Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer

Although polyploid cells were first described nearly two centuries ago, their ability to proliferate has only recently been demonstrated. It also becomes increasingly evident that a subset of tumor cells, polyploid giant cancer cells (PGCCs), play a critical role in the pathophysiology of breast cancer (BC), among other cancer types. In BC, PGCCs can arise in response to therapy-induced stress. Their progeny possess cancer stem cell (CSC) properties and can repopulate the tumor. By modulating the tumor microenvironment (TME), PGCCs promote BC progression, chemoresistance, metastasis, and relapse and ultimately impact the survival of BC patients. Given their pro- tumorigenic roles, PGCCs have been proposed to possess the ability to predict treatment response and patient prognosis in BC. Traditionally, DNA cytometry has been used to detect PGCCs.. The field will further derive benefit from the development of approaches to accurately detect PGCCs and their progeny using robust PGCC biomarkers. In this review, we present the current state of knowledge about the clinical relevance of PGCCs in BC. We also propose to use an artificial intelligence-assisted image analysis pipeline to identify PGCC and map their interactions with other TME components, thereby facilitating the clinical implementation of PGCCs as biomarkers to predict treatment response and survival outcomes in BC patients. Finally, we summarize efforts to therapeutically target PGCCs to prevent chemoresistance and improve clinical outcomes in patients with BC.

Hyperspectral microscopy combined with DAPI staining for the identification of hepatic carcinoma cells

In this study, the DAPI staining is firstly reported for use in the identification of hepatic carcinoma cells based on hyperspectral microscopy. Nuclei in cancer cells usually contain more aneuploidies than that in normal cells, leading to the change of DNA content. Here, we stain hepatic carcinoma tissues and normal hepatic tissues with 4',6-diamidino-2-phenylindole (DAPI) which is sensitive to the DNA content as a fluorochrome binds to DNA. Consequently, the difference in DNA content between hepatic carcinoma cells and normal hepatic cells can be identified by the fluorescent spectral characteristics. Harnessing the hyperspectral microscopy, we find that the fluorescent properties of these two kinds of cells are different not only in the intensity but also in the spectral shape. These properties are exploited to train a support vector machine (SVM) model for classifying cells. The results show that the sensitivity and specificity for the identification of 1000 hepatic carcinoma samples are 99.3% and 99.1%, respectively.