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

这个I-Corps项目的更广泛的影响是开发一个机器学习模型来预测一种化疗奥沙利铂对结肠癌患者的疗效。结直肠癌是第三大常见癌症，在癌症死亡中排名第二。2020年，估计结直肠癌的发病率为190万，预计到2030年将增加60%。大多数结肠癌患者接受手术后化疗（辅助治疗），以防止癌症复发。奥沙利铂是结直肠癌中最广泛使用的化疗药物，用于预防复发，约占所有癌症患者的10%。然而，超过一半的患者没有从奥沙利铂中获益。相反，奥沙利铂导致致残和持久的神经病变，使患者的生活质量恶化，并因治疗不必要的副作用而造成巨大的经济负担（每位患者每年18，000美元）。准确预测奥沙利铂的益处可能使肿瘤学家能够在食品和药物管理局批准的方案中进行选择，以通过将奥沙利铂限制在可能受益的患者中来最大限度地提高疗效并最大限度地减少不良反应。该解决方案可能会改善全球接受术后辅助治疗的结肠癌患者的预后。该I-Corps项目利用体验式学习以及对行业生态系统的第一手调查来评估该技术的转化潜力。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疗方案治疗结肠癌的疗效。切除的高危II/III期结肠癌患者通常接受根治性辅助化疗以防止复发。然而，化疗，奥沙利铂，可能会导致急性和慢性致残性周围神经毒性。机器学习模型是基于患者的个体化转录组数据开发的，用于预测癌细胞的药物敏感性。为了降低化疗剂量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维持疗效，同时减少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，被证明是预测奥沙利铂在结肠癌辅助治疗的好处在双盲临床试验的1,065例结肠癌患者的转录组数据和生存outcomes.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持.

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.