Identification of Molecular Biomarkers for Cancer Target Therapy Efficacy

2018-11-04 15:55:10 | BioPortfolio


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

Study Design




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


OmicsWay Corp.
United States




OmicsWay Corp.

Results (where available)

View Results


Published on BioPortfolio: 2018-11-04T15:55:10-0500

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