Biological changes that occur during metastatic progression of breast cancer are still incompletely characterized. In this study, we compared intrinsic molecular subtypes and gene expression in 123 paired primary and metastatic tissues from breast cancer patients. Intrinsic subtype was identified using a PAM50 classifier and χ² tests determined the differences in variable distribution. The rate of subtype conversion was 0% in basal-like tumors, 23.1% in HER2-enriched (HER2-E) tumors, 30.0% in luminal B tumors, and 55.3% in luminal A tumors. In 40.2% of cases, luminal A tumors converted to luminal B tumors, whereas in 14.3% of cases luminal A and B tumors converted to HER2-E tumors. We identified 47 genes that were expressed differentially in metastatic versus primary disease. Metastatic tumors were enriched for proliferation-related and migration-related genes and diminished for luminal-related genes. Expression of proliferation-related genes were better at predicting overall survival in metastatic disease (OSmet) when analyzed in metastatic tissue rather than primary tissue. In contrast, a basal-like gene expression signature was better at predicting OSmet in primary disease compared with metastatic tissue. We observed correlations between time to tumor relapse and the magnitude of changes of proliferation, luminal B, or HER2-E signatures in metastatic versus primary disease. Although the intrinsic subtype was largely maintained during metastatic progression, luminal/HER2-negative tumors acquired a luminal B or HER2-E profile during metastatic progression, likely reflecting tumor evolution or acquisition of estrogen independence. Overall, our analysis revealed the value of stratifying gene expression by both cancer subtype and tissue type, providing clinicians more refined tools to evaluate prognosis and treatment. Cancer Res; 77(9); 2213–21. ©2017 AACR.

A decade of EGFR inhibition in EGFR-mutated non small cell lung cancer (NSCLC): Old successes and future perspectives A decade of EGFR inhibition in EGFR-mutated non small cell lung cancer (NSCLC): Old successes and future perspectives

Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non–small cell lung cancer (NSCLC). CT scans from 109 treatment-naïve patients with NSCLC (21 EGFR-mutant and 88 EGFR-wild type) underwent radiomics analysis to develop a machine learning model able to recognize EGFR-mutant from EGFR-WT patients via CT scans. A “test–retest” approach was used to identify stable radiomics features. The accuracy of the model was tested on an external validation set from another institution and on a dataset from the Cancer Imaging Archive (TCIA). The machine learning model that considered both radiomic and clinical features (gender and smoking status) reached a diagnostic accuracy of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the datasets from TCIA and the external institution were 76.6% and 83.3%, respectively. Furthermore, 17 distinct radiomics features detected at baseline CT scan were associated with subsequent development of T790M during treatment with an EGFR inhibitor. In conclusion, our machine learning model was able to identify EGFR-mutant patients in multiple validation sets with globally good accuracy, especially after data optimization. More comprehensive training sets might result in further improvement of radiomics-based algorithms. Significance: These findings demonstrate that data normalization and “test–retest” methods might improve the performance of machine learning models on radiomics images and increase their reliability when used on external validation datasets.

Uncommon Epidermal Growth Factor Receptor (EGFR) mutations represent a distinct and highly heterogeneous subgroup of Non-Small Cell Lung Cancers (NSCLCs), that accounts for approximately 10% of all EGFR-mutated patients. The incidence of uncommon EGFR mutations is growing, due to the wider adoption of next-generation sequencing (NGS) for diagnostic purposes, which enables the identification of rare variants, usually missed with available commercial kits that only detect a limited number of EGFR mutations. However, the sensitivity of uncommon mutations to first- and second-generation EGFR Tyrosine Kinase Inhibitors (TKIs) is widely heterogeneous and less well known, compared with classic mutations (i.e., exon 19 deletions and exon 21 L858R point mutation), since most of the pivotal studies with EGFR TKIs in the first line, with few exceptions, excluded patients with rare and/or complex variants. Recently, the third generation EGFR TKI osimertinib further revolutionized the therapeutic algorithm of EGFR-mutated NSCLC, but its role in patients harboring EGFR mutations besides exon 19 deletions and/or L858R is largely unknown. Therefore, a better knowledge of the sensitivity of uncommon mutations to currently available EGFR TKIs is critical to guiding treatment decisions in clinical practice. The aim of this paper is to provide a comprehensive overview of the treatment of NSCLC patients harboring uncommon EGFR mutations with currently approved therapies and to discuss the emerging therapeutic opportunities in this peculiar subgroup of patients, including chemo-immunotherapy combinations, next-generation EGFR TKIs, and novel targeted agents.