High IGKC-Expressing Intratumoral Plasma Cells Predict Response to Immune Checkpoint Blockade
Abstract
:1. Introduction
2. Results
2.1. Clinicopathological Variables Associated with Response and Prognosis
2.2. B-Cell Transcriptomic Signature of the Response to PD1 Blockade
2.3. TNFRSF11B, IGLV6-57, IGHA1 and GRIA1 Are Technically Validated as Promising Markers of Response to Nivolumab
2.4. A Fraction of the Transcriptomic Signature of Response Is Prognostic
2.5. TMB Is Not Associated with Response to Nivolumab in Cutaneous Metastatic Melanoma Patients
2.6. Decoding the Response-Relevant Mutational Signatures in Patients with Cutaneous Metastatic Melanoma
2.7. Identification of the Specific Response-Associated Stromal Cell Subtypes in Single Cell RNA-seq Data
2.8. Specific Stromal Cell Population Composition in the TME of Our Cohort of Metastatic Melanma Patents in Treament with Nivolumab
2.9. Validation of the Bulk RNA-seq Transcriptomic Gene Signature Using Single-Cell RNA-seq Data
2.10. Validation of the B-Cell Signature by Multiplex Immunofluorescence
2.11. Higher Abundance and Diversity of BCR and HLA, but Not of TCR Clonotypes, Are Associated with Response to Nivolumab
2.12. Machine-Learning-Based Models Based on Our Transcriptomics Gene Signature Predict Response in Eight External Melanoma Cohorts Treated with ICB
3. Discussion
4. Materials and Methods
4.1. Subjects
- Good responders: patients with maintained partial or complete response for a year or in treatment during at least one year.
- Bad responders: progression in less than 3 months from the start of IT. Of these, a subgroup of “severe” bad responders was defined as those who progressed in fewer than 60 days.
4.2. Nucleic Acid Extraction
4.3. Technical Validation
4.4. Next Generation Sequencing
4.5. Bioinformatic Analysis
4.6. Statistical Analysis
4.7. Multiplex Immunofluorescence
4.8. Tissue Imaging, Spectral Unmixing and Phenotyping
4.9. Special Case
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Good Responders Count | Bad Responders Count | Clonotype Composition |
---|---|---|
1555 | 87 | IGKV3-20, IGKJ1, IGKC |
1364 | 8 | IGKV1-33, IGKJ4, IGKC |
917 | 129 | IGKV1-39, IGKJ2, IGKC |
818 | 49 | IGKV1-5, IGKJ1, IGKC |
816 | 47 | IGKV3-15, IGKJ2, IGKC |
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Onieva, J.L.; Xiao, Q.; Berciano-Guerrero, M.-Á.; Laborda-Illanes, A.; de Andrea, C.; Chaves, P.; Piñeiro, P.; Garrido-Aranda, A.; Gallego, E.; Sojo, B.; et al. High IGKC-Expressing Intratumoral Plasma Cells Predict Response to Immune Checkpoint Blockade. Int. J. Mol. Sci. 2022, 23, 9124. https://doi.org/10.3390/ijms23169124
Onieva JL, Xiao Q, Berciano-Guerrero M-Á, Laborda-Illanes A, de Andrea C, Chaves P, Piñeiro P, Garrido-Aranda A, Gallego E, Sojo B, et al. High IGKC-Expressing Intratumoral Plasma Cells Predict Response to Immune Checkpoint Blockade. International Journal of Molecular Sciences. 2022; 23(16):9124. https://doi.org/10.3390/ijms23169124
Chicago/Turabian StyleOnieva, Juan Luis, Qingyang Xiao, Miguel-Ángel Berciano-Guerrero, Aurora Laborda-Illanes, Carlos de Andrea, Patricia Chaves, Pilar Piñeiro, Alicia Garrido-Aranda, Elena Gallego, Belén Sojo, and et al. 2022. "High IGKC-Expressing Intratumoral Plasma Cells Predict Response to Immune Checkpoint Blockade" International Journal of Molecular Sciences 23, no. 16: 9124. https://doi.org/10.3390/ijms23169124