Mass spectrometry to decode PTM patterns and enhance the Biomarker utility of ER

描述（由申请人提供）：雌激素受体（ER， α亚型）是第一个被临床验证为癌症治疗反应预测因子的生物标志物，并且仍然是少数具有足够医学证据证明其在临床决策中常规使用的肿瘤生物标志物之一。肿瘤ER低表达或缺失（ER-）能准确预测对内分泌治疗缺乏反应性，而肿瘤ER过表达（ER+）则不能准确预测内分泌治疗的反应，平均准确率仅为50%。因此，提高目前仅测量肿瘤内质网含量的内质网检测的预测准确性是癌症研究中最重要的未解决问题之一。由于已知任何给定蛋白质上的翻译后修饰（PTMs）是一个决定构象、定位和细胞内功能的分子密码，我们组建了一个跨学科的翻译研究者和蛋白质化学家团队，他们开发了质谱（MS）方法，采用多种反应监测（MRM）能够检测和定量各种PTMs，包括Ser/Thr/Tyr磷酸化，内源性内源性内质网蛋白的6个结构域的赖氨酸修饰（乙酰化/甲基化/泛素化）和赖氨酸氧化。基于解码ER ptm对于提高ER+乳腺癌的生物标志物特异性和临床分型至关重要这一前提，我们提出了两个研究目标。Aim 1研究将充分优化MRM/MS程序，以定量配体依赖性（雌激素）和配体非依赖性（生长因子，氧化应激）在ER+人乳腺癌细胞系中对ER PTMs的诱导，这些细胞系是根据抗雌激素敏感性范围选择的。将特别强调内质网铰链和dna结合域，其中特定的PTM模式已知会改变内质网功能并决定抗雌激素敏感性。通过引入突变的ER PTM构建体并评估其对内源性ER依赖基因表达的细胞内影响，还将评估新型ER PTM的功能影响。在其他目标中，Aim 1的努力将产生预测细胞系抗雌激素耐药的候选PTM谱，以便在Aim 2肿瘤样本中进一步评估。Aim 2研究将采用Aim 1中完全优化的MRM/MS方案，使用两个不同的ER+原发性乳腺癌临床注释队列来评估肿瘤ER PTMs的患病率和谱。可用于MRM/MS分析的两个肿瘤队列是：1)验证我们的Aim 1导出的与细胞系他莫昔芬反应性相关的ER PTM谱，ii)独立导出并验证与临床对辅助他莫昔芬耐药和其他与早期临床复发相关的侵袭性肿瘤特征相关的肿瘤ER PTM谱。在完成这些研究目标后，定量MRM/MS分析将确定所有乳腺癌PTM的频谱和患病率，并且PTM模式与更具侵袭性和抗雌激素抗性的乳腺癌将被确定和验证。

DESCRIPTION (provided by applicant): Estrogen receptor (ER, alpha isoform) was the first biomarker to be clinically validated as a predictor of cancer therapy response, and still stands as one of the few tumor biomarkers with sufficient medical evidence to justify its routine use in clinical decision making. While low or absent tumor ER expression (ER-) accurately predicts lack of responsiveness to endocrine therapy, tumor overexpression of ER (ER+) is a poor predictor of response with an accuracy averaging only 50%. Hence, improving the predictive accuracy of current ER assays that measure only tumor ER content is one of the most important unresolved issues in cancer research. Since the constellation of posttranslational modifications (PTMs) on any given protein is known to be a molecular code dictating conformation, localization, and intracellular function, we assembled an interdisciplinary team of translational investigators and protein chemists who developed mass spectrometry (MS) approaches, employing multiple reaction monitoring (MRM) capable of detecting and quantitating diverse PTMs, including Ser/Thr/Tyr phosphorylations, Lys modifications (acetylation/methylation/ubiquitination), and Cys oxidation across the six domains of endogenously expressed ER protein. Two study aims are proposed based on the premise that decoding ER PTMs is essential for improving ER biomarker specificity and the clinical subtyping of ER+ breast cancers. Aim 1 studies will fully optimize MRM/MS procedures to quantitate ligand-dependent (estrogen) and ligand-independent (growth factor, oxidative stress) induction of ER PTMs across a panel of ER+ human breast cancer cell lines selected for their range of antiestrogen sensitivities. Special emphasis will be given to the ER hinge and DNA-binding domains where specific PTM patterns are known to alter ER functionality and determine antiestrogen sensitivity. The functional impact of novel ER PTMs will also be assessed by introducing mutated ER PTM constructs and evaluating their intracellular impact on endogenous ER dependent gene expression. Among other objectives, Aim 1 efforts will generate a candidate PTM profile predictive of cell line antiestrogen resistance for further evaluation in Aim 2 tumor samples. Aim 2 studies will employ fully optimized MRM/MS protocols from Aim 1 to evaluate the prevalence and spectrum of tumor ER PTMs using two different clinically annotated cohorts of ER+ primary breast cancers. The two tumor cohorts available for MRM/MS analysis are powered to: i) validate our Aim 1 derived ER PTM profile associating with cell line tamoxifen responsiveness, and ii) independently derive and validate a tumor ER PTM profile associating with clinical resistance to adjuvant tamoxifen and other aggressive tumor features linked to early clinical relapse. On completion of these study aims, quantitative MRM/MS assays will have defined the spectrum and prevalence of all breast cancer PTMs, and a PTM pattern associated with more aggressive and antiestrogen resistant breast cancers will have been identified and validated.