Patients receiving target drugs with the score value above 0,1 as monotherapy or in combination

Patients receiving only non-target drugs or target drugs with the score value equal to or below 0,1 as monotherapy or in combination

Next Generation Sequencing of RNA from tumor samples, rRNA-depleted.

RNA sequencing

Analysis of RNA-seq data using the Oncobox algorithm.

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

Anton A Buzdin, Ph.D., D.Sc

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

Formalin-fixed, paraffin-embedded pathological cancer samples obtained during surgery or core needle biopsy

Identification of Molecular Biomarkers for Cancer Target Therapy Efficacy

Study Protocol, Statistical Analysis Plan (SAP), NGS data, Clinical Study Report (CSR)

Tumor response according to the results of instrumental studies

Time to progression compared to the previous therapy lane

Oncobox Ltd

Vitamed LLC

N.N. Blokhin National Medical Research Center of Oncology

Kaluga Regional Clinical Oncology Center

Multidisciplinary medical holding SM-Clinic

Oncological Clinical Dispensary No. 1 of the Moscow City Health Department

OmicsWay Corp.

Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Breast Neoplasms

Breast Diseases

Skin Diseases

Antineoplastic Agents

All Drugs and Chemicals

Trastuzumab

Mitogens

Antineoplastic Agents, Immunological

Her2-positive breast cancer patients treated with Trastuzumab (monotherapy)

Her2-positive breast cancer patients treated with Trastuzumab in combination with other regimens of chemotherapy

Standard Trastuzumab treatment regimen recommended for breast cancer

Standard chemotherapy regimen recommended for breast cancer

Chemotherapy

The purpose of the study is to identify molecular markers at the level of molecular pathway activation to predict efficacy of anti-HER2 therapy with Trastuzumab.

Biopsy and/or surgical samples from Her2-positive breast cancer patients treated with Trastuzumab alone or in combinations with other regimens of chemotherapy with known clinical outcomes will be collected and subjected to mRNA sequencing. Expression profiles will be analysed using original bioinformatic platform Oncobox. Based on comparison of molecular pathways activation strength and clinical response to Trastuzumab and chemotherapy, this study aims to identify molecular markers predicting efficacy of therapy with Trastuzumab and the possibility of further relapse.

Formalin-fixed, paraffin embedded pathological samples of breast cancer obtained during radical mastectomy or core needle biopsy

Molecular Pathway Activation Markers Predicting Efficacy of Trastuzumab Therapy for HER2-positive Breast Cancer

Study Protocol Statistical Analysis Plan (SAP) NGS data Clinical Study Report (CSR)

Tumor response by RECIST v1.1 or pathological response according to Chevallier system

Oncobox Ltd., Russia

Vitamed LLC, Russia

Republican Oncological Dispensary of Karelia Republic, Russia

Chief Scientific Officer

Blood and Lymph Conditions

Rare Diseases

Leukemia

Leukemia, Myeloid

Leukemia, Myeloid, Acute

Neoplasms by Histologic Type

Hematologic Diseases

Myeloid Leukemia

Acute Myeloid Leukemia

Acute Non Lymphoblastic Leukemia

Acute Graft Versus Host Disease

Immunogenic profile in patients affected by primary or secondary AML at diagnosis

Immunogenic profile in patients affected by primary or secondary AML at relapse

RNA sequencing (RNAseq), Whole exome sequencing (WES), NeoAntigens (NeoAg), Immune checkpoints analysis, Gene expression profiling

Immunogenic profile

Oriana Nanni

Erika Brolenghi, MD

Giovanni Martinelli, MD

Giorgia Simonetti, PhD

Marianna Norata, MD

Martina Ghetti, PhD

Nicola Cascavilla, MD

Roberto Massimo Lemoli, MD

Daniele Vallisa

Pellegrino Musto, MD

Michela Rondoni, MD

Anna Maria Mianulli, MD

Monica Bocchia, MD

Daniela Cilloni, MD

Alessandro Cignetti, DR

Michele Gottardi, MD

Monia Lunghi

Anna Candoni, MD

Giovanni Martinelli

This is a multicenter, prospective, translational study, to evaluate the immunogenic profile of AML cases according to immune checkpoint molecule expression.

This is a multicenter, prospective, translational study. Pharmacological treatments will not be object of the present study. Patients will follow the prescribed therapeutic indications according to regular clinical practice. Biological samples and clinical data will be collected at fixed timepoints.

Aim of the study is to evaluate the immunogenic profile of AML cases according to immune checkpoint molecule expression.

A total of 75 AML patients affected by primary or secondary AML, at diagnosis ( 45 patients) or relapse (30 patients) will be enrolled in the protocol (18 months for patient enrolment). Blood,BM and saliva samples will be collected from each patients; for patients enrolled at diagnosis, samples will be collected before and after treatment with hypomethylating agents.

Bone marrow (BM) and peripheral blood (PB) and saliva.

Exploring Disease Immunogenicity and the Immunological Effects of Hypomethylating Agents in Acute Myeloid Leukemia

The immunogenic profile of AML cases obtained by NeoAgprediction will be related with established biological and clinical parameters (including karyotype, percentage of blasts, clinical outcome) performing RNA sequencing (RNAseq) and whole exome sequencing (WES) analysis

the relationship between leukemia immunogenicity and immune checkpoint activation through transcriptome analysis of downstream pathways by flow cytometric analysis

Investigation of the activation of immune checkpoint molecules under the selective pressure of hypomethylating agents treatment by gene expression profilling

Investigation of the association of the immune checkpoint status with clinical outcomes (OS, PFS) estimated according to Kaplan-Meier.

Investigation of the association of the immunogenic profile with clinical outcomes (OS, PFS) estimated according to Kaplan-Meier.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Identification of Molecular Biomarkers for Cancer Target Therapy Efficacy	NCT03724097	Cancer	RNA sequencing Transcriptome a... target drug wit... target drug wit... non-target drug palliative care	18 Years - 80 Years	OmicsWay Corp.
Molecular Pathway Activation Markers Predicting Efficacy of Trastuzumab Therapy for HER2-positive Breast Cancer	NCT03521245	HER2-positive B...	RNA sequencing Transcriptome a... Trastuzumab Chemotherapy	18 Years - 80 Years	OmicsWay Corp.
Exploring Disease Immunogenicity and the Immunological Effects of Hypomethylating Agents in Acute Myeloid Leukemia	NCT03789981	Acute Myeloid L...	Immunogenic pro...	18 Years -	Istituto Scientifico Romagnolo per lo Studio e la cura dei Tumori

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