Nan Sun

INTRODUCTION: Programmed death receptor-1 (PD-1) inhibitors have shown efficacy in first-line treatment of NSCLC; however, evidence of PD-1 inhibitor as neoadjuvant treatment is limited. This is a phase 1b study to evaluate the safety and outcome of PD-1 inhibitor in neoadjuvant setting. METHODS: Treatment-naive patients with resectable NSCLC (stage IA-IIIB) received two cycles of sintilimab (200 mg, intravenously, day 1 out of 22). Operation was performed between day 29 and 43. Positron emission tomography-computed tomography scans were obtained at baseline and before the operation. The primary end point was safety. Efficacy end points included rate of major pathologic response (MPR) and objective response rate. Expression of programmed cell death ligand 1 was also evaluated (registration number: ChiCTR-OIC-17013726). RESULTS: A total of 40 patients enrolled, all of whom received two doses of sintilimab and 37 underwent radical resection. A total of 21 patients (52.5%) experienced neoadjuvant treatment-related adverse events (TRAEs). Four patients (10.0%) experienced grade 3 or higher neoadjuvant TRAEs, and one patient had grade 5 TRAE. Eight patients achieved radiological partial response, resulting in an objective response rate of 20.0%. Among 37 patients, 15 (40.5%) achieved MPR, including six (16.2%) with a pathologic complete response in primary tumor and three (8.1%) in lymph nodes as well. Squamous cell NSCLC exhibited superior response compared with adenocarcinoma (MPR: 48.4% versus 0%). Decrease of maximum standardized uptake values after sintilimab treatment correlated with pathologic remission (p < 0.00001). Baseline programmed cell death ligand 1 expression of stromal cells instead of tumor cells was correlated with pathologic regression (p = 0.0471). CONCLUSIONS: Neoadjuvant sintilimab was tolerable for patients with NSCLC, and 40.5% MPR rate is encouraging. The decrease of maximum standardized uptake values after sintilimab might predict pathologic response.

Immune checkpoint inhibitors against PD-1/PD-L1 yield improved survival rates of KRAS-mutant NSCLC patients, who conferred a poor prognosis without effective targeted therapy until now. Yet, the underlying association between KRAS mutations and immune responses remains unclear. We performed an integrated analysis of the data from publicly available repositories and from clinical center cohorts to explore the association between KRAS mutation status and tumor immunity-associated features, including PD-L1 expression, CD8+ tumor-infiltrating lymphocytes (TILs) and tumor mutational burden (TMB). Our results revealed that KRAS mutations are correlated with an inflammatory tumor microenvironment and tumor immunogenicity, resulting in superior patient response to PD-1/PD-L1 inhibitors. Meanwhile, three-pool analysis further confirmed that KRAS-mutant NSCLC patients show remarkable clinical benefit from anti-PD-1/PD-L1 immunotherapy. In addition, a KRAS-mutant lung adenocarcinoma mouse model was established to estimate the relative efficacy of anti-PD-L1 monoclonal antibody monotherapy or combination treatment with docetaxel versus docetaxel alone. Most surprisingly, we found that PD-L1 blockade combined with docetaxel did not promote an anti-tumor response. These findings uncover that PD-1/PD-L1 blockade monotherapy may be the optimal therapeutic schedule in NSCLC patients harboring KRAS mutations.

Abstract Background Although immune checkpoint inhibitors (ICIs) against programmed cell death protein 1 (PD‐1) and its ligand PD‐L1 have demonstrated potency towards treating patients with non‐small cell lung carcinoma (NSCLC), the potential association between Kirsten rat sarcoma viral oncogene homolog ( KRAS ) oncogene substitutions and the efficacy of ICIs remains unclear. In this study, we aimed to find point mutations in the KRAS gene resistant to ICIs and elucidate resistance mechanism. Methods The association between KRAS variant status and the efficacy of ICIs was explored with a clinical cohort ( n = 74), and confirmed with a mouse model. In addition, the tumor immune microenvironment (TIME) of KRAS ‐mutant NSCLC, such as CD8 + tumor‐infiltrating lymphocytes (TILs) and PD‐L1 level, was investigated. Cell lines expressing classic KRAS substitutions were used to explore signaling pathway activation involved in the formation of TIME. Furthermore, interventions that improved TIME were developed to increase responsiveness to ICIs. Results We observed the inferior efficacy of ICIs in KRAS ‐G12D‐mutant NSCLC. Based upon transcriptome data and immunostaining results from KRAS ‐mutant NSCLC, KRAS ‐G12D point mutation negatively correlated with PD‐L1 level and secretion of chemokines CXCL10/CXCL11 that led to a decrease in CD8 + TILs, which in turn yielded an immunosuppressive TIME. The analysis of cell lines overexpressing classic KRAS substitutions further revealed that KRAS ‐G12D mutation suppressed PD‐L1 level via the P70S6K/PI3K/AKT axis and reduced CXCL10/CXCL11 levels by down‐regulating high mobility group protein A2 (HMGA2) level. Notably, paclitaxel, a chemotherapeutic agent, upregulated HMGA2 level, and in turn, stimulated the secretion of CXCL10/CXCL11. Moreover, PD‐L1 blockade combined with paclitaxel significantly suppressed tumor growth compared with PD‐L1 inhibitor monotherapy in a mouse model with KRAS ‐G12D‐mutant lung adenocarcinoma. Further analyses revealed that the combined treatment significantly enhanced the recruitment of CD8 + TILs via the up‐regulation of CXCL10/CXCL11 levels. Results of clinical study also revealed the superior efficacy of chemo‐immunotherapy in patients with KRAS ‐G12D‐mutant NSCLC compared with ICI monotherapy. Conclusions Our study elucidated the molecular mechanism by which KRAS ‐G12D mutation drives immunosuppression and enhances resistance of ICIs in NSCLC. Importantly, our findings demonstrate that ICIs in combination with chemotherapy may be more effective in patients with KRAS ‐G12D‐mutant NSCLC.

Importance: There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases. Objective: To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria. Design, Setting, and Participants: Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS). Main Outcomes and Measures: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity). Results: In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model. Conclusions and Relevance: This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.