Systems and methods for expression-based discrimination of distinct clinical disease states in prostate cancer
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Publication Number
US-10865452-B2
Publication Date
2020-12-15
Expiration Date
Abstract
A system for expression-based discrimination of distinct clinical disease states in prostate cancer is provided that is based on the identification of sets of gene transcripts, which are characterized in that changes in expression of each gene transcript within a set of gene transcripts can be correlated with recurrent or non-recurrent prostate cancer. The Prostate Cancer Prognostic system provides for sets of “prostate cancer prognostic” target sequences and further provides for combinations of polynucleotide probes and primers derived there from. These combinations of polynucleotide probes can be provided in solution or as an array. The combination of probes and the arrays can be used for diagnosis. The invention further provides further methods of classifying prostate cancer tissue.
Core Innovation
The invention relates to an expression-based diagnostic/prognostic system for prostate cancer that classifies clinical disease states using differential RNA transcript expression. It distinguishes between clinical disease states including NED, PSA biochemical relapse, and SYS systemic recurrence/metastasis by measuring transcript expression patterns from a patient sample.
A prostate cancer prognostic library is defined that comprises gene transcripts, and target sequences are derived from the library. The targets include coding target sequences and non-coding target sequences, and the distinct expression levels between coding and non-coding targets are used as the basis for determining whether a prostate cancer transcript profile is recurrent.
The system determines a recurrent prostate cancer transcript profile by utilizing expression levels of the at least one coding target sequence and the at least one non-coding target sequence. The expression-based assessment is performed using target measurement on nucleic acid arrays, including microarrays, and associated probe/primer sets, and it generates expression-model scores that support risk category designation and decisions regarding treatment selection or monitoring.
Transcript discovery and selection is described using statistical procedures such as PCA, hierarchical clustering, and linear regression filters, including nearest shrunken centroids for minimal signatures. Examples are provided showing metagenes and likelihood-style scoring that increase statistical separation among clinical groups, including multiple RNA signature sets and associated target-set sizes.
Claims Coverage
The document content grounds coverage primarily in one independent method claim directed to treating prostate cancer based on whether a recurrent prostate cancer transcript profile is present, using distinct coding and non-coding transcript expression levels. Dependent claims refine the measurement, definition of non-coding targets, and the decision model used for the recurrent-profile determination, including a linear computer model or algorithm.
Treating prostate cancer by coding versus non-coding expression profiling
Obtain an expression level of at least one coding target sequence and obtain an expression level of at least one non-coding target sequence from a prostate cancer cell in a sample from the subject, wherein the obtained expression level of the non-coding target sequence is distinct from the obtained expression level of the at least one coding target sequence.
Determining recurrence from a recurrent prostate cancer transcript profile
Determine whether the prostate cancer cell of the subject comprises a recurrent prostate cancer transcript profile utilizing the expression level of the at least one coding target sequence and the expression level of the at least one non-coding target sequence in the sample.
Selecting treatment or monitoring based on the recurrent profile determination
If the prostate cancer cell comprises the recurrent prostate cancer transcript profile, administer a prostate cancer treatment selected from the group consisting of adjuvant chemotherapy, systemic radiation therapy, and anti-androgen therapy, or a combination thereof; if the prostate cancer cell does not comprise the recurrent prostate cancer transcript profile, then monitor the subject without providing the prostate cancer treatment.
Measuring expression levels using selected assays
Determine the expression levels of at least one coding target sequence and/or at least one non-coding target sequence using a method selected from RT-PCR, Northern blotting, ligase chain reaction, array hybridization, or combinations thereof.
Defining non-coding targets as not translated into protein
Configure that the non-coding target sequence is not included in an RNA molecule that is translated into protein.
Defining non-coding targets as not associated with a gene locus
Configure that the non-coding target sequence is not associated with a gene locus.
Using a computer model or algorithm to determine recurrence
Determine whether prostate cancer has a recurrent prostate cancer transcript profile by assessing expression levels of coding and non-coding target sequences using a computer model or algorithm.
Restricting recurrence decision to a linear model
Configure that the computer model or algorithm is linear.
Overall claim coverage centers on using distinct expression levels of coding and non-coding target sequences to determine whether a recurrent prostate cancer transcript profile is present in a prostate cancer cell, and then selecting adjuvant chemotherapy, systemic radiation therapy, anti-androgen therapy, or monitoring without treatment accordingly. Dependent refinements include assay-based expression measurement, definitional constraints for non-coding targets, and recurrent-profile determination using a computer model or algorithm, including a linear model.
Stated Advantages
Enables classification of clinical disease states (NED, PSA biochemical relapse, SYS systemic recurrence/metastasis) using differential RNA transcript expression.
Supports determination of whether a subject comprises a recurrent prostate cancer transcript profile based on coding and non-coding expression levels.
Supports risk category designation and selection between providing prostate cancer treatment and monitoring without providing prostate cancer treatment.
In transcript discovery and selection examples, larger metagenes increase statistical separation among clinical groups.
Documented Applications
Application to treating a subject having prostate cancer by determining a recurrent prostate cancer transcript profile from coding and non-coding transcript expression levels and then administering selected therapy or monitoring without treatment.
Classification of clinical disease states including NED, PSA biochemical relapse, and SYS systemic recurrence/metastasis based on differential RNA transcript expression.
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