Immune cell profiles of metastatic HER2-positive breast cancer patients according to the sites of metastasis

Purpose Recent works have characterized that metastatic site can affect the tumour immune profiles and efficiency of cancer immunotherapies. The prognosis of HER2-positive breast cancer is associated with the characteristics of the tumour immune microenvironment, with immunological cells playing a central role in efficiency of HER2-targeted antibodies. Here we investigated the prognostic significance of different metastatic sites and their correlation to tumour immune profiles in HER2-positive breast cancer treated with trastuzumab. Methods We collected all (n = 54) HER2-positive metastatic breast cancer patients treated with trastuzumab containing regimens at Oulu University Hospital 2009-2014. Pathological and clinical data were collected from electronic patient records. The tumour immune profiles were analysed from pre-treatment primary tumours using well-characterized immunological markers with computer-assisted immune cell counting. Results Of the metastatic sites, only liver metastases were associated with poor prognosis (hazard ratio 1.809, 95% confidence interval 1.004-3.262), especially when presented as the primary site of metastases. Of the other sites, pulmonary metastases characterized a patient profile with trend to improved survival. Of the studied tumour immunological markers, patients with liver metastases had low densities of CD3(+) T cells (p = 0.030) and M1-like macrophages in their primary tumours (p = 0.025). Of the other studied markers and sites, patients with pulmonary metastases had low STAB1(+)-immunosuppressive macrophage density in their primary tumours. Conclusion Our results suggest that the site of metastasis is associated with prognosis in HER2-positive breast cancer, highlighted by the poor prognosis of liver metastases. Furthermore, liver metastases were associated with adverse tumour immune cell profiles.Peer reviewe

Immune cell profiles of metastatic HER2-positive breast cancer patients according to the sites of metastasis

Purpose Recent works have characterized that metastatic site can affect the tumour immune profiles and efficiency of cancer immunotherapies. The prognosis of HER2-positive breast cancer is associated with the characteristics of the tumour immune microenvironment, with immunological cells playing a central role in efficiency of HER2-targeted antibodies. Here we investigated the prognostic significance of different metastatic sites and their correlation to tumour immune profiles in HER2-positive breast cancer treated with trastuzumab. Methods We collected all (n = 54) HER2-positive metastatic breast cancer patients treated with trastuzumab containing regimens at Oulu University Hospital 2009-2014. Pathological and clinical data were collected from electronic patient records. The tumour immune profiles were analysed from pre-treatment primary tumours using well-characterized immunological markers with computer-assisted immune cell counting. Results Of the metastatic sites, only liver metastases were associated with poor prognosis (hazard ratio 1.809, 95% confidence interval 1.004-3.262), especially when presented as the primary site of metastases. Of the other sites, pulmonary metastases characterized a patient profile with trend to improved survival. Of the studied tumour immunological markers, patients with liver metastases had low densities of CD3(+) T cells (p = 0.030) and M1-like macrophages in their primary tumours (p = 0.025). Of the other studied markers and sites, patients with pulmonary metastases had low STAB1(+)-immunosuppressive macrophage density in their primary tumours. Conclusion Our results suggest that the site of metastasis is associated with prognosis in HER2-positive breast cancer, highlighted by the poor prognosis of liver metastases. Furthermore, liver metastases were associated with adverse tumour immune cell profiles.Peer reviewe

Prognostic significance of spatial and density analysis of T lymphocytes in colorectal cancer

Background Although high T cell density is a strong favourable prognostic factor in colorectal cancer, the significance of the spatial distribution of T cells is incompletely understood. We aimed to evaluate the prognostic significance of tumour cell-T cell co-localisation and T cell densities. Methods We analysed CD3 and CD8 immunohistochemistry in a study cohort of 983 colorectal cancer patients and a validation cohort (N = 246). Individual immune and tumour cells were identified to calculate T cell densities (to derive T cell density score) and G-cross function values, estimating the likelihood of tumour cells being co-located with T cells within 20 mu m radius (to derive T cell proximity score). Results High T cell proximity score associated with longer cancer-specific survival in both the study cohort [adjusted HR for high (vs. low) 0.33, 95% CI 0.20-0.52, P-trend < 0.0001] and the validation cohort [adjusted HR for high (vs. low) 0.15, 95% CI 0.05-0.45, P-trend < 0.0001] and its prognostic value was independent of T cell density score. Conclusions The spatial point pattern analysis of tumour cell-T cell co-localisation could provide detailed information on colorectal cancer prognosis, supporting the value of spatial measurement of T cell infiltrates as a novel, robust tumour-immune biomarker.Peer reviewe

Immunophenotype based on inflammatory cells, PD-1/PD-L1 signalling pathway and M2 macrophages predicts survival in gastric cancer

BackgroundImmune response against cancer has prognostic impact but its role in gastric cancer is poorly known. The aim of the study was to assess the prognostic significance of immune cell score (CD3+, CD8+), tumour immune escape (PD-L1, PD-1) and immune tolerance (Clever-1). MethodsAfter exclusion of Epstein-Barr virus positive (n = 4) and microsatellite instable (n = 6) tumours, the study included 122 patients with GC undergoing D2 gastrectomy. CD3+ and CD8+ based ICS, PD-L1, PD-1 and Clever-1 expressions were evaluated. Differences in survival were examined using Cox regression adjusted for confounders. The primary outcome was 5-year survival. Results The 5-year overall survival rate was 43.4%. High ICS was associated with improved overall survival (adjusted HR 0.48 (95% CI 0.26-0.87)) compared to low ICS. In the high ICS group, patients with PD-L1 expression (5-year survival 69.2 vs. 53.1%, p = 0.317), high PD-1 (5-year survival 70.6 vs. 55.3% p = 0.312) and high Clever-1 (5-year survival 72.0% vs. 45.5% (p = 0.070) had poor prognosis. Conclusions High ICS was associated with improved survival. In the high ICS group, patients with high PD-L1, PD-1 and Clever-1 had poor prognosis highlighting the importance of immune escape and immune tolerance in GC.</p

CD3+ and CD8+ T-Cell-Based Immune Cell Score and PD-(L)1 Expression in Pulmonary Metastases of Microsatellite Stable Colorectal Cancer

The objective of this study was to evaluate the prognostic value of CD3+ and CD8+ based immune cell score (ICS), programmed death -1 (PD-1) and programmed death ligand -1 (PD-L1) in pulmonary metastases of proficient mismatch repair colorectal cancer (CRC) patients. A total of 101 pulmonary metastases and 62 primary CRC tumours were stained for CD3+, CD8+, PD-1 and PD-L1 expression. The prognostic value of ICS, PD-1/PD-L1 expression in 67 first pulmonary metastases and 61 primary CRC tumour was analysed. Comparative analysis was also performed between primary tumours and pulmonary metastases, as well as between T-cell densities and PD-1/PD-L1 expression. The 5-year overall survival rates of low, intermediate, and high ICS in pulmonary metastases were 10.0%, 25.5% and 47.0% (p = 0.046), respectively. Patients with high vs. low ICS in pulmonary metastases had a significantly better 5-year survival (adjusted HR 0.25, 95% CI 0.09-0.75, p = 0.013). High tumour cell PD-L1 expression in the pulmonary metastases was associated with improved survival (p = 0.024). Primary tumour CD8+ expression was significantly correlated with all T-cell densities in pulmonary metastases. Conclusion: The ICS evaluated from the resected pulmonary metastases of CRC showed significant prognostic value. High PD-L1 expression in pulmonary metastases is associated with favourable prognosis.publishedVersionPeer reviewe

Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy

IntroductionDeep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&amp;E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and personalized treatment development. Conventionally, these models are trained on ground truth cell labels derived from IHC-stained tissue sections adjacent to H&amp;E-stained ones, which might be less accurate than labels from the same section. Although many such DL models have been developed, the impact of ground truth cell label derivation methods on their performance has not been studied.MethodologyIn this study, we assess the impact of cell label derivation on H&amp;E model performance, with CD3+ T-cells in lung cancer tissues as a proof-of-concept. We compare two Pix2Pix generative adversarial network (P2P-GAN)-based virtual staining models: one trained with cell labels obtained from the same tissue section as the H&amp;E-stained section (the ‘same-section’ model) and one trained on cell labels from an adjacent tissue section (the ‘serial-section’ model).ResultsWe show that the same-section model exhibited significantly improved prediction performance compared to the ‘serial-section’ model. Furthermore, the same-section model outperformed the serial-section model in stratifying lung cancer patients within a public lung cancer cohort based on survival outcomes, demonstrating its potential clinical utility.DiscussionCollectively, our findings suggest that employing ground truth cell labels obtained through the same-section approach boosts immunophenotyping DL solutions

The interplay of matrix metalloproteinase-8, transforming growth factor-beta 1 and vascular endothelial growth factor-C cooperatively contributes to the aggressiveness of oral tongue squamous cell carcinoma

Background: Matrix metalloproteinase-8 (MMP-8) has oncosuppressive properties in various cancers. We attempted to assess MMP-8 function in oral tongue squamous cell carcinoma (OTSCC). Methods: MMP-8 overexpressing OTSCC cells were used to study the effect of MMP-8 on proliferation, apoptosis, migration, invasion and gene and protein expression. Moreover, MMP-8 functions were assessed in the orthotopic mouse tongue cancer model and by immunohistochemistry in patient samples. Results: MMP-8 reduced the invasion and migration of OTSCC cells and decreased the expression of MMP-1, cathepsin-K and vascular endothelial growth factor-C (VEGF-C). VEGF-C was induced by transforming growth factor-beta 1 (TGF-beta 1) in control cells, but not in MMP-8 overexpressing cells. In human OTSCC samples, low MMP-8 in combination with high VEGF-C was an independent predictor of poor cancer-specific survival. TGF-beta 1 treatment also restored the migration of MMP-8 overexpressing cells to the level of control cells. In mouse tongue cancer, MMP-8 did not inhibit metastasis, possibly because it was eliminated in the peripheral carcinoma cells. Conclusions: The suppressive effects of MMP-8 in OTSCC may be mediated through interference of TGF-beta 1 and VEGF-C function and altered proteinase expression. Together, low MMP-8 and high VEGF-C expression have strong independent prognostic value in OTSCC.Peer reviewe

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer.

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifier, TSR values were predicted and the results were compared to a visual TSR estimation made by a pathologist. The results suggest that classification accuracy does not improve when domain-specific data are used in the pre-training of the convolutional neural network models in the task at hand. Classification accuracy for stroma, tumor and other reached 96.1% on an independent test set. Among the three classes the best model gained the highest accuracy (99.3%) for class tumor. When TSR was predicted with the best model, the correlation between the predicted values and values estimated by an experienced pathologist was 0.57. Further research is needed to study associations between computationally predicted TSR values and other clinicopathological factors of colorectal cancer and the overall survival of the patients