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

Abstract

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.

Keywords: Artificial intelligence; Breast cancer; Chemo resistance; Patient prognosis; Polyploid giant cancer cell.

Fig 1

Representative images of PGCCs in various cancer types. a Representative immunofluorescence images in (i) MDAMB-231 TNBC cells (ii) PC-3 prostate cancer cells (iii) Panc89 pancreatic cancer cells, and (iv) WIDR colon cancer cells. The arrowheads indicate PGCCs. Red: α-tubulin (microtubules); blue: Hoechst (nuclei). Images were acquired using an LSM 700 confocal microscope (magnification,63X). The yellow arrowheads indicate PGCCs. b Representative H&Estained cancer tissues. Blue: hematoxylin (nuclei); pink: eosin (cytoplasm). Images were acquired under a light microscope (i) PGCCs (yellow arrow) seen with irregular and multiple nuclei at the boundaries of poorly differentiated ductal carcinoma of the breast (magnification, 400X); (ii) hepatocellular carcinoma exhibiting sheets of large clear cells with distinct cell borders and scattered hyperchromatic tumor giant cells, some with multinucleation (yellow arrows) (magnification, 200X, H and E stain); (iii)Embryonal carcinoma of testicles showing PGCCs (yellow arrows)(magnification, 600X, H and E stain); (iv) Well differentiated neuroendocrine tumor with striking nesting of bland tumor cells with eccentric nuclei. Scattered hyperchromatic tumor giant cells are also present (yellow arrows) (magnification, 200X, H and E stain); (v) Clear cell renal cell carcinoma, bizarre tumor cells on the right (red arrows) compared to smaller clear cells on left with low nuclear to cytoplasmic ratio (magnification, 400X) (vi) Clusters of malignant cells with prominent cytoplasmic vacuoles are embedded in desmoplastic stroma. Note the presence of multi nucleated tumor giant cells containing bizarre irregular nuclei (red arrows) with coarse chromatin and prominent nucleoli.

Fig. 2.

Generation and fates of PGCCs and their progeny (adapted from Niu et al. and Chen et al.) [12,41].

Fig. 3.

A comparison of traditional and digital pathology and a brief overview of the development of a digital pathology pipeline to characterize PGCCs and other TME components.

Fig. 4.

A schema of the deep learning model based WSI analysis to characterize PGCCs and other key TME components.