EP-4735643-A1 - METHODS FOR EARLY DETECTION OF CANCER

Abstract

Described herein are gene signatures providing prognostic, diagnostic, treatment and molecular subtype classifications of cancers through genomic and epigenomic profiling, including immune checkpoint regulators such as programmed death ligand 1 (PDL-1). Using methods and compositions described herein, specific and sensitive detection of biomarkers of interest is provided. Such biomarkers are indicative of disease pathogenesis, which provides opportunity for selection of treatment, including treatment regimes directed at overcoming resistance mechanisms.

Inventors

• EPPLER, ROSS KEATING
• BARBACIORU, CATALIN
• TOLKUNOV, Denis
• RODRIGUEZ, Alejandra

Assignees

• Guardant Health, Inc.

Dates

Publication Date

20260506

Application Date

20240628

Claims (20)

1. 1. A method, comprising: detecting methylation in at least one of a plurality of sites; generating a plurality of one or more metrics for each of the plurality of sites; processing the one or more metrics to characterize a sample.
2. 2. The method of claim 1, wherein the one or more metrics are obtained from methylation calls from each of the plurality of sites.
3. 3. The method of claim 1, comprising obtaining a sample.
4. 4. The method of claim 1, comprising having obtained a sample.
5. 5. The method of claim 1, wherein characterizing the sample comprises determining gene expression of one or more biomarkers.
6. 6. The method of claim 4, wherein the one or more biomarkers comprise PD-L1, MSI and/or BRAF.
7. 7. The method of claim 1, further comprising building a binary classification model from methylation data of a set of training samples comprising PDL-1 status high and PDL-1 low.
8. 8. The method of claim 6, wherein the classification model is trained using cross-validation.
9. 9. The method of claim 7, wherein cross-validation comprises using 3-fold or 10-fold cross- validation.
10. 10. The method of claim 6, wherein regions are selected using penalized logistic regression, Least Absolute Shrinkage Selection Operator (LASSO) regularization.
11. 11. The method of claim 9, wherein the penalized logistic regression model comprises response variable PD-L1 and predictors methylation calls for each of the plurality of sites.
12. 12. The method of claim 1, wherein the sites comprise a custom panel.
13. 13. The method of claim 11, wherein the custom panel is configured in an in silico panel.
14. 14. The method of claim 11, wherein the custom panel is configured in a physical panel.
15. 15. The method of claim 11, wherein the custom panel comprises a set of oncogenes, promoter regions for a set of oncogenes, HRR genes, immuno-oncology (IO) genes, a cancer pathway, methylation peaks found in cancer or methylation peaks found in clinical samples.
16. 16. The method of claim 11, wherein the custom panel is refined based at least on literature annotations, common methylation peak positions, and/or public datasets.
17. 17. The method of claim 1, wherein PDL-1 status is determined based on gene expression data, PD-L1 promoter region nucleosomal position , or histology data.
18. 18. The method of claim 16, wherein the PD-L1 status is predictive of therapy response.
19. 19. The method of claim 17, wherein the therapy comprises one or more of an immune checkpoint inhibitor (ICI) , poly (ADP -ribose) polymerase (PARP) inhibitor, a kinase inhibitor, or an aromatase inhibitor, a CTLA4 inhibitors, PD-L1 inhibitor, PD-1 inhibitor alone or in combination with, fluoropyrimidine- and platinum-containing chemotherapy.
20. 20. The method of claim 18, wherein the immune checkpoint inhibitor is Pembrolizumab.

Description

METHODS FOR EARLY DETECTION OF CANCER CROSS REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit of U.S. provisional patent application no. 63/511,493, filed June 30, 2023, which is incorporated by reference herein in its entirety. FIELD OF THE INVENTION [0002] Described herein methods and compositions for prognostic, diagnostic, treatment and molecular subtype classifications of cancers through genomic, epigenomic, transriptomic profiling. BACKGROUND [0003] Cancer is a major cause of disease worldwide. Each year, tens of millions of people are diagnosed with cancer around the world, and more than half of the patients eventually die from it. In many countries, cancer ranks the second most common cause of death following cardiovascular diseases. Early detection is associated with improved outcomes for many cancers. [0004] To detect cancer, several screening tests are available. A physical exam and history survey general signs of health, including checking for signs of disease, such as lumps or other unusual physical symptoms. A history of a patient’s health habits and past illnesses and treatments will also be taken. Laboratory tests are another type of screening test and may include medical procedures to procure samples of tissue, blood, urine, or other substances in the body before conducting laboratory testing. Imaging procedures screen for cancer by generating visual representations of areas inside the body. Genetic tests detect certain gene deleterious mutations linked to some types of cancer. Genetic testing is particularly useful for a number of diagnostic methods. There is a great need in the art for genetic testing methods, including biomarkers, informative of diagnostic methods. [0005] Of interest is PD-L1, wherein PD-1/PD-L1 pathway plays a prominent role in immune regulation by delivering inhibitory signals to maintain the balance in T cell activation, tolerance, and immune-mediated tissue damage. Generally speaking, current approaches using a PD-L1 test measures what percentage of cells in a tumor that “express PD-L1” (programmed death ligand-1 protein) encoded by CD274 gene. This can be informative as PD-L1 levels may impact treatment options. Nevertheless, such techniques are of limited utility, laborious and timeconsuming as relying on tumor tissue and visual inspection using immunohistochemial staining. SUMMARY OF THE INVENTION [0006] Described herein is a method of building a binary classification model from methylation data by use of epigenomic panel normalized molecule counts in a hyper partition as a measure of methylation. In one application, lung cancer patients can have their PD-L1 levels tested via fluid sample such as blood. Based on expression levels, immunotherapy as first-line treatment may be recommended and/or administered, or in other instances, immunotherapy and/or chemotherapy. [0007] Described herein is a method, comprising: detecting methylation in at least one of a plurality of sites; generating a plurality of one or more metrics for each of the plurality of sites; processing the one or more metrics to characterize a sample. In other embodiments, the one or more metrics are obtained from methylation calls from each of the plurality of sites. In other embodiment, the method includes obtaining a sample. In other embodiment, the method includes having obtained a sample. In other embodiments, the characterizing of the sample comprises determining gene expression of one or more biomarkers. In other embodiments, the one or more biomarkers comprise PD-L1, MSI and/or BRAF. In other embodiments, the method includes building a binary classification model from methylation data of a set of training samples comprising PD-L1 status high and PD-L1 low. In other embodiments, the classification model is trained using cross-validation. In other embodiments, the cross-validation comprises using 3-fold or 10-fold cross-validation. In other embodiments, the regions are selected using penalized logistic regression, Least Absolute Shrinkage Selection Operator (LASSO) regularization. In other embodiment, the penalized logistic regression model comprises response variable PD-L1 and predictors methylation calls for each of the plurality of sites. In other embodiments, the sites comprise a custom panel. In other embodiments, the custom panel is configured in an in silico panel. In other embodiments, the custom panel is configured in a physical panel. In other embodiments, the custom panel comprises a set of oncogenes, promoter regions for a set of oncogenes, HRR genes, immuno-oncology (IO) genes, a cancer pathway, methylation peaks found in cancer or methylation peaks found in clinical samples. In other embodiments, the custom panel is refined based at least on literature annotations, common methylation peak positions, and/or public datasets. In other embodiments, PDL-1 status is determined based on gene expression data, PD-L1 promoter region nucleosomal position, or hist