Predicting therapeutic response to neoadjuvant immunotherapy based on an integration model in resectable stage IIIA (N2) non-small cell lung cancer.

OBJECTIVE: Accurately predicting response during neoadjuvant chemoimmunotherapy for resectable non-small cell lung cancer remains clinically challenging. In this study, we investigated the effectiveness of blood-based tumor mutational burden (bTMB) and a deep learning (DL) model in predicting major pathologic response (MPR) and survival from a phase 2 trial. METHODS: Blood samples were prospectively collected from 45 patients with stage IIIA (N2) non-small cell lung cancer undergoing neoadjuvant chemoimmunotherapy. An integrated model, combining the computed tomography-based DL score, bTMB, and clinical factors, was developed to predict tumor response to neoadjuvant chemoimmunotherapy. RESULTS: At baseline, bTMB were detected in 77.8% (35 of 45) of patients. Baseline bTMB ≥11 mutations/megabase was associated with significantly greater MPR rates (77.8% vs 38.5%, P = .042), and longer disease-free survival (P = .043), but not overall survival (P = .131), compared with bTMB <11 mutations/megabase in 35 patients with bTMB available. The developed DL model achieved an area under the curve of 0.703 in all patients. Importantly, the predictive performance of the integrated model improved to an area under the curve of 0.820 when combining the DL score with bTMB and clinical factors. Baseline circulating tumor DNA (ctDNA) status was not associated with pathologic response and survival. Compared with ctDNA residual, ctDNA clearance before surgery was associated with significantly greater MPR rates (88.2% vs 11.1%, P < .001) and improved disease-free survival (P = .010). CONCLUSIONS: The integrated model shows promise as a predictor of tumor response to neoadjuvant chemoimmunotherapy. Serial ctDNA dynamics provide a reliable tool for monitoring tumor response.

Brief Report: Acetaminophen Reduces Neoadjuvant Chemoimmunotherapy Efficacy in Patients With NSCLC by Promoting Neutrophil Extracellular Trap Formation: Analysis From a Phase 2 Clinical Trial.

Introduction: Neoadjuvant chemoimmunotherapy has recently been the standard of care for resectable locally advanced NSCLC. Factors affecting the neoadjuvant immunotherapy efficacy, however, remain elusive. Metabolites have been found to modulate immunity and associate with immunotherapeutic efficacy in advanced tumors. Therefore, we aimed to investigate the impact of plasma metabolites on the pathologic response after neoadjuvant chemoimmunotherapy. Methods: Patients with stage IIIA (N2) NSCLC who underwent neoadjuvant chemoimmunotherapy in a prospective phase 2 clinical trial (NCT04422392) were enrolled. Metabolomic profiling of the plasma before treatment was performed using liquid chromatography-mass spectrometry. A Lewis lung carcinoma mouse model was further used to investigate the underlying mechanisms. Proteomics and multiplexed immunofluorescence of the mice tumor were performed. Results: A total of 39 patients who underwent three cycles of anti-programmed cell death-protein 1 (anti-PD-1) (sintilimab) and chemotherapy were included. The level of acetaminophen (APAP) was found to be significantly elevated in patients who did not achieve major pathologic response. The level of APAP remained an independent predictor for major pathologic response in multivariate logistic analysis. In the Lewis lung carcinoma mouse model, combination of APAP and anti-PD-1 treatment significantly reduced the treatment efficacy compared with anti-PD-1 treatment alone. Proteomics of the tumor revealed that myeloid leukocyte activation and neutrophil activation pathways were enriched after APAP treatment. Tumor microenvironment featuring analysis also revealed that the combination treatment group was characterized with more abundant neutrophil signature. Further multiplexed immunofluorescence confirmed that more neutrophil extracellular trap formation was observed in the combination treatment group. Conclusions: APAP could impair neoadjuvant chemoimmunotherapy efficacy in patients with NSCLC by promoting neutrophil activation and neutrophil extracellular trap formation.

Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas.

INTRODUCTION: The International Association for the Study of Lung Cancer (IASLC) newly proposed grading system for lung adenocarcinomas (ADC) has been shown to be of prognostic significance. Hence, intraoperative consultation for the grading system was important regarding the surgical decision-making. Here, we evaluated the accuracy and interobserver agreement for IASLC grading system on frozen section (FS), and further investigated the prognostic performance. METHODS: FS and final pathology (FP) slides were reviewed by three pathologists for tumor grading in 373 stage I lung ADC following surgical resection from January to June 2013 (retrospective cohort). A prospective multicenter cohort (January to June 2021, n = 212) were included to confirm the results. RESULTS: The overall concordance rates between FS and FP were 79.1% (κ = 0.650) and 89.6% (κ = 0.729) with substantial agreement in retrospective and prospective cohorts, respectively. Presence of complex gland was the only independent predictor of discrepancy between FS and FP (presence versus. absence: odds ratio, 2.193; P = 0.015). The interobserver agreement for IASLC grading system on FS among three pathologists were satisfactory (κ = 0.672 for retrospective cohort; κ = 0.752 for prospective cohort). Moreover, the IASLC grading system by FS diagnosis could well predict recurrence-free survival and overall survival for patients with stage I invasive lung ADC. CONCLUSIONS: Our results suggest that FS had high diagnostic accuracy and satisfactory interobserver agreement for IASLC grading system. Future prospective studies are merited to validate the feasibility of using FS to match patients into appropriate surgical type.

Utility of 18F-FDG PET/CT uptake values in predicting response to neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer.

INTRODUCTION: Reliable predictive markers are lacking for resectable non-small cell lung cancer (NSCLC) patients treated with neoadjuvant chemoimmunotherapy. The present study investigated the utility of SUVmax values acquired from PET/CT to predict the response to neoadjuvant chemoimmunotherapy for resectable NSCLC. MATERAL AND METHODS: SUVmax, clinical and pathological outcomes, were collected from patients in 5 hospitals. Patients who received dynamic PET/CT surveillance were divided into cohorts A (chemoimmunotherapy) and B (chemotherapy), respectively, while cohort C (chemoimmunotherapy) comprised patients undergoing post-therapy PET/CT. Associations between SUVmax and major pathologic response (MPR) were evaluated through receiver operating characteristic (ROC) curves. RESULTS: A total of 129 cases with an MPR rate of 46.5 % was identified. In neoadjuvant chemoimmunotherapy, ΔSUVmax% (AUC: 0.890, 95 % CI: 0.761-0.949) and post-therapy SUVmax (AUC: 0.933, 95 % CI: 0.802-0.959) could accurately predict MPR. On the contrary, the baseline SUVmax was not associated with MPR (p = 0.184). Furthermore, an independent cohort C proved that post-therapy SUVmax could serve as an independent predictor (AUC: 0.928, 95 % CI: 0.823-0.958). In addition, robust predictive performance could be observed when we use the optimal cut-off point of both ΔSUVmax% (54.4 %, AUC: 0.912, 95 % CI: 0.824-0.994) and post-therapy SUVmax (3.565, AUC: 0.912, 95 % CI: 0.824-0.994) in neoadjuvant chemoimmunotherapy. The RNA data revealed that the expression of PFKFB4, a key enzyme in glycolysis, was positively correlated with SUVmax value and tumor cell proliferation after neoadjuvant chemoimmunotherapy. CONCLUSION: These findings highlighted that the ΔSUVmax% and remained SUVmax were accurate and non-invasive tests for the prediction of MPR after neoadjuvant chemoimmunotherapy.

Profiling the proteome dynamics during the cell cycle of human hepatoma cells.

Deregulation of cell cycle leads to cell transformation and cancer development. Here we present profiling the proteome dynamics using 2-DE and constructing the associated functional networks during the cell cycle of human hepatoma cells, Mahlavu. The protein dynamics was validated by hierarchical clustering analysis on the proteome, and by Northern blot assays on the selected 14-3-3 proteins. Of the 2665 protein spots, 201 with variation coefficient of expression dynamics >20% throughout the cell cycle were subjected to analysis. Degree of the global protein dynamics was phase dependent with the greatest in transitional phases of S/G2, G2/M, and G1/S. Concurrence of pathways coordinating cell-cycle progression versus arrest, and/or pathways regulating apoptosis versus antiapoptosis was always identified during the cell cycle, suggesting the existence of counteracting mechanisms for intracellular homeostasis. Data mining of the results suggested that the key transcription factors in G0/G1, G1/S, S, and G2/M were p53 and SP1, c-Myc, c-Myc and p53, and YY1 and c-Jun, respectively. Our findings for the first time provide insights into the regulation of mammalian cell-cycle progression at the proteome level, and grant a model to study disease mechanisms and to discover therapeutic targets for anticancer therapy.