Maxime Meylan, PhD @maximemeylan @crcordeliers #ESMO20 #kidneycancer Clear Cell Renal Cell Carcinoma Immune Classification

Maxime Meylan, PhD @maximemeylan @crcordeliers #ESMO20 #kidneycancer Clear Cell Renal Cell Carcinoma Immune Classification

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Maxime Meylan, PhD of the Centre de Recherche des Cordeliers explains the ESMO Virtual Congress 2020: Clear Cell Renal Cell Carcinoma Immune Classification Enhances the Predictive Value of T Effector and Angiogenesis Signatures in Response to Nivolumab abstract.

A real-world phase 2 multicenter analysis of nivolumab in metastatic clear cell renal cell carcinoma (ccRCC) was the NIVOREN GETUG-AFU26 clinical trial. Compared with the randomized phase 3 CheckMate 025 trial comparing nivolumab to everolimus, published in 2015, the patient results of this study were favorable. A translational program was launched in the NIVOREN trial to analyze molecular features associated with the response to nivolumab. For inclusion in this ancillary analysis, a total of 324 patients had molecular data produced.

The NIVOREN translational program's study plan is shown below. Immunohistochemistry on formalin-fixed paraffin embedded biopsy tissue examined 14 pre-identified markers. To add previously published gene expression signatures to samples as shown below, 3 'RNA profiling on FFPE tissue was also performed on 184 samples. Three signatures were used: the signatures of the Teffector and Angiogenesis produced from the IMmotion 150 study and the immune cell classification (KIC) score of the kidney ccRCC to classify the types of cells present in the biopsy specimen. KIC was first performed by unsupervised clustering for the above signature, then validated using a separate cohort to predict cell types present.

In contrast to the overall NIVOREN cohort, the characteristics of the 184 patients (divided into discovery and validation cohorts) are shown below. Differences were noted between the percentage of favorable risk IMDC patients as well as the rate of ORR between the cohorts of discovery and validation.

Samples with low Angiogenesis signature scores and high Teffector scores were enriched for objective response in the discovery cohort and, as shown below, had better progression-free survival compared to other signature combinations.

Unsupervised clustering using the KIC identified 5 clusters of cell populations inside the tumor in the discovery cohort. These were classified into high or low stromal cells or high / low immune cells using principal component analysis. The gene expression classification of cell types present was confirmed by immunohistochemistry.

The authors hypothesized that the immune cell enriched samples (KIC Immune-high) might also have high scores for Teffector and low gene expression for Angiogenesis. This, as the Venn Diagram below shows, was not obviously the case.

The result was then correlated with the KIC Immune-High signature. The authors discovered that in the Discovery cohort as well as the Validation cohort, samples with high scores suggesting high immune cell levels and low stromal cell levels were most correlated with objective response and progression-free survival.

In summary, the authors established several classifications of patient samples based on the application of gene expression signatures using samples from the NIVOREN GETUG-AFU26 real-world trial of nivolumab therapy in ccRCC. Samples with low IMmotion150 angiogenesis signature scores and high IMmotion150 Teffector signature scores were most closely correlated with good performance. Relatively superior results were also associated with samples with Immune-high and stromal-low cell populations as evaluated by KIC. However, there was no absolute sample overlap between Teffector high/angiogenesis low and KIC Immune-high / stromal-low samples, indicating that multiple biomarkers may be needed in ccRCC to predict immunotherapy response. Most interestingly , the results of immunohistochemistry correlated well with the signatures of KIC, indicating that IHC may be useful as a predictive tool.
Presented by: Maxime Meylan, PhD Student, Research Center Cordelier, Sorbonne, Paris , France

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