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Institute
The induction of apoptosis is a direct way to eliminate tumor cells and improve cancer therapy. Apoptosis is tightly controlled by the balance of pro- and antiapoptotic Bcl-2 proteins. BH3 mimetics neutralize the antiapoptotic function of Bcl-2 proteins and are highly promising compounds inducing apoptosis in several cancer entities including pediatric malignancies. However, the clinical application of BH3 mimetics in solid tumors is impeded by the frequent resistance to single BH3 mimetics and the anticipated toxicity of high concentrations or combination treatments. One potential avenue to increase the potency of BH3 mimetics is the development of immune cell-based therapies to counteract the intrinsic apoptosis resistance of tumor cells and sensitize them to immune attack. Here, we describe spheroid cultures of pediatric cancer cells that can serve as models for drug testing. In these 3D models, we were able to demonstrate that activated allogeneic Natural Killer (NK) cells migrated into tumor spheroids and displayed cytotoxicity against a wide range of pediatric cancer spheroids, highlighting their potential as anti-tumor effector cells. Next, we investigated whether treatment of tumor spheroids with subtoxic concentrations of BH3 mimetics can increase the cytotoxicity of NK cells. Notably, the cytotoxic effects of NK cells were enhanced by the addition of BH3 mimetics. Treatment with either the Bcl-XL inhibitor A1331852 or the Mcl-1 inhibitor S63845 increased the cytotoxicity of NK cells and reduced spheroid size, while the Bcl-2 inhibitor ABT-199 had no effect on NK cell-mediated killing. Taken together, this is the first study to describe the combination of BH3 mimetics targeting Bcl-XL or Mcl-1 with NK cell-based immunotherapy, highlighting the potential of BH3 mimetics in immunotherapy.
In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin–eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.