I-Corps: Translation potential of using machine learning to predict oxaliplatin chemotherapy benefit in early colon cancer

I-Corps：利用机器学习预测奥沙利铂化疗对早期结肠癌疗效的转化潜力

• 批准号: 2425300
• 负责人: Lujia Chen
• 金额: $ 5万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2425300&HistoricalAwards=false
• 关键词: Corps; Translation; potential; using; machine

The broader impact of this I-Corps project is the development of a machine learning model to predict the efficacy of one type of chemotherapy, oxaliplatin, for colon cancer patients. Colorectal cancer is the third most common cancer and ranks second in cancer death. In 2020, the estimated incidences of colorectal cancer were 1.9 million, and these are expected to increase 60% by 2030. Most colon cancer patients receive post-surgery chemotherapy (adjuvant therapy) to prevent cancer recurrence. Oxaliplatin is the most widely used chemotherapy agent in colorectal cancers to prevent recurrence, accounting for around 10% of all cancer patients. However, more than half of the patients do not benefit from oxaliplatin. Instead, oxaliplatin leads to disabling and lasting neuropathy that deteriorates the patient's quality of life and results in substantial financial burdens ($18,000 per patient per year) due to treatments for unnecessary side effects. Accurately predicting oxaliplatin benefits may enable oncologists to choose among Food and Drug Administration-approved regimens to maximize efficacy and minimize adverse effects by limiting oxaliplatin to patients who likely will benefit. This solution may improve the outcomes for colon cancer patients receiving post-surgery adjuvant therapy worldwide.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a machine learning model using the colon cancer transcriptome as an input feature to predict the efficacy of oxaliplatin-based chemotherapy regimens for the treatment of colon cancer. Patients with resected high-risk stage II/III colon cancer usually receive a curative adjuvant chemotherapy to prevent recurrence. However, the chemotherapy, oxaliplatin, may lead to acute and chronic disabling peripheral neurotoxicity. The machine learning model was developed to predict the cancer cells’ drug sensitivity based on patient’s individualized transcriptomic data. In an effort to de-escalate chemotherapy and avoid unnecessary side effects, clinical trials were conducted to examine whether a shorter duration can maintain efficacy and yet reduce oxaliplatin-induced neurotoxicity. The model, referred to as the colon oxaliplatin signature model, was shown to be predictive of oxaliplatin benefits in the colon cancer adjuvant setting in a double-blinded clinical trial of 1,065 colon cancer patients with both transcriptomic data and survival outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该I-Corps项目的更广泛的影响是开发机器学习模型，以预测一种化学疗法Oxaliptin对结肠癌患者的效率。结直肠癌是第三大常见的癌症，在癌症死亡中排名第二。在2020年，估计的结直肠癌发动机为190万，预计到2030年将增加60％。大多数结肠癌患者接受了手术后化学疗法（辅助治疗），以防止癌症复发。奥沙利铂是有色癌症中使用最广泛的化学疗法，以防止复发，占所有癌症患者的10％。但是，超过一半的患者不能从奥沙利铂中受益。取而代之的是，奥沙利铂会导致残疾和持久的神经病变，这决定了患者的生活质量，并导致大量金融伯恩根（每年每年$ 18,000），这是由于治疗了不必要的副作用。准确地预测奥沙利铂的益处可以使肿瘤学家能够在食品和药物管理批准的方案中进行选择，从而最大程度地提高有效性，并通过将奥沙利铂限制在可能受益的患者中，从而最大程度地减少不良反应。该解决方案可能会改善接受全球手术后调整疗法的结肠癌患者的结局。这个I-Corps项目利用专家学习以及行业生态系统的第一手投资来评估技术的翻译潜力。该解决方案基于使用结肠癌转录组作为输入特征的机器学习模型的开发，以预测基于奥沙利铂的化学疗法治疗方案对结肠癌的治疗效率。切除的高风险II/III期结肠癌患者通常会接受治疗性调整化疗，以防止复发。然而，化学疗法，草钙蛋白可能导致急性和慢性残疾周围神经毒性。开发机器学习模型是为了根据患者的个性化转录组数据来预测癌细胞的药物敏感性。为了降低化学疗法并避免不必要的副作用，进行了临床试验，以检查较短的持续时间是否可以保持有效性并降低奥沙利铂诱导的神经毒性。该模型被称为结肠奥沙利铂签名模型，被证明可以预测结肠癌中奥沙利铂的益处，以对1,065例具有转录数据和生存结果的双眼临床试验进行调整的环境，这是NSF的法定任务，并通过评估范围来构成依据，这表明了NSF的批准。