Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Clinical Pathology, University Hospital of North Norway, Tromso; Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso.
Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso; Department of Oncology, University Hospital of North Norway, Tromso, Norway.
Ann Oncol. 2023 Jul;34(7):578-588. doi: 10.1016/j.annonc.2023.04.005. Epub 2023 Apr 24.
We aim to implement an immune cell score model in routine clinical practice for resected non-small-cell lung cancer (NSCLC) patients (NCT03299478). Molecular and genomic features associated with immune phenotypes in NSCLC have not been explored in detail.
We developed a machine learning (ML)-based model to classify tumors into one of three categories: inflamed, altered, and desert, based on the spatial distribution of CD8+ T cells in two prospective (n = 453; TNM-I trial) and retrospective (n = 481) stage I-IIIA NSCLC surgical cohorts. NanoString assays and targeted gene panel sequencing were used to evaluate the association of gene expression and mutations with immune phenotypes.
Among the total of 934 patients, 24.4% of tumors were classified as inflamed, 51.3% as altered, and 24.3% as desert. There were significant associations between ML-derived immune phenotypes and adaptive immunity gene expression signatures. We identified a strong association of the nuclear factor-κB pathway and CD8+ T-cell exclusion through a positive enrichment in the desert phenotype. KEAP1 [odds ratio (OR) 0.27, Q = 0.02] and STK11 (OR 0.39, Q = 0.04) were significantly co-mutated in non-inflamed lung adenocarcinoma (LUAD) compared to the inflamed phenotype. In the retrospective cohort, the inflamed phenotype was an independent prognostic factor for prolonged disease-specific survival and time to recurrence (hazard ratio 0.61, P = 0.01 and 0.65, P = 0.02, respectively).
ML-based immune phenotyping by spatial distribution of T cells in resected NSCLC is able to identify patients at greater risk of disease recurrence after surgical resection. LUADs with concurrent KEAP1 and STK11 mutations are enriched for altered and desert immune phenotypes.
我们旨在为接受手术治疗的非小细胞肺癌（NSCLC）患者（NCT03299478）实施一种免疫细胞评分模型。目前尚未详细探讨与 NSCLC 免疫表型相关的分子和基因组特征。
我们开发了一种基于机器学习（ML）的模型，根据两个前瞻性（n=453；TNM-I 试验）和回顾性（n=481）Ⅰ-ⅢA 期 NSCLC 手术队列中 CD8+T 细胞的空间分布，将肿瘤分为三类：炎症型、改变型和荒漠型。采用 NanoString 检测和靶向基因 panel 测序来评估基因表达和突变与免疫表型的相关性。
在总共 934 例患者中，24.4%的肿瘤被归类为炎症型，51.3%为改变型，24.3%为荒漠型。ML 衍生的免疫表型与适应性免疫基因表达特征之间存在显著相关性。我们通过在荒漠型中发现 NF-κB 通路和 CD8+T 细胞排斥的阳性富集，确定了 NF-κB 通路和 CD8+T 细胞排斥的强烈关联。与炎症表型相比，非炎症型肺腺癌（LUAD）中 KEAP1（OR 0.27，Q=0.02）和 STK11（OR 0.39，Q=0.04）的突变明显共发生。在回顾性队列中，炎症表型是手术切除后疾病特异性生存和复发时间延长的独立预后因素（风险比 0.61，P=0.01 和 0.65，P=0.02）。
基于 ML 的免疫表型通过在切除的 NSCLC 中 T 细胞的空间分布来识别手术切除后疾病复发风险较高的患者。同时存在 KEAP1 和 STK11 突变的 LUAD 中，改变型和荒漠型免疫表型更为富集。
