Applying pathomics to establish a biosignature for aggressive skin melanoma.

项目摘要 我们建议开发侵袭性黑色素瘤的病理组学生物特征，以指导治疗决策， 手术切除黑色素瘤但仍有高复发和死亡风险的患者。这是 这是一个迫切的需求，因为患有II期和III期黑色素瘤的患者有大约30%的机会死于 黑色素瘤超过10年治疗已被证明可以降低复发风险，但它们是有毒的和昂贵的。 确定那些真正被手术治愈并且没有癌症的病人将是非常有用的 来指导病人护理几十年来人们已经知道，免疫系统限制了黑色素瘤的进展， 较高水平的肿瘤浸润淋巴细胞（TIL）预示着有利的结果。对TILs的评估， 然而，涉及病理学家使用定性标准的主观确定， 易于观察者之间的变化。发展早期预后生物标志物的一个障碍 黑色素瘤是肿瘤很小，大多数皮肤病理学家要求整个样本是福尔马林 固定和石蜡包埋（FFPE）进行仔细的形态学分析。为了克服这一障碍，我们的团队 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 基于组织内强免疫监视的证据表明复发率较低的假设 风险，包括使用数字软件定量TIL，使用 定量免疫荧光（qIF），以及使用NanoString测量干扰素特征 技术.这些方法中的每一种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，但不提供关于基因组的空间信息。 在肿瘤微环境中定位特定的细胞表型。例如，qIF显示 巨噬细胞在位于肿瘤间质内时特别具有不良预后。在目标1中， 我们建议使用来自Roswell的514个黑色素瘤样本验证三种先前发表的生物标志物 公园综合癌症研究所，不列颠哥伦比亚省大学，耶鲁大学医学院，和 盖辛格卫生系统接下来，在提案的目标2中，我们提出了一种综合系统生物学方法 包括转录组学、qIF、TILS的形态学分析以及标准临床和病理学特征， 创建一个多参数的生物签名首先，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，并确定新的成像和基因组靶点，可能会提高 我们的生物标记物的精确度。然后，我们构建了一个复合生物签名的基础上，这个网络。最后，我们测试 新的生物特征，以及Aim 1中最初经过多次验证的生物标志物的前瞻性回顾 时尚的样本从E1697试验的辅助干扰素，有超过10年的后续行动。的 回顾性前瞻性方法消除了回顾性研究引入的任何选择偏倚。

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.