Molecular determinants of response to PD-L1 blockade across tumor types
Romain Banchereau1, Ning Leng2, Oliver Zill2
1Genentech, South San Francisco, CA, USA. banchereau.romain@gene.com.
Abstract:
Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies.
Insights
Predicting response to immune checkpoint inhibitors is complex. This study found significant molecular heterogeneity across cancer types, suggesting personalized treatment strategies are crucial for effective cancer immunotherapy.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
Background:
- Immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis offer durable responses in various cancers.
- Predictive biomarkers like PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) have limitations.
- Identifying robust molecular profiles for ICI response remains a challenge.
Purpose of the Study:
- To identify unifying and indication-specific molecular profiles predicting response to PD-1/PD-L1 blockade.
- To investigate the utility of RNA sequencing (RNA-seq) in conjunction with IHC and TMB for response prediction.
- To explore cross-indication molecular heterogeneity in response to checkpoint inhibitors.
Main Methods:
- RNA-sequencing analysis of 366 patients with non-small cell lung cancer (NSCLC), urothelial carcinoma (mUC), and renal cell carcinoma (RCC).
- Application of multiple machine learning approaches to identify predictive transcriptional signatures.
- Comparison of identified signatures with previously reported biomarkers for ICI response.
Main Results:
- Machine learning models failed to identify a universal predictive transcriptional signature across indications.
- Previously reported ICI response signatures did not validate in this cohort.
- Significant molecular heterogeneity was observed between indications, particularly in PD-L1 positive tumors.
- Cell cycle and DNA damage repair genes correlated with response in PD-L1 negative mUC and NSCLC.
- The CDKN2A gene was identified as a significant transcriptional correlate of response, linking non-immune pathways to ICI outcomes.
Conclusions:
- A single molecular signature is insufficient for predicting ICI response across diverse cancer types.
- Significant heterogeneity exists between mUC, NSCLC, and RCC tumors regarding ICI response.
- Indication-specific molecular approaches are essential for optimizing treatment strategies and improving patient outcomes with checkpoint blockade therapy.
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