Differential transcriptomic profiles across multiple sites in HGSOC
We performed WGS and RNA-seq on 48 sites from nine treatment-naïve pathological HGSOC patients (Supplementary Table 1, Fig. 1a). Each site in the same patient had a similar proportion of tumor (Supplementary Table 1, Methods). Copy-number variation (CNV) and somatic mutations of these tumors were consistent with known HGSOC genomic patterns 10 (Fig S1a-b) with the exception of OV001, that despite high grade characteristics on histopathology, did not have TP53 mutations and instead with the only tumor with a NF1 aberration. High level gene amplifications present in ovarian cancer, such as CCNE1, MYC were present in 2/9 (Fig S1b). Eight of the nine HGSOC tumors had somatic TP53 mutations, while 2/9 patients harbored germline BRCA1/2 mutations (Fig S1a-b). Importantly, the majority of the CNV and somatic mutation events did not demonstrate spatial genomic heterogeneity among tumor sites (Fig S1b). The detection of TP53 mutations in metastases but not in ovarian sites of OV004 is one key exception. Manual inspection of sequence tracks failed to identify TP53 mutations in ovarian sites. To characterize the relationship between multiple sites in HGSOC, we first performed principal component analysis (PCA) on transcriptomic profiles of primary ovarian (Ov while HGSOC originate in the fallopian tube, the ovary represents the most frequent site of initial seeding consistent with the definition of primary), omental (Om) and other metastatic lesions (Ot). PCA demonstrated two drivers of heterogeneity, patient specific processes with tumors across different sites within a given individual tending to cluster together and tumors within different sites tending to cluster together for different patients (Fig. 1b). For example, while ovarian tumors from OV004, OV005, OV006 and OV008 were clearly separated from omental and other sites, ovarian tumors from OV001, OV002, OV003 and OV009 tended to cluster closer to their metastatic sites than to other ovarian tumors. Decomposition of immune cell proportions using ssGSEA analysis of RNA-seq data recapitulated the heterogeneity observed in the PCA analysis with information content specific to patients and also to tumor site (Fig. 1c). Figure 1c also demonstrates the robustness and the consistency of the analysis with tumors from the left and right ovary from the same patient clustering together and multiple different omental lesions from the same patient clustering together. Similar to the PCA analysis, immune cell-based clustering suggested that while most ovarian tumors were in a single cluster, OV001, OV002, OV003 and OV009 ovarian tumors tended to cluster with their metastatic sites. Interestingly, the estimated proportions of various immune components were low in ovarian tumors (Fig. 1c). In contrast the immune components were markedly higher in most omental sites compared to matched ovarian tumors; with other sites have lower immune content and indeed a subset of the other metastatic sites clustered with the ovarian tumors (Fig. 1c). A panel of 159 genes selected based on six different characteristics of T cell quantity or spatial distribution11 were used to further characterize the samples demonstrating highest T cell infiltration in omental lesions, with most of the ovarian tumors having low levels, indicating a “desert” T cell phenotype (Fig. 1d). In parallel, CD4+ and CD8+ T cell infiltration into the different lesions were evaluated based on immunohistochemistry (IHC) staining (Fig S1c) and flow cytometry analysis (Fig S1d). Consistent with the transcriptional profiling data, the density of CD4+ and CD8+ T cells was much lower in ovarian lesions than in omental lesions (Fig. 1e and f), indicating that the ovarian lesions are immune ‘cold’ lesions. FAP, a marker of activated stroma, in contrast, did not vary across lesion location (Fig. 1e).
Distinct characteristics and differential composition of TILs across different lesions in HGSOC by scRNA-seq
To further detail the landscape of infiltrated T cells and explore the heterogeneity among different lesions, we sorted CD45+CD3+ T cells from single-cell suspensions prepared from 13 ovarian (Ov), 7 omental (Om), 4 other distant metastatic (Ot) sites and 5 PBMCs of patients OV004, OV005, OV006, OV008, OV009, and OV010 and performed scRNA-seq and matched scTCR-seq using the 10x 5’ platform (Fig. 1a and S2a, Supplementary table 1). After removing confounding batch effects and patient-specific variability (see Methods), a total of 227,769 CD45+ CD3+ immune cells from all subjects were available for analysis.
Using unsupervised clustering of uniform manifold approximation and projection (UMAP), we identified 22 stable clusters, including 7 clusters for CD4+ and 15 clusters for CD8+ T cells, each with unique signature genes (Figs. 2a-b and S2b-e). In addition to typical CD8+ and CD4+ T cell clusters including naïve (Tn), effector (Teff), memory (Memory), mucosal associated invariant T cells (MAIT) of blood and tissue, conventional regulatory T (Treg), and dysfunctional “exhausted” T cells (Tex), we also identified two proliferative clusters that highly expressed MKI67: CD8_C05-TYMS expressing markers associated with exhaustion (designated as Tex.prol) and CD4_C04-TYMS expressing markers associated with Treg (designated as Treg.prol) (Fig. 2b and S2d-e). CD8_C03 (Tex) population showed the highest expression of CXCL13, HAVCR2, and the co-inhibitory receptor PDCD1, as well as increased expression of GZMB, GZMA and GZMH, indicating that cells in this cluster potentially have cytotoxic activity in addition to exhaustion features. Furthermore, a pre-dysfunctional cluster (CD8_C02, referred to as “transitional”) was defined by high expression of GZMK12 and a progenitor exhaustion cluster (CD8_C07) was defined by higher GPR18313 (a central memory marker) and lower PDCD1 (an exhaustion marker) than CD8_C03 (Tex). We identified additional CD8 positive subsets, including CD8_C04 (NK-like) and CD8_C15 (γδ-like). CD8_C04 (NK-like) highly expressed KLRD1 and NKG7, known markers of NK14/NKT15 cells and CD8_C15 (γδ-like) highly expressed TRDV2 and TRGV9, known markers of γδ T cells16 (Fig. 2b and S2e).
We next investigated the relative proportions of different clusters between ovarian, omental and other sites and blood (Fig. 2c-d and S2f-i). Interestingly, the proportion of dysfunctional cells, including CD8_C03 (Tex), CD8_C05 (Tex.prol), and CD8_C07 (Tex.prog) and immunosuppression cluster, CD4_C02 (Treg) were significantly enriched in ovarian tumors (Fig. 2d). CD4_C03 (Tex) exhibited a trend to increase in ovarian as compared to omental or other sites. In contrast, naïve, memory and transition functional state clusters were enriched in omental sites (Fig. 2d). Opal multiplex IHC showed that most T cells enriched in ovarian lesions were dysfunctional states (CD8+ PD-1+), whereas memory T cells (CD8+ PD-1− or CD8+GZMB−) enriched in omental lesions (Fig. 2e). The major cellular composition difference between ovarian and omental lesions was again observed by flow cytometry analysis (Fig. 2f-g and S3a). Treg and Tex cells were significantly increased in ovarian sites, while central memory T (Tcm) were enriched in omental lesions, and other subsets, including naïve, effector memory T and effector memory re-expressing CD45RA T cells (TEMRA) were comparable in these two sites (Fig S3b). Taken together, increased Tex and Treg is consistent with primary ovarian tumors being immunosuppressed.
Patient derived TMB is associated with skewed T cell differentiation
Tumor mutation burden (TMB), neoantigen burden and high genomic instability, including deficient mismatch repair (dMMR) and homologous recombination deficiency (HRD), have been associated with increased T-cell infiltration and better response to checkpoint inhibitors in some cancer types17, 18, 19. To explore whether heterogeneity in T cell infiltration in different tumor sites or different patients is related to genomic aberrations, TMB20, HRD21 score and COSMIC mutational signature22 of each sample were assessed according to previous analysis pipelines (Fig S4a-b). Concordant with a previous study in NSCLC23, the correlation matrix revealed that CD4_C03 (Tex) and CD8_C03 (Tex) clusters correlated with TMB, neoantigen burden and HRD score, suggesting that CD4_C03 (Tex) and CD8_C03 (Tex) may be antigen-engaged T cell subsets (Fig S4c-d). However, the association of TMB, HRD score, and COSMIC mutational signatures with CD4_C03 (Tex) and CD8_C03 (Tex) is observed at the patient level rather than site level within individual patients (Fig S4a-b). In addition, we also constructed multi-region evolutionary trees based on somatic single-nucleotide variants (SNV) and structural variants (SV) across tumor sites (Fig S4e). Compared to PBMC, spatial genomic heterogeneity among tumors within individual patients is low especially between ovarian and omental metastatic tumors. Thus, spatial genomic features including TMB, HRD score and COSMIC mutational signature and evolution trajectory fail to explain the differences in T cell infiltration across different lesion sites within patients.
Tumor-specific but exhausted CD8 + cells preferentially infiltrate primary ovarian tumors, while non-tumor specific bystander cells are enriched in omentum metastases
To further investigate functional differences of CD8+ T cell clusters across locations, we first assessed transcriptional features of terminal exhaustion and effector memory signatures among CD8 T cell clusters by functional scores derived from previous reports24, 25 (Fig. 3a-b and S5a). As expected, CD8_C03 (Tex) and CD8_C05 (Tex.prol) had the highest terminal exhaustion characteristic (Fig. 3b), while CD8_C02 (Tex,trans), CD8_C04 (NK-like) and CD8_C06 (Teff) had features associated with effector and memory (Fig S5a). Given that exhausted T cells are frequently generated as a consequence of persistent antigen exposure26, we next tested whether CD8_C03 (Tex) and CD8_C05 (Tex.prol) transcriptionally resemble neoantigen-reactive populations using a tumor-specific signature27. Consistent with the concept that exhausted cells have undergone chronic antigen stimulation, the tumor-specific signature was significantly enriched in these two exhausted T subsets (Fig. 3c). Intra-tumoral T cells can also be CD39− bystanders that recognize virus rather than tumor antigens28, 29. Bystander signatures, including virus-specific and CD39− CD69− signatures, were dramatically increased in CD8_C02 (Tex,trans), CD8_C04 (NK-like) and CD8_C06 (Teff) that are enriched in omental tumors (Fig S5b-c). Collectively, CD8_C03 (Tex) and CD8_C05 (Tex.prol) that are enriched in ovarian tumors exhibited high exhaustion, tumor-specific score and low bystander score, whereas CD8_C02 (Tex,trans) and CD8_C04 (NK-like) that are enriched in omental tumors exhibited the opposite characteristics (Fig. 3d-e). Overall, tumor-specific signatures were strongly positively correlated with a terminal exhausted signature and were negatively associated with a bystander signature (Fig. 3f-g). Spatially, ovarian lesions had profoundly higher tumor-specific and terminal exhaustion scores than omental samples (Fig. 3f and h). Conversely, omental lesions exhibited markedly higher bystander scores (Fig. 3g and h). A heatmap of all signature scores showed the same distribution (Fig. 3i). Consistently, flow cytometry analysis confirmed more CD8+ tumor-specific T cells, expressing CD39+, were enriched in ovarian lesions, whereas more CD8+ bystander T cells enriched in omental lesions (Fig. 3j and S5d).
In addition, we reconstructed CD8 T cell antigen receptor (TCR) sequences from the scTCR-seq data. More than 70% of cells in all the tumor subsets had matched TCR information, with the exception of the NK-like subsets indicating limited drop out (Fig S5e). Given that peptide-MHC complex (pMHC) are recognized by specific TCRs, neoantigen and associated TCRs should be present in the same tissue30. Accordingly, we first selected TCRs that had the same distribution as neoantigens and excluded neoantigen/TCR pairs identified in only one sample (Fig S5f-g). Peptide motifs in CDR3 are important for defining antigen specificity with a single antigen being recognized by multiple related TCRs. Consequently, clustering of CDR3 sequences is characteristic of an antigen-driven T cell response31. Thus, we calculated the pairwise similarity of CDR3 sequences between selected TCRs (same distribution as neoantigens) and randomly selected TCRs (Fig S5h). Selected CDR3 had higher similarity in each patient (Fig S5i). Finally, we calculated the proportion of cells corresponding to the selected and unselected TCRs in different clusters (Fig S5j). The proportion of selected cells were highest in CD8_C03 (Tex), followed by CD8_C06 (Teff) and lower in CD8_C02 (Tex,trans) (Fig S5k), which again supports the contention that CD8_C03 (Tex) represent a tumor specific cluster. We also compared bulk TCR data of each sample with three virus-specific TCR libraries (see Methods), with the results showing that omentum samples contained the highest proportion of virus-specific TCR (Fig S5l), further supporting their bystander T cell features.
More importantly, pseudotime analysis showed that omental TIL tends to be in early to mid-differentiation with continued transit, while TIL in ovarian tumors have limited transit consistent with terminally differentiated exhausted T cells (Fig. 3k-l). These results collectively indicated that the T cells infiltrating ovarian lesions were characterized by tumor-specific terminal exhaustion, while the T cells in the omentum were non-exhausted but also non-tumor specific.
Exhausted CD8 T cells enriched in primary ovarian tumors are clonally expanded
As noted above, we identified a proliferative CD8+ cluster (CD8_C05 (Tex.prol)) that highly expresses proliferation marker genes, such as TUBB, STMN1 and MKI67 that is enriched in ovarian tumors (Fig S6a-b). To better characterize this cluster, we used label transfer to interrogate the “second best” cluster for each proliferating cell32. Interestingly, the CD8_C05 (Tex.prol) cells were majorly regrouped into CD8_C03 (Tex) or CD8_C02 (Tex, trans) (Fig. 4a). Very few cells were reattributed to naïve or effector memory CD8+ T cell populations, suggesting that proliferating cells were transcriptionally closer to late-differentiated T exhaustion cells. Differential expression analyses in the regrouped CD8_C02 (Tex,trans) and CD8_C03 (Tex) cells after label transfer showed that proliferation-related pathways, including G2M checkpoint, mitotic spindle, DNA-repair, oxidative phosphorylation33, and E2F targets34 pathways were concurrently elevated in this subclass (Fig S6c).
Then we performed differential analysis of functional markers between CD8_C02 (Tex,trans), CD8_C03 (Tex), CD8_C05 (Tex.prol) and CD8_C06 (Teff). As expected, CD8_C02 (Tex,trans) showed increased GZMK, GZMM, GZMA, which are markers of transition status (Fig. 4b and S6d). Compared with CD8_C02 (Tex,trans), CD8_C05 (Tex.prol) had modestly increased levels of co-inhibition and co-stimulation genes (PDCD1, LAG3, TIGIT, CTLA4, TNFRSF4/9/14/18 and ICOS) and transcription factors (TOX, RBPJ, IRF9) (Fig. 4b and S5d), which are necessary and sufficient to induce major features of Tex cells35. Of note, these co-inhibition, co-stimulation, and transcription factors were most highly expressed in CD8_C03 (Tex) consistent with exhaustion status (Fig. 4b and S5d). Cytotoxic markers (GZMB, PRF1, GNLY, GZMH) were low in both CD8_C05 (Tex.prol) and CD8_C02 (Tex,trans) indicating poor cytotoxic effector function. Notably, although weaker than CD8_C06 (Teff) cells, CD8_C03 (Tex) exhibited moderate GZMB, and PRF1 in the context of high FASLG and IFNG effector genes (Fig. 4b and S6d). Consistently, we observed the triple positive (CD8+PD-1+GZMB+) T cells in ovarian sample, but not in omental samples, by using opal multiplex IHC stains from site-matched FFPE sections, indicating the exist of CD8_C03 (Tex) cells exclusively in ovarian lesions and having modest cytotoxic activity (GZMB+) despite the expression of exhaustion markers (PD-1+) (Fig. 4c). Furthermore, we found that the CD8_C03 (Tex) gene signature score was associated with better overall survival, longer disease specific survival and better predicted response to ICB36 in TCGA ovarian cancer patients (Fig S6e-g), which further suggests that the Tex population in ovarian cancer may have cytolytic activity and may contribute to response to ICB and improved outcomes.
To further explore the relationship between exhausted and cytotoxic functions, we calculated effector and exhaustion scores after label transfer. The positive correlation of exhaustion score and effector score in both proliferating and non-proliferating cells suggests that CD8 T cells in HGSOC concurrently exhibit cytotoxic capacity and exhaustion status (Fig. 4d). Proliferating T cells displayed lower effector and exhaustion scores, with a clone size that was much smaller than that of non-proliferating T exhausted cells (Fig. 4e). The STARTRAC-expansion index13 also showed that the CD8_C05 (Tex.prol) subclass had modest clonal expansion while the CD8_C03 (Tex) subclass had the highest degree of clonal expansion (Fig. 4f). When the dysfunction population was divided into decile according to exhaustion score, we found that as exhaustion scores increase, the proportion of proliferating cells first increased slightly, and then decreased sharply (Fig. 4g). The most exhausted cells completely lost proliferative ability (Fig. 4g). These results together are consistent with the exhausted CD8 T cell subclass developing from an early differentiation state with high proliferative capacity. Remarkably, we found there was a higher proportion of proliferating cells in each interval in ovarian tumors than in omental tumors (Fig. 4g).
Exhausted CD8 T cells are a consequence of differentiation
We performed STARTRAC-transition analysis to reveal T cell state transitions among CD8 cells. As expected, the probability of the same TCR being present between CD8_C02 (Tex,trans), CD8_C03 (Tex) and CD8_C05 (Tex.prol) was markedly higher compared to other clusters, indicating their considerable developmental state transitions exist across them (Fig. 5a-d). A UMAP of representative clonal sharing among CD8_C02 (Tex,trans), CD8_C03 (Tex) and CD8_C05 (Tex.prol) is shown in Fig. 5e. To further investigate clonal sharing among CD8_C02 (Tex,trans), CD8_C03 (Tex) and CD8_C05 (Tex.prol), we selected the top 30 clonal TCRs shared between CD8_C02 (Tex,trans) and CD8_C03 (Tex) clusters with or without proliferative status (CD8_C05 (Tex.prol)), and calculated the proportion of clonotype in each subclass. Interestingly, most of the top shared clones across the three subclasses were most frequently expressed as CD8_C03 (Tex), especially the TOP10 clones to the left of the dotted line (Fig. 5f). As the proliferative cells decreased (CD8_C05 (Tex.prol)), T cells in these clone types tend to be more in CD8_C02 (Tex,trans) status (Fig. 5f, left). In particular, the vast majority of TCR clones shared between the non-proliferating CD8_C02 (Tex,trans) and CD8_C03 (Tex) clusters were in the CD8_C02 (Tex,trans) cluster (Fig. 5f, right). On the whole, these results further support that the exhausted CD8 T cells develop following proliferation and clonal expansion. Label transfer of CD8_C02 (Tex,trans) cells showed that many of these cells were regrouped into CD8_C03 (Tex) (Fig. 5g-h) supporting the concept that CD8_C02 (Tex,trans) are transiting to the CD8_C03 (Tex) subclass.
The results presented thus far are consistent with the hypothesis that transition between CD8_C02 (Tex,trans) and CD8_C03 (Tex) clusters occurs while cells are proliferating. To test this possibility, we measured the frequency of proliferative cells among clones shared between the CD8_C02 (Tex,trans) and CD8_C03 (Tex) clusters across different sites (Fig. 5i-j) using CD8_C04 (NK-like) as a comparator. As shown in Fig. 5i, clone sharing predominantly occurred between CD8_C02 (Tex,trans) and CD8_C03 (Tex), with most of these shared clones also being present in proliferating cells. Specifically, compared with omental and other tumor sites, primary ovarian sites had a higher frequency of state transitions driven by proliferation between CD8_C02 (Tex,trans) and CD8_C03 (Tex) (Fig. 5j). Together this suggests that terminal exhaustion T cell differentiation preferentially occurs in primary ovarian sites.
CD4 Treg suppress the immune microenvironment in primary ovarian tumor sites
For CD4+ T cells (see Fig. 6a for a UMAP of CD4 T cells), CD4_C02 (Treg) and CD4_C03 (Tex) were enriched in ovarian tumors (Fig. 2e-f), while naïve, memory and transition functional state clusters were mainly present in omental tumors (Fig. 2e-f). We next assessed the expression of tumor-specific and bystander gene signatures in the CD4+ clusters (Fig. 6b-c). Notably, tumor-specific signature was significantly enriched in CD4_C03 (Tex), followed by CD4_C02 (Treg) population (Fig. 6b), while bystander signature was enriched in other naïve and effector/memory clusters, including CD4_C01/C05/C06 (Fig. 6c). Similar to CD8+ T cells, CD4+ cells in primary ovarian tumors displayed the highest tumor-specific and terminal exhaustion scores (Fig. 6d). Again, similar to CD8+ T cells, CD4+ T cells in omental sites exhibited the highest bystander score (Fig. 6e). Furthermore, CD4+, similar to CD8+, exhausted clusters expressed co-inhibitory and co-stimulatory receptor genes, including TIGIT, HAVCR2, CTLA4, PDCD1, and TNFRSF14 (Fig. 6f). There were differences with for example the co-stimulatory receptors TNFRSF4/18 and the co-inhibitory receptor LAG3 being highly expressed in CD4 Tex cluster, while TNFRSF9 was enriched in the CD8 Tex cluster (Fig. 6f). Of note, unlike CD8 Tex cells, almost all cytotoxic makers, including GZMA, GZMB, PRF1, GZMK, GNLY and CCL5, were absent in CD4_C03 (Tex), indicating a lack of cytotoxic activity (Fig. 6f).
A CD4 T cell cluster with proliferation characteristics expressed MKI67 and FOXP3 (Fig. 2b and S7a). Unlike proliferative CD8_C05 (Tex.prol), label transfer of CD4_C04 (Treg.prol) showed that these cells are exclusively related to CD4_C02 (Treg) but not exhausted CD4_C03 (Tex) cells (Fig. 6g). So, in addition to the lack of cytotoxicity noted above (Fig. 6f), CD4_C03 (Tex) did not exhibit proliferative capacity which was further supported by the relatively small clone size compared to exhausted CD8 T cells (CD8_C03) (Fig S7b). TCR similarity analysis by STARTRAC-transition showed that CD4_C04 (Treg.prol) shared TCRs with CD4_C02 (Treg) rather than CD4_C03 (Tex) (Fig. 6h, Fig S7c). Moreover, the transition between CD4_C02 (Treg) and CD4_C04 (Treg.prol) mainly occurred in ovarian tumors, represented by the green line (Fig S7d). Monocle 2 reconstructed a trajectory capturing the progression of CD4 reprogramming with a root at the highest naïve state (CD4_C07) and ending with two termini (Treg (CD4_C02 and CD4_C04) and Tex (CD4_C03)) corresponding to two distinct reprogramming outcomes (Fig. 6i). More importantly, while the terminal differentiated T cell clusters were enriched in ovarian tumors, early differentiated T cells were more frequent in omental tumors (Fig. 6j). Meanwhile, we computed the Treg score for each cell in Treg cells and calculated the proportion of proliferating cells in each score interval (Fig. 6k). Within the Treg cell pool in ovarian tumors, as the Treg score increased, the proportion of proliferating cells decreases sharply (Fig. 6k). More importantly, the proportion of proliferating cells in each interval is higher in ovarian tumors than that in omental tumors (Fig. 6k).
CD4 T cells can support effective anti-tumor CD8 function, but their cross-talk within the TME is not well characterized. To investigate molecular links underlying the intercellular communication of CD4+ and CD8+ T cell in HGSOC, CellphoneDB analysis37 was used to identify molecular interactions between ligand-receptor pairs and major cell types in order to construct cellular communication networks. We found that interactions between Treg clusters, including CD4_C02 (Treg) and CD4_C04 (Treg.prol), and CD8 dysfunctional clusters, such as CD8_C03/05/07 rather than CD8_C01/02/04/06/09 non-dysfunctional subsets were commonly observed (Fig S7e). We subsequently analyzed detailed reciprocal connections between CD4_C02 (Treg) and all CD8 populations and identified markedly different ligand-receptor pairs between ovarian and omental tumors (Fig S7f). Notably, the KLRC1-HLA-E axis, a novel checkpoint in the TME38 was exclusively enriched in ovarian tumors, whereas ICAM1/ICAM2 that has been characterized as a site for the cellular entry of human rhinovirus39 and production of proinflammatory effects40 was enriched in omental tumors which is in line with the bystander characteristics of omental tumors.
Inherent TME characteristics contribute to spatial differences of TIL status
To explore mechanisms underlying differences in infiltration of the Tex classes in tumor lesions, we performed pairwise STARTRAC-migration analysis of CD8_02/03/05 clusters between different lesions. We did not find evidence for T cell clusters in omental or other tumors preferentially migrating to ovarian tumors or vice versa (Fig. 7a). Moreover, migration of T cells between blood and different tumor lesions was extremely low with no evidence for preference for different tumor sites (Fig S8a). Therefore, spatial specific migration of individual T cell clusters is limited or absent. We subsequently analyzed the top 10 TCR clones per cluster in blood (Fig. 7b, top) or in tumors (Ovarian and Omental tumors) (Fig. 7b, bottom) for potentially transcriptional reprogramming between blood and different tumor foci. Notably, the top 10 TCR clonotypes from CD8_C03 (Tex) and C05 (Tex.prol) exhausted clusters were not observed in blood, consistent with these clones expanding intratumorally. Together the data argue that the preferential infiltration of CD8_C03 (Tex) and C05 (Tex.prol) in ovarian tumors is not due to migration from blood or other tumor sites.
Intra-tumoral T cell dysfunction has recently been suggested to be associated with reactivity to tumor antigens12, 41. Consistent with this concept, TMB correlated with the proportion of dysfunctional T cells in primary ovarian tumors (Fig S3c-d), the differentiation process being associated with neoantigen recognition. We computed the CDR3 sequence similarity to investigate whether this differentiation process is antigen driven (not all patients shown, Fig S8b). Shared CDR3 sequences between transition states (CD8_C02) and exhausted states (CD8_C03 (Tex) and CD8_C05 (Tex.prol) were significantly elevated compared to unshared CDR3 sequences in different patients but not in different tumor regions (Fig. 7c and S8c). This suggests that while neoantigen may drive differentiation towards exhausted states, this does not explain the differences in exhausted T cells between different tumor sites.
We next performed differential analysis of signaling pathways between primary ovarian tumors and omental tumors on bulk transcriptomic profiles. Compared with omental tumors, proliferation-related pathways (G2M checkpoint, mitotic spindle, DNA-repair, oxidative phosphorylation, and E2F targets) and interferon signaling were concurrently increased in ovarian tumors (Fig. 7d). Consistently, these pathways were enriched in total (Fig. 7e, left), CD8+ (Fig. 7e, middle) or CD4+ T cells (Fig. 7e right) in primary ovarian tumors compared to omental tumors, indicating inherent TME characteristics contribute to spatial differences of TIL status. Notably, proliferation-related pathways, oxidative phosphorylation, glycolysis were all associated with T cell proliferation and function.
In contrast, consistent with the decreased interferon signaling in omental metastasis, MHC-I in tumor area detected by IHC was lower in omental metastasis (Fig. 7f and S8d). To further validate the differential expression of MHC-I in tumor cells of different lesions, we collected an additional 6 patients (36 samples, including 13 ovarian, 8 omental, 7 ascites and 8 other metastasis lesions) to perform scRNA-seq using the BGI droplet platform (Fig S8e, Supplementary table 2, see Methods). After quality control (see Methods), a total of 158,620 cells were available for analysis (Supplementary table 2). Using UMAP, we identified 7 stable clusters, including DCs, plasma, macrophages, T, endothelial, fibroblast and epithelial cells, each with unique signature genes (Figs S8e-f). In consonance with the differential expression of MHC-I detected by IHC staining, genes encoding for MHC-I processing and presentation were lowest in omental lesions, while HLA-B/C/E/F/G were highly expressed in ovarian lesions and HLA-A, CALR, PSMB6/8/9 and so on were highest in tumor cells of other lesions (Fig. 7g). As interferon increases antigen presentation42 and MHC-I reflecting antigen presentation ability and provides a marker of inflamed T cell infiltration 11, the results suggested that omental tumors have lower antigen presentation ability.
Meanwhile, IHC staining of T cells exhibited that both CD4 and CD8 T cells were preferentially located in stroma than in tumor of omental tumors (Fig. 7h-i and S8g), indicating that most of the T cells in omental masses are excluded from contact with tumor cells.