Lunit在2025年AACR上展示了从罕见到常见肿瘤的AI病理学癌症研究进展-动脉网

Lunit Showcases AI Pathology Advances in Cancer Research from Rare to Common Tumors at AACR 2025

CISION 等信源发布 2025-04-22 20:58



可切换为仅中文







SEOUL, South Korea

韩国，首尔

，

April 22, 2025

2025年4月22日

/PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced the presentation of seven posters at the American Association for Cancer Research (AACR) Annual Meeting 2025, taking place April 25–30 in

/PRNewswire/ -- Lunit（KRX:328130.KQ），一家领先的癌症诊断和治疗人工智能解决方案提供商，今天宣布在2025年4月25日至30日举行的美国癌症研究协会（AACR）年会上展示七张海报。

Chicago, Illinois

伊利诺伊州芝加哥市

. The presentations introduce Lunit's latest research in AI-based histopathology, featuring studies powered by the Lunit SCOPE

这些报告介绍了Lunit在基于人工智能的组织病理学方面的最新研究，重点展示了由Lunit SCOPE支持的研究成果。

suite across a range of cancers—from rare salivary gland tumors to common types like lung cancer.

适用于多种癌症的套件——从罕见的唾液腺肿瘤到常见的肺癌等。

继续阅读

Lunit presents seven studies—including joint research with global pharma leaders—at AACR 2025 Annual Meeting, showcasing how Lunit SCOPE® enables novel biomarker discovery across a range of tumors.

Lunit在2025年AACR年会上展示了包括与全球制药领导者联合研究在内的七项研究，展示了Lunit SCOPE®如何在多种肿瘤中实现新型生物标志物的发现。

The poster lineup includes two collaborative studies with global pharma leaders, including AstraZeneca. One study—conducted in partnership with AstraZeneca—presents the development and validation of an AI model that predicts EGFR mutations from H&E slides, enabling faster, more accessible mutation testing for NSCLC patients.

海报展示阵容包括与全球制药业领袖（包括阿斯利康）的两项合作研究。其中一项与阿斯利康合作进行的研究展示了一种人工智能模型的开发和验证，该模型能够从H&E切片中预测EGFR突变，从而为非小细胞肺癌患者提供更快、更便捷的突变检测。

Another, co-authored with a major global biotech company, applies Lunit SCOPE IO® to phase II and III clinical trial data to predict benefit from atezolizumab, revealing that AI-based histologic profiling can help stratify patients based on likely immunotherapy response..

另一篇与一家全球主要生物技术公司共同撰写的文章将Lunit SCOPE IO®应用于II期和III期临床试验数据，以预测对atezolizumab的受益情况，揭示了基于人工智能的组织学分析可以帮助根据患者可能的免疫治疗反应进行分层。

Among the seven studies to be presented, three feature high-impact findings with particular clinical and scientific relevance.

在即将展示的七项研究中，有三项具有特别临床和科学意义的高影响力发现。

One study addresses the challenge of predicting response to neoadjuvant immuno-chemotherapy in patients with resectable salivary gland cancer (SGC)—a rare and aggressive cancer. To better understand treatment outcomes, the study applied a multi-modal approach that combined single-cell RNA sequencing, T cell receptor (TCR) analysis, spatial transcriptomics (Xenium), and AI-powered histological profiling using Lunit SCOPE IO.

一项研究针对可切除的唾液腺癌（SGC）患者中新辅助免疫化疗反应预测的挑战——这是一种罕见且侵袭性强的癌症。为了更好地理解治疗结果，该研究采用了一种多模式方法，结合了单细胞RNA测序、T细胞受体（TCR）分析、空间转录组学（Xenium）以及使用Lunit SCOPE IO的AI驱动组织学分析。

on surgically resected tumor samples. Responders were found to have more CD8+ dysfunctional and memory T cells, along with increased TCR clonality and reduced diversity—patterns suggestive of clonal expansion. Non-responders, in contrast, showed a higher presence of tumor-associated macrophages. Lunit SCOPE IO.

在手术切除的肿瘤样本上，应答者被发现拥有更多的CD8+功能障碍和记忆T细胞，同时伴随TCR克隆性增加和多样性减少——这些模式提示克隆扩增。相反，无应答者显示出更高水平的肿瘤相关巨噬细胞。Lunit SCOPE IO。

contributed to detailed morphological profiling, enabling cell type identification and validation of spatial patterns. These findings suggest that combining advanced molecular tools with AI-powered histology may help uncover tumor microenvironment features predictive of response to immunotherapy.

有助于详细的形态学分析，实现细胞类型识别和空间模式验证。这些研究结果表明，结合先进的分子工具与人工智能驱动的组织学分析，可能有助于揭示预测免疫治疗反应的肿瘤微环境特征。

Another study explores potential biomarkers linked to treatment resistance in salivary duct carcinoma (SDC) by combining Lunit SCOPE IO

另一项研究通过结合Lunit SCOPE IO，探索了与唾液腺导管癌（SDC）治疗耐药性相关的潜在生物标志物。

with Xenium spatial transcriptomics. The researchers analyzed over 915,000 cells from surgically resected salivary gland tumors, including SDC cases treated with neoadjuvant immunotherapy. In one relapsed case, the tumor showed higher expression of genes associated with immune evasion and epithelial-to-mesenchymal transition (EMT), despite similar morphology to a non-relapsed case.

通过Xenium空间转录组学，研究人员分析了超过915,000个细胞，这些细胞来自手术切除的唾液腺肿瘤，包括接受新辅助免疫治疗的SDC病例。在一个复发病例中，尽管肿瘤形态与非复发病例相似，但其与免疫逃逸和上皮-间质转化（EMT）相关的基因表达更高。

A lower presence of CXCL9-expressing tumor-infiltrating lymphocytes was also noted, which may reflect a less immunologically active tumor microenvironment. These findings offer additional insight into potential resistance-related features that may not be evident through conventional histology..

同时，CXCL9表达的肿瘤浸润淋巴细胞的存在较低，这可能反映出免疫活性较低的肿瘤微环境。这些发现为潜在的耐药相关特征提供了更多见解，而这些特征通过常规组织学可能并不明显。

In a third study, Lunit developed an AI model to identify EGFR-mutant NSCLC tumors with morphologic features similar to small cell lung cancer (SCLC)—a pattern clinically linked to early histologic transformation from NSCLC to SCLC and resistance to EGFR tyrosine kinase inhibitors (TKIs), particularly in patients with RB1 mutations.

在第三项研究中，Lunit 开发了一种 AI 模型，用于识别具有与小细胞肺癌（SCLC）相似形态特征的 EGFR 突变非小细胞肺癌（NSCLC）肿瘤——这种模式在临床上与从 NSCLC 到 SCLC 的早期组织学转化以及对 EGFR 酪氨酸激酶抑制剂（TKI）的耐药性相关，尤其是在携带 RB1 突变的患者中。

The study used deep learning to analyze H&E-stained tumor slides from 106 advanced-stage EGFR-mutant NSCLC patients, performing cell-level tumor heterogeneity analysis based on AI-discovered morphological features. Patients in the top 25% for SCLC-like morphology—defined as the SCLC-like group—had significantly smaller nuclear area (56 µm² vs.

该研究使用深度学习分析了106名晚期EGFR突变非小细胞肺癌患者的H&E染色肿瘤切片，并基于人工智能发现的形态学特征进行了细胞水平的肿瘤异质性分析。在小细胞肺癌样形态中排名前25%的患者（被定义为类SCLC组）其核面积显著较小（56 µm² vs.

102 µm²) and darker nuclear staining. Clinically, they experienced shorter progression-free survival after TKI therapy and were more likely to later transform into SCLC upon rebiopsy (15.8% vs. 2.0%). This study is the first to demonstrate that AI-based morphologic profiling at diagnosis can identify patients at risk for small cell transformation and early TKI resistance, offering a new path toward risk-adapted treatment planning..

102 µm²）以及较深的核染色。在临床上，这些患者在接受TKI治疗后无进展生存期较短，并且在重新活检时更容易转化为小细胞肺癌（15.8% 对 2.0%）。本研究首次证明，基于人工智能的形态学分析可以在诊断时识别出有小细胞转化和早期TKI耐药风险的患者，为风险适应性治疗规划提供了新途径。

The remaining studies further demonstrate the breadth of Lunit's research capabilities and AI expertise. These include studies on cell surface target discovery in prostate cancer and preclinical immunotherapy enhancement in colon cancer.

其余的研究进一步证明了Lunit的研究能力和人工智能专业知识的广度。这些研究包括前列腺癌细胞表面靶点发现和结肠癌临床前免疫治疗增强的研究。

'At AACR 2025, we're showcasing how Lunit's AI technologies are driving a new wave of biomarker discovery and clinical insight,' said

“在2025年AACR会议上，我们将展示Lunit的AI技术如何推动新一轮的生物标志物发现和临床洞察，”

Brandon Suh

布兰登·苏

, CEO of Lunit. 'From salivary gland cancer to lung cancer, our studies reveal how AI-powered histopathology—especially through Lunit SCOPE IO

，Lunit公司首席执行官。“从唾液腺癌到肺癌，我们的研究揭示了人工智能驱动的组织病理学——特别是通过Lunit SCOPE IO

—can uncover critical tumor microenvironment patterns and transformation risks, and even predict how tumors may respond to targeted therapies well before clinical progression is observed. These insights can play a meaningful role in shaping more precise, responsive cancer care.'

—可以揭示关键的肿瘤微环境模式和转化风险，甚至可以在临床进展被观察到之前预测肿瘤对靶向治疗的反应。这些见解可以在制定更精准、更有效的癌症护理中发挥重要作用。

To learn more about Lunit's latest research and activities at AACR 2025, visit

要了解更多关于Lunit在2025年AACR的最新研究和活动，请访问

Booth #2843

展位号：2843

。

Lunit's featured presentations at AACR 2025 include:

Lunit在2025年AACR上的专题报告包括：

[Poster #2463/15]

[海报 #2463/15]

AI-based EGFR-mutation prediction from haematoxylin and eosin (H&E) images in non-small cell lung cancer (NSCLC): A global multi-cohort validation study

基于人工智能的非小细胞肺癌（NSCLC）苏木精和伊红（H&E）图像中EGFR突变预测：一项全球多队列验证研究

，

April 28

4月28日

，

9:00 AM - 12:00 PM

上午9:00 - 上午12:00

, Section 46

，第46条

[Poster #2458/10]

[海报 #2458/10]

Prediction of atezolizumab benefit in NSCLC by computational pathology: Retrospective validation in a phase III trial

通过计算病理学预测阿特珠单抗在非小细胞肺癌中的益处：III 期试验的回顾性验证

，

April 28

4月28日

，

9:00 AM - 12:00 PM

上午9:00 - 上午12:00

, Section 46

，第46节

[Poster #157/8]

[海报 #157/8]

Exploratory analysis of tumor microenvironment using scRNA, scTCR, and spatial transcriptomics in salivary gland cancer with surgical sample after neoadjuvant immuno-chemotherapy

使用scRNA、scTCR和空间转录组学对接受新辅助免疫化疗后的手术样本进行唾液腺癌肿瘤微环境的探索性分析

，

April 27

4月27日

，

2:00 PM - 5:00 PM

下午2:00 - 下午5:00

, Section 7

，第7节

[Poster #171/22]

[海报 #171/22]

A novel single-cell level approach integrating artificial intelligence (AI)-powered histomorphology labeling and spatial transcriptomics enables biomarker identification of treatment-resistance in salivary gland cancer (SGC)

一种新颖的单细胞水平方法，整合了人工智能（AI）驱动的组织形态学标记和空间转录组学，能够识别唾液腺癌（SGC）治疗抵抗的生物标志物。

，

April 27

4月27日

，

2:00 PM - 5:00 PM

下午2:00 - 下午5:00

, Section 7

，第七节

[Poster #4659/17]

[海报 #4659/17]

AI-powered assessment of morphologic likeness to small cell lung cancer (SCLC) predicts progression to SCLC and TKI response in EGFR-mutant NSCLC

人工智能驱动的形态学相似性评估预测EGFR突变型非小细胞肺癌（NSCLC）向小细胞肺癌（SCLC）的进展及TKI药物反应。

，

April 29

4月29日

，

9:00 AM - 12:00 PM

上午9:00 - 上午12:00

, Section 32

，第32节

[Poster #2542/21]

[海报 #2542/21]

Artificial Intelligence(AI)-powered delineation of the prostate cancer surfaceome through subcellular-level expression profiling from immunohistochemistry (IHC) images

通过免疫组织化学（IHC）图像的亚细胞水平表达谱分析，人工智能（AI）驱动的前列腺癌表面组描绘

，

April 28

4月28日

，

2:00 PM - 5:00 PM

下午2:00 - 下午5:00

, Section 1

，第一节

[Poster #4790/26]

[海报 #4790/26]

Immunostimulatory effects of GENA-104A16, a monoclonal anti-CNTN4 antibody, on tumor-infiltrating lymphocytes in a colon cancer liver metastasis model

GENA-104A16（一种单克隆抗CNTN4抗体）在结肠癌肝转移模型中对肿瘤浸润淋巴细胞的免疫刺激作用

April 29

4月29日

，

9:00 AM - 12:00 PM

上午9:00 - 上午12:00

, Section 36

，第36节

About Lunit

关于Lunit

Founded in 2013, Lunit (KRX:328130.KQ) is a medical AI company on a mission to conquer cancer through AI. Lunit harnesses AI-powered medical image analytics and biomarker analysis to ensure accurate diagnosis and optimal treatment for each cancer patient. The FDA-cleared Lunit INSIGHT suite for cancer screening serves over 4,800 medical institutions across 55+ countries.

Lunit（KRX:328130.KQ）成立于2013年，是一家致力于通过人工智能战胜癌症的医疗AI公司。Lunit利用人工智能驱动的医学影像分析和生物标志物分析，为每位癌症患者提供精确诊断和最佳治疗方案。获得FDA批准的Lunit INSIGHT癌症筛查套件已服务于55多个国家的4800多家医疗机构。

Lunit clinical studies have been published in top journals, including the .

Lunit的临床研究已发表在顶级期刊上，包括。

Journal of Clinical Oncology

临床肿瘤学杂志

and the

和

Lancet Digital Health

柳叶刀数字健康

, and presented at global conferences such as the ASCO and RSNA. Headquartered in

，并在全球性会议如ASCO和RSNA上展示。总部位于

Seoul, South Korea

韩国首尔