Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data

Abstract

Background: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. Methods: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. Results: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84–0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. Conclusion: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC.

Targeted Inhibitors of EGFR: Structure, Biology, Biomarkers, and Clinical Applications

“…The fact of EGFR-positivity (overexpression of the receptor and/or amplification of the gene encoding it), in principle, allows the narrowing down of the population to be treated to a significant extent but has insufficient predictive power. For example, unlike the EGFR gene expression level itself, which is a poor predictor of cetuximab treatment response in colorectal cancer [ 250 ], a broader view of the quantitively measured activation of relevant molecular pathways has shown promising results [ 251 ]. Thus, exploring the underlying cellular and molecular mechanisms that cause resistance to EGFR inhibitor treatments and utilizing personalized predictive approaches can reveal innovative strategies to improve the efficacy of EGFR-targeted therapies.…”

Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers

“…When used to discriminate between nine human cancer types, PAL values showed better accuracy than expression levels of individual genes [ 23 ]. On its own, PAL was also found to be a good predictor of tissue type in bladder cancer [ 9 ] and of sensitivities to some cancer drugs [ 13 , 24 , 25 , 26 , 27 , 28 ]. Additionally, PALs demonstrated better stability against experimental noise and lower batch bias compared to single gene expression levels in both transcriptomic (microarray and RNAseq) and proteomic data [ 14 , 23 , 29 ].…”

Gene Cluster Expression Index (GCEI) and Potential Indications for Targeted Therapy and Immunotherapy

“…The current standard approach for selection of targeted therapy is via matching particular gene mutations [39], and the selection of immunotherapy is via routine tests such as pathological immunoassay (IHC) for protein expression of PD1 or PD-L1, via DNA-based NGS assessment of Tumor Mutation Burden (TMB), Mismatch Repair (MMR), and Micro-satellite Instability (MSI). Transcriptome profiling analysis has emerged as promising biomarkers to cancer treatment and showed encouraging clinical results [10,57]. In NSCLC, study showed that gene expression profiling might have better prognostic prediction power than mutation status [38] in particular scenarios.…”