A multimodal approach for precision immuno-oncoloy in lymphoma treated with CAR-T cells

项目摘要/摘要 自体CD19导向的嵌合抗原受体T细胞(CAR-T)引起的异常应答 复发率和难治性大B细胞淋巴瘤(LBCL)。然而，超过60%的CD19-CAR-T接受者 将经历疾病复发或进展。这些患者中的大多数将死于他们的疾病。 CAR-T治疗失败的机制部分被了解，生物标记物影响患者的结果 和管理方面的临床应用有限。我们的中心假设是正交模式(例如，临床， 分子、基因组和放射[来自医学图像的定量测量])相辅相成， 共同提供有关耐药机制和患者预后的信息 通过任何单独的方式。我们提出的结果表明，机器学习是一种有效的 将多个数据源合成和建模的方法。癌细胞利用基因组 异质性，以逃避免疫疗法施加的压力，如免疫检查点抑制剂。我们的 初步数据还表明，TP53基因改变强烈地决定了对CAR-T的反应。 此外，利用转录图谱，我们发现癌细胞途径需要有效的 在TP53基因改变的淋巴瘤中，CAR-T细胞毒信号的传递被扭曲。这些早期的发现提供了 在CAR-T之后，基因组学在告知疾病生物学和风险方面的效用的概念验证。 我们假设CAR-T细胞利用细胞通路中的肿瘤遗传异常来发挥细胞毒作用 通过使癌细胞对CAR-T刺激不敏感并支持免疫来提高治疗耐药性 逃走。在目标1中，我们将在CAR-T前后使用综合的基因和表型肿瘤分析 研究先验决定的基因和通路在调节固有和获得性治疗中的作用 抵抗。我们还假设，患者和肿瘤简档的正交模式是互补的， 将它们整合到一个统一的多模式模型中可以准确地预测CAR-T的结果。在目标2中，我们 将综合来自多个医疗设备的数据并使用机器学习算法来预测CAR-T响应 并确定新的生物标记物。 为了实现我们的目标，我们编制了最大的CAR-T患者和样本生物库之一。一群 免疫学、遗传学、病理学、放射学、机器学习和生物信息学方面的顶尖专家将指导 这项多学科工作的候选人。如果成功，我们预计我们的组合方法将发现 研究CAR-T固有和获得性耐药性的遗传特征，并确定新的可用药靶点。 此外，我们的机器学习方法将通过建立决策来支持个性化治疗 支持系统和识别高危患者的生物标记物。最后，我们将介绍新的方法 用于模拟CAR-T结果，可扩展到其他形式的治疗。

PROJECT SUMMARY/ABSTRACT Autologous CD19-directed chimeric antigen receptor T-cells (CAR-T) have resulted in extraordinary response rates in relapsing and refractory large B-cell lymphoma (LBCL). However, over 60% of CD19-CAR-T recipients will experience disease recurrence or progression. Most of these patients will die from their disease. Mechanisms of CAR-T treatment failure are partially understood and biomarkers informing patient outcomes and management have limited clinical utility. Our central hypothesis is that orthogonal modalities (e.g., clinical, molecular, genomic, and radiomic [quantitative measures from medical images]) complement one another, together providing information on resistance mechanisms and patient outcomes beyond that accessible through any individual modality. We present results suggesting that machine learning is an effective methodology for synthesizing and modeling multiple sources of data together. Cancer cells harness genomic heterogeneity to evade pressure applied by immunotherapies, such as immune checkpoint inhibitors. Our preliminary data also demonstrate that TP53 genomic alterations strongly determine response to CAR-T. Furthermore, using transcriptomic profiling, we found that cancer cellular pathways required for effective transmission of CAR-T cytotoxic signals are distorted in TP53-altered lymphoma. These early findings provide a proof-of-concept for the utility of genomics to inform disease biology and risk after CAR-T. We hypothesize that tumor genetic aberrations in cellular pathways used by CAR-T cells to exert cytotoxicity drive treatment resistance by rendering cancer cells insensitive to CAR-T stimuli and supporting immune escape. In Aim 1, we will use comprehensive genotypic and phenotypic tumor profiling before and after CAR-T to study the role of a priori determined genes and pathways in mediating inherent and acquired treatment resistance. We also hypothesize that orthogonal modalities for patient and tumor profiling are complementary, and their integration into a unified, multimodal model could accurately predict CAR-T outcomes. In Aim 2, we will synthesize data from multiple modalities and use machine learning algorithms to predict CAR-T response and identify novel biomarkers. To meet our goals, we have compiled one of the largest CAR-T patient and sample biobanks. A group of leading experts in immunology, genetics, pathology, radiology, machine learning, and bioinformatics will guide the candidate in this multidisciplinary work. If successful, we expect our combinatorial approach to uncover genetic features underlying inherent and acquired CAR-T resistance and identify new druggable targets. Furthermore, our machine learning approach will support treatment personalization by establishing decision support systems and identifying biomarkers of high-risk patients. Finally, we will introduce novel methodologies for modeling CAR-T outcomes, which are extendable to other forms of treatment.