Example: The National Cancer Institute (NCI) seeks research co-development partners and/or licensees for a collection of T-cell receptors (TCRs) that specifically target the mutated KRAS antigen.
This technology encompasses a DNA methylation–based diagnostic platform designed to improve the accuracy and consistency of cancer classification, with demonstrated utility for tumors of the central nervous system, kidney, and hematopoietic system. By identifying disease-specific methylation signatures, the approach reduces interobserver variability and enhances diagnostic confidence. The central nervous system (CNS) classifier is built from a curated reference set of 16,567 methylation profiles and organizes tumors into 22 families and 133 clinically relevant diagnostic classes, including 21 newly developed methylation classes not represented in other existing tools. Across multiple independent validation cohorts (n = 5,875), the classifier demonstrated robust performance, and in a clinical-impact analysis of 1,204 NIH validation cases, methylation profiling materially influenced final diagnosis in 74.4% of cases by refining, increasing precision, or reclassifying. The CNS classifier was deployed as a user-facing software tool, MethylScape Analysis, which streamlines methylation-based classification workflows for CNS tumors (https://methylscape.ccr.cancer.gov/). The development of multiple specialized classifiers supports a more granular understanding of tumor biology and informed clinical decision-making.
Accurate CNS tumor classification can be challenging when tumors show overlapping histology, limited tissue or atypical features. A meaningful fraction of cases remains unclassified or assigned with limited confidence with current molecular tools. These diagnostic ambiguities directly affect subtype and grade assignment which, in turn, influence treatment planning, prognosis, and clinical trial eligibility. Variability across observers and institutions can lead to additional testing, delays, and inconsistent diagnoses.
The NCI/Bethesda classifier addresses this gap using DNA methylation patterns as a robust molecular fingerprint. It applies a stratified machine learning framework to extend diagnostic coverage and improve assignment confidence for CNS tumors. The classifier was developed from a rigorously curated reference set of over 16k methylation profiles, structured into 22 tumor families and 133 clinically relevant diagnostic classes and includes 21 recently developed methylation classes not represented in existing tools. The approach has been validated across multiple independent cohorts (n = 5,875) and supports deployment through MethylScape, a public web-based portal that streamlines classifier execution for broad accessibility. In an NIH validation cohort analysis of 1,204 high-confidence matches with pre-methylation diagnoses available, methylation profiling confirmed the initial diagnosis in 25.6% of cases while driving clinically meaningful diagnostic evolution in the remainder, including refined diagnosis (subtyping) in 14.6%, new diagnosis with increased precision in 54.7%, and substantial diagnostic reclassification in 5.0%—changes that are expected to affect patient management in the reclassification subset.
Renal neoplasms present a parallel diagnostic challenge due to morphologic and molecular heterogeneity, overlapping microscopic features, and interobserver variability. As a result, a subset of cases are unclassifiable even after immunohistochemical, mutation and cytogenetic workups. To address this, the Kidney Classifier component of this platform leverages genome-wide DNA methylation profiling (feasible on formalin-fixed paraffin-embedded tissue using robust array-based methods). It was developed through examination of methylation signatures from over 2,000 renal neoplasms, identifying 23 coherent methylation groups that correlate with known tumor types and reveal clinically relevant novel subtypes. A machine learning classifier trained on 1,284 samples was externally tested on 287 renal neoplasms, demonstrating >90% concordance between expected neoplasm type and high-score methylation-based classification, with discordant cases highlighting opportunities for diagnostic reclassification and improved precision in challenging renal tumor evaluations.
Licensing and collaboration opportunities include commercial development of methylation-based diagnostic tests and/or software-enabled classification solutions for clinical laboratories, reference labs, and diagnostic companies. Partners may engage in external validation (retrospective and prospective), assay standardization, integration into pathology workflows and reporting systems, and extension of classifier coverage to additional tumor types and multi-institutional datasets. Consistent with consensus recommendations for complementary classifiers, the inventors are also interested in collaborations that: (1) operationalize multi-classifier strategies (concordant/complementary prediction to increase confidence; (2) leverage discordance to trigger orthogonal follow-up) and (3) accelerate clinical translation through scalable deployment models- including CLIA laboratory workflows and regulated diagnostic pathways.