下一代肿瘤不可知靶点即将出现
Next-generation tumor-agnostic targets on the horizon.
肿瘤不可知药物开发将肿瘤学重新聚焦于共享的分子依赖性而非组织来源,从而能够针对跨肿瘤的罕见可操作驱动因素进行高效开发。
英文原题:Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.
Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.
标准化人工sTIL评分具有可重复性,数字和基于AI的方法尽管平台间仅呈中度相关,但显示出一致的预后分层及治疗获益分层的潜力。AI空间指标提供了超越sTIL密度的补充信息,可支持更具可扩展性的免疫评估。未来需要在独立队列中验证这些方法,并阐明其在当代HER2靶向治疗分层中的临床效用。
间质TIL(肿瘤浸润淋巴细胞)(sTILs)在早期HER2阳性乳腺癌中具有预后价值,但其在双HER2阻断治疗背景下的作用仍未明确。我们利用3期APHINITY试验的肿瘤样本,评估了人工、数字化和人工智能(AI)为基础的sTIL定量,以及AI衍生的空间指标,用于预后和治疗获益分层。
在APHINITY试验中,4805例患者被随机分配接受化疗联合曲妥珠单抗及帕妥珠单抗,或化疗联合曲妥珠单抗及安慰剂。中位随访时间为74·1个月(IQR 68·3-75·4)。我们使用人工评估、自动化数字方法、基于AI的淋巴细胞定量(AI淋巴细胞百分比)以及两种AI衍生的空间特征(AI-TIL和免疫热点)分析了4262张苏木精-伊红染色图像。观察者间可重复性在262个随机选择的肿瘤样本中进行评估,由五位病理学家独立评分。多变量Cox模型用于评估TIL水平与侵袭性无病生存期(APHINITY的主要结局)、远处无复发生存间期和总生存期之间的关联。帕妥珠单抗获益的异质性通过亚组分析、亚群治疗效果模式图分析以及包含治疗-by-生物标志物交互项的嵌套Cox模型进行评估。
人工评分显示出较高的观察者间可重复性(组内相关系数0·84 [95% CI 0·79-0·88])。人工方法与自动化方法之间的一致性为中等。基于AI的评分(AI淋巴细胞百分比)将1035例淋巴结阳性肿瘤中的120例(11·6%)从免疫低(按人工评分)重新分类为免疫高;与人工和基于AI方法一致分类为免疫低的患者相比,该亚组患者在帕妥珠单抗组与安慰剂组之间5年侵袭性无病生存曲线的分离更大。较高的TIL水平与所有sTIL测量方法和空间测量的侵袭性无病生存改善相关(风险比[HR] 0·41-0·93)。在所有测量方法中,帕妥珠单抗与较高sTIL水平下的侵袭性无病生存改善相关(HR 0·36-0·48),但与较高的空间测量值无关。帕妥珠单抗最大的6年绝对改善见于淋巴结阳性疾病且肿瘤在人工sTIL评分中处于最高免疫浸润水平的患者(≥70·0%;平均绝对改善12·1个百分点[SD 2·8])。在嵌套预后和预测模型中,基于AI的免疫热点评分与任何sTIL测量联合时提供了最一致的额外信息(所有p<0·010)。
BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74·1 months (IQR 68·3-75·4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0·010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.
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