Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fβ-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms

Effects of iron modulation on mesenchymal stem cell-induced drug resistance in estrogen receptor-positive breast cancer

Patients with estrogen receptor-positive (ER+) breast cancer, the most common subtype, remain at risk for lethal metastatic disease years after diagnosis. Recurrence arises partly because tumor cells in bone marrow become resistant to estrogen-targeted therapy. Here, we utilized a co-culture model of bone marrow mesenchymal stem cells (MSCs) and ER+ breast cancer cells to recapitulate interactions of cancer cells in bone marrow niches. ER+ breast cancer cells in direct contact with MSCs acquire cancer stem-like (CSC) phenotypes with increased resistance to standard antiestrogenic drugs. We confirmed that co-culture with MSCs increased labile iron in breast cancer cells, a phenotype associated with CSCs and disease progression. Clinically approved iron chelators and in-house lysosomal iron-targeting compounds restored sensitivity to antiestrogenic therapy. These findings establish iron modulation as a mechanism to reverse MSC-induced drug resistance and suggest iron modulation in combination with estrogen-targeted therapy as a promising, translatable strategy to treat ER+ breast cancer