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: 10545113
• 负责人: Rui Chang
• 金额: --
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 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 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10545113
• 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; 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; machine learning model; 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.

项目总结 我们建议开发侵袭性黑色素瘤的病理组学生物标志，以指导治疗决定 黑色素瘤手术切除但仍有复发和死亡的高风险的患者。这是 非常需要，因为II和III期黑色素瘤患者有大约30%的机会死于 黑色素瘤10年以上。治疗方法已被证明可以降低复发风险，但它们毒性大，成本高。 确定真正被手术治愈并且没有癌症的患者将是非常有用的。 指导病人护理。几十年来，人们已经知道免疫系统限制黑色素瘤的进展和 较高水平的肿瘤浸润性淋巴细胞(TIL)预示着良好的结局。评估TILs， 然而，这涉及到病理学家使用定性标准的主观判断，这种方法是 容易出现观察者间的变异性。早期预测生物标记物发展的一个障碍 黑色素瘤是指肿瘤很小，大多数皮肤病理学家要求整个样本都是福尔马林。 固定和石蜡包埋(FFPE)，用于仔细的形态分析。为了克服这一障碍，我们团队 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 基于这样一种假设，即有证据表明组织内的免疫监视较强，表明复发率较低 风险，包括使用数字软件对TIL进行定量，使用 定量免疫荧光(QIF)和使用纳米串测量干扰素信号 技术这些方法中的每一个都提供了关于肿瘤免疫微环境的独特信息。 例如，NanoString提供基因组信息，但不提供有关 特定细胞表型在肿瘤微环境中的位置，就像qIF一样。例如，qif透露 当巨噬细胞位于肿瘤间质内时，预后尤其差。在目标1中 建议我们使用罗斯韦尔的514个黑色素瘤样本来验证之前发表的三个生物标志物 帕克综合癌症研究所，不列颠哥伦比亚大学，耶鲁医学院，以及 盖辛格健康系统公司。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、QIF、TIL的形态分析以及标准的临床和病理特征 创建多参数生物签名。首先，我们使用原始的临床和病理组学数据来建立模型 侵袭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，我们识别出 确定复发表型并确定新的成像和基因组靶点，可能会增强 我们生物标记物的精确度。然后，我们基于该网络构造了一个复合生物签名。最后，我们测试 在前瞻性回顾中，新的生物签名以及来自AIM 1的原始多重验证生物标记物 以E1697辅助干扰素试验的样本为时尚，该试验已有10多年的跟踪调查。这个 回溯性前瞻性方法消除了回溯性研究引入的任何选择偏向。