Peripherally-driven myeloid NFkB and IFN/ISG responses predict malignancy risk, survival, and immunotherapy regime in ovarian cancer

Abstract

Background: Tumors can influence peripheral immune macroenvironment, thereby creating opportunities for non-invasive serum/plasma immunobiomarkers for immunostratification and immunotherapy designing. However, current approaches for immunobiomarkers' detection are largely quantitative, which is unreliable for assessing functional peripheral immunodynamics of patients with cancer. Hence, we aimed to design a functional biomarker modality for capturing peripheral immune signaling in patients with cancer for reliable immunostratification.

Methods: We used a data-driven in silico framework, integrating existing tumor/blood bulk-RNAseq or single-cell (sc)RNAseq datasets of patients with cancer, to inform the design of an innovative serum-screening modality, that is, serum-functional immunodynamic status (sFIS) assay. Next, we pursued proof-of-concept analyses via multiparametric serum profiling of patients with ovarian cancer (OV) with sFIS assay combined with Luminex (cytokines/soluble immune checkpoints), CA125-antigen detection, and whole-blood immune cell counts. Here, sFIS assay's ability to determine survival benefit or malignancy risk was validated in a discovery (n=32) and/or validation (n=699) patient cohorts. Lastly, we used an orthotopic murine metastatic OV model, with anti-OV therapy selection via in silico drug-target screening and murine serum screening via sFIS assay, to assess suitable in vivo immunotherapy options.

Results: In silico data-driven framework predicted that peripheral immunodynamics of patients with cancer might be best captured via analyzing myeloid nuclear factor kappa-light-chain enhancer of activated B cells (NFκB) signaling and interferon-stimulated genes' (ISG) responses. This helped in conceptualization of an 'in sitro' (in vitro+in situ) sFIS assay, where human myeloid cells were exposed to patients' serum in vitro, to assess serum-induced (si)-NFκB or interferon (IFN)/ISG responses (as active signaling reporter activity) within them, thereby 'mimicking' patients' in situ immunodynamic status. Multiparametric serum profiling of patients with OV established that sFIS assay can: decode peripheral immunology (by indicating higher enrichment of si-NFκB over si-IFN/ISG responses), estimate survival trends (si-NFκB or si-IFN/ISG responses associating with negative or positive prognosis, respectively), and coestimate malignancy risk (relative to benign/borderline ovarian lesions). Biologically, we documented dominance of pro-tumorigenic, myeloid si-NFκB response^HIGHsi-IFN/ISG response^LOW inflammation in periphery of patients with OV. Finally, in an orthotopic murine metastatic OV model, sFIS assay predicted the higher capacity of chemo-immunotherapy (paclitaxel-carboplatin plus anti-TNF antibody combination) in achieving a pro-immunogenic peripheral milieu (si-IFN/ISG response^HIGHsi-NFκB response^LOW), which aligned with high antitumor efficacy.

Conclusions: We established sFIS assay as a novel biomarker resource for serum screening in patients with OV to evaluate peripheral immunodynamics, patient survival trends and malignancy risk, and to design preclinical chemo-immunotherapy strategies.

Keywords: computational biology; immunological techniques; immunotherapy; tumor biomarkers; tumor microenvironment.

Figure 1

Interrogation of the tumour derived bulk-RNAseq or tumour/blood single-cell (sc)-RNAseq data of patients with cancer. (A, B) The t-distributed stochastic neighbor embedding (tSNE) visualizations of indicated immune cell types, from scRNAseq data from patients with renal cell and large cell neuroendocrine carcinoma (n=4 patients in total) (derived from GSE139555) isolated from tumor tissue (A) or peripheral blood (B). These immune cells were further colored for NFκB and IFN/ISG response gene-signature levels. Herein, the arrows highlight main immune populations with overlaps between tumor and blood for these signatures, and the circles indicate different myeloid cell subsets. (C) PCA analyses of median expression for each gene across cancer types (from online supplemental figure S5A) and median HR values (from online supplemental figure S5B). Venn diagram represents the portion of NFκB and ISGs in each cluster. (D, E) Visualization of the hazard ratio (HR) ±95% confidence interval (CI) for the impact of expression of NFκB signaling gene signature (D) or ISG signaling gene signature (E), for the indicated TCGA cancer datasets wherein the signature expression cut-off for binary (high vs low expression) patient stratification was based on best-performing threshold principle for OS of indicated TCGA patients with cancer (LIHC, n=371; PAAD, n=177; LUSC, n=501; LUAD, n=513; HNSC, n=500; CESC, n=304; BLCA, n=405; UCEC, n=543; OV, n=374; SARC, n=259; BRCA; n=1090; KIRC; n=530) (Mantel-Cox test, *p<0.05). (F–H) tSNE visualizations of indicated immune cell-types, from scRNAseq data from patients with OV (n=5 patients) (derived from GSE146026) isolated from tumor tissue. These immune cells were furthered colored for NFκB (G) and ISG signature expression levels (H). BLCA, bladder cancer; CESC, cervical cancer; HNSC, head and neck cancer; IFN, interferon; ISG, interferon-stimulated gene; KIRC, renal cell cancer; LIHC, liver cancer; LUSC, lung cancer; LUAD, lung adenocarcinoma; NFκB, nuclear factor kappa-light-chain enhancer of activated B cells; OS, overall survival; OV, ovarian cancer; PAAD, pancreatic cancer; SARC, sarcoma; BRCA, breast cancer;TCGA, The Cancer Genome Atlas; UCEC, endometrial cancer.

Figure 2

Standardization of sFIS assay. (A) Overview of the sFIS assay. THP1-dual reporter cells containing a reporter for NFκB and IFN/ISG response (see the Methods section) were stimulated with human serum of a patient with cancer, healthy donor serum (baseline) or LPS (positive control) for 24 h. Subsequently, THP1 media were checked for LUCIA and SEAP activity. (B, C) Bar graph of THP1-dual reporter cells, exposed to indicated concentrations of different agonists, reporting NFκB (B) or IFN/ISG (C) responses at 48 h post-treatment (n=3, one-way analysis of variance with Fisher’s Least Significant Difference (LSD) test; *p<0.05). (D–G) Bar graph of THP1 dual reporter cells exposed to indicated concentrations of different cytokines (D, E) or soluble immune checkpoints (F, G) reporting NFκB (D, F) or IFN/ISG (E, G) responses at 48 h post-treatment (min–max normalized, n=4, Kruskal-Wallis test; *p<0.05). cGAMP, cyclic guanosine monophosphate–adenosine monophosphate; IFN, interferon; IL, interleukin; IRF, interferon regulatory factor; ISG, interferon-stimulated gene; ISRE, interferon-stimulated response element; LPS, lipopolysaccharide; NFκB, nuclear factor kappa-light-chain enhancer of activated B cells; PD-1, programmed cell death protein 1; PDL-1, programmed cell death-ligand 1; PRR, pattern recognition receptor; TIM3, T-cell immunoglobulin and mucin domain 3; TNF, tumor necrosis factor; TLR, toll-like receptor; TRAIL, TNF-related apoptosis-inducing ligand; SEAP, secreted embryonic alkaline phosphatase; sFIS, serumfunctional immunodynamic status; -STAT, signal transducer and activator of transcription.

Figure 3

Immunological benchmarking and sFIS assay testing in discovery cohort of patients with OV. (A) Eligible serum samples from the UZL-CSI cohort were profiled for different immunological factors (n=74) and CA125 (n=95). Colors indicate association with categories mentioned in the legend. (B) REACTOME-Wikipathway GSEA based on concentrations of immunological biomarkers or CA125 (top 10 significance method). Violin plot (C) and sample-paired analysis (D) of UZL-CSI serum samples profiled for the si-NFκB or si-IFN/ISG response (n=96, Wilcoxon matched-pairs signed-rank test; *p<0.05). ‘§’ depicts serum samples wherein the si-IFN/ISG response values exceeded the si-NFκB response values. (E) Heatmap representation of Pearson correlation matrix of si-NFκB and si-IFN/ISG response, CA125 and immunological factors profiled from the serum of the UZL-CSI OV dataset (n=74). (F, G) Network analyses of NFκB (F) or IFN/ISG (G) response activating transcription program. Genes in blue indicate immunologically relevant factors enumerated by the PID analyses. (H, I) Bar graph of THP1-dual reporter cells exposed to indicated concentrations of TGF-β reporting IFN/ISG (H) or NFκB (I) responses at 48 h post-treatment (n=3, one-way analysis of variance corrected for false discovery rate via Benjamini, Krieger and Yekutieli test; *p<0.05). CA125, carbohydrate antigen 125; GSEA, gene set enrichment analysis; IFN, interferon; ISG, interferon-stimulated gene; NFκB, nuclear factor kappa-light-chain enhancer of activated B cells; OV, ovarian cancer; PID, Pathway Interaction Database; sFIS, serum-functional immunodynamic status; si, serum-induced; UZL-CSI, UZ Leuven-Cell Stress Immunity cohort.

Figure 4

Immunological characteristics of si-NFκB/si-IFN/ISG responses and their prognostic impact on patients with OV. (A) PCA representation of Spearman’s correlation analyses between PFS or OS of patients with OV with si-NFκB and si-IFN/ISG response, CA125 and immunological factors profiled from the serum of the UZL-CSI OV dataset (fold change to baseline/healthy serum) (median value-based data integration for multiple serum samples per patient, n=32). (B–E) Cubic spline analyses of si-NFκB or si-IFN/ISG responses (B, C) or CA125 concentration (D, E) profiled from the serum of the UZL-CSI OV cohort dataset (median value-based data integration for multiple serum samples per patient; fold change to baseline/healthy serum) and distributed as per PFS (B, D) or OS values (C–E) (n=32). (F) Radar plot of Pearson correlation values of CA125 and si-NFκB and si-IFN/ISG response with blood cell counts (n=66). (G–I) Pearson correlation of CA125 (G), si-NFκB (fold change to baseline/healthy serum) (H) and si-IFN/ISG (fold change to baseline/healthy serum) (I) responses, to NLR (CA125, n=64; si-NFκB/si-IFN/ISG response, n=65; *p<0.05). CA125, carbohydrate antigen 125; IFN, interferon; ISG, interferon-stimulated gene; NFκB, nuclear factor kappa-light-chain enhancer of activated B cells; NLR, neutrophil-to-lymphocyte ratio; OS, overall survival; OV, ovarian cancer; PFS, progression-free survival; si, serum-induced; UZL-CSI, UZ Leuven-Cell Stress Immunity cohort.

Figure 5

Prognostic impact of si-NFκB or si-IFN/ISG responses of patients with OV. (A–F) Violin plots of si-NFκB responses (A, B) or si-IFN/ISG responses (C, D) and CA125 concentration (E, F) (median value-based data integration for multiple serum samples per patient) profiled from the serum of the UZL-CSI OV cohort (fold change to baseline/healthy serum). These were subdivided as longer and shorter PFS (CA125, longer PFS n=5 vs shorter PFS n=26; si-NFκB or si-IFN/ISG resp., longer PFS n=5 vs shorter PFS n=27) (A, C, E) or OS (CA125, longer OS n=8 vs shorter OS n=23; si-NFκB or si-IFN/ISG resp., longer PFS n=9 vs shorter PFS n=23) (B, D, F) based on a cut-off of 2 years of PFS or 3 years of OS (Mann-Whitney test, two-tailed; *p<0.05). (g–j) KM plots of si-NFκB (G, I) or si-IFN/ISG (H, J) responses (median value-based data integration for multiple serum samples per patient) of the UZL-CSI OV cohort. Subdivision of PFS (G, H) or OS (I, J) as high or low were based on the 75th percentile cut-off (si-NFκB or si-IFN/ISG resp. vs PFS/OS, HIGH n=8 vs low n=24). The plots depict the HR ±95% CI (log-rank Mantel-Cox test, *p<0.05). CA125, carbohydrate antigen 125; IFN, interferon; ISG, interferon-stimulated gene; KM, Kaplan-Meier; NFκB, nuclear factor kappa-light-chain enhancer of activated B cells; OS, overall survival; OV, ovarian cancer; PFS, progression-free survival; si, serum-induced; UZL-CSI, UZ Leuven-Cell Stress Immunity cohort.

Figure 6

sFIS assay testing to estimate malignancy risk in a validation cohort of patients with ovarian cancer. (A, B) Heatmap representation of CA125 (A) or si-NFκB and si-IFN/ISG response (B) per histology category profiled from the serum of the TRANS-IOTA cohort (fold change to baseline/healthy serum, averaged values; for full patient details and numbers, see online supplemental table 2). ‘*’ indicates inclusion of patients with cysts at the ultrasound. (C, D) Violin plot of CA125 (C) or si-NFκB response (D) of patients with benign (CA125, n=302; si-NFκB response, n=404), borderline (CA125, n=85; si-NFκB response, n=90) and malignant (CA125, n=198; si-NFκB response, n=205) cancers, profiled from the serum of the TRANS-IOTA cohort (fold change to baseline/healthy serum, Kolmogorov-Smirnov test; *p<0.05). (E, F) Violin plot of PFS based on CA125 (low, n=42; high, n=14) (E) or si-NFκB response (low, n=41; high, n=15) (F) of patients with cancer (fold change to baseline/healthy serum) (Mann-Whitney test, *p<0.05). Subdivision of CA125 and si-NFκB (E, F) as high or low were based on the 75th percentile cut-off. CA125, carbohydrate antigen 125; IFN, interferon; ISG, interferon-stimulated gene; NFκB, nuclear factor kappa-light-chain enhancer of activated B cells; PFS, progression free survival; si, serum-induced; sFIS, serum-functional immunodynamic status.

Figure 7

sFIS assay-based prediction of chemoimmunotherapy regime’s design and in vivo testing in murine metastatic ovarian cancer model. (A) In silico drug-prediction analyses based on the NFκB response signature (cut-offs: multiple adjustment test, Bonferroni; significance level, adjusted p value=0.01). Colors indicate overlapping genes between NFκB response signature and literature-associated drug–gene sets. (B) Overview of the tumor inoculation, serum collection, and therapeutic treatment schedules for the mice experiments. (C) si-IFN/ISG and si-NFκB response of J774 dual-reporter cell lines exposed to mouse serum obtained from day 49 as a ratio to day 13 (control, n=8; anti-TNF Ab, n=8; PARPi, n=7; PARPi and anti-TNF Ab, n=8; PTX-CBP, n=7; PTX-CBP and anti-TNF Ab, n=6). (D, E) Kaplan-Meier plots of overall survival (D) or survival while considering the first drainage of ascitic fluid (E) of metastatic ID8 tumor-bearing mice treated with different therapy regimes (control, n=8; anti-TNF Ab, n=8; PARPi, n=8; PARPi and anti-TNF Ab, n=8; PTX-CBP, n=7; PTX-CBP and anti-TNF Ab, n=7) (log-rank Mantel-Cox test; *p<0.05, **p<0.01). IFN, interferon; ISG, interferon-stimulated gene; NFκB, nuclear factor kappa-light-chain enhancer of activated B cells; PARPi, PARP inhibitor; PTX-CBP, paclitaxel +carboplatin; sFIS, serum-functional immunodynamic status.