| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | April 10, 2024 |
| Latest Amendment Date: | April 10, 2024 |
| Award Number: | 2425300 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Ruth Shuman
rshuman@nsf.gov (703)292-2160 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | April 15, 2024 |
| End Date: | March 31, 2025 (Estimated) |
| Total Intended Award Amount: | $50,000.00 |
| Total Awarded Amount to Date: | $50,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4200 FIFTH AVENUE PITTSBURGH PA US 15260-0001 (412)624-7400 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4200 FIFTH AVENUE PITTSBURGH PA US 15260-0001 |
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Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | I-Corps |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
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.
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