Multi-Omic Lung Nodule Screening for Higher Detection Accuracy
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Solution Overview
Problem
Current methods for detecting diseases such as cancer lack accuracy and specificity, particularly at early stages, which affects treatment and prognosis.
Innovation Solution
A multi-omic approach using proteomic and nucleic acid sequencing measurements from biofluid samples, combined with classifiers, to enhance disease detection accuracy, utilizing enrichment protocols, mass spectrometry, and nanoparticle adsorption of proteins, achieving an average ROC AUC of at least 0.9 in controlled trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-omic data is used for disease detection, then the method is simpler and less costly, but the detection accuracy and specificity are insufficient
Solution Approach 1:
The patent combines multiple omic data types (proteomic, metabolomic, transcriptomic, and/or genomic data) into an integrated multi-omic dataset for disease detection. This merging of different biological data layers enables comprehensive molecular profiling that significantly improves detection accuracy and specificity compared to single-omic approaches, while the automated computational pipeline manages the complexity through systematic data integration and classifier algorithms.
2Measurement precision
If multi-omic data integration is implemented, then detection accuracy improves by 4% or more, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex multi-omic data processing task into distinct analytical components: individual omic data type processing, feature selection for each data type, and integrated classifier application. This segmentation allows systematic handling of each data layer separately before integration, reducing the overall processing difficulty while maintaining the accuracy benefits of multi-omic integration through structured computational pipelines.
Solution Approach 2:
The patent introduces computational classifiers and bioinformatics pipelines as intermediary layers between raw multi-omic data and disease detection results. These intermediaries automatically integrate the diverse omic data types, perform feature selection, and generate diagnostic predictions, thereby managing the computational complexity and making the multi-omic integration process tractable and scalable.
3Measurement precision
If enrichment protocols and mass spectrometry are used for proteomic measurements, then the sensitivity of protein detection is improved, but the time and resource requirements increase
Solution Approach 1:
The patent applies enrichment protocols to biofluid samples before mass spectrometry analysis to pre-concentrate and isolate target proteins and peptides. This preliminary action increases the sensitivity of protein detection by ensuring sufficient analyte concentration in the mass spectrometry assay, while the enriched samples require less instrumental analysis time compared to analyzing dilute samples without enrichment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The multi-omic method improves disease detection performance by 4% or more compared to single-omic data, with a ROC AUC of 0.9, enabling precise identification and tailored treatment strategies.
Implementation Method 1
the proteomic measurements are generated from proteins adsorbed to nanoparticles
Data Source
AI summary
Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.