Unraveling tumor–immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy

Abstract

In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 5 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and imagebased quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared sitematched and site-unmatched tumors before and after neoadjuvant chemotherapy.Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment..

Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of Combination Immunotherapy

“…However, BPPNM tumors were unresponsive to single-agent PD-L1 and only produced long-term survival when ICB was combined with Olaparib, reflecting reported clinical data (46,49,63). These data suggest that PD-L1 expression and inflammatory status alone may not be sufficient on their own predict the success of ICB in these tumors and that immune-stimulating alterations to the tumor microenvironment incurred by adjuvant treatments such as chemotherapy may be necessary for a significant response to checkpoint inhibitors (43,64). Nonetheless, more generally, targeted therapy based on inhibiting the DNA damage response pathway in combination with ICB therapy in ovarian cancer offers the potential to tailor treatment for patients with BRCA1 mutant HGSC.…”

“…Altogether, the scRNA seq, flow cytometry, and ascitic cytokine data combined to describe distinct immunosuppressive microenvironments across the spectrum of the tumor-cell genotypes compared here, this being reminiscent of the heterogeneity seen among tumors borne by HGSC patients (42,43).…”

Tumor Microenvironment in Ovarian Cancer: Function and Therapeutic Strategy

“…The tumouricidal treatment causes cell death, incurring multiple changes in the TME. After neoadjuvant chemotherapy (NACT), researchers adopted immunogenic analysis in HGSOC tumors and found increased NK cell infiltration and oligoclonal expansion of T cells, suggesting chemotherapy can potentiate immunogenicity of the primary tumor (Jiménez-Sánchez et al, 2020). Another gene expression analysis revealed ovarian cancer patients treated with paclitaxel had an enriched gene signature linked to M1 TAM activation (Wanderley et al, 2018).…”

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

“…We studied the differences in the molecular signaling pathways between the low-risk and high-risk patient groups using the 50 hallmark gene sets [26], extracted using single sample gene-set enrichment (ssgsea) analysis of the RNA samples [25]. The signaling pathways were categorized into immune, oncogenic, stromal, cellular, and other [24]. Patients were dichotimized using the median value (cluDiss = 68.6) of cluDiss (low-risk<median and vice versa).…”

“…The aims of this study were to (i) validate cluDiss as a predictor of outcomes using an internal and external multi-institutional cohort, and (ii) evaluate whether an integrated iRCG measure of HGSOC outcomes was more accurate than average heterogeneity radiomic (aRCG) and conventional imaging (CCG) measures. Finally, we attempted to establish the biological basis of the prognostic radiomics measures by studying their correlation with underlying biological processes characterized by a well-defined molecular HALLMARK gene set pathways, stromal and immune scores of the tumor microenvironment (TME), and established 18 cell types of the TME extracted using the consensus TME method [23,24] by using patient-level single sample gene set analysis (ssgsea) [25] from RNA-sequencing data.…”