Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.

1Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and 2 molecular profiles obtained prior to administration of the drug, can play a significant role 3 in individualized medicine. Machine learning models have the potential to address this 4 issue, but training them requires data from a large number of patients treated with each 5 drug, limiting their feasibility. While large databases of drug response and molecular 6 profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear 7 whether preclinical samples can be used to predict CDR of real patients. 8 9We designed a systematic approach to evaluate how well different algorithms, trained on 10 gene expression and drug response of CCLs, can predict CDR of patients. Using data from 11 two large databases, we evaluated various linear and non-linear algorithms, some of 12 which utilized information on gene interactions. Then, we developed a new algorithm 13 called TG-LASSO that explicitly integrates information on samples' tissue of origin with 14 gene expression profiles to improve prediction performance. Our results showed that 15 regularized regression methods provide significantly accurate prediction. However, 16including the network information or common methods of including information on the 17 tissue of origin did not improve the results. On the other hand, TG-LASSO improved the 18 predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. 19Additionally, TG-LASSO identified genes associated with the drug response, including 20 known targets and pathways involved in the drugs' mechanism of action. Moreover, 21 utilizes a new approach for explicitly incorporating the tissue of origin of samples in the 43 prediction task. Our results show that TG-LASSO outperforms all other algorithms and can 44 accurately distinguish resistant and sensitive patients for the majority of the tested drugs. 45Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity 46 in each cancer type. 47

BackgroundHuman glomerulonephritis (GN)—membranous nephropathy (MN), focal segmental glomerulosclerosis (FSGS) and IgA nephropathy (IgAN), as well as diabetic nephropathy (DN) are leading causes of chronic kidney disease. In these glomerulopathies, distinct stimuli disrupt metabolic pathways in glomerular cells. Other pathways, including the endoplasmic reticulum (ER) unfolded protein response (UPR) and autophagy, are activated in parallel to attenuate cell injury or promote repair.MethodsWe used publicly available datasets to examine gene transcriptional pathways in glomeruli of human GN and DN and to identify drugs.ResultsWe demonstrate that there are many common genes upregulated in MN, FSGS, IgAN, and DN. Furthermore, these glomerulopathies were associated with increased expression of ER/UPR and autophagy genes, a significant number of which were shared. Several candidate drugs for treatment of glomerulopathies were identified by relating gene expression signatures of distinct drugs in cell culture with the ER/UPR and autophagy genes upregulated in the glomerulopathies (“connectivity mapping”). Using a glomerular cell culture assay that correlates with glomerular damage in vivo, we showed that one candidate drug – neratinib (an epidermal growth factor receptor inhibitor) is cytoprotective.ConclusionThe UPR and autophagy are activated in multiple types of glomerular injury. Connectivity mapping identified candidate drugs that shared common signatures with ER/UPR and autophagy genes upregulated in glomerulopathies, and one of these drugs attenuated injury of glomerular cells. The present study opens the possibility for modulating the UPR or autophagy pharmacologically as therapy for GN.