The Mechanical Fingerprint of Circulating Tumor Cells (CTCs) in Breast Cancer Patients

Label-free detection and enumeration of rare circulating tumor cells by bright-field image cytometry and multi-frame image correlation analysis

Identification and enumeration of circulating tumor cells (CTCs) in peripheral blood are proved to correlate with the progress of metastatic cancer and can provide valuable information for diagnosis and monitoring of cancer. Here, we introduce a bright-field image cytometry (BFIC) technique, assisted by a multi-frame image correlation (MFIC) algorithm, as a label-free approach for tumor cell detection in peripheral blood. For this method, images of flowing cells in a wide channel were continuously recorded and cell types were determined simultaneously using a deep neural network of YOLO-V4 with an average precision (AP) of 98.63%, 99.04%, and 98.95% for cancer cell lines HT29, A549, and KYSE30, respectively. The use of the wide microfluidic channel (400 μm width) allowed for a high throughput of 50 000 cells per min without clogging. Then erroneous or missed cell classifications caused by imaging angle differences or accidental misinterpretations in single frames were corrected by the multi-frame correlation analysis. This further improved the AP to 99.40%, 99.52%, and 99.47% for HT29, A549, and KYSE30, respectively. Meanwhile, cell counting was also accomplished in this dynamic process. Moreover, our imaging cytometry method can readily detect as few as 10 tumor cells from 100 000 white blood cells and was unaffected by the EMT process. Furthermore, CTCs from 8 advanced-stage cancer clinical samples were also successfully detected, while none for 6 healthy control subjects. Although this method is implemented for CTCs, it can also be used for the detection of other rare cells.

Identification of Secondary Breast Cancer in Vital Organs through the Integration of Machine Learning and Microarrays

Breast cancer includes genetic and environmental factors and is the most prevalent malignancy in women contributing to the pathogenesis and progression of cancer. Breast cancer prognosis metastasizes towards bones, the liver, brain, and lungs, and is the main cause of death in patients. Furthermore, the selection of features and classification is significant in microarray data analysis, which suffers from huge time consumption. To address these issues, this research uniquely integrates machine learning and microarrays to identify secondary breast cancer in vital organs. This work firstly imputes the missing values using K-nearest neighbors and improves the recursive feature elimination with cross-validation (RFECV) using the random forest method. Secondly, the class imbalance is handled by employing K-means synthetic object oversampling technique (SMOTE) to balance minority class and prevent noise. We successfully identified the 16 most essential Entrez gene ids responsible for predicting metastatic locations in the bones, brain, liver, and lungs. Extensive experiments are conducted on NCBI Gene Expression Omnibus GSE14020 and GSE54323 datasets. The proposed methods have handled class imbalance, prevented noise, and appropriately reduced time consumption. Reliable results were obtained on four classification models: decision tree; K-nearest neighbors; random forest; and support vector machine. Results are presented having considered confusion matrices, accuracy, ROC-AUC and PR-AUC, and F1-score.

Pancreatic Ductal Adenocarcinoma Cortical Mechanics and Clinical Implications

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers due to low therapeutic response rates and poor prognoses. Majority of patients present with symptoms post metastatic spread, which contributes to its overall lethality as the 4th leading cause of cancer-related deaths. Therapeutic approaches thus far target only one or two of the cancer specific hallmarks, such as high proliferation rate, apoptotic evasion, or immune evasion. Recent genomic discoveries reveal that genetic heterogeneity, early micrometastases, and an immunosuppressive tumor microenvironment contribute to the inefficacy of current standard treatments and specific molecular-targeted therapies. To effectively combat cancers like PDAC, we need an innovative approach that can simultaneously impact the multiple hallmarks driving cancer progression. Here, we present the mechanical properties generated by the cell’s cortical cytoskeleton, with a spotlight on PDAC, as an ideal therapeutic target that can concurrently attack multiple systems driving cancer. We start with an introduction to cancer cell mechanics and PDAC followed by a compilation of studies connecting the cortical cytoskeleton and mechanical properties to proliferation, metastasis, immune cell interactions, cancer cell stemness, and/or metabolism. We further elaborate on the implications of these findings in disease progression, therapeutic resistance, and clinical relapse. Manipulation of the cancer cell’s mechanical system has already been shown to prevent metastasis in preclinical models, but it has greater potential for target exploration since it is a foundational property of the cell that regulates various oncogenic behaviors.