to how effective the standard of care is within the patient cohort. We observed that, within the TME, the interactions between B cells (CD19⁺) and macrophages (CD68⁺), as well as interactions between CD4 T cells and macrophages, were associated with better overall survival outcomes. Higher infiltration of B cells into tumor regions was also associated with better survival outcomes. Using Cox proportional hazards models, interactions between macrophages and B cells or CD4 T cells were shown to be better predictors of survival probability than the presence of any immune cell type alone. Taken together, elevated macrophage-B cell and macrophage–CD4 T-cell interactions within tumors suggest a more favorable response to chemotherapy.

Previous studies have defined how chemotherapy remodels the HGSOC TME. Notably, chemotherapy often induces a robust IL6-mediated inflammatory response that may directly remodel the TME (refs. 18, 25). In HGSOC, IL6 induction is mainly described as tumor promoting; thus, dampening inflammatory or IL6-driven responses may ultimately be beneficial. While the mechanisms driving the improved survival benefit from B-cell/macrophage interactions are not well understood, we speculate that B cell–dependent paracrine signaling via immunomodulatory cytokines, such as IL10, serves to attenuate inflammation. Moreover, macrophages can present antigens to B cells, potentially enriching for tumor-specific antigen antibody production, leading to a more robust antitumor immune response following chemotherapy. Dissecting these B-cell/macrophage antitumor mechanisms will be the subject of future investigations.

Small populations of immune cells, as well as intratumoral heterogeneity, can obfuscate investigations into the TME by making it difficult to ascertain whether results will replicate to other similar datasets. Larger studies with explicit a priori hypothesis about immune cell reactions in the TME are needed to confirm these findings. In addition, while we did not observe CD8⁺ T cells were significantly associated with improved survival, there was a trend toward improved survival that may be observable in future, larger studies. To better understand the relationships between different immune cells within the TME, further research will also need to be conducted with larger TME samples and with stronger phenotyping of immune cells (e.g., double-positive or double-negative T cells were not a focus of this study). Regardless, based on previous studies (refs. 17, 18, 25), there is reason to believe that immune cell interaction within the TME leads to better overall survival outcomes, as demonstrated by the “interaction” of B cells (CD19⁺) and macrophages (CD68⁺) in this dataset. Understanding which immune cell interactions are most important in the TME may lead to more accurate diagnoses and the development of novel therapy techniques.

B.G. Bitler reports grants from Department of Defense Award (BGB OC170228), American Cancer Society Research Scholar Award (134106-RSG-19-129-01-DDC), and NIH/NCATS Colorado CTSA (UL1 TR002535 to J. Wrobel) during the conduct of the study. No disclosures were reported by the other authors.

B. Steinhart: Software, formal analysis, investigation, visualization, methodology, writing–original draft, writing–review and editing. K.R. Jordan: Data curation, funding acquisition, writing–review and editing. J. Bapat: Conceptualization, writing–original draft, writing–review and editing. M.D. Post: Funding acquisition, writing–review and editing. L.W. Brubaker: Writing–review and editing. B.G. Bitler: Conceptualization, resources, data curation, supervision, funding acquisition, visualization, writing–original draft, project administration, writing–review and editing. J. Wrobel: Conceptualization, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, project administration, writing–review and editing.

This work was supported by grants from the Department of Defense Award (BGB OC170228, OC200302, and OC200225), the American Cancer Society Research Scholar Award (134106-RSG-19-29-01-DDC), NIH/NCI (R37CA261987), The University of Colorado Cancer Center Support Grant (P30CA046934), and by NIH/NCATS Colorado CTSA (UL1 TR002535). Contents are the authors' sole responsibility and do not necessarily represent official NIH views.