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151 Background: Colorectal cancer (CRC) is the fourth most common cancer worldwide with relatively poor patient survival. Transcriptome assay could be used to personalize CRC treatment thus complementing standard mutation analysis. Methods: We performed retrospective hybrid experimental and meta-analysis of CRC patient gene expression data with available progression-free survival (PFS) information and/or targeted drug response status. In total we analyzed 243 gene expression profiles from four publicly available (TCGA and three datasets from Gene Expression Omnibus GSE19860, GSE19862, GSE104645), and one experimental (PRJNA663280) patient cohorts. Each gene expression profile was analyzed using bioinformatic second-opinion platform Oncobox to calculate balanced drug efficiency scores (BES) to build personalized ratings of potentially effective targeted drugs. Area under the ROC curve (AUC) metric and Cox regression analysis were used to assess Oncobox capacity to predict tumor response and PFS, respectively. Results: Patients from GSE19860 (n = 12), GSE19862 (n = 14), GSE104645 (n = 81) received bevacizumab as monotherapy or in combination with chemotherapy as the nearest line of treatment after biopsy collection. Oncobox correctly classified treatment responders vs non-responders with AUC 0.94, 0.90 and 0.84, respectively. BES value was strongly associated with PFS (HR = 0.53, CI 0.33-0.84, log-rank test p-value 0.0057) in the GSE104645 cohort. However, BES was ineffective for predicting response and PFS after second-line (after biopsy collection) treatment with cetuximab. BES also predicted treatment response with AUC 0.74 in the TCGA cohort (n = 17) treated with 4 different targeted drugs. Thirty clinical outcomes were collected for 14 patients from our experimental cohort PRJNA663280. Patients were treated with 10 different targeted drugs. BES was an effective biomarker that could predict treatment outcomes with AUC 0.74 for all lines of therapy and 0.94 for the first line therapy (after biopsy), and could predict PFS after first-line treatment (HR 0.14, CI 0.027-0.73, log-rank test p-value 0.0091). Conclusions: Our results suggest that RNA profiling in tumor samples may be helpful for personalizing prescriptions of targeted therapeutics in CRC. Using recent biopsies is essential to obtain robust estimates of targeted drugs efficacy.

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High‐risk neuroblastoma (NB) is one of the most aggressive childhood tumors that lacks effective treatment. Targeted drugs, such as receptor tyrosine kinase (RTK) inhibitors, have shown potential in treating high‐risk NB, but their efficacy is likely impaired by the cancer cells’ ability to adapt to drugs. Unlike other cancers, NB has a very low frequency of recurrent mutations, and only a few of them, such as MYCN amplification, can be used as prognostic markers. Here we reasoned that activation of growth factor signaling could promote NB therapeutic resistance and NB progression. We performed a transcriptome analysis of 60 NB tumor samples and found that enhanced growth factor signaling pathways such as erythropoietin (EPO), HGF, and NGF are associated with metastasis and poor response to therapy. We validated our results using data for more than 1000 NB samples from several independent patient cohorts and showed that high EPO receptor (EPOR) expression is a robust marker for NB poor prognosis and high relapse occurrence. Notably, EPOR expression does not depend on MYCN amplification and can be potentially used as a prognostic marker for MYCN‐non‐amplified high‐risk NB. We developed a new computational method based on gene set analyses to predict gene‐associated biological processes that drive the NB progression. This method proposes a novel strategy for NB risk stratification based on growth factor‐related gene sets. In a therapeutic setting, receptor tyrosine kinase inhibitors potentiate NB cells to EPO and NGF stronger pro‐survival action by increasing the receptors expression and basal ERK activity levels. ERK activity was measured on a single cell level using kinase translocation reporter, and receptor expression changes were measured by RT‐PCR and confirmed by antibody staining. Our research provides new insights into the importance of growth factor signaling in NB progression and highlights growth factor‐driven NB subtypes’ existence.