Cytokine networks and cancer stem cells

Cell-to-cell communication is an integral function of multicellular organisms. Many of these signals are received by a myriad of cell-surface receptors that utilize a range of intracellular signaling pathways to communicate this to the nucleus, rapidly impacting on the transcription of target genes in order to elicit the desired response, such as proliferation, differentiation, activation, and survival. Dysregulation of these important signaling pathways, and networks, often lead to pathological conditions due to inappropriate cell responses with negative consequences. The aberrant signaling pathways have been associated with many diseases, including cancer. Cytokines and chemokines convey a multitude of messages to the target cell, many of which are beneficial for cancers and cancer stem cells, such as proliferation, survival and migration. By hijacking this communication network, cancers and cancer stem cells can become invasive and more pathogenic. Furthermore, by using these communication systems, cancer stem cells are able to evade current therapies. Therefore, novel therapies may be developed to break the communication systems of the cancer stem cells. This chapter explores the role of the cytokines TGF-β, TNF-α, IL-1 and IL-6 and chemokine CXCL8 as well as NF-κB and their role in cancer stem cell survival and maintenance. Emerging therapies are beginning to target the cancer stem cell population, either specifically or synergistically with existing therapeutic options. These novel therapies may hold the key to breaking the communication network of cancer stem cells.

Toxicity prediction in cancer using multiple instance learning in a multi-task framework

Treatments of cancer cause severe side effects called toxicities. Reduction of such effects is crucial in cancer care. To impact care, we need to predict toxicities at fortnightly intervals. This toxicity data differs from traditional time series data as toxicities can be caused by one treatment on a given day alone, and thus it is necessary to consider the effect of the singular data vector causing toxicity. We model the data before prediction points using the multiple instance learning, where each bag is composed of multiple instances associated with daily treatments and patient-specific attributes, such as chemotherapy, radiotherapy, age and cancer types. We then formulate a Bayesian multi-task framework to enhance toxicity prediction at each prediction point. The use of the prior allows factors to be shared across task predictors. Our proposed method simultaneously captures the heterogeneity of daily treatments and performs toxicity prediction at different prediction points. Our method was evaluated on a real-word dataset of more than 2000 cancer patients and had achieved a better prediction accuracy in terms of AUC than the state-of-art baselines.