Preclinical studies performed in appropriate models could help identify optimal timing of combined chemotherapy and immunotherapy

Immune checkpoint inhibitors (ICI) have been revolutionary in the field of cancer therapy. However, their success is limited to specific indications and cancer types. Recently, the combination treatment of ICI and chemotherapy has gained more attention to overcome this limitation. Unfortunately, many clinical trials testing these combinations have provided limited success. This can partly be attributed to an inadequate choice of preclinical models and the lack of scientific rationale to select the most effective immune-oncological combination. In this review, we have analyzed the existing preclinical evidence on this topic, which is only limitedly available. Furthermore, this preclinical data indicates that besides the selection of a specific drug and dose, also the sequence or order of the combination treatment influences the study outcome. Therefore, we conclude that the success of clinical combination trials could be enhanced by improving the preclinical set up, in order to identify the optimal treatment combination and schedule to enhance the anti-tumor immunity

Opposite Macrophage Polarization in Different Subsets of Ovarian Cancer: Observation from a Pilot Study

The role of the innate immune system in ovarian cancer is gaining importance. The relevance of tumor-associated macrophages (TAM) is insufficiently understood. In this pilot project, comprising the immunofluorescent staining of 30 biopsies taken from 24 patients with ovarian cancer, we evaluated the presence of total TAM (cluster of differentiation (CD) 68 expression), M1 (major histocompatibility complex (MHC) II expression), and M2 (anti-mannose receptor C type 1 (MRC1) expression), and the blood vessel diameter. We observed a high M1/M2 ratio in low-grade ovarian cancer compared to high-grade tumors, more total TAM and M2 in metastatic biopsies, and a further increase in total TAM and M2 at interval debulking, without beneficial effects of bevacizumab. The blood vessel diameter was indicative for M2 tumor infiltration (Spearman correlation coefficient of 0.65). These data mainly reveal an immune beneficial environment in low-grade ovarian cancer in contrast to high-grade serous ovarian cancer, where immune suppression is not altered by neoadjuvant therapy.status: publishe

Trial watch: dendritic cell vaccination for cancer immunotherapy

Dendritic- cells (DCs) have received considerable attention as potential targets for the development of anticancer vaccines. DC-based anticancer vaccination relies on patient-derived DCs pulsed with a source of tumor-associated antigens (TAAs) in the context of standardized maturation-cocktails, followed by their reinfusion. Extensive evidence has confirmed that DC-based vaccines can generate TAA-specific, cytotoxic T cells. Nonetheless, clinical efficacy of DC-based vaccines remains suboptimal, reflecting the widespread immunosuppression within tumors. Thus, clinical interest is being refocused on DC-based vaccines as combinatorial partners for T cell-targeting immunotherapies. Here, we summarize the most recent preclinical/clinical development of anticancer DC vaccination and discuss future perspectives for DC-based vaccines in immuno-oncology.status: publishe

Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm

Abstract Background Assessing malignancy risk is important to choose appropriate management of ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian tumor is benign, borderline malignant, stage I primary invasive, stage II-IV primary invasive, or secondary metastatic. Methods This retrospective cohort study used 5909 patients recruited from 1999 to 2012 for model development, and 3199 patients recruited from 2012 to 2015 for model validation. Patients were recruited at oncology referral or general centers and underwent an ultrasound examination and surgery ≤ 120 days later. We developed models using standard multinomial logistic regression (MLR), Ridge MLR, random forest (RF), XGBoost, neural networks (NN), and support vector machines (SVM). We used nine clinical and ultrasound predictors but developed models with or without CA125. Results Most tumors were benign (3980 in development and 1688 in validation data), secondary metastatic tumors were least common (246 and 172). The c-statistic (AUROC) to discriminate benign from any type of malignant tumor ranged from 0.89 to 0.92 for models with CA125, from 0.89 to 0.91 for models without. The multiclass c-statistic ranged from 0.41 (SVM) to 0.55 (XGBoost) for models with CA125, and from 0.42 (SVM) to 0.51 (standard MLR) for models without. Multiclass calibration was best for RF and XGBoost. Estimated probabilities for a benign tumor in the same patient often differed by more than 0.2 (20% points) depending on the model. Net Benefit for diagnosing malignancy was similar for algorithms at the commonly used 10% risk threshold, but was slightly higher for RF at higher thresholds. Comparing models, between 3% (XGBoost vs. NN, with CA125) and 30% (NN vs. SVM, without CA125) of patients fell on opposite sides of the 10% threshold. Conclusion Although several models had similarly good performance, individual probability estimates varied substantially

Increased Immunosuppression Is Related to Increased Amounts of Ascites and Inferior Prognosis in Ovarian Cancer

BACKGROUND/AIM: The presence of ascites in ovarian cancer patients is considered a negative prognostic factor. The underlying mechanisms are not clearly understood. MATERIALS AND METHODS: The amount of ascites was evaluated, preferably, using diffusion-weighted MRI at primary diagnosis in a retrospective cohort of 214 women with ovarian cancer, in an ordinal manner (amount of ascites: none, limited, moderate, abundant). In a prospective cohort comprising 45 women with ovarian cancer, IL-10 (interleukin), VEGF (vascular endothelial growth factor), TGF-β (transforming growth factor) and CCL-2 [chemokine (C-C) motif ligand 2] were measured at diagnosis (and at interval debulking, when available). RESULTS: Gradually increasing amounts of ascites were correlated significantly, even after correction for FIGO stage, with reduced survival (p<0.0001) and stronger immunosuppression (IL10 and VEGF). Neoadjuvant chemotherapy reduced immunosuppression, which was observed as a reduction in CCL-2, IL-10 and VEGF. CONCLUSION: The amount of ascites is an independent predictor of survival and correlates with increased immunosuppression.status: publishe

Neo-Adjuvant chemotherapy reduces, and surgery increases immunosuppression in first-line treatment for ovarian cancer

SIMPLE SUMMARY: The immune system plays an important role in the development and progression of cancer. The current treatments for ovarian cancer (surgery and chemotherapy) create changes in the immune system, but it is not clear how. Nevertheless, if immunotherapy is associated on top of this, then it seems crucial to understand what is changing in the current state of the art. In this study, we measured immune-related proteins in the serum of ovarian cancer patients throughout their treatment. We discovered that carboplatin–paclitaxel as a chemotherapeutic treatment reduces immunosuppression and promotes immunostimulation, meaning that the immune system be-comes less hostile and more in favour of the patient. Therefore, chemotherapy seems to induce a temporary window of opportunity to insert immunotherapy during the current treatment of ovarian cancer patients. ABSTRACT: In monotherapy, immunotherapy has a poor success rate in ovarian cancer. Upgrading to a successful combinatorial immunotherapy treatment implies knowledge of the immune changes that are induced by chemotherapy and surgery. Methodology: Patients with a new ovarian cancer diagnosis underwent longitudinal blood samples at different time points during primary treatment. Results.: Ninety patients were included in the study (33% primary debulking surgery (PDS) with adjuvant chemotherapy (ACT), 61% neo-adjuvant chemotherapy (NACT) with interval debulking surgery (IDS), and 6% debulking surgery only). Reductions in immunosuppression were observed after NACT, but surgery reverted this effect. The immune-related proteins showed a pronounced decrease in immune stimulation and immunosuppression when primary treatment was completed. NACT with IDS leads to a transient amelioration of the immune microenvironment compared to PDS with ACT. Conclusion: The implementation of immunotherapy in the primary treatment schedule of ovarian cancer cannot be induced blindly. Carboplatin–paclitaxel seems to ameliorate the hostile immune microenvironment in ovarian cancer, which is less pronounced at the end of primary treatment. This prospective study during primary therapy for ovarian cancer that also looks at the evolution of immune-related proteins provides us with an insight into the temporary windows of opportunity in which to introduce immunotherapy during primary treatment

Assessment of protein biomarkers for preoperative differential diagnosis between benign and malignant ovarian tumors

Objective. To estimate the diagnostic value of tumor and immune related proteins in the discrimination between benign and malignant adnexal masses, and between different subgroups of tumors. Methods. In this exploratory diagnostic study, 254 patientswith an adnexal mass scheduled for surgery were consecutively enrolled at the University Hospitals Leuven (128 benign, 42 borderline, 22 stage I, 55 stage II-IV, and 7 secondary metastatic tumors). The quantification of 33 serum proteins was done preoperatively, using multiplex high throughput immunoassays (Luminex) and electrochemiluminescence immuno-assay (ECLIA). We calculated univariable areas under the Receiver Operating Characteristic Curves (AUCs). To discriminate malignant from benign tumors, multivariable ridge logistic regression with backward elimination was performed, using bootstrapping to validate the resulting AUCs. Results. CA125 had the highest univariable AUC to discriminate malignant from benign tumors (0.85, 95% confidence interval 0.79-0.89). Combining CA125 with CA72.4 and HE4 increased the AUC to 0.87. For benign vs borderline tumors, CA125 had the highest univariable AUC (0.74). For borderline vs stage I malignancy, no proteins were promising. For stage I vs II-IV malignancy, CA125, HE4, CA72.4, CA15.3 and LAP had univariable AUCs ≥0.80. Conclusions. The results confirm the dominant role of CA125 for identifying malignancy, and suggest that other markers (HE4, CA72.4, CA15.3 and LAP) may help to distinguish between stage I and stage II-IV malignancies. However, further research is needed, also to investigate the added value over clinical and ultrasound predictors of malignancy, focusing on the differentiation between subtypes of malignancy.status: accepte

Myeloid-derived suppressor cells at diagnosis may discriminate between benign and malignant ovarian tumors

BACKGROUND: The behavior of the immune system as a driver in the progression of ovarian cancer has barely been studied. Our knowledge is mainly limited to the intra-tumoral adaptive immune system. Because of the widespread metastases of ovarian cancer, an assessment of the circulating immune system seems more accurate.To demonstrate the presence of immune cells in blood samples of patients with ovarian neoplasms. METHODS: In this exploratory prospective cohort study, peripheral blood mononuclear cells were collected at diagnosis from 143 women, including 62 patients with benign cysts, 13 with borderline tumor, 41 with invasive ovarian cancer, and 27 age-matched healthy controls. Immune profile analyses, based on the presence of CD4 (cluster of differentiation), CD8, natural killer cells, myeloid-derived suppressor cells, and regulatory T cells, were performed by fluorescence activated cell sorting. RESULTS: In a multivariable analysis, six immune cells (activated regulatory T cells, natural killer cells, myeloid-derived suppressor cells, monocytic myeloid-derived suppressor cells, exhausted monocytic myeloid-derived suppressor cells, and total myeloid cells) were selected as independent predictors of malignancy, with an optimism-corrected area under the receiver operating characteristic curve (AUC) of 0.858. In contrast, a profile based on CD8 and regulatory T cells, the current standard in ovarian cancer immunology, resulted in an AUC of 0.639. CONCLUSIONS: Our immune profile in blood suggests an involvement of innate immunosuppression driven by myeloid-derived suppressor cells in the development of ovarian cancer. This finding could contribute to clinical management of patients and in selection of immunotherapy.status: publishe

Additional file 1 of Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm

Supplementary Material 1. Predictor selection. Supplementary Material 2. Sample size argumentation. Supplementary Material 3. Hyperparameter tuning. Supplementary Material 4. Multiple imputation for CA125. Supplementary Material 5. Flowcharts for modeling and validation procedure. Table S1. Descriptive statistics by reference standard (final diagnosis). Table S2. List of centers in the development and validation data. Table S3. Pairwise area under the receiver operating characteristic curve (AUROC) values (with 95% CI) for models with CA125 on external validation data. Table S4. Pairwise area under the receiver operating characteristic curve (AUROC) values for models without CA125. Table S5. Percentage of patients on validation data falling on opposite sides of the 10% risk of malignancy threshold when comparing two models. Figure S1. Polytomous discrimination index for models with CA125 on external validation data. Figure S2. AUROC for benign tumors vs any malignancy for models with CA125. Figure S3. Polytomous Discrimination Index (PDI) for models without CA125. Figure S4. AUROC for benign tumors vs any malignancy for models without CA125. Figure S5. Box plots of estimated probabilities for standard MLR with CA125. Figure S6. Box plots of estimated probabilities for ridge MLR with CA125. Figure S7. Box plots of estimated probabilities for random forest with CA125. Figure S8. Box plots of estimated probabilities for extreme gradient boosting (XGBoost) with CA125. Figure S9. Box plots of estimated probabilities for neural network with CA125. Figure S10. Box plots of estimat ed probabilities for support vector machine with CA125. Figure S11. Flexible calibration curves for models without CA125. Figure S12. Box plots of estimated probabilities for standard MLR without CA125. Figure S13. Box plots of estimated probabilities for ridge MLR without CA125. Figure S14. Box plots of estimated probabilities for random forest without CA125. Figure S15. Box plots of estimated probabilities for extreme gradient boosting (XGBoost) without CA125. Figure S16. Box plots of estimated probabilities for neural network without CA125. Figure S17. Box plots of estimated probabilities for support vector machine without CA125. Figure S18. Decision curves for models with CA125 on external validation data. Figure S19. Decision curves for models without CA125 on external validation data. Figure S20. Differences between the highest and lowest estimated probability for each outcome across the six models with CA125 (panel A) and the six models without CA125 (panel B) for patients in the external validation dataset. Each dot denotes the difference between the highest and the lowest estimated probability for one patient. This means that each patient is shown five times in each panel, once for each outcome category. For example, at the far left, the difference between the highest and lowest estimated probability for a benign tumor is shown for all 3199 patients in the dataset. The box represents the interquartile range which contains the middle 50% of the differences. The line inside the box indicates the median. Whiskers correspond to the 5th and 95th percentile. Figure S21. Scatter plots of the estimated risk of a benign tumor for each pair of models without CA125. Figure S22. Scatter plots of the estimated risk of a borderline tumor for each pair of models without CA125. Figure S23. Scatter plots of the estimated risk of a stage I primary invasive tumor for each pair of models without CA125. Figure S24. Scatter plots of the estimated risk of a stage II-IV primary invasive tumor for each pair of models without CA125. Figure S25. Scatter plots of the estimated risk of a secondary metastatic tumor for each pair of models without CA125