Molecular Features Identified That Predict Response to Checkpoint Inhibitors in NSCLC
Key findings
- This study is a joint analysis of the Stand Up To Cancer–Mark Foundation non-small cell lung cancer cohort (NSCLC) cohort, a dataset of whole-exome and RNA sequencing from 393 patients with NSCLC who were treated with a PD-1 or PD-L1 inhibitor
- Several associations were identified between molecular features and response, including favorable (e.g., ATM-altered) and unfavorable (e.g., TERT-amplified) genomic subgroups and expression of immunoproteasome subunit genes
- A de-differentiated tumor-intrinsic subtype of NSCLC was identified that showed particularly high response to checkpoint blockade
- These and additional results demonstrate the complexity of the biological determinants of immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large cancer-specific cohorts
Only one in five patients with non-small cell lung cancer (NSCLC) responds to anti–PD-1 or anti–PD-L1 immune checkpoint inhibitors, and intensive effort is underway to guide the selection of good candidates.
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Justin Gainor, MD, director of the Center for Thoracic Cancers Program and director of Targeted Immunotherapy at the Mass General Cancer Center, and colleagues at eight other cancer centers recently conducted the first joint multi-omics analysis of the Stand Up To Cancer–Mark Foundation NSCLC cohort. The dataset comprised 393 patients with advanced NSCLC who had careful imaging-based documentation of response to one of these medications.
In Nature Genetics the researchers report biomarkers of response and resistance that may improve the selection of candidates and perhaps even aid the development of personalized immunotherapy based on a patient's molecular profile.
Somatic and Transcriptional Predictors
Two of the top genomic features identified were:
- A disabling mutation in ATM, a DNA-repair gene (associated with favorable response to immunotherapy)
- Amplification of TERT, which is thought to protect against telomere crisis (associated with negative outcomes)
Transcriptomic analysis identified immunoproteasome subunit genes as additional key predictors of response.
Immune Signatures
Of 11 immune signatures evaluated, exhausted CD8+ T-cells were most strongly associated with response, and monocyte/macrophage and dendritic cell signatures were most strongly associated with resistance. Beyond individual cell types, higher-level organization of the strongest genes associated with response and resistance identified microenvironmental signatures previously associated with immune states:
- M-1, Wound healing—The immune system appeared dampened, as if early in healing a physical injury
- M-2, Immune-activated—Immune cells displayed signatures of previously high activity that had waned
- M-3, Immune desert—This signature was defined by a paucity of myeloid and lymphoid cells
Response to checkpoint blockade was significantly increased with M-2 relative to M-1 and M-3. At least two distinct transcriptional states may be associated with checkpoint blockade resistance in NSCLC.
Patients with both M-2 and a dedifferentiated tumor-intrinsic subtype of NSCLC (lower levels of canonical adenocarcinoma and squamous markers but high levels of markers associated with neighboring endodermal lineages) had the highest responsiveness to checkpoint blockade, overall response rate 67%.
Integrative Analysis
Integrative analysis of the genomic features identified in this study and previously reported signatures relevant to immune and tumor biology suggested complex interplay between certain signaling pathways (e.g., CXCL9 vs. TGF-β) and cell types (e.g., myeloid cells vs. fibroblasts).
Collectively, the study results show multiple factors underlie response to PD-1/PD-L1 inhibition. Stratifying tumors by mutations and gene expression patterns not currently assessed in routine care may eventually make this immunotherapy effective for a broader population.
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