A prognostic and predictive computational pathology immune signature for ductal carcinoma in situ: retrospective results from a cohort within the UK/ANZ DCIS trial

IF 23.8 1区医学 Q1 MEDICAL INFORMATICS

Lancet Digital Health Pub Date : 2024-08-01 DOI:10.1016/S2589-7500(24)00116-X

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引用次数: 0

Abstract

Background

The density of tumour-infiltrating lymphocytes (TILs) could be prognostic in ductal carcinoma in situ (DCIS). However, manual TIL quantification is time-consuming and suffers from interobserver and intraobserver variability. In this study, we developed a TIL-based computational pathology biomarker and evaluated its association with the risk of recurrence and benefit of adjuvant treatment in a clinical trial cohort.

Methods

In this retrospective cohort study, a computational pathology pipeline was developed to generate a TIL-based biomarker (CPath TIL categories). Subsequently, the signature underwent a masked independent validation on H&E-stained whole-section images of 755 patients with DCIS from the UK/ANZ DCIS randomised controlled trial. Specifically, continuous biomarker CPath TIL score was calculated as the average TIL density in the DCIS microenvironment and dichotomised into binary biomarker CPath TIL categories (CPath TIL-high vs CPath TIL-low) using the median value as a cutoff. The primary outcome was ipsilateral breast event (IBE; either recurrence of DCIS [DCIS-IBE] or invasive progression [I-IBE]). The Cox proportional hazards model was used to estimate the hazard ratio (HR).

Findings

CPath TIL-score was evaluable in 718 (95%) of 755 patients (151 IBEs). Patients with CPath TIL-high DCIS had a greater risk of IBE than those with CPath TIL-low DCIS (HR 2·10 [95% CI 1·39–3·18]; p=0·0004). The risk of I-IBE was greater in patients with CPath TIL-high DCIS than those with CPath TIL-low DCIS (3·09 [1·56–6·14]; p=0·0013), and the risk of DCIS-IBE was non-significantly higher in those with CPath TIL-high DCIS (1·61 [0·95–2·72]; p=0·077). A significant interaction (p_interaction=0·025) between CPath TIL categories and radiotherapy was observed with a greater magnitude of radiotherapy benefit in preventing IBE in CPath TIL-high DCIS (0·32 [0·19–0·54]) than CPath TIL-low DCIS (0·40 [0·20–0·81]).

Interpretation

High TIL density is associated with higher recurrence risk—particularly of invasive recurrence—and greater radiotherapy benefit in patients with DCIS. Our TIL-based computational pathology signature has a prognostic and predictive role in DCIS.

Funding

National Cancer Institute under award number U01CA269181, Cancer Research UK (C569/A12061; C569/A16891), and the Breast Cancer Research Foundation, New York (NY, USA).

查看原文本刊更多论文

导管原位癌的预后和预测性计算病理学免疫特征：英国/新西兰 DCIS 试验队列的回顾性结果。

背景：肿瘤浸润淋巴细胞（TIL）的密度可作为导管原位癌（DCIS）的预后指标。然而，人工定量 TIL 不仅耗时，而且存在观察者之间和观察者内部的差异。在本研究中，我们开发了一种基于TIL的计算病理学生物标志物，并在临床试验队列中评估了其与复发风险和辅助治疗获益的相关性：在这项回顾性队列研究中，开发了一个计算病理学管道，以生成基于TIL的生物标志物（CPath TIL类别）。随后，对英国/新西兰 DCIS 随机对照试验中 755 名 DCIS 患者的 H&E 染色全切片图像进行了独立的掩蔽验证。具体来说，连续生物标志物 CPath TIL 评分计算为 DCIS 微环境中的平均 TIL 密度，并以中值作为分界点，将其分为二元生物标志物 CPath TIL 类别（CPath TIL 高 vs CPath TIL 低）。主要结果是同侧乳腺事件（IBE；DCIS复发[DCIS-IBE]或浸润性进展[I-IBE]）。采用 Cox 比例危险模型估算危险比 (HR)：在 755 例患者（151 例 IBE）中，有 718 例（95%）的 CPath TIL 评分可进行评估。CPath TIL 高的 DCIS 患者比 CPath TIL 低的 DCIS 患者发生 IBE 的风险更高（HR 2-10 [95% CI 1-39-3-18]; p=0-0004）。CPath TIL高的DCIS患者发生I-BE的风险高于CPath TIL低的DCIS患者（3-09 [1-56-6-14]; p=0-0013），CPath TIL高的DCIS患者发生DCIS-IBE的风险无显著性差异（1-61 [0-95-2-72]; p=0-077）。CPath TIL类别与放疗之间存在明显的交互作用（pinteraction=0-025），CPath TIL高的DCIS（0-32 [0-19-0-54]）比CPath TIL低的DCIS（0-40 [0-20-0-81]）在预防IBE方面的放疗获益更大：高TIL密度与DCIS患者较高的复发风险（尤其是侵袭性复发）和更大的放疗获益相关。我们基于TIL的计算病理学特征对DCIS具有预后和预测作用：美国国立癌症研究所（获奖号：U01CA269181）、英国癌症研究中心（C569/A12061; C569/A16891）和美国纽约乳腺癌研究基金会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Lancet Digital Health Multiple-

CiteScore

41.20

自引率

1.60%

发文量

232

审稿时长

13 weeks

期刊介绍： The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health. The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health. We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.