RU2018143409A - Способы классификации пациентов с солидным раком - Google Patents
Способы классификации пациентов с солидным раком Download PDFInfo
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- RU2018143409A RU2018143409A RU2018143409A RU2018143409A RU2018143409A RU 2018143409 A RU2018143409 A RU 2018143409A RU 2018143409 A RU2018143409 A RU 2018143409A RU 2018143409 A RU2018143409 A RU 2018143409A RU 2018143409 A RU2018143409 A RU 2018143409A
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- 206010028980 Neoplasm Diseases 0.000 title claims 17
- 238000000034 method Methods 0.000 title claims 16
- 201000011510 cancer Diseases 0.000 title claims 9
- 210000004027 cell Anatomy 0.000 claims 24
- 239000000090 biomarker Substances 0.000 claims 17
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- 102100034922 T-cell surface glycoprotein CD8 alpha chain Human genes 0.000 claims 8
- 108050005493 CD3 protein, epsilon/gamma/delta subunit Proteins 0.000 claims 6
- 102000017420 CD3 protein, epsilon/gamma/delta subunit Human genes 0.000 claims 6
- 108090000623 proteins and genes Proteins 0.000 claims 6
- 230000028993 immune response Effects 0.000 claims 4
- 239000007787 solid Substances 0.000 claims 4
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- 101710149815 C-C chemokine receptor type 2 Proteins 0.000 claims 2
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- 206010009944 Colon cancer Diseases 0.000 claims 2
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- 101000974815 Homo sapiens BTB/POZ domain-containing protein KCTD11 Proteins 0.000 claims 2
- 101000797762 Homo sapiens C-C motif chemokine 5 Proteins 0.000 claims 2
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- 101000598002 Homo sapiens Interferon regulatory factor 1 Proteins 0.000 claims 2
- 101000998146 Homo sapiens Interleukin-17A Proteins 0.000 claims 2
- 101000987581 Homo sapiens Perforin-1 Proteins 0.000 claims 2
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- 101000579218 Homo sapiens Renin Proteins 0.000 claims 2
- 101000713602 Homo sapiens T-box transcription factor TBX21 Proteins 0.000 claims 2
- 101000946860 Homo sapiens T-cell surface glycoprotein CD3 epsilon chain Proteins 0.000 claims 2
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- 101000845170 Homo sapiens Thymic stromal lymphopoietin Proteins 0.000 claims 2
- 102100022341 Integrin alpha-E Human genes 0.000 claims 2
- 102100037850 Interferon gamma Human genes 0.000 claims 2
- 102100036981 Interferon regulatory factor 1 Human genes 0.000 claims 2
- 102000003812 Interleukin-15 Human genes 0.000 claims 2
- 108090000172 Interleukin-15 Proteins 0.000 claims 2
- 102100033461 Interleukin-17A Human genes 0.000 claims 2
- 102100028467 Perforin-1 Human genes 0.000 claims 2
- 102100040120 Prominin-1 Human genes 0.000 claims 2
- 102100036840 T-box transcription factor TBX21 Human genes 0.000 claims 2
- 102100035794 T-cell surface glycoprotein CD3 epsilon chain Human genes 0.000 claims 2
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- 102100031294 Thymic stromal lymphopoietin Human genes 0.000 claims 2
- 230000033289 adaptive immune response Effects 0.000 claims 2
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- 238000000338 in vitro Methods 0.000 claims 2
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- 102100022790 BTB/POZ domain-containing protein KCTD11 Human genes 0.000 claims 1
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- 102100039498 Cytotoxic T-lymphocyte protein 4 Human genes 0.000 claims 1
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- 101150063370 Gzmb gene Proteins 0.000 claims 1
- 102100034458 Hepatitis A virus cellular receptor 2 Human genes 0.000 claims 1
- 101710083479 Hepatitis A virus cellular receptor 2 homolog Proteins 0.000 claims 1
- 101000897480 Homo sapiens C-C motif chemokine 2 Proteins 0.000 claims 1
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- 101000858060 Homo sapiens C-X-C motif chemokine 11 Proteins 0.000 claims 1
- 101000858064 Homo sapiens C-X-C motif chemokine 13 Proteins 0.000 claims 1
- 101000947172 Homo sapiens C-X-C motif chemokine 9 Proteins 0.000 claims 1
- 101000889276 Homo sapiens Cytotoxic T-lymphocyte protein 4 Proteins 0.000 claims 1
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- 101001040751 Homo sapiens Granulysin Proteins 0.000 claims 1
- 101001009599 Homo sapiens Granzyme A Proteins 0.000 claims 1
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- 101000582950 Homo sapiens Platelet factor 4 Proteins 0.000 claims 1
- 101000611936 Homo sapiens Programmed cell death protein 1 Proteins 0.000 claims 1
- 101000946863 Homo sapiens T-cell surface glycoprotein CD3 delta chain Proteins 0.000 claims 1
- 101000808011 Homo sapiens Vascular endothelial growth factor A Proteins 0.000 claims 1
- -1 ICOS Proteins 0.000 claims 1
- 102100026214 Indian hedgehog protein Human genes 0.000 claims 1
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- 102000017578 LAG3 Human genes 0.000 claims 1
- 206010058467 Lung neoplasm malignant Diseases 0.000 claims 1
- 206010033128 Ovarian cancer Diseases 0.000 claims 1
- 206010061535 Ovarian neoplasm Diseases 0.000 claims 1
- 102100030304 Platelet factor 4 Human genes 0.000 claims 1
- 102100040678 Programmed cell death protein 1 Human genes 0.000 claims 1
- 206010060862 Prostate cancer Diseases 0.000 claims 1
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- 102100035891 T-cell surface glycoprotein CD3 delta chain Human genes 0.000 claims 1
- 208000007097 Urinary Bladder Neoplasms Diseases 0.000 claims 1
- 108010019530 Vascular Endothelial Growth Factors Proteins 0.000 claims 1
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- 201000005202 lung cancer Diseases 0.000 claims 1
- 208000020816 lung neoplasm Diseases 0.000 claims 1
- 201000005112 urinary bladder cancer Diseases 0.000 claims 1
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Claims (28)
1. Способ in vitro прогнозирования времени выживания пациента, страдающего от солидного рака, включающий следующие стадии:
а) количественное определение двух или больше биологических маркеров, показывающих статус иммунного ответа указанного пациента против указанного рака, причем каждый биологический маркер, показывающий статус иммунного ответа, определяют количественно в образце опухоли, полученном от указанного пациента;
b) сравнение каждой из величин, полученных на стадии а) для указанных двух или больше биологических маркеров, с распределением величин, полученных для каждого из указанных двух или больше биологических маркеров от контрольной группы пациентов, страдающих от указанного рака;
с) определение для каждой величины, полученной на стадии а) для указанных двух или больше биологических маркеров, процентиля распределения, которому соответствуют величины, полученные на стадии а);
d) вычисление среднеарифметического значения или медианы процентиля; и
е) сравнение среднеарифметического значения или медианы процентиля, полученных на стадии d), с предварительно установленным контрольным среднеарифметическим значением или предварительно установленной контрольной медианой процентиля, которые коррелируют с временем выживания.
2. Способ оценки in vitro восприимчивости пациента, страдающего от солидного рака, к противораковому лечению, который включает следующие стадии:
а) количественное определение двух или больше биологических маркеров, показывающих статус иммунного ответа указанного пациента против указанного рака, причем каждый биологический маркер, показывающий статус иммунного ответа, определяют количественно в образце опухоли, полученном от указанного пациента;
b) сравнение каждой из величин, полученных на стадии а) для указанных двух или больше биологических маркеров, с распределением величин, полученных для каждого из указанных двух или больше биологических маркеров от контрольной группы пациентов, страдающих от указанного рака;
с) определение для каждой величины, полученной на стадии а) для указанных двух или больше биологических маркеров, процентиля распределения, которому соответствуют величины, полученные на стадии а);
d) вычисление среднеарифметического значения или медианы процентиля; и
е) сравнение среднеарифметического значения или медианы процентиля, полученных на стадии d), с предварительно установленным контрольным среднеарифметическим значением или предварительно установленной контрольной медианой процентиля, которые коррелируют с реакцией на указанное противораковое лечение.
3. Способ по п. 1 или 2, причем указанный солидный рак представляет собой колоректальный рак, рак молочной железы, рак легких, рак головы и шеи, рак мочевого пузыря, рак яичников или рак предстательной железы.
4. Способ по п.3, причем солидный рак представляет собой колоректальный рак.
5. Способ по любому из пп. 1-4, причем два или больше биологических маркеров включают клеточную плотность клеток из иммунной системы.
6. Способ по п.5, причем два или больше биологических маркеров включают плотность CD3+ клеток, плотность CD8+ клеток, плотность CD45RO+ клеток, плотность GZM-B+ клеток, плотность B-клеток и/или плотность DC клеток.
7. Способ по п.6, причем два или больше биологических маркеров включают плотность CD3+ клеток и плотность CD8+ клеток, плотность CD3+ клеток и плотность CD45RO+ клеток, плотность CD3+ клеток и плотность GZM-B+ клеток, плотность CD8+ клеток и плотность CD45RO+ клеток, плотность CD8+ клеток и плотность GZM-B+ клеток или плотность CD45RO+ клеток и плотность GZM-B+ клеток.
8. Способ по любому из пп. 5-7, причем плотность клеток из иммунной системы определяют количественно в центре опухоли и/или в инвазивном крае опухоли.
9. Способ по п.8, причем два или больше биологических маркеров включают плотность CD3+ клеток в центре опухоли, плотность CD8+ клеток в центре опухоли, плотность CD3+ клеток в инвазивном крае и плотность CD8+ клеток в инвазивном крае.
10. Способ по любому из пп. 5-9, причем плотность измеряют в участке образца опухоли, где плотность является наименьшей.
11. Способ по любому из пп. 5-9, причем плотность измеряют в участке образца опухоли, где плотность является наибольшей.
12. Способ по любому из пп. 1-11, причем два или больше биологических маркеров включают уровень экспрессии одного или нескольких генов из группы, включающей CCR2, CD3D, CD3E, CD3G, CD8A, CXCL10, CXCL11, GZMA, GZMB, GZMK, GZMM, IL15, IRF1, PRF1, STAT1, CD69, ICOS, CXCR3, STAT4, CCL2 и TBX21.
13. Способ по любому из пп. 1-12, причем два или больше биологических маркеров включают уровень экспрессии одного или нескольких генов из группы, включающей GZMH, IFNG, CXCL13, GNLY, LAG3, ITGAE, CCL5, CXCL9, PF4, IL17A, TSLP, REN, IHH, PROM1 и VEGFA.
14. Способ по любому из пп. 1-12, причем два или больше биологических маркеров включают уровень экспрессии по меньшей мере одного гена, характеризующего адаптивный иммунный ответ человека, и уровень экспрессии по меньшей мере одного гена, характеризующего иммуносупрессорный ответ человека.
15. Способ по п.14, причем по меньшей мере одни ген, характеризующий адаптивный иммунный ответ человека, выбирают из группы, включающей
и указанный по меньшей мере одни ген, характеризующий иммуносупрессорный ответ человека, выбирают из группы, включающей
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| WO2019149817A1 (en) * | 2018-01-31 | 2019-08-08 | Ventana Medical Systems, Inc. | Methods and systems for evaluation of immune cell infiltrate in stage iii colorectal cancer |
| FR3079617B1 (fr) | 2018-03-29 | 2023-12-22 | Office National Detude Et De Rech Aerospatiales Onera | Methode de detection de cellules presentant au moins une anomalie dans un echantillon cytologique |
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| CN110988324B (zh) * | 2019-11-29 | 2021-08-24 | 广州市雷德医学检验实验室有限公司 | 免疫状态确定系统、方法、装置及存储介质 |
| CN111257563B (zh) * | 2020-01-22 | 2022-08-23 | 广州泛恩生物科技有限公司 | Cxcl13检测剂在制备预测免疫治疗效果的试剂盒中的用途 |
| CN111999503B (zh) * | 2020-05-28 | 2022-05-20 | 首都医科大学附属北京地坛医院 | 一组用于预测急性病毒性呼吸道传染病重症化的标志物及其应用和试剂盒 |
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| CN113174439B (zh) * | 2021-03-30 | 2022-06-28 | 中国医学科学院肿瘤医院 | 一种基于免疫基因对评分体系在预测非小细胞肺癌患者免疫治疗效果中的应用 |
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| CA3245129A1 (en) | 2022-03-17 | 2023-09-21 | Univ Paris Cite | METHODS FOR PREDICTING RESPONSE TO IMMUNOTHERAPEUTIC TREATMENT IN A CANCER PATIENT |
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