Multi-class Cancer Classification via cfDNA Binning and Dimensionality Reduction
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Solution Overview
Problem
Current cancer diagnosis methods using noninvasive serum-based biomarkers suffer from low specificity, leading to high false-positive results, necessitating the development of more accurate techniques for classifying cancer conditions through cell-free nucleic acids in body fluids.
Innovation Solution
The method involves representing the reference genome into non-overlapping bins, obtaining bin counts from sequencing data, applying dimensionality reduction, and using these counts to train classifiers for accurate cancer classification based on nucleic acid fragments in biological samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If noninvasive serum-based biomarkers are used for cancer detection, then patient compliance and screening feasibility are improved, but diagnostic specificity deteriorates leading to high false-positive results
Solution Approach 1:
The patent segments the diagnostic process into multiple independent classification stages. First, a primary classifier screens for any cancer presence using cfDNA markers. Then, secondary classifiers specifically detect ovarian and breast cancer components. This segmented approach maintains the noninvasive advantage while improving diagnostic specificity by breaking down the complex classification problem into manageable, specialized stages.
Solution Approach 2:
The patent transitions from traditional single-marker serum biomarker analysis to multi-dimensional cfDNA analysis. By examining multiple cfDNA characteristics including fragment size distribution, methylation patterns, and sequence variations across different genomic regions, the system adds dimensional depth to the diagnosis, thereby improving specificity while maintaining noninvasive sampling.
2Device complexity
If traditional single-marker biomarkers are used for cancer screening, then test simplicity is maintained, but diagnostic accuracy deteriorates
Solution Approach 1:
The patent merges multiple cfDNA analysis dimensions into a unified diagnostic workflow. It combines fragment size analysis, methylation status detection, and sequence variation identification into an integrated multi-classifier system. This merging approach maintains relative test simplicity by using a single blood draw while achieving high diagnostic accuracy through the synergistic combination of multiple analytical modalities.
3Adaptability or versatility
If multiple cancer classes are classified simultaneously, then comprehensive cancer screening is achieved, but classification complexity increases
Solution Approach 1:
The patent segments the multi-cancer classification task into specialized classifier modules. Each classifier is trained to detect specific cancer types (ovarian, breast, lung, colorectal, prostate) using cancer-type-specific cfDNA markers. This segmentation allows comprehensive cancer screening across multiple classes while managing complexity through modular, specialized detection routines rather than a single monolithic classifier.
Solution Approach 2:
The patent introduces intermediary classifier components that process cfDNA data through intermediate representation layers. These intermediaries translate raw cfDNA measurements into cancer-type-specific feature vectors that can be processed by specialized classifiers. This intermediary processing stage simplifies the overall classification complexity by creating standardized intermediate representations that bridge raw data and final multi-cancer classification decisions.
Data Source
AI summary
Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.