Fluorescence-Based Quantitative and Spatial Analysis of Tumour Spheroids: A Proposed Tool to Predict Patient-Specific Therapy Response

Tumour spheroids are widely used to pre-clinically assess anti-cancer treatments. They are an excellent compromise between the lack of microenvironment encountered in adherent cell culture conditions and the great complexity of in vivo animal models. Spheroids recapitulate intra-tumour microenvironment-driven heterogeneity, a pivotal aspect for therapy outcome that is, however, often overlooked. Likely due to their ease, most assays measure overall spheroid size and/or cell death as a readout. However, as different tumour cell subpopulations may show a different biology and therapy response, it is paramount to obtain information from these distinct regions within the spheroid. We describe here a methodology to quantitatively and spatially assess fluorescence-based microscopy spheroid images by semi-automated software-based analysis. This provides a fast assay that accounts for spatial biological differences that are driven by the tumour microenvironment. We outline the methodology using detection of hypoxia, cell death and PBMC infiltration as examples, and we propose this procedure as an exploratory approach to assist therapy response prediction for personalised medicine.

A Hybrid Interpolation Weighted Collaborative Filtering Method for Anti-cancer Drug Response Prediction

Individualized therapies ask for the most effective regimen for each patient, while the patients' response may differ from each other. However, it is impossible to clinically evaluate each patient's response due to the large population. Human cell lines have harbored most of the same genetic changes found in patients' tumors, thus are widely used to help understand initial responses of drugs. Based on the more credible assumption that similar cell lines and similar drugs exhibit similar responses, we formulated drug response prediction as a recommender system problem, and then adopted a hybrid interpolation weighted collaborative filtering (HIWCF) method to predict anti-cancer drug responses of cell lines by incorporating cell line similarity and drug similarity shown from gene expression profiles, drug chemical structure as well as drug response similarity. Specifically, we estimated the baseline based on the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. The similarity scores were then shrunk and weighted by the correlation coefficients drawn from the know response between each pair. Before used to find the K most similar neighbors for further prediction, they went through the case amplification strategy to emphasize high similarity and neglect low similarity. In the last step for prediction, cell line-oriented and drug-oriented collaborative filtering models were carried out, and the average of predicted values from both models was used as the final predicted sensitivity. Through 10-fold cross validation, this approach was shown to reach accurate and reproducible outcome for those missing drug sensitivities. We also found that the drug response similarity between cell lines or drugs may play important role in the prediction. Finally, we discussed the biological outcomes based on the newly predicted response values in GDSC dataset.

Prolonged re-expression of the hypermethylated gene EPB41L3 using artificial transcription factors and epigenetic drugs

Epigenetic silencing of tumor suppressor genes (TSGs) is considered a significant event in the progression of cancer. For example, EPB41L3, a potential biomarker in cervical cancer, is often silenced by cancer-specific promoter methylation. Artificial transcription factors (ATFs) are unique tools to re-express such silenced TSGs to functional levels; however, the induced effects are considered transient. Here, we aimed to improve the efficiency and sustainability of gene re-expression using engineered zinc fingers fused to VP64 (ZF-ATFs) or DNA methylation modifiers (ZF-Tet2 or ZF-TDG) and/or by co-treatment with epigenetic drugs [5-aza-2'-deoxycytidine or Trichostatin A (TSA)]. The EPB41L3-ZF effectively bound its methylated endogenous locus, as also confirmed by ChIP-seq. ZF-ATFs reactivated the epigenetically silenced target gene EPB41L3 (∼ 10-fold) in breast, ovarian, and cervical cancer cell lines. Prolonged high levels of EPB41L3 (∼ 150-fold) induction could be achieved by short-term co-treatment with epigenetic drugs. Interestingly, for otherwise ineffective ZF-Tet2 or ZF-TDG treatments, TSA facilitated re-expression of EPB41L3 up to twofold. ATF-mediated re-expression demonstrated a tumor suppressive role for EPB41L3 in cervical cancer cell lines. In conclusion, epigenetic reprogramming provides a novel way to improve sustainability of re-expression of epigenetically silenced promoters.