Abstract: Cancer research is moving into the frontiers of how to assign the correct drug(s) to a given patient. We have recently developed a computational, thermodynamic-based strategy allowing to accurately predict the response of cells to drug treatments or to rationally design anti-cancer personalized drug combinations. While conventional approaches classify cancers based on characteristic biomarkers, we identify patient-specific, central protein nodes representing a patient-specific signaling signature (PaSSS),consisting of several distinct subnetworks, or unbalanced processes (i.e. a set of altered co-expressed, protein-protein subnetworks deviating from the steady state in each tumor). 
Our recent in–vivo results in melanoma, breast, lung and oral cancers indeed show that PaSSSs successfully dictate the design of patient-specific drug cocktails. We demonstrate that simultaneous inhibition of central protein targets from the entire set of distinct unbalanced processes blocks the patient-specific altered signaling flux and is more effective than the treatments used in clinics.
 
Lately we took a step forward and extended the approach to the single cell analysis. This development allows quantifying a set of unbalanced processes in each individual cell, named cell specific signaling signature (CSSS), and then mapping distinct subpopulations in every tumor without a need for large datasets, usually including multiple tissues from different patients. A high number of measured cells (>500,000) from one tumor are compared instead of many patients, thereby providing an accurate and statistically significant information about different unbalanced processes within each sample.
Using these cell specific signatures, distinct subpopulations evolving within the tumor in response to an outside perturbation (e.g. anticancer treatments) were revealed and mapped. CSSSs were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using breast cancer models and patient-derived tumor tissues known to switch phenotypes in response to radiotherapy (RT). 
The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.