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Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

3791 - bMSI better predicts the responses to immune checkpoint inhibitors (ICI) than MMR/MSI from historical tissue specimens in metastatic gastrointestinal cancer patients

Date

20 Oct 2018

Session

Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

Presenters

Zhenghang Wang

Citation

Annals of Oncology (2018) 29 (suppl_8): viii14-viii57. 10.1093/annonc/mdy269

Authors

Z. Wang1, H. Qin2, M. Wang3, J. Gong1, X. Wang4, J. Li4, J. Gao4, Z. Li5, D. Wang6, S. Cai7, Y. Bai7, L. Xiong2, F. Li2, L. Shen4

Author affiliations

  • 1 Department Of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, 100142 - Beijing/CN
  • 2 Research And Development Institute Of Precision Medicine, 3D Medicine Inc., Shanghai, China., 201114 - Shanghai/CN
  • 3 Oncology Department, Changhai Hospital, Shanghai/CN
  • 4 Department Of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing/CN
  • 5 Department Of Pathology, Peking University Cancer Hospital & Institute, Beijing/CN
  • 6 Shanghai 3dmed Clinical Laboratory, 3D medicines Inc., 201114 - Shanghai/CN
  • 7 The Medical Department, 3D medicines Inc., 201114 - Shanghai/CN
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Resources

Abstract 3791

Background

Microsatellite instability (MSI) has been approved as the first pan-cancer biomarker in immune checkpoint inhibitors (ICI) therapies. The tumor tissues of most metastatic cancer patients receiving ICI therapies are usually unavailable. However, polymerase chain reaction (PCR) or immunohistochemistry (IHC), the two conventional MSI evaluation methods, could only be applied to the tumor tissues. Hence, we aimed to develop a next-generation sequencing based method to detect MSI from blood circulating tumor DNA (bMSI).

Methods

A training cohort of 40 metastatic cancers patients before first-line treatments were collected to train a linear-based detection model. Then, a validation cohort of 47 metastatic gastrointestinal cancer patients before ICI therapies were collected. The prediction to the responses of ICI by bMSI was compared with that by the mismatch repair (MMR) or MSI from historical tissue specimens.

Results

bMSI showed 87.5% accuracy to predict the MMR/MSI status from tissue specimens in the training cohort, and 95.2% sensitivity in the validation cohort. bMSI-H patients had 31.4% objective response rate (ORR) and 45.7% disease control rate (DCR), which were comparable to the dMMR of historical FFPE specimens (33.3% and 47.6% respectively). However, 57.7% pMMR patients were classified as bMSI-H and showed similar ORR (27%) , DCR (40%) and progress free survival to those of dMMR patients. Furthermore, 17% bMSI-H patients with high bMSI scores (larger or equal to 28) showed 66.7% ORR and 100% DCR. Finally, 91.7% patients with controlled diseases over 6 months showed decreasing bMSI scores, and 60% patients with progressive diseases showed increasing bMSI scores during therapies.

Conclusions

A significant proportion of pMMR metastatic gastrointestinal cancer patients could be rescued by bMSI and get benefits from ICI. bMSI could further classify the patients to three groups and more precisely predict the response of ICI. The level of bMSI is dynamically related to the response during the therapies. bMSI could potentially improve clinical practices in the future.

Clinical trial identification

Legal entity responsible for the study

Peking University Cancer Hospital & Institute.

Funding

National Key Research and Development Program of China (No. 2016YFC0905302).

Editorial Acknowledgement

Disclosure

H. Qin, D. Wang, S. Cai, Y. Bai, Z. Xie: Employee: 3D Medicines Inc. L. Xiong: Employee, stock holder, chairman: 3D Medicines Inc. F. Li: Employee and stock holder: 3D Medicines Inc. All other authors have declared no conflicts of interest.

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