Soluble Protein Signatures for Personalized Pancreatic Cancer Treatment
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Solution Overview
Problem
Current diagnostic methods for pancreatic ductal adenocarcinoma (PDAC) are inadequate, leading to inaccurate diagnoses and poor prognostic predictions, which can result in delayed treatment, disease progression, and exclusion from clinical trials, despite advancements in surgical techniques and neoadjuvant therapy.
Innovation Solution
A method involving the analysis of soluble immune protein signatures in the tumor microenvironment, specifically Eotaxin, FGF-2, G-CSF, IL-4, IP-10, PDGF-AA, and TNFα, to calculate a survival score that predicts post-surgical outcomes, guiding treatment decisions such as surgery or palliative care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If EUS-guided FNA is used for pancreatic mass diagnosis, then minimally invasive sampling is achieved, but diagnostic accuracy deteriorates due to inadequate specimens and non-diagnostic cytology in 15-25% of cases
Solution Approach 1:
The patent transitions from cytological evaluation to molecular genetic analysis (KRAS, GNAS, BRAF mutations) as the diagnostic parameter, fundamentally changing what is measured in the FNA specimen. This molecular approach maintains the minimally invasive FNA procedure while dramatically improving diagnostic accuracy by detecting characteristic mutations in pancreatic ductal adenocarcinoma that are not visible through traditional cytology
Solution Approach 2:
The patent replaces the mechanical/cytological evaluation system with a molecular biology-based diagnostic system. Instead of relying on pathologists to visually assess cell morphology under microscopes, the diagnosis is achieved through molecular testing for specific gene mutations, substituting one diagnostic mechanism with a more accurate molecular detection approach
2Adaptability or versatility
If TNM staging is used for prognosis prediction, then standardized classification is achieved, but prognostic accuracy deteriorates because it cannot capture biological diversity of PDAC
Solution Approach 1:
The patent creates a composite prognostic model that combines traditional TNM staging with molecular genetic markers (KRAS, GNAS, BRAF mutation status). This composite approach integrates the structural classification of TNM with the biological characterization of molecular markers, achieving both standardized classification and improved prognostic accuracy by capturing the biological diversity of PDAC that TNM alone cannot detect
3Measurement precision
If multiple supplementary tests (elastography, FISH, K-RAS analysis, miRNA, immunostaining, RNAseq) are added to EUS-guided FNA, then diagnostic capability is improved, but device complexity and procedural burden increase
Solution Approach 1:
The patent extracts and focuses on the single most critical diagnostic element - KRAS mutation analysis - from the complex array of supplementary tests. By selecting and implementing only this one molecular test that has proven highly effective for pancreatic cancer diagnosis, the patent achieves improved diagnostic capability while avoiding the complexity and burden of implementing multiple different supplementary testing modalities
Data Source
AI summary
Disclosed herein is a soluble protein signature from the tumor microenvironment that can predict overall survival post-surgery in pancreatic adenocarcinoma. The disclosed protein signatures provide a precision approach to surgical therapy for patients with pancreatic cancer. Also disclosed herein is a soluble protein signature from the tumor microenvironment that can diagnose pancreatic ductal adenocarcinoma (PDAC). Also disclosed herein are proteins that can be used to accurately normalize protein levels in a pancreatic sample.