AlgenML: Drug target discovery platform for transcriptional reprogramming of MYCN-driven neuroblastoma

AlgenML：MYCN驱动的转录重编程药物靶点发现平台 神经母细胞瘤 项目总结 对于生物制药来说，药物发现是一项费力、耗时和昂贵的工作。肿瘤学尤其是 由于新药在临床试验中只有3.4%的成功几率，这是一个困难的问题。此应用程序用于 传统药物靶点发现在肿瘤学中的重大挑战依赖于细胞活性或报告 过于简单化细胞状态的分析。单细胞RNA表达谱的新进展使我们能够 通过定量绘制癌细胞中的转录依赖关系并快速 探测漏洞以重新编程致癌信令网络。转录因子MYCN和MYC是 迄今为止，尽管它们经常是高价值的抗癌药物靶标，但它们不能被小分子药物下药 放大的基因，并在整个癌症谱系中导致不良结果。间接阻止MYCN的代理 从合成致死活性筛查中鉴定出来的结果是，在正在进行的 临床试验。Algen的专有机器学习平台(AlgenML)识别阻止致癌的目标 CRISPR干扰(CRISPRi)基因单细胞RNA在MYCN上的转录成瘾 击倒对手。全基因组范围的单细胞RNA表达谱测量每个细胞10,000个基因，每个高基因 吞吐量分析通常一次捕获16万个细胞。使用CRISPRi基因敲除文库和 通过多重分析，数百个基因可以同时被敲除，我们可以将单细胞rna 对每个CRISPRi基因敲除200个细胞进行测序。这就形成了一个非常丰富的数据集，超过400个 AlgenML分析的百万个RNA表达数据的数据点。我们的药物发现方法是创新的 因为，与传统方法不同，AlgenML平台不识别导致细胞 而是以一种公正的方式选择药物靶点，其抑制可以重新编程疾病- 相关的转录依赖性。由此产生的药物应该更安全，耐受性更好。在这里，我们的方法 是优化AlgenML以监控和重新编程MYCN在新的遗传定义模型中的转录活动 由MYCN引起的神经母细胞瘤。我们将重点放在神经母细胞瘤上，因为MYCN扩增在 疾病，基因定义的模型允许检测MYCN癌基因的精确贡献 与同基因对照相比。在目标1中，我们定义了MYCN转录特征，提名了目标基因，并 根据靶基因重新编程MYCN转录依赖的能力，在体外测试靶基因。目标2 评估靶向抑制对新诱导的人类肿瘤缩小和延长寿命的体内疗效 来自加州大学旧金山分校的多能干细胞(IPSC)和神经母细胞瘤啮齿动物模型。我们的调查团队位于 Algen和UCSF在开发RNA签名以间接靶向MYC和 MYCN通过合成致命性。成熟的初创企业Algen BioTechnologies拥有一个充满活力的投资者生态系统， 创新者，并成长于诺贝尔奖获得者詹妮弗·杜德纳博士在加州大学伯克利分校的实验室，他一直是一名科学工作者 自成立以来一直担任顾问。在加州大学旧金山分校的威廉·韦斯博士的帮助下，我们建立了一个强大的环境。

AlgenML: Drug target discovery platform for transcriptional reprogramming of MYCN-driven neuroblastoma PROJECT SUMMARY Drug discovery is a laborious, time-consuming, and expensive undertaking for biopharma. Oncology is especially difficult with new drugs in clinical trials having just 3.4% probability of success. This application addresses significant challenges of traditional drug target discovery in oncology that relies on cell viability or reporter assays which oversimplifies cell state. New advancements in single-cell RNA expression profiling allows us to overcome these challenges by quantitatively mapping transcriptional dependencies in cancer cells and rapidly probing vulnerabilities to reprogram the oncogenic signaling networks. Transcription factors MYCN and MYC are to date non-druggable by small molecules despite being high value cancer drug targets as they are frequently amplified genes and drive poor outcome across the cancer spectrum. Agents that block MYCN indirectly identified from synthetic lethal viability screens have resulted in only modest or short-lived responses in ongoing clinical trials. Algen’s proprietary machine learning platform (AlgenML) identifies targets that block oncogenic transcription addiction on MYCN using single-cell RNA expression of CRISPR interference (CRISPRi) gene knockdown. Genome-wide single-cell RNA expression profiling measures 10,000 genes per cell and each high- throughput assay routinely captures 160,000 cells at once. Using CRISPRi gene knockdown libraries and multiplexing the assays, hundreds of genes can be knocked down simultaneously and we single-cell RNA sequence 200 cells per CRISPRi gene knockdown. This makes for an extremely rich data set with over 400 million data points of RNA expression data which AlgenML analyzes. Our drug discovery approach is innovative because, unlike traditional approaches, the AlgenML platform does not identify essential genes that cause cell death, but rather selects drug targets in an unbiased manner whose suppression can reprogram the disease- related transcriptional dependencies. Resulting drugs should be safer and better tolerated. Here, our approach is to optimize AlgenML to monitor and reprogram MYCN transcriptional activity in new genetically defined models of MYCN-driven neuroblastoma. We focus on neuroblastoma because MYCN amplifications are common in the disease, and the genetically defined models allow detection of the precise contribution of MYCN oncogene compared to isogenic controls. In Aim 1, we define MYCN transcriptional signature, nominate target genes, and test target genes in vitro based on their ability to reprogram the MYCN transcriptional dependency. Aim 2 evaluates in vivo efficacy of target inhibition to shrink tumors and extend lifespan in new human induced pluripotent stem cell (iPSC) and rodent models of neuroblastoma from UCSF. Our team of investigators at Algen and UCSF has decades of experience in developing RNA signatures to indirectly targeting MYC and MYCN via synthetic lethality. Mature startup Algen Biotechnologies has a vibrant ecosystem of investors, innovators, and grew out of Nobel Laureate Dr. Jennifer Doudna’s lab at UC Berkeley, who has been a scientific advisor since its founding. With Dr. William Weiss at UCSF, we have assembled a powerhouse environment.