Myeloid cell networks govern re-establishment of original immune landscapes in recurrent ovarian cancer

Abstract

Immunotherapy has shown limited success in recurrent ovarian cancer (OC), with prognostic insights largely derived from treatment-naive tumors. We analyzed 697 tumor samples (566 primary and 131 recurrent) from 595 OC patients across five independent cohorts, capturing tumor-infiltrating lymphocytes (TILs) heterogeneity and identifying four immune phenotypes linked to prognosis and TIL:myeloid networks driving malignant progression. We found that in preclinical mouse models, mirroring inflamed human OCs, the recurrent Brca1^mut tumors maintained activated TILs:dendritic cells (DCs) niches but evaded immune control through upregulation of COX/PGE₂ signaling. Conversely, recurrent Brca1^wt tumors displayed loss of TILs:DCs niches and accumulated immunosuppressive tumor microenvironment (TME) networks featuring Trem2/ApoE^high tumor associated macrophages (TAMs) and Nduf4l2^high/Galectin3^high malignant states. Recurrent tumors recapitulate the immunogenic landscapes of original cancers. Our findings reveal BRCA-dependent TIL:myeloid crosstalk as key to persistent immunogenicity in recurrent OC and propose new targets to enhance chemotherapy efficacy.

Keywords: PGE(2) signaling; TREM2; immune phenotype; myeloid-T cell networks; ovarian cancer; recurrence; tumor microenvironment.

Figure 1.. Multiplexed immunofluorescence imaging reveals four different CD8⁺-based OC immune phenotypes which correlate with clinical outcome.

(A) Schematic representation of the OC primary-recurrent samples collection coming from five different clinical cohorts. (B) Schematic of FFPE tissues imaging analysis from IHC to mIF. (C) Kaplan-Meier curve of overall survival (OS) in the IMCOL and UPENN cohort (treatment-naïve samples only) according to the mIF cut-off of 21CD8⁺/mm². (D) Representative mIF images of the four immune phenotypes, scale bar 100 um. (E) Pie charts representing the percentage of the four immune categories in the treatment-naïve samples of our training cohorts (UPENN and IMCOL). (F) Kaplan-Meier curve of OS in the IMCOL and UPENN cohorts according to immune phenotypes. (G) Pie chart representing the percentage of the four immune-categories in the treatment-naive tumors from the validation cohort HiTide-UPENN. (H) Kaplan-Meier curve of OS in the HiTide-UPENN cohort according to immune phenotypes. (I) Schematic representation of our CD8⁺-based immune phenotype algorithm according to the fractions of inflamed ROIs in tumour (X axis) and stroma (Y axis) in the NHS I/II study cohort (N=418). (J) Reconstructed images of original tumour microarrays (TMAs) of the NHS I/II cohort according to the immune phenotype. (K) Cox multivariate model of OS in the NHS I/II dataset according to immune category. Statistical analysis: Log-Rank test (C,F,H), p values <0.05 considered significant. Also see Figure S1.

Figure 2.. OC immune phenotypes are characterized by distinct TILs and TME states.

(A) Schematic representation of the HiTide-UPENN treatment-naive OC cohort (N=51 FFPE tissues for mIF imaging and N=71 snap frozen material for bulkRNA). (B) Example of mIF panel with deconvoluted images for each marker: arrows indicate the TILs subset of interest as labelled in the upper part. (C) Proportions of T cell subset of interest out of total CD8⁺ T cell profiled by mIF according to immune phenotypes. Total CD8⁺ T cell density (cells/mm²) according to immune phenotypes reported in the upper line. (D) Proportions of CD8⁺PD1⁺ T cell subset out of total CD8⁺ T cell profiled by mIF according to immune phenotypes. (E) Left: schematic representation of the NHS I/II cohort stained by mIF for a T cell exhaustion panel (N=270) and a T cell resident panel (N=237). Right: heatmap showing the median cell density (log₁₀ scale) of the different T cell subsets clustered according to immune phenotype. (F-G) Fractions of CD3⁺PD1⁺ and CD3⁺CD8⁺CD69⁺CD103⁺ T cell subsets identified by mIF according to immune category. (H) Selected significant differential pathways from bulkRNA sequencing analysis in the HiTide-UPENN cohort among the four immune categories (full list in Supplementary Table 2). Data shown as mean ± SD in (C,D,F,G,H). Statistical analysis: unpaired, two-tailed Wilcoxon-rank test (C,D,F.G), corrected by Bonferroni correction (H). p values <0.05 considered significant. Also see Figure S2.

Figure 3.. TILs: myeloid crosstalk varies vastly across OC CD8⁺ immune phenotypes in treatment-naïve tumors.

(A) Schematic representation of the tissue site of origin for samples harvested at primary surgery (treatment-naïve tumors for the UPENN and IMCOL cohorts merged). (B) Example of the mIF panel with deconvoluted images for each marker: the immune population of interest is indicated by arrows and color-coded as labelled in the upper part. (C-D) Cell density (cells/mm², log₁₀ scale) profiled by mIF for CD11c⁺ and CD68⁺ according to immune phenotypes in the UPENN cohort. (E) Left: schematic representation of the NHS I/II cohort (treatment naïve samples) stained by mIF (N=246). Right: fraction of the CD68⁺CD86⁺pSTAT1⁺ subset identified by mIF according to immune category. (F) Heatmaps showing the normalized frequency of mutual cell interaction at a 20um neighboring radii in the UPENN cohort. Immune cell population interaction of interest in lines and immune categories as columns. Color-code scale bar showing the normalized frequency by row. (G) Digital tissue reconstruction showing Kernel density estimation of the frequency of triple mutual interaction CD8+:CD68+:CD11c+ according to immune phenotypes. (H-K) Frequency of mutual interaction between the indicated cell types according to immune phenotypes. Data shown as mean ± SD in (C-E and H-K). Statistical analysis: unpaired, two-tailed Wilcoxon-rank test (C-E and H-K). p values <0.05 considered significant. Also see Figure S3.

Figure 4.. Myeloid crosstalk at recurrence define the evolution of OC TME architecture together with HRD status.

(A) Schematic representation of the tissue site of origin for samples harvested at recurrence (UPENN and IMCOL recurrent tumors, cohorts merged). (B-D) The evolution of immune phenotypes for patient-matched samples (UPENN and IMCOL cohorts merged) in the BRCA/HRD and HRP subgroups separately. (E) The percentage of the immune phenotypes at recurrence in the BRCA/HRD and HRP subgroups. (F) Kaplan-Meier curves of OS according to immune phenotype evolution at recurrence. (G) Heatmaps showing the normalized frequency of mutual interaction between cell types at a 20um neighboring radii at recurrence for UPENN cohort. Immune cell population interaction of interest in lines and immune categories as columns. Color-code scale bar showing the normalized frequency by each row. (H-I) Frequency of mutual interaction between the indicated cell types according to immune phenotypes at recurrence. (J-K) Frequency of mutual interaction between the indicated cell types according to HRD status at primary and recurrence (L) Left: Schematic representation of the EORTC-1508-GCG cohort analyzed by mIF; right: frequency of mutual interaction between the indicated cell types in responders (Rs) and non-responders (NRs) according to RECIST v.1.1 criteria. Data shown as mean ± SD in (H-L). Statistical analysis: Log-rank test (F), unpaired, two-tailed Wilcoxon-rank test (C-E and H-K). p values <0.05 considered significant.

Figure 5.. Brca1^wt tumors lose TILs:APC interactions and upregulate immunosuppressive TAMs at recurrence which can be target in-vivo to delay OC recurrence

(A) Tumor growth kinetics of ID8Luc Trp53^−/− Brca1^wt and Brca1^mut during treatment in the control (vehicle or primary) or chemotherapy group (CTX, recurrence) (n=6-7 mice per group). (B) Percentages of immune phenotypes in the Brca1^wt and Brca1^mut tumors at primary and recurrence. (C) CD8⁺:CD11c⁺ niches assessed by IHC between Brca1^mut and Brca1^wt tumors at baseline and recurrence. (D) t-SNE map of the in-vivo single-cell transcriptomic data displaying the identified myeloid clusters. (E-G) Proportion of the indicated myeloid classes and subclasses between Brca1^mut and Brca1^wt tumors at baseline and at recurrence. (H) Circos plot of interactome analysis by MultiNicheNet displaying finer subclasses interaction within the top 5 cell type interactions between Brca1^mut and Brca1^wt recurrent tumors. (I-J) Tumor growth kinetics of ID8Luc Trp53^−/− Brca1^wt during treatment with chemotherapy (CTX) and CTX+anti-CSFR1 (left) or CTX+anti-TREM2 Ab (right) (n=6-7 mice per group). (K-L) Ex-vivo FACS data comparing the percentage of CD45⁺ cells and the reduction ratio of DC and macrophages in previous experiment (I-J). Statistical analysis: two-way ANOVA (A,I,J), unpaired, two-tailed Wilcoxon-rank test (E-G and K-L). p values <0.05 considered significant. Also see Figures S4–S7.

Figure 6:. Tumor-intrinsic mechanism of resistance to CTX of Brca1^mut tumors include the PGE₂-axis upregulation.

(A) t-SNE map of the in-vivo single-cell transcriptomic data displaying the identified malignant clusters. (B) Proportion of the indicated malignant subclasses between Brca1^mut and Brca1^wt tumours at baseline and at recurrence. (C) Heatmap displaying gene non-negative matrix factorization (NMF) and the nine metaprograms (MPs) identified in the malignant compartment. (D) Pathway enrichment analysis for each MP using both the Hallmarks and the Reactome pathway collections. (E) Heatmap showing signature scores (z-score) for each MPs in each malignant subpopulation. (F) Tumour growth kinetics of ID8Luc Trp53^−/− Brca1^wt during treatment with chemotherapy (CTX) and CTX+anti-IFNAR1 Ab (n=6-7 mice per group). (G) FACS data analysis from in-vivo experiment in panel F. (H) PGE₂ expressed by Brca1^wt and Brca1^mut cell lines assessed by ELISA at the indicated time-point and according to the labeled conditions. (I) Tumour growth kinetics of ID8Luc Trp53^−/− Brca1^mut during treatment with chemotherapy (CTX) and CTX+celecoxib (n=6-7 mice per group). (J) Survival curve from in-vivo experiment in panel I according to the different maintenance treatment groups. Statistical analysis: unpaired, two-tailed Wilcoxon-rank test (B,G), two-way ANOVA (A,I,), Log-rank test (J); p values <0.05 considered significant. Also see Figure S8.