Gene expression signatures as candidate biomarkers of response to PD-1 blockade in non-small cell lung cancers

Imaging diagnosis and efficacy monitoring by [89Zr]Zr-DFO-KN035 immunoPET in patients with PD-L1-positive solid malignancies

Rationale: Although programmed death-ligand 1 (PD-L1) inhibitors have achieved efficacy in cancer therapy, their response rate is low. Differences in the prognosis of patients with cancer under anti-PD-L1 treatment are related to the PD-L1 level in tumors. Accurate PD-L1 detection can optimize the accuracy of tumor immunotherapy and avoid ineffective clinical diagnosis and treatments. Methods: We investigated the imaging efficiency and therapy monitoring capacity of [89Zr]Zr-DFO-KN035 immunoPET for tumors. We labeled the monodomain anti-PD-L1 antibody KN035 with the radionuclide zirconium-89 and used this tracer for PET imaging. [89Zr]Zr-DFO-KN035 uptakes in patients with PD-L1-positive tumors, including primary and metastatic tumors, as well as in normal tissues, were comparatively assessed by using positron emission tomography/computed tomography imaging. Results: In PD-L1-positive patients, [89Zr]Zr-DFO-KN035 was sensitive in tumor-targeting imaging and could detect multiple metastatic foci, including multiple bone metastases (tumor-to-muscle ratios of 7.102 and 6.118 at 55 and 120 h, respectively) and lymph-node metastases (tumor-to-muscle ratios of 11.346 and 6.542 at 55 and 120 h, respectively). The needed radioactive dose of [89Zr]Zr-DFO-KN035 (55.5-92.5 MBq) used in this study was considerably lower than that of [18F]FDG (370-555 MBq). [89Zr]Zr-DFO-KN035 monitored and predicted the site of adverse reactions in antitumor immunotherapy. Moreover, after antitumor treatment, [89Zr]Zr-DFO-KN035 enabled observational imaging for therapeutic efficacy evaluation, which can help predict patient prognosis. Conclusion: [89Zr]Zr-DFO-KN035 can be used for the diagnosis and therapy monitoring of PD-L1-positive tumors and provide noninvasive and comprehensive observations for tumor diagnostic imaging, prognosis prediction, and efficacy evaluation.

Gene Expressions and High Lymphocyte Count May Predict Durable Clinical Benefits in Patients with Advanced Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

BACKGROUND

Not all patients with advanced non-small cell lung cancer (NSCLC) benefit from immune checkpoint inhibitors (ICIs). Therefore, we aimed to assess the predictive potential of gene expression profiling (GEP), peripheral immune cell counts, and clinical characteristics.

METHODS

The primary endpoint of this prospective, observational study was a durable clinical benefit (DCB) defined as progression-free survival >6 months. In a subgroup with histological biopsies of sufficient quality (n = 25), GEP was performed using the nCounter® PanCancer IO 360 panel.

RESULTS

DCB was observed in 49% of 123 included patients. High absolute lymphocyte count (ALC) and absence of liver metastases were associated with DCB (OR = 1.95, p = 0.038 and OR = 0.36, p = 0.046, respectively). GEP showed clustering of differentially expressed genes according to DCB, and a strong association between PD-L1 assessed by GEP (CD274) and immunohistochemistry (IHC) was observed (p = 0.00013). The TGF-β, dendritic cell, and myeloid signature scores were higher for patients without DCB, whereas the JAK/STAT loss signature scores were higher for patients with DCB (unadjusted p-values < 0.05).

CONCLUSIONS

ALC above 1.01 × 109/L and absence of liver metastases were significantly associated with DCB in ICI-treated patients with NSCLC. GEP was only feasible in 20% of the patients. GEP-derived signatures may be associated with clinical outcomes, and PD-L1 could be assessed by GEP rather than IHC.

Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer

Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.