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

AlgenML：MYCN驱动的转录重编程的药物靶点发现平台 神经母 项目摘要 对于生物制药来说，药物发现是一项费力、耗时且昂贵的工作。肿瘤学尤其是 临床试验中的新药只有3.4%的成功概率。本申请涉及 依赖于细胞活力或报告基因的肿瘤学中传统药物靶点发现的重大挑战 过度简化细胞状态的测定。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性来克服这些挑战， 探测漏洞以重新编程致癌信号网络。转录因子MYCN和MYC是 迄今为止，尽管小分子是高价值的癌症药物靶点， 扩增的基因，并导致整个癌症谱的不良结果。间接阻止MYCN的代理 从合成致死活性筛选中鉴定的这些基因，在正在进行的研究中， 临床试验Algen的专有机器学习平台（AlgenML）识别阻断致癌基因的靶点 使用CRISPR干扰（CRISPRi）基因的单细胞RNA表达对MYCN的转录成瘾 击倒。全基因组单细胞RNA表达谱测量每个细胞10，000个基因，每个高表达基因组 通量测定通常一次捕获160，000个细胞。使用CRISPRi基因敲低文库和 通过多重检测，可以同时敲除数百个基因， 每次CRISPRi基因敲除对200个细胞进行测序。这使得一个非常丰富的数据集，超过400 AlgenML分析的100万个RNA表达数据点。我们的药物发现方法是创新的 因为与传统方法不同，AlgenML平台不能识别导致细胞凋亡的必需基因， 死亡，而是以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 优化AlgenML，以在新的遗传定义模型中监测和重编程MYCN转录活性 MYCN驱动的神经母细胞瘤我们专注于神经母细胞瘤，因为MYCN扩增在神经母细胞瘤中很常见。 疾病，并且遗传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，命名了靶基因， 基于靶基因重编程MYCN转录依赖性的能力，在体外测试靶基因。目的2 评估靶向抑制在新人类诱导的肿瘤中缩小肿瘤和延长寿命的体内功效 多能干细胞（iPSC）和来自UCSF的神经母细胞瘤的啮齿动物模型。我们的调查团队在 Algen和UCSF在开发间接靶向MYC的RNA特征方面拥有数十年的经验， MYCN通过合成致死。成熟的创业公司Algen Biotechnologies拥有一个充满活力的投资者生态系统， 创新者，并成长于诺贝尔奖得主詹妮弗杜德纳博士在加州大学伯克利分校的实验室，谁一直是一个科学 自成立以来，威廉韦斯博士在加州大学旧金山分校，我们已经组装了一个发电站的环境。

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.