Antiangiogenic Therapy as a New Strategy in the Treatment of Endometriosis? The First Case Report

Angiogenesis plays a pivotal role in implantation and development of ectopic endometrial lesions. Thus, the potential usefulness of anti-angiogenic therapies has been speculated. Several reports describe their usefulness in animal models. Nonetheless this therapy has not been tested on humans yet. Here we report the outcome of a patient treated for a severe endometriosis with Bevacizumab (Avastin®), a monoclonal antibody directed against the vascular endothelial growth (VEGF). After a first-look laparoscopy with confirmatory biopsies was performed, three doses of Bevacizumab at 2-week intervals were administered. The therapy showed a well-tolerated profile and the prompt disappearance of the therapy-refractory chronic dysmenorrhea. A suppression of metabolic activity at the PET-scan compared to the basal one performed at diagnosis was also recorded. Furthermore, compared to the diagnostic biopsies prior the treatment, we documented a shift in the hormonal receptors profile toward a higher expression of progesterone and estrogen receptors in the endometriotic lesions.

P-OGC57 Predicting survival and response to therapy using diagnostic biopsies: A machine learning approach to facilitate treatment decisions for oesophageal adenocarcinoma

Background Standard of care for locally advanced oesophageal adenocarcinoma is neoadjuvant chemotherapy or chemoradiotherapy followed by surgery. Only a minority of patients (<25%) derive significant survival benefit from neoadjuvant treatment and there are no reliable means of establishing prior to treatment in whom this benefit will occur. Moreover, accurate prediction of survival prior to treatment is also not possible. The availability of machine learning techniques provides the potential to use complex data sources to answer these problems. In this study, we assessed the utility of high-resolution digital microscopy of pre-treatment biopsies in predicting both response to neoadjuvant therapy and overall survival. Methods A total of 157 cases were included in the study. Pre-treatment clinical information, including neoadjuvant treatment, was obtained, along with diagnostic biopsies. Diagnostic biopsies were converted into high-resolution whole slide-images and features extracted using the pre-trained convolutional neural network Xception. Single representative images were converted into patches from which predictive models were trained. Elastic net regression classifiers were derived and validated with bootstrapping and 1000 resampled datasets. The response to treatment was considered according to Mandard tumour regression grade (TRG). Model performance was quantified using the C-index (for TRG) and time-dependent AUC (tAUC, fo Overall survival) along with calibration plots. Results Median survival was 78.9months (95%CI 35.9 months – not reached). Survival at 5-years was 52.1%. Neoadjuvant treatment was received by 123 patients (78.3%), with a significant response seen in 45 cases (36.6%). A response was more likely in those patients who received chemoradiotherapy than chemotherapy (53.3% vs 23.1% p < 0.001) and in older patients (median age 69.4 vs 66.0 years, p = 0.038), with other characteristics similar. A predictive model for response to neoadjuvant treatment derived from image features and clinical data achieved good discrimination (C-index 0.767, 95%CI 0.701-0.833) and calibration. Accuracy of prediction of overall survival was more modest (tAUC 0.640, 95%CI 0.518-0.762). Conclusions Using a small dataset, utility of a feature extraction pipeline in prediction of patient level outcomes has been demonstrated. This was more marked in prediction of response to neoadjuvant treatment than overall survival, which may reflect the importance of pre-treatment clinical data in determining the former outcome. Further study to refine the methodology and confirmation in larger datasets are required before expansion to clinical settings.

Modification of Breast Cancer Milieu with Chemotherapy Plus Dendritic Cell Vaccines: An Approach to Select Best Therapeutic Strategies

Purpose: The addition of dendritic cell vaccines (DCV) to neoadjuvant chemotherapy (NAC) could induce immune biomarker changes in those patients with residual disease (RD) by transforming tumor microenvironment. Methods: Core diagnostic biopsies and surgical specimens from 80 patients (38 in the Vaccinated Group plus NAC (VG) and 42 in the Control Group (CG) treated only with NAC) were selected. We quantify TILs (CD8, CD4 and CD45RO) using Immunohistochemistry (IHC) and the Automated Cellular Imaging System (ACIS III) in the core-diagnostic biopsies and in the surgical specimens, to compare the amount of TILs in each group. Results: A CD8 rise in TNBC samples was observed after NAC plus DCV, changing from 4.48% in the biopsy to 6.70% in the surgical specimen, not reaching statistically significant differences (p = 0.11). TNBC patients in the CG showed a TILs drop from 2.71% in the biopsy to 0.18% in the surgical specimen (p = 0.5). We also found that 66.7% (4/6) of TNBC patients from VG registered an increase in TILs after treatment as compared to 20% (1/5) of TNBC patients in the CG (p=0.24). This phenomenon is not observed in the other biologic subtypes. An association between before NAC CD8 TILs (4% cut-off point ) and pathological complete response in the VG was found in univariate and multivariate analysis (OR=1.41, IC95% 1.05-1.90; p=0.02, and OR=2.0, IC95% 1.05-3.9; p=0.03, respectively).Conclusion: Our findings suggest that patients with TNBC especially benefit from the stimulation of the antitumor immune system by using DCV pulsed with tumor antigens.

Association of anesthetic and surgical risk factors with outcomes of initial diagnostic biopsies in a current cohort of children with anterior mediastinal masses

BackgroundDiagnostic biopsies of pediatric anterior mediastinal masses (AMMs) are high-risk procedures in which general anesthesia (GA) is traditionally avoided. However, awareness of historically recognized risk factors and corresponding perioperative management have improved over time and may now no longer strictly preclude the use of GA. Therefore, in this study, we examined the association of anesthetic and surgical risk factors and modalities with resulting procedural and survival outcomes in a current patient cohort.MethodsWe retrospectively reviewed charts of 35 children with AMMs who underwent initial diagnostic biopsies between January 2001 and August 2019, and determined tracheal compression and deviation from archival CT scans and procedural and disease outcomes.ResultsTwenty-three (65%) patients underwent GA while 12 (35%) received sedation. Among patients with available CT measurements, 13 of 25 (52%) had >50% anteroposterior tracheal diameter reduction. Patients with >50% anteroposterior tracheal compression received sedation more frequently (p=0.047) and were positioned upright (p=0.015) compared with patients with ≤50% compression, although 4 of 13 and 9 of 12, respectively, still received GA. Intraoperative adverse events (AEs) occurred in four (11.4%) patients: three received GA, and all were positioned supine or lateral. AEs were not associated with radiographic airway risk factors but were significantly associated with morphine and sevoflurane use (p<0.001) and with thoracoscopic biopsies (p=0.035). There were no on-table mortalities, but four delayed deaths occurred (three related to disease and one from late procedural complications).ConclusionsIn a current cohort of pediatric AMM biopsies, patients with >50% anteroposterior tracheal compression were more frequently managed with a conservative perioperative management strategy, though not completely excluding GA. The corresponding reduction in frequency of procedural AEs in this traditionally high-risk group suggests that increased awareness of procedural risk factors and appropriate risk-guided perioperative management choices may obviate the procedural mortality historically associated with pediatric AMM biopsies.

Modification of Breast Cancer Milieu with Chemotherapy Plus Dendritic Cell Vaccines: An Approach to Select Best Therapeutic Strategies

BackgroundThe addition of Dendritic cell vaccines (DCV) to neoadjuvant chemotherapy (NAC) could induce immune biomarker changes in those patients with residual disease (RD) by transforming tumor microenvironment.MethodsCore-diagnostic biopsies and surgical specimens from 80 patients (38 in the Vaccinated Group plus NAC (VG) and 42 in the Control Group (CG) treated only with NAC) were selected. We quantify TILs (CD8, CD4 and CD45RO) using Immunohistochemistry (IHC) and the Automated Cellular Imaging System (ACIS III) in the core-diagnostic biopsies and in the surgical specimen, to compare the amount of TILs in each group.ResultsA CD8 rise in TNBC samples was observed after NAC plus DCV, changing from 4.48% in the biopsy to 6.70% in the surgical specimen, not reaching statistically significant differences (p = 0.11). TNBC patients in the CG showed a TILs drop from 2.71% in the biopsy to 0.18% in the surgical specimen (p = 0.5). We also found that a 66.7% (4/6) of TNBC patients from VG registered an increase in TILs after treatment as compared with 20% (1/5) of TNBC patients in the CG (p=0.24). This phenomenon is not observed in the other biologic subtypes.An association between before NAC CD8 TILs (4% cut-off point ) and pathological complete response in VG was found in univariate and multivariate analysis (OR=1.41, IC95% 1.05-1.90; p=0.02, and OR=2.0, IC95% 1.05-3.9; p=0.03, respectively).ConclusionOur findings suggest that patients with TNBC especially benefit from the stimulation of the antitumor immune system by using DCV pulsed with tumor antigens.Trial registration: NCT01431196. Registred 19 May 2016. EudraCT 2009- 017402-36.

Modification of Breast Cancer Milieu With Chemotherapy Plus Dendritic Cell Vaccines: An Approach To Select Best Therapeutic Strategies

BackgroundThe addition of Dendritic cell vaccines (DCV) to neoadjuvant chemotherapy (NAC) could induce immune biomarker changes in those patients with residual disease (RD) by transforming tumor microenvironment.MethodsCore-diagnostic biopsies and surgical specimens from 80 patients (38 in the Vaccinated Group plus NAC (VG) and 42 in the Control Group (CG) treated only with NAC) were selected. We quantify TILs (CD8, CD4 and CD45RO) using Immunohistochemistry (IHC) and the Automated Cellular Imaging System (ACIS III) in the core-diagnostic biopsies and in the surgical specimen, to compare the amount of TILs in each group.ResultsA CD8 rise in TNBC samples was observed after NAC plus DCV, changing from 4.48% in the biopsy to 6.70% in the surgical specimen, not reaching statistically significant differences (p = 0.11). TNBC patients in the CG showed a TILs drop from 2.71% in the biopsy to 0.18% in the surgical specimen (p = 0.5). We also found that a 66.7% (4/6) of TNBC patients from VG registered an increase in TILs after treatment as compared with 20% (1/5) of TNBC patients in the CG (p=0.24). This phenomenon is not observed in the other biologic subtypes.An association between before NAC CD8 TILs (4% cut-off point ) and pathological complete response in VG was found in univariate and multivariate analysis (OR=1.41, IC95% 1.05-1.90; p=0.02, and OR=2.0, IC95% 1.05-3.9; p=0.03, respectively).ConclusionOur findings suggest that patients with TNBC especially benefit from the stimulation of the antitumor immune system by using DCV pulsed with tumor antigens.Trial registration: NCT01431196. Registred 19 May 2016. EudraCT 2009- 017402-36.

O4 Neural network image capture to predict response of oesophageal adenocarcinoma to neoadjuvant therapy

Introduction Locally advanced oesophageal adenocarcinoma is typically treated with neoadjuvant chemotherapy (NACT) or chemoradiotherapy (NACRT) followed by surgery. Significant benefit to neoadjuvant treatment however is confined to a minority of patients (&lt;25%) and there are no reliable means of establishing prior to treatment in whom this benefit will occur. In this study, we assessed the utility of features extracted from high-resolution digital microscopy of pre-treatment biopsies in predicting response to neoadjuvant therapy in a machine-learning based modelling framework. Method A total of 102 cases were included in the study. Pre-treatment clinical information, including TNM staging, was obtained, along with diagnostic biopsies. Diagnostic biopsies were converted into high-resolution whole slide-images and features extracted using a pre-trained convolutional neural network (Xception). Elastic net regression models were then trained and validated with bootstrapping with 1000 resampled datasets. The response was considered according to Mandard tumour regression grade (TRG). Result There were 45 (44.1%) responders (TRG1-2) and 57 (57%) non-responders (TRG3-5) in the dataset. 34 patients (33.3%) received NACT and 68 (66.7%) received NACRT. A model trained with RNA-seq data achieved fair performance only in predicting response (AUC 0.598 95% CI 0.593–0.603), which was far exceeded by use of segmented diagnostic biopsy images (AUC 0.872 95% CI 0.869–0.875), which also produced well calibrated predictions of risk. Conclusion Despite using a small dataset, impressive performance in classifying response to neoadjuvant treatment can be achieved, particularly using automated image classification. Further study to refine the methodology is required before expansion to clinical settings. Take-home Message Response to neoadjuvant treatment for oesophageal cancer can be predicted from diagnostic biopsies