Mass Spectrometry Classifiers for Cancer Immunotherapy Patient Stratification
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Solution Overview
Problem
Current methods for guiding cancer treatment with immunotherapy drugs, such as nivolumab, struggle to accurately identify patients who will benefit from long-term treatment, particularly those with poor prognosis, leading to misclassification and suboptimal treatment outcomes.
Innovation Solution
Development of new classifiers, including New classifier 1 and New classifier 2, which utilize mass spectrometry data from blood samples to hierarchically classify patients, distinguishing between those with durable benefit from immunotherapy and those with poor prognosis, and guiding treatment decisions based on these classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current classification methods (e.g., BDX008) are used to guide immunotherapy treatment, then treatment guidance is provided for cancer patients, but patients with poor prognosis are misclassified and do not receive appropriate durable benefit
Solution Approach 1:
The patent divides the classification process into multiple sequential classifiers (first classifier identifies poor prognosis patients, second classifier identifies durable benefit within that subgroup). This segmentation allows each classifier to focus on specific aspects of patient stratification, improving overall classification accuracy and preventing misclassification of patients with durable benefit.
Solution Approach 2:
The patent adds a new dimension to the classification by introducing a second classification layer specifically for identifying durable benefit within the poor prognosis subgroup. This dimensional extension captures previously lost information about patients who may have favorable outcomes despite overall poor prognosis, thereby resolving the information loss problem.
2Adaptability or versatility
If immunotherapy is administered to all cancer patients regardless of prognosis classification, then treatment access is maximized, but patients with poor prognosis and no durable benefit receive suboptimal treatment
Solution Approach 1:
The patent segments the patient population into distinct subgroups through sequential classification: first identifying poor prognosis patients, then identifying those with durable benefit within that subgroup. This segmentation enables tailored treatment guidance that adapts to different patient categories, improving treatment precision without requiring a single overly complex classifier.
Solution Approach 2:
The patent performs preliminary classification to identify poor prognosis patients before conducting the second classification for durable benefit. This preliminary action allows the system to efficiently route patients through appropriate classification pathways, providing adaptable treatment guidance while managing system complexity through structured preprocessing.
Data Source
AI summary
A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.