Identification of Molecular Biomarkers for Cancer Target Therapy Efficacy

Cancer

biomarkers, target therapy, NGS, RNAseq, mRNA, transcriptome analysis, signaling pathway, intracellular molecular pathway, Oncobox, Next generation sequencing, RNA sequencing

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) 2. Afatinib 3. Aflibercept 4. Alectinib 5. Alemtuzumab 6. Alitretinoin 7. Anastrozole 8. Apalutamide, ARN-509 9. Arsenic trioxide 10. Atezolizumab 11. Avelumab 12. Axitinib 13. Belinostat 14. Bevacizumab 15. Bexarotene 16. Bicalutamide 17. Binimetinib (MEK162) 18. Blinatumomab 19. Bortezomib 20. Bosutinib 21. Brentuximab vedotin 22. Brigatinib 23. Cabazitaxel 24. Cabozantinib 25. Carfilzomib 26. Ceritinib (Zykadia, LDK378) 27. Cetuximab 28. Cobimetinib 29. Crizotinib 30. CYT387 (Momelotinib) 31. Dabrafenib 32. Daratumumab 33. Dasatinib 34. Degarelix 35. Denileukin diftitox (Ontac) 36. Denosumab 37. Docetaxel 38. Dovitinib 39. Durvalumab 40. Elotuzumab 41. Encorafenib 42. Enzalutamide 43. Erlotinib 44. Estramustine 45. Everolimus 46. Exemestane 47. Flavopiridol (Alvociclib) 48. Foretinib 49. Fulvestrant 50. Ganetespib (STA-9090) 51. Gefitinib 52. Goserelin 53. Homoharringtonine (Omacetaxine mepesuccinate) 54. Ibritumomab tiuxetan 55. Ibrutinib 56. Idelalisib 57. Imatinib 58. Inotuzumab ozogamicin 59. Ipilimumab 60. Ixabepilone 61. Ixazomib (MLN9708) 62. Lapatinib 63. Lenalidomide 64. Lenvatinib 65. Letrozole 66. Leuprolide 67. Lomustine 68. Masitinib 69. Medroxyprogesterone acetate (MPA) 70. Megestrol 71. Methyltestosterone 72. Midostaurin 73. Mogamulizumab 74. Moxetumomab pasudotox 75. Necitumumab 76. Nilotinib 77. Nilutamide 78. Nimotuzumab 79. Nintedanib (BIBF 1120) 80. Niraparib 81. Nivolumab (BMS-936558) 82. Obinutuzumab 83. Ofatumumab 84. Olaparib 85. Olaratumab 86. Osimertinib 87. Paclitaxel 88. Palbociclib 89. Panitumumab 90. Panobinostat 91. Pazopanib 92. Pembrolizumab 93. Perifosine 94. Pertuzumab 95. Pomalidomide 96. Ponatinib 97. Ramucirumab (Cyramza) 98. Regorafenib 99. Ribociclib 100. Rigosertib 101. Rituximab 102. Romidepsin 103. Rucaparib 104. Ruxolitinib 105. Selumetinib 106. Siltuximab 107. Sonidegib (LDE225) 108. Sorafenib 109. Sunitinib 110. Tamoxifen 111. Tecemotide (Emepepimut-S, L-BLP25) 112. Temozolomide 113. Temsirolimus 114. Thalidomide 115. Tivantinib 116. Tivozanib 117. Toremifene 118. Trametinib (Mekinst) 119. Trastuzumab 120. Trebananib 121. Vandetanib 122. Veliparib 123. Vemurafenib 124. Venetoclax 125. Vinblastine 126. Vincristine 127. Vindesine 128. Vinorelbine 129. Vismodegib 130. Vorinostat

Russia, United States