Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT03724097 |
| Other study ID # |
OB0052018 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
October 17, 2017 |
| Est. completion date |
November 1, 2022 |
Study information
| Verified date |
September 2021 |
| Source |
OmicsWay Corp. |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
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.
Description:
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