Tumor Chemosensitivity Assays Are Helpful for Personalized Cytotoxic Treatments in Cancer Patients

Tumor chemosensitivity assays (TCAs), also known as drug response assays or individualized tumor response tests, have been gaining attention over the past few decades. Although there have been strong positive correlations between the results of these assays and clinical outcomes, they are still not considered routine tests in the care of cancer patients. The correlations between the assays’ results (drug sensitivity or resistance) and the clinical evaluations (e.g., response to treatment, progression-free survival) are highly promising. However, there is still a need to design randomized controlled prospective studies to secure the place of these assays in routine use. One of the best ideas to increase the value of these assays could be the combination of the assay results with the omics technologies (e.g., pharmacogenetics that gives an idea of the possible side effects of the drugs). In the near future, the importance of personalized chemotherapy is expected to dictate the use of these omics technologies. The omics relies on the macromolecules (Deoxyribonucleic acid -DNA-, ribonucleic acid -RNA-) and proteins (meaning the structure) while TCAs operate on living cell populations (meaning the function). Therefore, wise combinations of TCAs and omics could be a highly promising novel landscape in the modern care of cancer patients.

Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer

Mathematical and computational oncology has increased the pace of cancer research towards the advancement of personalized therapy. Serving the pressing need to exploit the large amounts of currently underutilized data, such approaches bring a significant clinical advantage in tailoring the therapy. CHIMERA is a novel system that combines mechanistic modelling and machine learning for personalized chemotherapy and surgery sequencing in breast cancer. It optimizes decision-making in personalized breast cancer therapy by connecting tumor growth behaviour and chemotherapy effects through predictive modelling and learning. We demonstrate the capabilities of CHIMERA in learning simultaneously the tumor growth patterns, across several types of breast cancer, and the pharmacokinetics of a typical breast cancer chemotoxic drug. The learnt functions are subsequently used to predict how to sequence the intervention. We demonstrate the versatility of CHIMERA in learning from tumor growth and pharmacokinetics data to provide robust predictions under two, typically used, chemotherapy protocol hypotheses.

Mechanism of Overcoming Oxaliplatin Resistance Using HT29 Subcloning

Background: Cancer cells are different from normal healthy cells even though they reside within the same tissue and adapt to others. This intra-tumoral heterogeneity is the reason for chemo-resistance, and the emergence of one or more clones, which are resistant to chemotherapeutic drugs, causing clonal outgrowth to form recurrent cancer mass. The need for personalized chemotherapy is increasing because of the tumor heterogeneity and diverse mechanism of chemotherapy resistance. The recent accumulation of extensive evidence for the existence of cancer stem cells strengthens the cancer stem cell hypothesis for chemotherapy resistance and relapse. The development of a primary culture of cancer cells is essential for functional analysis like the sensitivity of chemotherapeutic drugs and the evaluation of the characteristics of cancer stem cells. Methods: We used a clonal cylinder to establish sub-clones originated from a single cell. Twenty-two sub-clones were successfully established, and eleven clones were selected according to their growth rate and analyzed. The sub-clones with low expression of BRAF, MEK2, and ERK, but not EGFR or KRAS, showed a correlation with the doubling time. We grouped the sub-clones as the fast-growing group and the slow-growing group of the HT29 cell line. Three out of five slow-growing sub-clones showed resistance to oxaliplatin treatment. All oxaliplatin-resistant sub-clones overexpressed ABCC2, and no relevance was found with ABCB1 and ABCG2. Active efflux of the drug by ABCC2, but not by ABCB1 or ABCG2, was confirmed with the inhibitor study using each specific inhibitor. The viability of resistant sub-clones decreased after the MK571 treatment, but other clones were not responsive. CD44 expression in oxaliplatin-resistant sub-clones was higher than that of sensitive clones. Conclusions: This study provides definite evidence of heterogeneity using a cancer cell line. Based on our studies, it appears that intra-tumoral heterogeneity of human cancer tissue is responsible for the development of chemotherapy resistance in cancer. These sub-clones are an excellent model for testing efficacy of anti-cancer drug candidates for advanced cancer therapy. The experimental design and concept of this study could be applied to personalized chemotherapy.