Robust Prediction of Immune Checkpoint Inhibition Therapy for Non-Small Cell Lung Cancer.

The development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICI treatment. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value. Two published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified six immune-related pathways whose mutational status was significantly associated with overall survival after ICIs treatment. Then these pathways mutational status combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response. We firstly proved that TMB, PD-L1, and mutant-allele tumor heterogeneity (MATH) were independent biomarkers. The survival analysis of six immune-related pathways revealed the mutational status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80. We developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger data sets, further studies are warranted to refine the predictive performance of the approach.

Jiang J ，Jin Z ，Zhang Y ，Peng L ，Zhang Y ，Zhu Z ，Wang Y ，Tong D ，Yang Y ，Wang J ，Yang Y ，Xiao K ... -  《Frontiers in Immunology》

Integration of comprehensive genomic profiling, tumor mutational burden, and PD-L1 expression to identify novel biomarkers of immunotherapy in non-small cell lung cancer.

This study aimed to explore the novel biomarkers for immune checkpoint inhibitor (ICI) responses in non-small cell lung cancer (NSCLC) by integrating genomic profiling, tumor mutational burden (TMB), and expression of programmed death receptor 1 ligand (PD-L1). Tumor and blood samples from 637 Chinese patients with NSCLC were collected for targeted panel sequencing. Genomic alterations, including single nucleotide variations, insertions/deletions, copy number variations, and gene rearrangements, were assessed and TMB was computed. TMB-high (TMB-H) was defined as ≥10 mutations/Mb. PD-L1 positivity was defined as ≥1% tumor cells with membranous staining. Genomic data and ICI outcomes of 240 patients with NSCLC were derived from cBioPortal. EGFR-sensitizing mutations, ALK, RET, and ROS1 rearrangements were associated with lower TMB and PD-L1+/TMB-H proportions, whereas KRAS, ALK, RET, and ROS1 substitutions/indels correlated with higher TMB and PD-L1+/TMB-H proportions than wild-type genotypes. Histone-lysine N-methyltransferase 2 (KMT2) family members (KMT2A, KMT2C, and KMT2D) were frequently mutated in NSCLC tumors, and these mutations were associated with higher TMB and PD-L1 expression, as well as higher PD-L1+/TMB-H proportions. Specifically, patients with KMT2C mutations had higher TMB and PD-L1+/TMB-H proportions than wild-type patients. The median progression-free survival (PFS) was 5.47 months (95% CI 2.5-NA) in patients with KMT2C mutations versus 3.17 months (95% CI 2.6-4.27) in wild-type patients (p = 0.058). Furthermore, in patients with NSCLC who underwent ICI treatment, patients with TP53/KMT2C co-mutations had significantly longer PFS and greater durable clinical benefit (HR: 0.48, 95% CI: 0.24-0.94, p = 0.033). TP53 mutation combined with KMT2C or KRAS mutation was a better biomarker with expanded population benefit from ICIs therapy and increased the predictive power (HR: 0.46, 95% CI: 0.26-0.81, p = 0.007). We found that tumors with different alterations in actionable target genes had variable expression of PD-L1 and TMB in NSCLC. TP53/KMT2C co-mutation might serve as a predictive biomarker for ICI responses in NSCLC. Cancer immunotherapies, especially immune checkpoint inhibitors (ICIs), have revolutionized the treatment of non-small cell lung cancer (NSCLC); however, only a proportion of patients derive durable responses to this treatment. Biomarkers with greater accuracy are highly needed. In total, 637 Chinese patients with NSCLC were analyzed using next-generation sequencing and IHC to characterize the unique features of genomic alterations and TMB and PD-L1 expression. Our study demonstrated that KMT2C/TP53 co-mutation might be an accurate, cost-effective, and reliable biomarker to predict responses to PD-1 blockade therapy in NSCLC patients and that adding KRAS to the biomarker combination creates a more robust parameter to identify the best responders to ICI therapy.

Shi Y ，Lei Y ，Liu L ，Zhang S ，Wang W ，Zhao J ，Zhao S ，Dong X ，Yao M ，Wang K ，Zhou Q ... -  《Cancer Medicine》

Association of Survival and Immune-Related Biomarkers With Immunotherapy in Patients With Non-Small Cell Lung Cancer: A Meta-analysis and Individual Patient-Level Analysis.

Yu Y ，Zeng D ，Ou Q ，Liu S ，Li A ，Chen Y ，Lin D ，Gao Q ，Zhou H ，Liao W ，Yao H ... -  《JAMA Network Open》

Machine learning reveals a PD-L1-independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context.

Current predictive biomarkers for PD-1 (programmed cell death protein 1)/PD-L1 (programmed death-ligand 1)-directed immunotherapy in non-small cell lung cancer (NSCLC) mostly focus on features of tumour cells. However, the tumour microenvironment and immune context are expected to play major roles in governing therapy response. Against this background, we set out to apply context-sensitive feature selection and machine learning approaches on expression profiles of immune-related genes in diagnostic biopsies of patients with stage IV NSCLC. RNA expression levels were determined using the NanoString nCounter platform in formalin-fixed paraffin-embedded tumour biopsies obtained during the diagnostic workup of stage IV NSCLC from two thoracic oncology centres. A 770-gene panel covering immune-related genes and control genes was used. We applied supervised machine learning methods for feature selection and generation of predictive models. Feature selection and model creation were based on a training cohort of 55 patients with recurrent NSCLC treated with PD-1/PD-L1 antibody therapy. Resulting models identified patients with superior outcomes to immunotherapy, as validated in two subsequently recruited, separate patient cohorts (n = 67, hazard ratio = 0.46, p = 0.035). The predictive information obtained from these models was orthogonal to PD-L1 expression as per immunohistochemistry: Selecting by PD-L1 positivity at immunohistochemistry plus model prediction identified patients with highly favourable outcomes. Independence of PD-L1 positivity and model predictions were confirmed in multivariate analysis. Visualisation of the models revealed the predictive superiority of the entire 7-gene context over any single gene. Using context-sensitive assays and bioinformatics capturing the tumour immune context allows precise prediction of response to PD-1/PD-L1-directed immunotherapy in NSCLC.

Wiesweg M ，Mairinger F ，Reis H ，Goetz M ，Kollmeier J ，Misch D ，Stephan-Falkenau S ，Mairinger T ，Walter RFH ，Hager T ，Metzenmacher M ，Eberhardt WEE ，Zaun G ，Köster J ，Stuschke M ，Aigner C ，Darwiche K ，Schmid KW ，Rahmann S ，Schuler M ... -  《-》

Efficacy of Immune Checkpoint Inhibitors in KRAS-Mutant Non-Small Cell Lung Cancer (NSCLC).

KRAS mutation is the most frequent molecular alteration found in advanced NSCLC; it is associated with a poor prognosis without available targeted therapy. Treatment options for NSCLC have been recently enriched by the development of immune checkpoint inhibitors (ICIs), and data about its efficacy in patients with KRAS-mutant NSCLC are discordant. This study assessed the routine efficacy of ICIs in advanced KRAS-mutant NSCLC. In this retrospective study, clinical data were extracted from the medical records of patients with advanced NSCLC treated with ICIs and with available molecular analysis between April 2013 and June 2017. Analysis of programmed death ligand 1 (PD-L1) expression was performed if exploitable tumor material was available. A total of 282 patients with ICI-treated (in the first line or more) advanced NSCLC (all histological subgroups) who were treated with ICIs (anti-programmed death 1, anti-PD-L1, or anti-cytotoxic T-lymphocyte associated protein 4 antibodies), including 162 (57.4%) with KRAS mutation, 27 (9.6%) with other mutations, and 93 (33%) with a wild-type phenotype, were identified. PD-L1 analysis was available for 128 patients (45.4%), of whom 45.3% and 19.5% had PD-L1 expression of 1% or more and 50%, respectively (49.5% and 21.2%, respectively, in the case of the 85 patients with KRAS-mutant NSCLC). No significant difference was seen in terms of objective response rates, progression-free survival, or overall survival between KRAS-mutant NSCLC and other NSCLC. No significant differences in overall survival or progression-free survival were observed between the major KRAS mutation subtypes (G12A, G12C, G12D, G12V, and G13C). In KRAS-mutant NSCLC, unlike in non-KRAS-mutant NSCLC, the efficacy of ICIs is consistently higher, even though not statistically significant, for patients with PD-L1 expression in 1% or more of tumor cells than for those with PD-L1 expression in less than 1% of tumor cells, and this finding is especially true when PD-L1 expression is high (PD-L1 expression ≥50%). For patients with KRAS-mutant NSCLC (all mutational subtypes), the efficacy of ICI is similar to that for patients with other types of NSCLC. PD-L1 expression seems to be more relevant for predicting the efficacy of ICIs in KRAS-mutant NSCLC than it is in other types of NSCLC.

Jeanson A ，Tomasini P ，Souquet-Bressand M ，Brandone N ，Boucekine M ，Grangeon M ，Chaleat S ，Khobta N ，Milia J ，Mhanna L ，Greillier L ，Biemar J ，Nanni I ，Ouafik L ，Garcia S ，Mazières J ，Barlesi F ，Mascaux C ... -  《-》