Multisite Tumor Sampling Reveals Extensive Heterogeneity of Tumor and Host Immune Response in Ovarian Cancer

Abstract

Background/Aim: Ovarian cancer (OVCA) is characterized by genomic/molecular intra-patient heterogeneity (IPH). Tissue histology and morphological features are surrogates of the underlying genomic/molecular contexture. We assessed the morphological IPH of OVCA tumor compartments and of lymphocytic infiltrates in multiple matched samples per patient. Materials and Methods: We examined 294 hematoxylin & eosin (H&E) OVCA tumor whole sections from 70 treatmentnaïve patients who had undergone cytoreductive surgery. We assessed morphological subtypes as immunoreactive (IR), solid-proliferative (SD), papilloglandular (PG), and mesenchymal transition (MT); subtype load per patient; stromal tumorinfiltrating lymphocyte (sTIL) density as average per sample; and, as maximal sTIL values (max-TILs) among all samples per patient, ovaries and implants. Results: Among all 294 tumor sections, the most frequent primary morphological subtype was PG (n=150, 51.0%), followed by MT (71, 24.1%), SD (48, 16.3%) and IR (15, 5.1%). Subtype combinations were observed in 67/294 sections (22.8%) and IPH in 48/70 patients (68.6%). PG prevailed in ovaries (p<0.001), SD and MT in implants (p=0.023 and p<0.001, respectively). sTILs were higher in SD compared to non-SD (p=0.019) and lower in PG, respectively (p<0.001). sTIL density was higher in implants than in ovaries (p<0.001). Higher max-TILs were associated with stage IV disease (p=0.043), upper abdominal dissemination (p=0.024), endometrioid histology (p=0.013), and grade 3 tumors (p=0.021). Favorable prognosticators were higher max-TILs per patient (PFS, OS) and higher SD-load (PFS). Conclusion: Clinically relevant morphological and host immune-response IPH appear to be the norm in OVCA. This may complicate efforts to decipher sensitivity of the tumor to certain treatment modalities from a single pre-operative biopsy. Pathologic diagnosis of epithelial ovarian cancer (OVCA) takes into account the different origin, pathogenesis and prognosis of major histologic types with minimal or no consideration of the biological characteristics of the tumors (1, 2). OVCA histological types are considered for patient management, while genetic counselling and tumor genotyping with particular emphasis on BRCA1/2, homologous recombination and mismatch repair deficiency were only recently recommended for maintenance treatment options (3, 4). OVCA is not among the first 10 causes of cancer-related morbidity but it ranks fifth with respect to cancer-related mortality (5, 6), necessitating improvement at all steps of patient management and a deeper understanding of tumor biology.

Tumor-Infiltrating Lymphocytes (TILs) in Epithelial Ovarian Cancer: Heterogeneity, Prognostic Impact, and Relationship with Immune Checkpoints

“…Murakami established an IHC classification that distinguishes four subgroups: mesenchymal transition, immune reactive, solid and proliferative, and papilloglandular [ 57 ]. The new classification of IHC has been used in 70 ovarian or peritoneal samples and confirms the heterogeneity in sTIL density [ 58 ]. In particular, the heterogeneity in TILs is observable in the primary tumor versus recurrence [ 59 ].…”

“…The proportion of CD3 TILs does not change after NACT [ 129 , 130 ], whereas NACT induces a decrease in the density of sCD3 TILs in HGSOC patients [ 131 ], or an increase in the proportions of sCD3, sCD8, and iCD8 TILs [ 69 , 137 ]. sTILs are associated with platinum sensitivity in 70 patients with advanced-stage SOC [ 58 ]. Chemotherapy induces an upregulation of PD-L1 [ 69 , 130 ].…”

Towards Personalized Sampling in Clear Cell Renal Cell Carcinomas

“…Jie et al [19] have compared the performance of routine sampling and MSTS in 182 oral and oropharyngeal squamous-cell carcinomas. The authors included in the comparison histological, immunohistochemical, and molecular parameters, and concluded that MSTS was more informative than routine sampling in detecting Aside from CCRCC, the usefulness of MSTS in CCRCC has been confirmed by subsequent histological, immunohistochemical, and molecular studies in ovarian carcinoma, mesothelioma, and head and neck squamous cell carcinoma [17][18][19][20]. Lakis et al [17] have analyzed 294 tumor sections from 70 treatment naïve patients who had undergone cytoreductive surgery of ovarian cancer and have observed not only the high histological variability of tumors across different regions, but also the irregular qualitative and quantitative distribution of tumor-associated lymphocyte, information with obvious prognostic and therapeutic implications.…”

“…The authors included in the comparison histological, immunohistochemical, and molecular parameters, and concluded that MSTS was more informative than routine sampling in detecting Aside from CCRCC, the usefulness of MSTS in CCRCC has been confirmed by subsequent histological, immunohistochemical, and molecular studies in ovarian carcinoma, mesothelioma, and head and neck squamous cell carcinoma [17][18][19][20]. Lakis et al [17] have analyzed 294 tumor sections from 70 treatment naïve patients who had undergone cytoreductive surgery of ovarian cancer and have observed not only the high histological variability of tumors across different regions, but also the irregular qualitative and quantitative distribution of tumor-associated lymphocyte, information with obvious prognostic and therapeutic implications. They conclude that ITH in ovarian cancer may limit the usefulness of pre-operative biopsies to make some therapeutic decisions.…”

Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset

“…The majority of published works using machine learning (ML) or deep learning (DL) techniques for classification or segmentation are mainly focused on H&E histopathology images across different types of tissue and disease [1][2][3]. Some of them use patches of samples [4][5][6] while more recent publications are dealing with whole slide images [7][8][9]. Notably, there are few scientific papers employing ML techniques on fluorescence data, possibly due to the fact that the number of annotated fluorescence image datasets publicly available is limited.…”