Applying pathomics to establish a biosignature for aggressive skin melanoma.

应用病理学建立侵袭性皮肤黑色素瘤的生物特征。

• 批准号: 10214049
• 负责人: Rui Chang
• 金额: $ 69.35万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2021-10-04
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10214049
• 关键词: Academic Medical Centers; Adjuvant; Adjuvant Study; Adjuvant Therapy; Artificial Intelligence; Bayesian Network; Biological Markers; British Columbia; CD8B1 gene; Cells; Cessation of life; Clinical; Clinical Data; Clinical Pathology; Clinical Protocols; Computer Vision Systems; Computer software; Cutaneous; Cutaneous Melanoma; DNA Sequence Alteration; Data; Development; Elements; Exposure to; Fluorescence; Formalin; Gene Expression; Genes; Genomic approach; Genomics; Growth; Health Care Costs; Health system; Hematoxylin and Eosin Staining Method; Image; Image Analysis; Immune; Immune system; Immunity; Immunologic Markers; Immunologic Surveillance; Individual; Institutes; Institution; Interferons; Interobserver Variability; Location; Machine Learning; Malignant Neoplasms; Measurement; Medical center; Messenger RNA; Methods; Modeling; Molecular; Molecular Computations; Morphology; Nuclear; Operative Surgical Procedures; Outcome; Paraffin Embedding; Pathologist; Pathology; Pathway Analysis; Patient Care; Patient Triage; Patients; Phenotype; Positioning Attribute; Prognosis; Prognostic Marker; Publishing; RNA; Recurrence; Reproducibility; Retrospective Studies; Risk; Role; Roswell Park Cancer Institute; Sampling; Selection Bias; Skin; Spatial Distribution; Speed; Staging; Stains; Standardization; Systems Biology; T-Lymphocyte; Technology; Testing; Tissue Embedding; Tissues; Toxic effect; Treatment-related toxicity; Tumor-Infiltrating Lymphocytes; Tumor-infiltrating immune cells; Universities; Update; base; biosignature; clinical application; clinical care; cohort; computerized tools; cost; digital; digital imaging; digital pathology; disorder risk; follow-up; genomic data; high risk; improved; macrophage; medical schools; melanoma; mortality risk; multi-scale modeling; nano-string; network models; prospective; quantitative imaging; transcriptome; transcriptomics; tumor; tumor growth; tumor microenvironment; tumor progression; tumor-immune system interactions

PROJECT SUMMARY We propose to develop a pathomics biosignature for aggressive melanoma to guide treatment decisions for patients who have had a melanoma surgically removed but remain at high risk of recurrence and death. This is a critical need because patients with stage II and III melanoma have an approximate 30% chance of dying of melanoma over 10 years. Therapies have been shown to lessen recurrence risk, but they are toxic and costly. Identifying patients who have truly been cured by the surgery and are cancer free would be tremendously useful to guide patient care. It has been known for decades that the immune system limits melanoma progression and that higher levels of tumor infiltrating lymphocytes (TILs) portend a favorable outcome. Assessment of TILs, however, involves a subjective determination by the pathologist using qualitative criteria and this approach is prone to inter-observer variability. One barrier to the development of prognostic biomarkers in early stage melanoma is that the tumors are tiny and most dermato-pathologists require that the entire sample be formalin fixed and paraffin embedded (FFPE) for careful morphology analysis. In order to overcome this barrier, our team has developed and published three digital pathology methods to estimate recurrence risk. These biomarkers are based on the hypothesis that evidence of strong immune surveillance within the tissue indicates lower recurrence risk and include quantitation of TILs using digital software, staining for macrophages and T cells using quantitative- immune-fluorescence (qIF), and measurement of an interferon signature using NanoString technology. Each of these methods provides unique information about the tumor immune micro-environment. For example, NanoString provides genomic information but does not provide spatial information regarding the locations of specific cell phenotypes within the tumor microenvironment as qIF does. For instance, qIF revealed the macrophages confer a poor prognosis specifically when located within the tumor stroma. In Aim 1 of the proposal we validate three previously published biomarkers using 514 melanoma samples from Roswell Park Comprehensive Cancer Institute, The University of British Columbia, Yale School of Medicine, and Geisinger Health Systems. Next, in Aim 2 of the proposal we propose an integrative systems biology approach including transcriptomic, qIF, morphology analysis of TILS, and standard clinical and pathology features to create a multi-parameter biosignature. First, we use the raw clinical and pathomics data to build a model multiscale biomarker network of aggressive skin melanoma. Using a Bayesian network, we identify nodes that determine the recurrence phenotype and identify new imaging and genomic targets that may enhance the precision of our biomarker. We then construct a composite biosignature based on this network. Finally, we test the new biosignature, as well as the original multiply validated biomarkers from Aim 1 in prospective retrospective fashion on samples from the E1697 trial of adjuvant interferon for which there is over 10 years of follow up. The retrospective prospective approach removes any selection bias introduced by retrospective study.

项目摘要 我们建议为侵略性黑色素瘤开发一种致病性生物签名，以指导治疗决策 患有外科手术的黑色素瘤的患者仍处于复发和死亡的高风险。这是 急需需求，因为患有II期和III期黑色素瘤的患者死于约30％ 黑色素瘤超过10年。疗法已被证明会降低复发风险，但它们具有毒性和昂贵。 识别真正通过手术治愈并无癌的患者将非常有用 指导患者护理。几十年来，免疫系统限制了黑色素瘤的进展和 较高水平的肿瘤浸润淋巴细胞（TILS）预示了有利的结果。评估tils， 但是，使用定性标准涉及病理学家的主观确定，这种方法是 容易出现观察者间的变异性。早期预后生物标志物发展的一个障碍 黑色素瘤是肿瘤很小，大多数皮肤病学家都要求整个样品都是福尔马林 固定和石蜡嵌入（FFPE）进行仔细的形态分析。为了克服这一障碍，我们的团队 已经开发并发表了三种数字病理学方法来估计复发风险。这些生物标志物是 基于以下假设：组织内强烈免疫监测的证据表明复发较低 风险并包括使用数字软件对TIL进行定量，使用数字软件染色 定量免疫荧光（QIF），并使用纳米串来测量干扰素特征 技术。这些方法中的每一种都提供了有关肿瘤免疫微环境的独特信息。 例如，纳米串提供了基因组信息，但没有提供有关该信息的空间信息 像QIF一样，肿瘤微环境中特定细胞表型的位置。例如，Qif揭示了 巨噬细胞在肿瘤基质内时特别赋予了较差的预后。在目标1中 提案我们使用罗斯威尔（Roswell）的514个黑色素瘤样品验证了三个先前发表的生物标志物 公园综合癌症研究所，不列颠哥伦比亚大学，耶鲁大学医学院和 Geisinger Health Systems。接下来，在提案的目标2中，我们提出了一种综合系统生物学方法 包括转录组，QIF，TIL的形态分析以及标准的临床和病理特征 创建一个多参数生物签名。首先，我们使用原始的临床和病原体数据来构建模型 侵略性皮肤黑色素瘤的多尺度生物标志物网络。使用贝叶斯网络，我们识别节点 确定复发表型并确定可能增强的新成像和基因组靶标 我们生物标志物的精度。然后，我们基于此网络构建一个复合生物签名。最后，我们测试 新的生物签名以及原始的倍数验证的生物标志物，来自AIM 1的潜在回顾 E1697辅助干扰素试验的样品中的时尚，有10年的随访。这 回顾性前瞻性方法消除了回顾性研究引入的任何选择偏差。