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Neoadjuvant Therapy in Lung Cancer: What Is Most Important: Objective Response Rate or Major Pathological Response?

ABSTRACT: Lung cancer is the most fatal and frequently diagnosed malignant tumor. Neoadjuvant therapy is a promising approach for prolonging survival and increasing the chance of cure rates for patients with potentially resectable disease. Currently, many therapeutic alternatives, including chemotherapy, targeted therapy, and immunotherapy, are continually being explored to enrich the content of neoadjuvant therapy. However, neoadjuvant therapy remains to have no unified evaluation standards. Overall survival (OS) is the "gold standard" for evaluating the clinical benefit of cancer treatment, but it needs years for a reliable evaluation. Hence, researchers need to identify surrogate endpoints that can predict OS accurately and reliably without long follow-up periods. In this review, we describe the research progress of different neoadjuvant therapies and explore their response evaluation, aiming to identify stronger predictors of OS.

SUBMITTER: Chen X

PROVIDER: S-EPMC8535119 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Neoadjuvant Therapy in Lung Cancer: What Is Most Important: Objective Response Rate or Major Pathological Response?

Chen Xi X Ma Kewei K

Current oncology (Toronto, Ont.) 20211013 5

Lung cancer is the most fatal and frequently diagnosed malignant tumor. Neoadjuvant therapy is a promising approach for prolonging survival and increasing the chance of cure rates for patients with potentially resectable disease. Currently, many therapeutic alternatives, including chemotherapy, targeted therapy, and immunotherapy, are continually being explored to enrich the content of neoadjuvant therapy. However, neoadjuvant therapy remains to have no unified evaluation standards. Overall surv ...[more]

PMID: 34677268

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Project description:BackgroundThis study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction.Methods274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR. To reveal the underlying biological basis of the deep learning score, a genetic analysis was conducted based on 25 patients with RNA-sequencing data.FindingsMPR was achieved in 54.0% (n = 148) patients. The area under the curve (AUC) of the deep learning score to predict MPR was 0.73 (95% confidence interval [CI]: 0.58-0.86) and 0.72 (95% CI: 0.58-0.85) in the internal validation and external validation cohorts, respectively. After integrating the clinical characteristic into the deep learning score, the combined model achieved satisfactory performance in the internal validation (AUC: 0.77, 95% CI: 0.64-0.89) and external validation cohorts (AUC: 0.75, 95% CI: 0.62-0.87). In the biological basis exploration for the deep learning score, a high deep learning score was associated with the downregulation of pathways mediating tumour proliferation and the promotion of antitumour immune cell infiltration in the microenvironment.InterpretationThe proposed deep learning model could effectively predict MPR in NSCLC patients treated with neoadjuvant chemoimmunotherapy.FundingThis study was supported by National Key Research and Development Program of China, China (2017YFA0205200); National Natural Science Foundation of China, China (91959126, 82022036, 91959130, 81971776, 81771924, 6202790004, 81930053, 9195910169, 62176013, 8210071009); Beijing Natural Science Foundation, China (L182061); Strategic Priority Research Program of Chinese Academy of Sciences, China (XDB38040200); Chinese Academy of Sciences, China (GJJSTD20170004, QYZDJ-SSW-JSC005); Shanghai Hospital Development Center, China (SHDC2020CR3047B); and Science and Technology Commission of Shanghai Municipality, China (21YF1438200).

Project description:Studies validating the prognostic accuracy of the tumor-node-metastases (TNM) classification in patients with lung cancer treated by neoadjuvant therapy are scarce. Tumor regression, particularly major pathological response (MPR), is an acknowledged prognostic factor in this setting. We aimed to validate a novel combined prognostic score. This retrospective single-center study was conducted on 117 consecutive patients with non-small cell lung cancer resected after neoadjuvant treatment at a Swiss University Cancer Center between 2000 and 2016. All cases were clinicopathologically re-evaluated. We assessed the prognostic performance of a novel prognostic score (PRSC) combining T-category, lymph node status, and MPR, in comparison with the eighth edition of the TNM classification (TNM8), the size adapted TNM8 as proposed by the International Association for the Study of Lung Cancer (IASLC) and MPR alone. The isolated ypT-category and the combined TNM8 stages accurately differentiated overall survival (OS, stage p = 0.004) and disease-free survival (DFS, stage p = 0.018). Tumor regression had a prognostic impact. Optimal cut-offs for MPR emerged as 65% for adenocarcinoma and 10% for non-adenocarcinoma and were statistically significant for survival (OS p = 0.006, DFS p < 0.001). The PRSC differentiated between three prognostic groups (OS and DFS p < 0.001), and was superior compared to the stratification using MPR alone or the TNM8 systems, visualized by lower Akaike (AIC) and Bayesian information criterion (BIC) values. In the multivariate analyses, stage III tumors (HR 4.956, p = 0.003), tumors without MPR (HR 2.432, p = 0.015), and PRSC high-risk tumors (HR 5.692, p < 0.001) had significantly increased risks of occurring death. In conclusion, we support 65% as the optimal cut-off for MPR in adenocarcinomas. TNM8 and MPR were comparable regarding their prognostic significance. The novel prognostic score performed distinctly better regarding OS and DFS.

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Neoadjuvant Therapy in Lung Cancer: What Is Most Important: Objective Response Rate or Major Pathological Response?

Publications

Neoadjuvant Therapy in Lung Cancer: What Is Most Important: Objective Response Rate or Major Pathological Response?

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