Transcriptomic properties of her2+ ductal carcinoma in situ of the breast associate with absence of immune cells

SIMPLE SUMMARY: Tumor-infiltrating lymphocytes (TILs) are likely to play a role in the biological behavior of HER2+ ductal carcinoma in situ (DCIS). To prevent invasiveness, the potential of targeted immune-modulating treatment of HER2+ DCIS has been explored. We identified a 29-gene expression profile that was associated with the density of TILs. These genes included CCND3, DUSP10 and RAP1GAP, which may guide towards more rationalized choices with respect to immune-mediated therapy in HER2+ DCIS, such as targeted vaccine therapy. ABSTRACT: The identification of transcriptomic alterations of HER2+ ductal carcinoma in situ (DCIS) that are associated with the density of tumor-infiltrating lymphocytes (TILs) could contribute to optimizing choices regarding the potential benefit of immune therapy. We compared the gene expression profile of TIL-poor HER2+ DCIS to that of TIL-rich HER2+ DCIS. Tumor cells from 11 TIL-rich and 12 TIL-poor DCIS cases were micro-dissected for RNA isolation. The Ion AmpliSeq Transcriptome Human Gene Expression Kit was used for RNA sequencing. After normalization, a Mann–Whitney rank sum test was used to analyze differentially expressed genes between TIL-poor and TIL-rich HER2+ DCIS. Whole tissue sections were immunostained for validation of protein expression. We identified a 29-gene expression profile that differentiated TIL-rich from TIL-poor HER2+ DCIS. These genes included CCND3, DUSP10 and RAP1GAP, which were previously described in breast cancer and cancer immunity and were more highly expressed in TIL-rich DCIS. Using immunohistochemistry, we found lower protein expression in TIL-rich DCIS. This suggests regulation of protein expression at the posttranslational level. We identified a gene expression profile of HER2+ DCIS cells that was associated with the density of TILs. This classifier may guide towards more rationalized choices regarding immune-mediated therapy in HER2+ DCIS, such as targeted vaccine therapy

Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Introduction: Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC. Methods: Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [18F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC. Results: In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively). Conclusion: CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients

Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Introduction: Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC. Methods: Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [18F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC. Results: In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively). Conclusion: CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients