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

I-Corps项目更广泛的影响是开发一种机器学习模型，以预测一种化疗药物奥沙利铂对结肠癌患者的疗效。结直肠癌是第三大最常见的癌症，在癌症死亡中排名第二。2020年，结直肠癌的估计发病率为190万，预计到2030年将增加60%。大多数结肠癌患者术后接受化疗（辅助治疗）以预防癌症复发。奥沙利铂是预防结直肠癌复发最广泛使用的化疗药物，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，奥沙利铂会导致致残和持久的神经病变，使患者的生活质量恶化，并由于治疗不必要的副作用而导致巨大的经济负担（每位患者每年18,000美元）。准确预测奥沙利铂的疗效可以使肿瘤学家在食品和药物管理局批准的方案中进行选择，通过将奥沙利铂限制在可能受益的患者身上，从而最大化疗效并最小化不良反应。这种解决方案可以改善全世界接受术后辅助治疗的结肠癌患者的预后。这个I-Corps项目利用体验式学习和对行业生态系统的第一手调查来评估该技术的翻译潜力。该解决方案基于一种机器学习模型的开发，该模型使用结肠癌转录组作为输入特征来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治疗性辅助化疗以防止复发。然而，化疗，奥沙利铂，可能导致急性和慢性致残性周围神经毒性。该机器学习模型的开发是为了根据患者的个体化转录组数据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保持疗效并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特征模型，在1065名结肠癌患者的双盲临床试验中，该模型被证明可以预测奥沙利铂在结肠癌辅助治疗中的益处，包括转录组数据和生存结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.