In silico drug sensitivity predicts subgroup-specific therapeutics in medulloblastoma patients.

Anna M Jermakowicz, Luz Ruiz, Jonathan Chu, Nitish Jange, Robert K Suter, Nina S Kadan-Lottick, Derek Hanson, Nagi G Ayad
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Abstract

Background: Medulloblastoma is the most common malignant pediatric brain tumor. Survival rates vary widely between subgroups, with an average overall survival of 70%. Recurrent medulloblastoma is highly aggressive, treatment-resistant, and usually fatal. In addition, current treatments are highly toxic to the developing brain and surviving patients suffer from lifelong side effects. Therefore, novel therapeutic options are urgently needed.

Methods: To inform risk-based, personalized therapy, we developed a novel platform called DrugSeq, which allows predictions of drug sensitivities in patients across medulloblastoma subgroups. We used a perturbagen-response dataset to calculate transcriptional response signatures for each drug and compared this to patient medulloblastoma tumor gene expression. We then stratified patients by molecular subgroup and used an ANOVA analysis to identify drugs that selectively targeted each subgroup.

Results: We found distinct differences in transcriptional profiles and predicted drug sensitivity for each medulloblastoma subgroup. We identified several kinase inhibitors, epigenetic inhibitors, and several drugs that have been investigated in drug repositioning studies for cancer.

Conclusions: We posit that DrugSeq may identify novel therapies and facilitate patient stratification in clinical trials, leading to more successful targeted medulloblastoma therapies that improve tumor response while minimizing late toxicities. This computational tool can also be used for other cancers to stratify patients based on any clinical or molecular feature.

Key points: DrugSeq calculates drug sensitivity for medulloblastoma tumors stratified by subgroup.DrugSeq platform may inform patient stratification strategies in clinical trials.

Importance of the study: Medulloblastoma is the most common malignant pediatric brain tumor. Current standard-of-care typically includes surgical resection, multi-agent chemotherapy, and radiation. However, survival rates vary widely between subgroups, ranging from 45 to 90%, depending on age and molecular features. In addition, surviving children frequently suffer from debilitating late side effects of therapy including neurocognitive impairment, epilepsy, stroke, subsequent cancer, endocrinopathies, and early mortality. Therefore, novel therapeutic options are urgently needed. However, a one-size-fits-all approach for therapy is unlikely to be effective given the well-characterized intertumor heterogeneity of medulloblastoma.

计算机药物敏感性预测成神经管细胞瘤患者亚组特异性治疗。
背景:髓母细胞瘤是儿童最常见的恶性脑肿瘤。亚组之间的存活率差异很大,平均总存活率为70%。复发性髓母细胞瘤具有很强的侵袭性,治疗难治性,通常是致命的。此外,目前的治疗方法对发育中的大脑有很高的毒性,存活下来的患者会遭受终生的副作用。因此,迫切需要新的治疗方案。方法:为了告知基于风险的个性化治疗,我们开发了一个名为DrugSeq的新平台,该平台可以预测髓母细胞瘤亚组患者的药物敏感性。我们使用摄动反应数据集来计算每种药物的转录反应特征,并将其与髓母细胞瘤患者的肿瘤基因表达进行比较。然后,我们按分子亚组对患者进行分层,并使用方差分析来确定选择性靶向每个亚组的药物。结果:我们发现了转录谱的明显差异,并预测了每个髓母细胞瘤亚组的药物敏感性。我们确定了几种激酶抑制剂,表观遗传抑制剂和几种已经在癌症药物重新定位研究中进行了研究的药物。结论:我们认为,DrugSeq可能会在临床试验中发现新的治疗方法,并促进患者分层,从而导致更成功的靶向髓母细胞瘤治疗,提高肿瘤反应,同时最大限度地减少晚期毒性。这种计算工具也可以用于其他癌症,根据任何临床或分子特征对患者进行分层。重点:DrugSeq计算按亚组分层的成神经管细胞瘤的药物敏感性。DrugSeq平台可以为临床试验中的患者分层策略提供信息。研究意义:髓母细胞瘤是儿童最常见的恶性脑肿瘤。目前的标准治疗包括手术切除、多药化疗和放疗。然而,存活率在不同亚组之间差异很大,根据年龄和分子特征,从45%到90%不等。此外,幸存的儿童经常遭受治疗的晚期副作用,包括神经认知障碍、癫痫、中风、随后的癌症、内分泌疾病和早期死亡。因此,迫切需要新的治疗方案。然而,考虑到髓母细胞瘤的肿瘤间异质性,一刀切的治疗方法不太可能有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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