Proteomic TIL Response Prediction for Personalized Cancer Therapy
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Solution Overview
Problem
Existing treatments for bulky, refractory cancers using adoptive autologous transfer of tumor infiltrating lymphocytes (TILs) have limited success, and there is a need for improved methods to predict which cancer patients are likely to benefit from TIL therapy.
Innovation Solution
A method involving obtaining an analytical signature from a patient's blood-derived sample, comparing it with a training set of class-labeled signatures from other cancer patients, and classifying the sample to predict the likelihood of benefiting from TIL administration, using mass spectrometry or other methods to identify specific protein correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If TIL therapy is administered to cancer patients, then treatment effectiveness may be improved, but patient selection difficulty increases
Solution Approach 1:
The patent applies preliminary action by performing proteomic profiling of patient samples before TIL therapy administration. The system analyzes protein expression patterns in advance to predict treatment response, allowing clinicians to identify suitable candidates prior to treatment initiation. This pre-screening approach resolves the contradiction by making patient selection objective and predictable rather than relying on trial-and-error methods.
Solution Approach 2:
The patent replaces subjective clinical assessment with objective proteomic analysis. Instead of relying on manual evaluation of patient characteristics, the system uses mass spectrometry-based proteomics to generate molecular fingerprints that objectively predict treatment response. This substitution transforms the patient selection process from a mechanical, experience-based approach to a precise, data-driven scientific method.
2Measurement precision
If personalized treatment strategies are implemented, then treatment precision is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the treatment decision-making process into distinct modular components: (1) sample acquisition, (2) proteomic profiling using mass spectrometry, (3) data analysis through machine learning algorithms, and (4) treatment recommendation. This segmentation allows each component to be optimized independently and facilitates implementation while maintaining high precision through specialized tools at each stage.
Solution Approach 2:
The patent introduces proteomic profiling as an intermediary between patient diagnosis and treatment administration. This intermediary layer generates objective molecular data that serves as a bridge, enabling precise patient selection without requiring direct complex interactions between all treatment components. The proteomic signature acts as a mediator that simplifies the overall system architecture while maintaining high treatment precision.
3Reliability
If predictive modeling is used for patient selection, then treatment outcome prediction is improved, but data requirements increase
Solution Approach 1:
The patent extracts and focuses on specific proteomic features that are most predictive of treatment response. Rather than requiring comprehensive analysis of all possible molecular parameters, the system identifies and extracts the key protein expression patterns that drive treatment outcomes. This extraction approach reduces the data burden while maintaining high prediction accuracy by concentrating on the most informative variables.
Solution Approach 2:
The patent changes the parameter space from traditional clinical variables to proteomic parameters. By transforming the data type from conventional patient demographics and history to molecular protein expression profiles, the system achieves more accurate predictions with manageable data quantities. The proteomic parameters provide higher signal-to-noise ratio, enabling better prediction without requiring excessive data volume.
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
Enables accurate prediction of patient response to TIL therapy, allowing for personalized treatment strategies with potential progression-free survival of up to 60 months or more.
Implementation Method 1
the analytical signature is obtained by a mass spectrometry method
Data Source
AI summary
The invention provides systems and methods for determining and predicting the effect of providing a population of tumor infiltrating lymphocytes (TILs) on a condition associated with an entity, for example the effect of providing a population of tumor infiltrating lymphocytes (TILs) on a subject having cancer. The systems and methods rely on acquiring a computer readable analytical signature from a sample of the entity, obtaining a trained model output value for the entity by inputting the computer readable analytical signature into a tier trained model panel, and classifying the entity based upon the trained model output value with a time-to-event class in an enumerated set of time-to-event classes, each of whom is associated with a different effect of providing a population of TILs to the entity.The invention provides methods of treating cancer in a patient by administering a therapeutically effective population of TILs to the patient, which is at the same determined to be likely to benefit from the administration of TILs comparative to other cancer patients that have been administered TILs. Such methods of treatment include obtaining from the patient a tumor fragment, contacting the tumor fragment with one or more cell culture mediums, thereby performing one or more expansions of population of TILs existing in the tumor, and producing one or more subsequent populations of TILs. The invention also provides methods of treating cancer in a patient exhibiting an increased or decreased level of expression of various biological markers.