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This is a prospective trial for a computation-based efficacy prediction method for anticancer target therapies. The original computational algorithm utilizes individual transcriptome data of a cancer sample and assesses changes at the level of gene expression and intracellular signaling pathways. By applying the database of known molecular targets of anticancer target drugs it allows to rank potential efficacies of target drugs.
Original computational algorithm Oncobox was developed to determine molecular features of individual tumors. It represents the solution for a personalized selection of target anticancer therapies. The method is based on the analysis of gene expression profile of a cancer sample in comparison with the corresponding normal tissue biosamples in order to select the most effective molecular targets for their inhibition and, accordingly, to identify more effective target drugs for cancer treatment. Histological material obtained from cancer patients during surgery or core-needle biopsy as part of standard treatment will be used for the analysis. Total RNA extracted from the tumor material will be subjected to next-generation sequencing (NGS). By comparing transcriptome profile of the tumor sample with the profiles of the corresponding normal tissue samples the rate of molecular pathways activation/deactivation will be calculated, as well as the case-to-normal ratios for the individual gene products - molecular targets of drugs. Based on these data, each target drug will be assigned with a score reflecting its potential efficacy for each individual tumor treatment. A drug with the score value above 0.1 will be considered potentially effective, a drug with the score value equal to or below 0.1 - as potentially ineffective. Following Oncobox test, 130 target anticancer drugs will be rated according to their predicted effectiveness (see the list of eligible target drugs below). This information will be fully available to a patient and his/her doctor. The doctor will prescribe treatment according to his/her consideration, e.g. based on the standards of care and the patient's life indications. After the appointment of therapy, the patients will be divided naturally into the following three observation groups. The first group will be formed from patients receiving target drugs with the score value above 0,1 as monotherapy or in combination. The second group - patients receiving only non-target drugs or target drugs with the score value equal to or below 0,1 as monotherapy or in combination. Third group will be formed by patients receiving palliative care. Within this study, these three groups will be compared by response to the therapy according to the results of instrumental studies, by time to progression and by time to progression compared to the previous line of therapy (if any). Additionally, overall survival will be measured in all three groups.
Eligible target drugs:
1. Abemaciclib (LY2835219)
8. Apalutamide, ARN-509
9. Arsenic trioxide
17. Binimetinib (MEK162)
21. Brentuximab vedotin
26. Ceritinib (Zykadia, LDK378)
30. CYT387 (Momelotinib)
35. Denileukin diftitox (Ontac)
47. Flavopiridol (Alvociclib)
50. Ganetespib (STA-9090)
53. Homoharringtonine (Omacetaxine mepesuccinate)
54. Ibritumomab tiuxetan
58. Inotuzumab ozogamicin
61. Ixazomib (MLN9708)
69. Medroxyprogesterone acetate (MPA)
74. Moxetumomab pasudotox
79. Nintedanib (BIBF 1120)
81. Nivolumab (BMS-936558)
97. Ramucirumab (Cyramza)
107. Sonidegib (LDE225)
111. Tecemotide (Emepepimut-S, L-BLP25)
118. Trametinib (Mekinst)
RNA sequencing, Transcriptome analysis, target drug with the score above 0,1, target drug with the score equal or below 0,1, non-target drug, palliative care
Published on BioPortfolio: 2018-11-04T15:55:10-0500
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