Unraveling the surface proteomic profile of multiple myeloma to reveal new immunotherapeutic targets and markers of drug resistance.
Unraveling the surface proteomic profile of multiple myeloma to reveal new immunotherapeutic targets and markers of drug resistance.
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细胞表面蛋白质组（“表面组”）充当患病细胞与其局部微环境之间的界面。在癌症中，这个区室不仅对于定义肿瘤生物学至关重要，而且还是潜在治疗靶点和诊断标记物的丰富来源。最近，我们对血癌多发性骨髓瘤（一种无法治愈的浆细胞恶性肿瘤）的表面组进行了分析。虽然现有的小分子药物可以促进骨髓瘤的初步缓解，但不可避免地会出现耐药性。针对骨髓瘤表面抗原的几种新型免疫疗法，包括抗体疗法和嵌合抗原受体 (CAR) T 细胞，可以进一步延长生存期。然而，对于复发的人仍然需要新的方法。因此，我们将糖蛋白细胞表面捕获（CSC）方法应用于多发性骨髓瘤细胞系，识别恶性浆细胞的关键表面蛋白特征。我们表征了浆细胞上最丰富的表面蛋白，指定 CD48 作为高密度抗原，有利于可能的基于亲合力的策略来增强 CAR-T 功效。在对一线治疗蛋白酶体抑制剂产生长期耐药后，我们发现骨髓瘤细胞表面特征发生显着改变，包括 CD50、CD361/EVI2B 和 CD53 下调，而对另一种一线治疗来那度胺的耐药性则导致 CD33 和 CD45/PTPRC 增加。相比之下，来那度胺的短期治疗导致表面抗原MUC-1上调，从而增强MUC-1靶向CAR-T细胞的功效。将我们的蛋白质组学数据与可用的转录组数据集相结合，我们开发了一个评分系统来对潜在的独立免疫治疗靶点进行排名。感兴趣的新靶标包括 CCR10、TXNDC11 和 LILRB4。我们使用其天然配体 CCL27 作为抗原识别域，开发了针对 CCR10 的原理验证 CAR-T 细胞。最后，我们开发了 CSC 方法的“小型化”版本，并将其应用于原发性骨髓瘤患者标本。总的来说，我们的工作为骨髓瘤社区创造了独特的资源。这项研究还支持无偏见的表面蛋白质组学分析作为识别新治疗靶点和耐药标记物的富有成效的策略，这可能有助于改善骨髓瘤患者的预后。类似的方法可以很容易地应用于其他肿瘤类型，甚至源自其他疾病的模型/组织。
The cell surface proteome (“surfaceome”) serves as the interface between diseased cells and their local microenvironment. In cancer, this compartment is critical not only for defining tumor biology but also serves as a rich source of potential therapeutic targets and diagnostic markers. Recently, we profiled the surfaceome of the blood cancer multiple myeloma, an incurable plasma cell malignancy. While available small molecule agents can drive initial remissions in myeloma, resistance inevitably occurs. Several new classes of immunotherapies targeting myeloma surface antigens, including antibody therapeutics and chimeric antigen receptor (CAR) T-cells, can further prolong survival. However, new approaches are still needed for those who relapse. We thus applied the glycoprotein cell surface capture (CSC) methodology to panel of multiple myeloma cell lines, identifying key surface protein features of malignant plasma cells. We characterized the most abundant surface proteins on plasma cells, nominating CD48 as a high-density antigen favorable for a possible avidity-based strategy to enhance CAR-T efficacy. After chronic resistance to proteasome inhibitors, a first-line therapy, we found significant alterations in the surface profile of myeloma cells, including down-regulation of CD50, CD361/EVI2B, and CD53, while resistance to another first-line therapy, lenalidomide, drove increases in CD33 and CD45/PTPRC. In contrast, short-term treatment with lenalidomide led to upregulation of the surface antigen MUC-1, thereby enhancing efficacy of MUC-1 targeting CAR-T cells. Integrating our proteomics data with available transcriptome datasets, we developed a scoring system to rank potential standalone immunotherapy targets. Novel targets of interest included CCR10, TXNDC11, and LILRB4. We developed proof-of-principle CAR-T cells versus CCR10 using its natural ligand, CCL27, as an antigen recognition domain. Finally, we developed a “miniaturized” version of the CSC methodology and applied it to primary myeloma patient specimens. Overall, our work creates a unique resource for the myeloma community. This study also supports unbiased surface proteomic profiling as a fruitful strategy for identifying new therapeutic targets and markers of drug resistance, that could have utility in improving myeloma patient outcomes. Similar approaches could be readily applied to additional tumor types or even models/tissues derived from other diseases.