Comprehensive polygenic risk assessment for breast cancer

Abstract

Provided herein is a method for evaluating the risk of a trait of a subject by selecting a plurality of ancestry-informative SNP markers based on a design criterion of interest, measuring the genotype of the subject, obtaining trait-associated SNP markers, and calculating the comprehensive polygenic risk score for the risk of the trait of the subject based on a plurality of ancestry-informative SNP markers and trait-associated SNP markers.The trait can be the risk of cancer.Also provided is a method for evaluating the ancestry of a subject.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 153,231, filed February 24, 2021, the entire contents of which are incorporated herein by reference.

[0002] Technical Field The present invention relates to the fields of genetics and medicine. More particularly, the present invention relates to methods for assessing and predicting breast cancer risk and treating breast cancer in polygenic traits and pharmaceutical applications. [Background technology]

[0003] It is desirable to use polygenomic risk scores to assess the likelihood of a subject's clinical trait or condition, such as the risk of a particular disease. Risk scores derived from genomic data depend on the identification of polymorphic loci to be used.

[0004] For example, traditional methods for trait prediction of breast cancer risk have identified various breast cancer-associated genes. However, germline pathogenic variants in breast cancer-associated genes introduce complications that reduce the accuracy and predictive power of traditional methods. Traditional methods may also rely on genomic data from a single race.

[0005] A significant drawback of conventional methods for characterizing the risk of traits from genomic data is that normative data for a trait in one particular population may not accurately predict the same trait in a different population of a different race. Conventional methods using genomic data derived from a selected population of one race may overestimate the risk of a particular trait in a different population. Overestimation of risk is a significant drawback, especially for disease traits.

[0006] Another drawback of conventional methods for determining traits such as cancer risk includes the problem that calculations using genomic data often rely on self-reported racial information, which can prevent proper determination of comprehensive population cancer risk.

[0007] A significant shortcoming of traditional methods for determining risk of traits is the lack of discrimination between low and high risk of traits in different populations. For example, traditional methods for breast cancer risk based on genomic data from one race may not be able to discriminate between low and high risk in different races. This shortcoming of traditional methods may confound prevention and treatment strategies of disease traits and jeopardize patient outcomes.

[0008] Conventional methods of polygenomic risk score may rely on SNPs discovered through genome-wide association studies (GWAS). However, such SNPs are usually not causative and may be in linkage disequilibrium (LD) with causative variants. What is needed is a set of SNPs for polygenomic risk estimation that discriminates risk for all racial groups and populations. It is also desirable to obtain a set of SNP markers for polygenomic risk estimation that does not bias calculations by population and provides accurate results for all racial groups and populations.

[0009] What is needed is a highly calibrated and accurate method for determining polygenic risk scores for traits such as breast cancer risk and preventing overestimation. There is a need for such a method that is useful for all racial populations and does not involve self-reported patient data. Informative clinical risk algorithms can improve medical care and patient treatment.

[0010] There is a pressing need for methods of assessing traits such as breast cancer risk that have good discrimination of risk levels in all populations regardless of race. There is a need for methods that can be efficiently delivered to clinical practice. Summary of the Invention

[0011] The present invention provides methods for determining polygenic traits, such as, for example, risk of breast cancer. The methods of the present invention can be used in medicine and in the treatment of diseases for which risk is identified and / or assessed.

[0012] In some aspects, the methods of the present invention may provide superior prediction of clinical risk in breast cancer patients. The methods of the present invention may provide polygenic risk prediction of breast cancer that is comprehensively applicable to all patients of all ethnic groups.

[0013] The comprehensive polygenomic risk scores of the present invention can be used to assess the likelihood of a clinical trait or condition, such as cancer.

[0014] Aspects of the invention can characterize an individual's risk of a trait from genomic data obtained for the trait in one particular race or population, where the individual may be of a different race or population. Embodiments of the invention can provide a comprehensive polygenomic risk score for an individual's trait using genomic data from a population selected from a different race than the individual, without overestimating the individual's risk of the trait.

[0015] In a further aspect, the present invention contemplates using genomic data of individuals who self-report ethnicity information to accurately determine traits such as cancer risk. The comprehensive polygenic risk scores of the present invention can be used to accurately determine cancer risk for the entire world population, regardless of any errors in the self-reported ethnicity information.

[0016] In an additional aspect, the invention provides methods for determining risk of a trait with good discrimination between low and high risk of the trait in different populations.

[0017] In some embodiments, the present invention includes methods for breast cancer risk based on a comprehensive polygenic risk score that can distinguish between low and high risk for any ethnic group or individual. The methods of the disclosure can provide prevention and treatment strategies for disease traits and improve patient outcomes.

[0018] In further embodiments, the present invention provides highly calibrated and accurate methods of determining comprehensive polygenic risk scores for traits such as breast cancer risk that prevent overestimation. The disclosed methods may be useful for all ethnic populations regardless of self-reported patient data and may improve medical care and patient treatment.

[0019] In additional embodiments, the present invention provides methods for assessing traits such as breast cancer risk with improved discrimination of risk levels for all populations regardless of race. The methods of the present disclosure can be efficiently provided to clinical practice.

[0020] The disclosed method further contemplates the use of various trait risk markers, which can be single nucleotide polymorphisms (SNPs). The disclosed SNPs can be associated with breast cancer risk in one or more different ethnic groups. A combination of SNPs can be used to provide a comprehensive polygenic risk score (gPRS), which can stratify disease-free patients with or without a family history of the disease for breast cancer risk.

[0021] The present invention provides a method of polygenomic risk scoring that relies on a specific set of SNPs discovered through specified criteria.This specific set of SNPs for polygenomic risk estimation can discriminate risk for all ethnic groups and populations.The set of SNP markers for polygenomic risk estimation disclosed herein is unbiased for virtually any population or ethnic group, and provides accurate results for all ethnic groups and populations.

[0022] Additional markers or types of factors may include age, family history, breast density, and hormone exposure.

[0023] In certain embodiments, the clinical utility of the present invention may include superior prediction of clinical risk for breast cancer patients of all ancestry.

[0024] The comprehensive polygenic scores obtained by the methods of the present invention can provide a surprising increase in accuracy in determining breast cancer risk.

[0025] The methods of the present invention, by including and evaluating a wide range of markers for different ancestries, can provide surprisingly accurate polygenic trait and risk determinations.

[0026] Embodiments of the present invention contemplate the determination of polygenic traits and levels of risk in the form of a score based on various genomic risk loci that can be individually identified and defined such that accurate determinations can be made by genotyping the subject.

[0027] In certain embodiments, the genomic risk loci can include genomic risk markers for breast cancer, which are combined with additional risk markers that can be specifically breast cancer-informative.

[0028]

[0013] Embodiments of the invention include a method of assessing the ancestry of a subject, the method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; and Calculating the racial ratio of the subject's genotype for each different racial population based on the plurality of ancestry-informative SNP markers, which may have different frequencies in three or more different racial populations, such as African, European, and East Asian racial populations.

[0029] The plurality of ancestry-informative SNP markers can be between 10 and 50,000 SNP markers, or between 10 and 56 SNP markers.

[0030] 1. A method for assessing risk of a trait in a subject, the method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; Obtaining trait-associated SNP markers; and The method includes calculating a comprehensive polygenic risk score for the risk of the trait in the subject based on a plurality of ancestry-informative SNP markers and trait-associated SNP markers.The step of calculating a comprehensive polygenic risk score for the risk of the trait in the subject can be done with additional clinical variables of the subject, such as the subject's age, personal medical history, family medical history, etc.The trait can be the risk of a disease of the subject, such as cancer.

[0031] The plurality of ancestry-informative SNP markers may be 10 to 50,000 SNP markers, or 10 to 56 SNP markers. The trait-associated SNP markers may be a plurality of cancer-associated SNP markers. The trait-associated SNP markers may be a plurality of 10 to 50,000 breast cancer-associated SNP markers, or 10 to 93 breast cancer-associated SNP markers.

[0032] In the above method, the step of calculating the comprehensive polygenic risk score for the risk of trait in the subject can be performed with the training of the clinical data of the reference group or with the validation of the clinical data of the reference group.The genotype of the subject can be measured by NGS or by using sequencing chip.

[0033] In the above methods, the multiple ancestry-informative SNP markers can determine the racial ratios of a subject's genotypes for each of three or more different ethnic populations, such as African, European, and East Asian ethnic populations.

[0034] In the above methods, a comprehensive polygenic risk score for the risk of the trait in a subject can be accurate for subjects of three or more different ethnic groups, such as African, European, and East Asian ethnic groups, even when the ethnic groups are self-reported.

[0035] In the above methods, the comprehensive polygenic risk score for the risk of the trait in a subject can be calibrated for subjects of three or more different racial groups so that the risk of the trait is not overestimated in any one racial group, such as, for example, African, European, and East Asian racial groups.

[0036] In the above methods, the comprehensive polygenic risk score for the risk of the trait in a subject can distinguish between low risk and high risk for subjects of three or more different ethnic groups, e.g., African, European, and East Asian ethnic groups.

[0037] In the above methods, the trait is risk of a disease, such as cancer, in the subject.

[0038] In the above methods, calculation of the comprehensive polygenic risk score may include using a clinical cohort of women of self-reported African ancestry, self-reported East Asian ancestry, and self-reported European ancestry.

[0039] In the above methods, calculating the global polygenic risk score may involve using a sum of ancestry-specific polygenic risk scores weighted according to the proportion of ancestry makeup.

[0040] In the above methods, the comprehensive polygenic risk score may be strongly associated with breast cancer in a reference cohort and in subcohorts defined by self-reported ancestry.

[0041] In the above methods, a comprehensive polygenic risk score can be combined with clinical and / or biological risk factors for accurate risk stratification for all women of all ancestry.

[0042] In the above methods, the calculation of the comprehensive polygenic risk score may involve a linear combination of the risk alleles according to formula III.

[0043]

number

[0044]

[0023] Embodiments of the present invention further contemplate a method of treating a disease in a subject in need thereof, the method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; Obtaining disease-associated SNP markers; and calculating a comprehensive polygenic risk score for the subject's risk of disease based on the plurality of ancestry-informative SNP markers and the disease-associated SNP markers, where the score indicates the need to treat the subject; and The method includes administering to the subject a treatment for the disease. The calculation of the comprehensive polygenic risk score can be performed with additional variables of age, personal medical history, and family medical history. The disease can be cancer. The treatment can be a cancer treatment selected from one or more of surgery, cryoablation, radiation therapy, bone marrow transplantation, chemotherapy, immunotherapy, hormone therapy, stem cell therapy, drug therapy, biological therapy, and administration of a pharmaceutical, preventive, or therapeutic compound. The disease can be breast cancer. The treatment can be breast cancer treatment.

[0045] The present invention includes a method of diagnosing or prognosing a subject having a disease, the method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; Obtaining disease-associated SNP markers; and The method includes calculating a comprehensive polygenic risk score for the subject's risk of the disease based on the plurality of ancestry-informative SNP markers and the disease-associated SNP markers, wherein the score indicates a diagnosis or prognosis for the subject. The disease can be cancer.

[0046] The present invention includes a method of generating data for assessing a trait of a subject, the method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; The method includes measuring trait-associated SNP markers in the subject's genotype. The method may further include determining additional clinical variables of the subject, such as the subject's age, personal medical history, family medical history, etc. The trait may be the subject's risk of a disease, such as cancer. The plurality of ancestry-informative SNP markers is between 10 and 50,000 SNP markers, or between 10 and 56 SNP markers. The trait-associated SNP markers may be a plurality of cancer-associated SNP markers. The trait-associated SNP markers may be a plurality of 10 to 50,000 breast cancer-associated SNP markers, or between 10 and 93 breast cancer-associated SNP markers.

[0047] The present invention further includes a system for assessing a subject's risk of disease, the system comprising: a processor for receiving a subject's genotype; one or more processors for performing a step of calculating a comprehensive polygenic risk score for the subject's risk of disease based on the plurality of ancestry-informative SNP markers, the plurality of disease-associated SNP markers of their genotypes, and additional variables of age, personal medical history, and family medical history; and A display for displaying and / or reporting the risk score is included. The disease can be cancer.

[0048] An additional embodiment includes a non-transitory machine-readable storage medium having recorded thereon instructions for execution by a processor, causing the processor to perform steps of a method for assessing a subject's risk of disease, the method comprising: receiving a subject's genotype; calculating a comprehensive polygenic risk score for the subject's risk of disease based on the plurality of ancestry-informative SNP markers, the plurality of disease-associated SNP markers of their genotypes, and additional variables of age, personal medical history, and family medical history; and and transmitting the risk score to a processor output for display and / or reporting.The disease can be cancer. [Brief description of the drawings]

[0049] [Figure 1] Figure 1 shows an ancestry map with contributions from different continents. [Diagram 2] FIG. 2 shows ancestry-based genotype distribution diagrams for Hispanic, White / Non-Hispanic, Black / African, and Asian genotypes. [Diagram 3] FIG. 3 shows an illustration of an embodiment of the invention showing that the distribution of comprehensive polygenic risk scores for breast cancer risk is centered around 0 for patients of all ancestral genotypes, removing ancestral bias from the risk estimates. [Figure 4] FIG. 4 shows a diagram of an embodiment of the invention in which the distribution of comprehensive polygenic risk scores for breast cancer risk is centered around 0 for Hispanic patients who do not carry the 6q25 SNP (rs140068132). [Diagram 5] FIG. 5 shows a comparison of ancestry-specific differences in the distribution of polygenic risk scores based on different breast cancer allele frequencies. [Figure 6] FIG. 6 shows a plot of historical breast cancer incidence by ancestry. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0050] Detailed Description of the Invention The present invention includes methods for determining a comprehensive polygenic risk score that is predictive of a trait of interest.

[0051] Comprehensive polygenic risk scores can predict breast cancer risk assessment.

[0052] In some aspects, the invention provides methods of comprehensive polygenic risk prediction with surprising improvements in accuracy of risk assessment for traits of interest.

[0053] Embodiments of the present invention further provide reliable breast cancer risk associations that are applicable to all populations of all ancestry.

[0054] The present disclosure provides various methods of clinical risk management, risk magnitude assessment, and comprehensive polygenic risk scores, as well as non-clinical trait prediction. The methods of the present invention can provide surprisingly accurate predictive capabilities for populations of all ancestry.

[0055] Aspects of the disclosure include predicting risk of a trait, such as the degree of expression of a clinical symptom or biological trait, by genotyping a subject with various markers that are associated with a disease and combining the genotypes in a comprehensive polygenic risk score.

[0056] In a further embodiment, multiple trait risk markers can be used to provide a comprehensive polygenic risk prediction for the trait.

[0057] The multiple trait risk markers may include a variety of disease-associated genetic markers.

[0058] In some embodiments, the plurality of trait risk markers may include between 1 and 1,000,000 SNP markers.

[0059] In certain embodiments, the plurality of trait risk markers can include 1-10,000 SNP markers, or 1-1000 SNP markers, or 1-100 SNP markers. The plurality of trait risk markers can be 1-1000 breast cancer SNP markers, or 1-500 breast cancer SNP markers, or 1-100 breast cancer SNP markers.

[0060] In certain embodiments, the plurality of trait risk markers may include between 56 SNP markers and 149 SNP markers.

[0061] The present invention provides methods for determining polygenic traits, such as risk of disease, including breast cancer. The methods of the invention can be used to treat diseases where risk is determined by polygenomic scoring.

[0062] In some aspects, the methods of the invention may provide superior prediction of clinical risk in breast cancer patients. The methods of the invention may provide comprehensive polygenic risk prediction of diseases such as breast cancer that is universally applicable to all patients of all ethnic groups.

[0063] The comprehensive polygenomic risk scores of the present invention can be used to assess the likelihood of a clinical trait or condition, such as cancer, in a subject.

[0064] In certain embodiments, the present invention can calculate an individual's risk of a trait from genomic data obtained for the trait in one particular racial group or population, where the individual may be from a different racial group or population. Embodiments of the present invention can thus provide a comprehensive polygenomic risk score for an individual's trait using genomic data from a population drawn from a different race than the individual belongs to or self-identifies as, without overestimating the individual's risk of the trait.

[0065] In further embodiments, the present invention contemplates using genomic data of individuals who self-report racial information to accurately determine traits such as cancer risk. The comprehensive polygenic risk scores of the present invention can be used to accurately determine cancer risk for subjects of any race, despite any errors in the self-reported racial information.

[0066] In additional embodiments, the present invention provides methods for determining an individual's risk for a trait with sufficient discrimination between low and high risk for the trait, regardless of the racial group or population to which the subject belongs or self-identifies.

[0067] In some embodiments, the present invention comprises methods for breast cancer risk based on comprehensive polygenic risk scores that are surprisingly capable of distinguishing between low and high risk for individuals of any race.

[0068] The methods of the disclosure can provide prevention and treatment strategies for disease traits and improve patient outcomes.

[0069] In further embodiments, the present invention may provide a highly calibrated and accurate comprehensive polygenic risk score that may be used in methods of determining traits, such as a subject's risk of breast cancer, that prevent overestimation. The methods of the present disclosure may be useful for all racial groups and / or populations, regardless of the use of self-reported patient data, and may improve medical care and patient treatment.

[0070] In additional embodiments, the present invention provides methods for assessing traits such as breast cancer risk with improved discrimination of risk levels for all populations regardless of race. The methods of the present disclosure can be efficiently provided to clinical practice.

[0071] The disclosed method further contemplates the use of various trait risk markers, which can be single nucleotide polymorphisms (SNPs). The disclosed SNPs can be associated with breast cancer risk in one or more different ethnic groups. A combination of SNPs can be used to provide a comprehensive polygenic risk score (gPRS), which can stratify disease-free patients with or without a family history of the disease for breast cancer risk.

[0072] Additional markers or types of factors may include age, family history, breast density, and hormone exposure.

[0073] In certain embodiments, the clinical utility of the present invention may include superior prediction of clinical risk for breast cancer patients of all ancestry.

[0074] The comprehensive polygenic scores obtained by the methods of the present invention can provide a surprising increase in accuracy in determining breast cancer risk.

[0075] The methods of the present invention can provide surprisingly accurate polygenic trait and risk determinations by including and assessing the contributions of a wide range of markers for different ancestries.

[0076] Embodiments of the present invention contemplate the determination of polygenic traits and levels of risk in the form of a score based on various genomic risk loci that can be individually identified and defined such that accurate determinations can be made by genotyping the subject.

[0077] In certain embodiments, the genomic risk loci can include genomic risk markers for breast cancer, which are combined with additional risk markers that can be specifically informative of breast cancer.

[0078] In additional embodiments, the plurality of trait risk markers may include between 1 and 100 family history elements, or between 1 and 20 family history elements, or between 1 and 10 family history elements.

[0079] Embodiments of the invention may include a plurality of trait risk markers, such as, for example, 1-100 clinical elements, or 1-20 clinical elements, or 1-10 clinical elements.

[0080] Embodiments herein may provide improved comprehensive polygenic risk prediction for breast cancer.

[0081] Comprehensive risk assessment, combining polygenic SNP scoring methods with other risk factors and components, can improve the accuracy of risk estimates and may facilitate decision-making for women with pathogenic variants in moderate penetrance genes.

[0082] In a further aspect, the polygenic risk scores of the present invention can be surprisingly more accurate for breast cancer than using conventional methods.

[0083] In certain embodiments, the association between the comprehensive polygenic risk score and breast cancer can be assessed by fixed stratification methods, which can adjust for age and family history, among other variables and factors.

[0084] Embodiments of the present invention may provide women with improved accuracy in estimating their lifetime risk of breast cancer. Such risk estimates are useful for informing threshold-based decisions for more aggressive screening, such as consideration of breast magnetic resonance imaging (MRI).

[0085] In some aspects, disclosed herein are methods that can utilize breast cancer SNP markers to provide a comprehensive polygenic risk score for breast cancer.

[0086] Some examples of breast cancer risk markers are given in Prediction of breast cancer risk based on profiling with common genetic variants, Mavaddat et al., J Natl Cancer Inst., 2015, April 8, Vol. 107(5), djv03.

[0087] Some examples of breast cancer risk markers are presented in Michailidou et al., Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer, Nat Genet., 2015, Vol. 47, pp. 373.

[0088] Some examples of breast cancer risk markers are presented in Characterizing Genetic Susceptibility to Breast Cancer in Women of African Ancestry, Feng et al., Cancer Epidemiol Biomarkers Prev., 2017, July, Vol. 26(7), pp. 1016-1026.

[0089] Some examples of breast cancer risk markers are given in Rainville, I. et al., Breast Cancer Research and Treatment, 2020, Vol. 180, pp. 503-509.

[0090] Some examples of breast cancer risk markers are given in Early Diagnosis of Breast Cancer, Wang et al., Sensors (Basel), 2017, July, Vol. 17(7), p. 1572.

[0091] Some examples of genetic modifiers of breast cancer risk are presented in Muranen TA, et al., Genetics in Medicine, 2017, Vol. 19(5), pp. 599-603.

[0092] Some examples of breast cancer risk scores are given in Kuchenbaecker K, et al., J Natl Cancer Inst., 2017, Vol. 109(7), djw302.

[0093] Some examples of cancer risk are given in Perencevich M, et al., Gastroenterology & Hepatology, 2011, Vol. 7(6), pp. 420-423.

[0094] Some examples of genetic analysis are given in Lek et al., Nature, 2016, Vol. 536.7616, pp. 285.

[0095] Ancestor information SNP Generally, polygenic determination of a trait in a subject can be performed using a set of polygenic SNP markers. In some embodiments, the trait can be ancestry.

[0096] Aspects of the present invention provide advantages in characterizing a subject's genotype by ancestry.

[0097] In certain embodiments, the method of the present disclosure may use SNPs associated with one or more different racial groups.A combination of SNPs may be used to evaluate the ancestry of a subject.The genotype of a subject may be determined based on the ancestry ratio of one or more different racial groups.

[0098] The present invention provides a method for evaluating the ancestry of a subject by selecting a plurality of ancestry-informative SNP markers. The ancestry-informative SNP markers can be based on one or more criteria, such as, for example, the ability to cover substantially the entire human genome, having a genomic frequency of at least 1%, and having different frequencies in different racial groups. By obtaining the genotype of the subject, the racial ratio of the genotype of the subject can be calculated for each different racial group based on the plurality of ancestry-informative SNP markers.

[0099] In some embodiments, the ancestry-informative SNP markers may have different frequencies in three or more different racial populations, such as African, European, and East Asian racial populations. A plurality of 10-50,000 ancestry-informative SNP markers may be used.

[0100] In some embodiments, the plurality of ancestry-informative SNP markers may include between 1 and 1,000,000 SNP markers.

[0101] In certain embodiments of the invention, a plurality of 10-56 ancestry-informative SNP markers may be used.

[0102] The method of the present disclosure can combine the use of ancestry-informative SNP markers with additional SNP markers that can be associated with biological traits.The combination of SNPs can be used to provide a comprehensive polygenic risk score (gPRS), which can stratify subjects for the risk of the trait regardless of race.The comprehensive polygenic risk score can incorporate genomic information that is essentially based on ancestry ratio.

[0103] The present invention provides a method for comprehensive polygenomic risk scoring that relies on a unique set of SNPs discovered through design criteria.This unique set of SNPs for comprehensive polygenomic risk estimation can discriminate risk for all ethnic groups and populations.The unique set of SNP markers for polygenomic risk estimation disclosed herein is unbiased for virtually any population or ethnic group, and provides accurate results for all ethnic groups and populations.

[0104] In certain aspects, a unique set of ancestry-informative SNP markers was discovered using estimates of individual SNP risk betas for different ancestry groups. In some embodiments, for each ancestry, the individual SNP risk betas were determined from known values, from data obtained in myRisk patients, and through meta-analysis of prior combined data. The estimates of individual SNP risk betas in different ancestry groups can be used to determine a unique set of SNP markers that can provide a comprehensive polygenic risk score for the risk of a trait, such as cancer risk, that can stratify disease-free patients for risk regardless of ancestry.

[0105] For example, in some embodiments, African SNP risk betas may be determined from 1,000 or more, or 5,000 or more, or 10,000 or more myRisk measurements for patients of self-reported African ancestry. Approximately 70 Asian SNP risk betas may be determined from Shu et al., Nat Commun., 2020, Vol. 11, pp. 1217-1226. Hispanic SNP risk betas may be determined from 1,000 or more, or 5,000 or more, or 10,000 or more Hispanic myRisk measurements for patients of self-reported Hispanic ancestry.

[0106] Embodiments of the invention can provide comprehensive polygenic risk scores for traits that can be clinically valid for all women in all racial groups and populations.

[0107] The comprehensive polygenic risk scores of the present disclosure can provide meaningful risk discrimination of traits for all women in all racial groups and populations.

[0108] The comprehensive polygenic risk scores of the present disclosure may provide a statistical distribution of scores for a trait in a population, where the scores may be unbiased for any ancestry-specific subpopulations and centered around 0.

[0109] Figure 1 shows an ancestry map with contributions from different continents.

[0110] FIG. 2 shows ancestry-based genotype distribution diagrams for Hispanic, White / Non-Hispanic, Black / African, and Asian genotypes.

[0111] A unique set of ancestry-informative SNPs for comprehensive polygenomic risk estimation can be obtained by characterizing a subject's ancestry in terms of contributions from different continents.

[0112] In a further embodiment, a unique set of ancestry-informative SNP markers may be obtained with design criteria to distinguish between three continental ancestry: African, East Asian and European.

[0113] In a further embodiment, a unique set of 56 ancestry-informative SNP markers may be obtained with design criteria for distinguishing between ancestry from three or more continents.

[0114] Ancestry-informative SNPs of the present disclosure include those in Table 1.

[0115] [Table 1] TIFF2024507542000004.tif139146

[0116] Comprehensive polygenomic risk estimation for cancer Embodiments of the present invention further contemplate combining a unique set of ancestry-informative SNP markers with additional SNP markers associated with traits such as cancer risk.

[0117] In some embodiments, the methods of the present invention can combine the use of ancestry-informative SNP markers with additional SNP markers that may be associated with cancer risk in one or more different ethnic groups. Such combinations of SNPs can be used to provide a comprehensive polygenic risk score (gPRS) for cancer risk, which can stratify disease-free patients for cancer risk, regardless of ancestry and family history of the disease.

[0118] In further embodiments, the methods of the present invention can combine the use of ancestry-informative SNP markers with additional SNP markers that may be associated with breast cancer risk in women of one or more different ethnic groups. Such combinations of SNPs can be used to provide a comprehensive polygenic risk score (gPRS) for breast cancer risk, which can stratify disease-free women for breast cancer risk, regardless of ancestry and family history of the disease.

[0119] In some embodiments, comprehensive polygenomic risk estimation may characterize a subject's risk of cancer regardless of the subject's genetic ancestry by using a combination of ancestry-informative SNP markers and cancer-associated SNPs. The cancer-associated SNPs may be from one or more different ethnic groups or populations.

[0120] In some embodiments, comprehensive polygenomic risk estimation can characterize a woman's risk of breast cancer regardless of genetic ancestry by using a combination of ancestry-informative SNP markers and breast cancer-associated SNPs. The breast cancer-associated SNPs can be from one or more different ethnic groups or populations.

[0121] In some embodiments, a comprehensive polygenomic risk score may characterize a woman's risk of breast cancer regardless of genetic ancestry by using a combination of 10-56 ancestry-informative SNP markers and 10-93 breast cancer-associated SNPs, which may include up to 92 European breast cancer-associated SNPs and one Hispanic breast cancer SNP 6q25 (rs140068132).

[0122] In certain embodiments, a comprehensive polygenomic risk score may characterize a woman's risk of breast cancer regardless of genetic ancestry by using a combination of 56 ancestry-informative SNP markers and 93 breast cancer-associated SNPs, consisting of 92 European breast cancer-associated SNPs and one Hispanic breast cancer SNP 6q25 (rs140068132).

[0123] The comprehensive polygenomic risk scores of the present invention can achieve high levels of accuracy for all women in all ethnic groups and populations, with surprisingly high risk discrimination and excellent accuracy of calibration.

[0124] Breast cancer-associated SNPs of the present disclosure include those in Table 2.

[0125] [Table 2] TIFF2024507542000006.tif246142TIFF2024507542000007.tif246142TIFF2024507542000008.tif97143

[0126] Polygenic Risk Scores In another example, polygenic estimation of breast cancer risk can be performed using an 86-SNP polygenic risk score. The 86-SNP polygenic risk score can provide an association with the risk of developing breast cancer in women with pathogenic variants in low-to-moderate penetrant genes, such as ATM, CHEK2, and PALB2. The absolute risk of breast cancer by age 80 can be calculated, illustrating the potential clinical utility of polygenic stratification in women with pathogenic variants in BRCA1 / 2, ATM, CHEK2, and PALB2.

[0127] A polygenic risk score can be defined as a linear combination of centered risk alleles according to Formula III.

[0128]

number

[0129] In some embodiments, SNP coefficients may be estimated for polygenic risk scores.

[0130] In some embodiments, SNP coefficients can be estimated and standard errors for multiple relevant SNPs can be obtained based on the incidence cohort. These coefficients are expressed as {b_devk | k=1, 2, ..., N SNP} and the standard error is {σ_ devk | k=1, 2,…, N SNP}, where N SNP is the number of SNPs used. These values ​​can be estimated from a single multivariate logistic regression model with breast cancer status as the dependent variable, as well as the following independent variables: SNP N represents the number of alleles for a SNP SNP Numeric variables {xk | k=1, 2,…, N SNP}, age, ancestry, personal and family history of cancer. The variables age, ancestry, personal and family history of cancer can be coded as described above. SNP coefficients are the literature-based coefficients {b_litk | k=1, 2,…, N SNP}, and standard error {σ_litk | k=1, 2,…, N SNP Linkage disequilibrium between SNPs can be accounted for by co-estimating effects in a multivariate regression model, with one model for each gene.

[0131] Finally, polygenic risk score coefficients were derived from a meta-analysis of incidence cohort and literature-based coefficients, {bk | k=1, 2,…, N SNP Polygenic risk score coefficients may be calculated as a weighted average of the coefficients from the incidence cohort and the literature, with weights inversely proportional to the squared standard errors. The ratio of the squared standard errors can be replaced by the median.

[0132] More specifically, for multiple SNPs and with non-missing σ_litk values, the median ratio can be calculated according to Formula IV.

[0133]

number

[0134]

number

[0135] In a further embodiment, the informativeness of each SNP can be calculated.

[0136] The informativeness of a SNP can be a function of its effect size and its general population allele frequency. SNP For each k through k, the informativeness of the kth SNP can be calculated according to Equation VI.

[0137]

number

[0138] In additional embodiments, the SNPs may be ordered by informativeness: by designation, b1 may be designated the most informative SNP, b2 the second most informative SNP, and so on.

[0139] A chi-square likelihood ratio test (LRT) statistic can be calculated to assess the contribution of each SNP to the polygenic risk score (PRS). For SNPs from a linked set, only the single most informative representative SNP from each gene may be included, with one less N for assessment. SNP may be omitted. 1 to N SNP For each k through k, an analysis can be performed on the incident cohort according to the following steps: First, calculate the k-SNP PRS for all patients according to Equation VII.

[0140]

number

[0141] Second, we used breast cancer status as the dependent variable and PRS k Multivariate logistic regression models were constructed using the independent variables of age, ancestry, and personal and family history of cancer. Third, the LRT statistic was recorded to compare the full model and the excluded PRS. k Compare with a nested model with

[0142] In further embodiments, SNPs for the PRS may be selected according to the highest likelihood ratio test (LRT) value. If representative SNPs are selected for inclusion, all linked SNPs from a gene may be included.

[0143] The identities of the SNPs incorporated into the 86-SNP score embodiment are shown in Table 3. Chromosomal locations are indicated according to hg19.

[0144] [Table 3] TIFF2024507542000015.tif250147TIFF2024507542000016.tif251148TIFF2024507542000017.tif38160

[0145] Cancer Methods and Treatments Cancer treatments may include surgery, cryoablation, radiation therapy, bone marrow transplantation, chemotherapy, immunotherapy, hormone therapy, stem cell therapy, drug therapy, biological therapy, and administration of pharmaceutical, prophylactic or therapeutic compounds, e.g., biologically or exogenously active agents.

[0146] Examples of treatments include bariatric surgical intervention, physical therapy, diet, and dietary supplementation.

[0147] Examples of cancer biologic therapies include adoptive cell transfer, angiogenesis inhibitors, Bacillus Calmette-Guerin therapy, biochemotherapy, cancer vaccines, chimeric antigen receptor (CAR) T-cell therapy, cytokine therapy, gene therapy, immune checkpoint regulators, immunoconjugates, monoclonal antibodies, oncolytic virus therapy, and targeted drug therapy.

[0148] Examples of cancer surgery include lumpectomy, partial mastectomy, total mastectomy, simple mastectomy, modified radical mastectomy, radical mastectomy, and Halsted radical mastectomy.

[0149] Examples of cancer drugs include drugs approved to prevent breast cancer, including Evista (raloxifene hydrochloride), raloxifene hydrochloride, and tamoxifen citrate.

[0150] Examples of cancer drugs include drugs approved to treat breast cancer, such as abemaciclib, Abraxane (paclitaxel albumin-stabilized nanoparticle formulation), Ado-trastuzumab emtansine, Afinitor (everolimus), Afinitor Disperz (everolimus), alpelisib, anastrozole, Aredia (disodium pamidronate), Arimidex (anastrozole), Aromasin (exemestane), atezolizumab, capecitabine, cyclophosphamide, docetaxel, doxorubicin hydrochloride, Ellence (epirubicin hydrochloride), Enhertu (Fam-Trastuzumab Deruxtecan-nxki), epirubicin hydrochloride, eribulin mesylate, everolimus, exemestane, 5-FU (fluorouracil injection), Fam-Trastuzumab Deruxtecan-nxki, Fareston (toremifene), Faslodex (fulvestrant), Femara (letrozole), fluorouracil injection, fulvestrant, gemcitabine hydrochloride, Gemzar (gemcitabine hydrochloride), goserelin acetate, Halaven (eribulin mesylate), HerceptinHylecta (trastuzumab and hyaluronidase-oysk), Herceptin (trastuzumab), Ibrance (palbociclib), ixabepilone, Ixempra (ixabepilone), Kadcyla (Ado-trastuzumab emtansine), Kisqali (ribociclib), lapatinib ditosylate, letrozole, Lynparza (olaparib), megestrol acetate, methotrexate, neratinib maleate, Nerlynx (neratinib maleate), olaparib, paclitaxel, paclitaxel-albumin-stabilized nanoparticle formulation, palbociclib, pamidronate diaminobenzyl These include Thorium, Perjeta (pertuzumab), pertuzumab, Piqray (alpelisib), ribociclib, talazoparib tosylate, Talzenna (talazoparib tosylate), tamoxifen citrate, Taxotere (docetaxel), Tecentriq (atezolizumab), thiotepa, toremifene, trastuzumab, trastuzumab and hyaluronidase-oysk, Trexall (methotrexate), Tykerb (lapatinib ditosylate), Verzenio (abemaciclib), vinblastine sulfate, Xeloda (capecitabine), and Zoladex (goserelin acetate).

[0151] The term "disease" as used herein includes any disorder, condition, malady, such as a chronic illness that manifests itself in an organ, part, structure, or system of the body that is disordered or functioning incorrectly.

[0152] The term "sample" as used herein includes any biological sample isolated from a subject. Samples may include, but are not limited to, single or multiple cells, cell fragments, aliquots of bodily fluids, whole blood, platelets, serum, plasma, red blood cells, white blood cells (or leucocytes), endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, peritoneal fluid, and interstitial or extracellular fluid. The term "sample" also encompasses the fluid in the space between cells, including synovial fluid, gingival crevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva, mucus, sputum, semen, sweat, urine, or any other bodily fluid. Blood samples may include whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells (or leucocytes), platelets, serum, and plasma.

[0153] As used herein, the term "subject" includes humans, which generally include women and men, as well as other non-binary individuals.

[0154] In some embodiments, the present invention can provide methods of recommending a therapeutic regimen, including withdrawal from a therapeutic regimen.

[0155] In further embodiments, the odds ratio can provide the clinician with a prognostic picture of the subject's biological condition. Such embodiments can provide subject-specific prognostic information that can inform treatment decisions and also facilitate monitoring of treatment response. Such embodiments can surprisingly result in improved treatment, e.g., better management of disease, or an increase in the proportion of subjects achieving symptomatic remission.

[0156] As used herein, the terms "biologic," "biotherapy," and / or "biopharmaceutical" may include pharmaceutical therapeutic products manufactured or extracted from biological material. Biologicals may include vaccines, blood or blood components, allergenics, somatic cells, gene therapy, tissues, recombinant proteins, and live cells; may be composed of sugars, proteins, nucleic acids, live cells or tissues, or combinations thereof.

[0157] As used herein, the terms "therapeutic regimen," "treatment," and / or "treatment" can include any clinical management and intervention of a subject, whether biological, chemical, physical, or a combination thereof, that is intended to sustain, ameliorate, improve, or otherwise alter the subject's condition.

[0158] The term "administering" as used herein may include placing the composition into a subject by a method or route that results in at least partial localization of the composition at a desired site to produce a desired effect. The administration route includes both local administration and systemic administration. Usually, local administration delivers more of the composition to a specific location compared to the entire body of the subject, while systemic administration delivers to essentially the entire body of the subject. "Administering" also includes performing physical actions on the subject's body, including physical therapy, as well as chiropractic care, massage, and acupuncture.

[0159] Apparatus and system The term machine-readable storage medium as used herein can include, for example, data storage material encoded with machine-readable data or data arrays. Data and machine-readable storage medium can be used for various purposes when a machine programmed with instructions for using said data is used. Such purposes include storing, accessing, and manipulating information regarding the risk of a subject or population over time, or the risk of response to treatment, or for drug discovery of inflammatory diseases. Data including genomic measurements can be executed by a computer program running on a programmable computer, which can include a processor, a data storage system, one or more input devices, and one or more output devices. Program code can be input to input data to perform the functions described herein and generate output information. The output information can then be input to one or more output devices. The computer can be, for example, a personal computer, a microcomputer, or a workstation.

[0160] The term computer program as used herein may be instruction code executed in a high-level procedural language or object-oriented programming language to communicate with a computer system. The program may be executed in a machine language or assembly language. The programming language may also be a compiled or interpreted language. Each computer program may be stored in a storage medium or device, such as a ROM or a magnetic diskette, and may be readable by a programmable computer, so as to configure and operate the computer when the storage medium or storage device is read by the computer to execute the described procedures. The health-related or genomic data management system may be considered to be executed as a computer-readable storage medium configured with a computer program, where the storage medium causes the computer to operate in a particular manner to perform various functions.

[0161] conclusion All publications, patents, and literature specifically mentioned herein are incorporated by reference in their entirety for all purposes.

[0162] Reference SNP ID numbers, or "rs" IDs, are identification tags assigned by NCBI to groups or clusters of co-located SNPs. rs ID numbers or rs tags are assigned after submission. The submitted SNP is verified to see if it is co-located with a previously submitted SNP, and if so, the submitted SNP is linked to a reference set of existing reference SNP records. These SNP rs IDs are mapped to external resources or databases, including NCBI databases. The SNP rs ID numbers are annotated in these external resource and database records to point users back to the original dbSNP record. Reference SNP records are stored in NCBI|rs<NCBI SNP ID> It has the form:

[0163] Words specifically defined herein are provided in the context of this disclosure as a whole and have the meaning typically understood by one of ordinary skill in the art. As used herein, the singular forms "a," "an," and "the" include the plural.

[0164] While the present disclosure will be described in conjunction with various embodiments, it is not intended that the present disclosure be limited to such embodiments. On the contrary, the present disclosure encompasses various alternatives, modifications, and equivalents, as will be recognized by those skilled in the art.

[0165] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention pertains. Similar or equivalent methods and materials to those described herein can be used in the practice or testing of the present invention, but suitable methods and materials are described below. In addition, the materials, methods, and examples in the present specification are illustrative only and are not intended to be limiting.

[0166] Although the foregoing disclosure has been described in some detail by way of illustration and example for clarity of understanding, it will be understood by those skilled in the art that various changes and modifications may be made within the scope of the invention and the appended claims. EXAMPLES

[0167] [Example 1] Comprehensive polygenic breast cancer risk assessment Breast cancer risk assessment was determined by the use of single nucleotide polymorphisms (SNPs) with small effects combined into a comprehensive polygenic risk score (gPRS), developed and validated primarily for populations of European descent. To make the gPRS meaningful for all women in all racial groups and populations, a novel comprehensive PRS (gPRS) that utilizes the genetic makeup of individual ancestry was determined and validated.

[0168] An ancestry-specific comprehensive polygenic risk scores corresponding to ancestry across the three continents were determined and validated using 149 SNPs, consisting of 93 breast cancer-associated SNPs and 56 ancestry-informative SNPs. The African polygenic risk score was determined using a cohort of 31,126 self-reported African American patients referred for hereditary cancer testing. The East Asian polygenic risk score was developed based on published data from the Asia Breast Cancer Consortium. The European polygenic risk score was determined using data from the Breast Cancer Association Consortium and 24,259 European hereditary cancer testing patients. For each patient, the ancestry-informative SNPs were used to calculate the proportion of ancestry attributable to each of the three continents. The gPRS was the sum of the ancestry-specific polygenic risk scores weighted according to genetic ancestry composition. In an independent validation cohort (N=62,707), the discrimination and calibration of the gPRS were evaluated and compared with the performance of a previously described 86-SNP PRS for women of European descent. Associations of SNPs and polygenic risk scores with breast cancer were analyzed using logistic regression adjusted for personal history of cancer, family history of cancer, age, and ancestry. Odds ratios (ORs) were reported per standard deviation within the corresponding patient population. P values ​​were recorded as two-sided.

[0169] FIG. 3 shows a diagram of an embodiment of the invention illustrating that the distribution of comprehensive polygenic risk scores for breast cancer risk is centered around 0 for patients of all ancestral genotypes, removing ancestral bias from the risk estimates.

[0170] As shown in Table 4, the gPRS was strongly associated with breast cancer in the full validation cohort and in subcohorts defined by self-reported ancestry.

[0171] [Table 4]

[0172] With respect to Table 4, 95% (88 / 93) of the breast cancer SNPs had a risk allele frequency of ≥ 1% in each of the self-reported populations. Compared to the previously described 86-SNP PRS, the gPRS of the present invention showed improved discrimination overall and within each subcohort, except for the Asian population, where sample sizes were too small to demonstrate superiority of either score. The gPRS was adequately calibrated for all women.

[0173] In conclusion, the 149-SNP gPRS has been validated and calibrated for women of all ancestry. In combination with clinical and biological risk factors, the 149-SNP gPRS may provide significantly improved risk stratification for all women, regardless of ancestry, population, or race.

[0174] [Example 2] Comprehensive polygenic breast cancer risk determination A comprehensive polygenomic risk estimate for breast cancer across all genetic ancestries was defined using a combination of 93 breast cancer-associated SNPs consisting of 92 European breast cancer-associated SNPs and one Hispanic breast cancer-associated SNP, 6q25 (rs140068132).

[0175] European SNP breast cancer risk beta was based on a meta-analysis of known SNP signatures and SNP signatures derived from myRisk patient data. African SNP breast cancer risk beta was based on data from 31,126 myRisk patients of self-reported African ancestry. Asian SNP breast cancer risk beta for approximately 70 SNPs was based on Shu et al., Nat Commun., 2020, Vol. 11, pp. 1217-1226. European SNP breast cancer risk beta was determined directly. Hispanic SNP breast cancer risk beta was determined from data from approximately 9,000 Hispanic myRisk patients.

[0176] Comprehensive polygenomic risk estimates for breast cancer were determined by primary analyses that included evaluation of the discrimination of comprehensive polygenic risk scores in a large cohort of myRisk patients of all ancestries.

[0177] Comprehensive polygenomic risk estimates for breast cancer were further determined by secondary analyses that involved calculating the improvement in breast cancer risk discrimination for all populations using the 93 breast cancer-associated SNPs compared with use of the 86-SNP PRS in the full cohort. The secondary analyses were repeated in subcohorts defined by self-reported ancestry.

[0178] The comprehensive polygenomic risk estimates for breast cancer were further determined by additional analyses, confirming that the comprehensive polygenomic risk score was centered on 0 in disease-free patients overall and in each subpopulation except for Hispanic 6q25 SNP (rs140068132) carriers. Centering on 0 indicated that the comprehensive polygenomic risk score was not biased toward any particular racial group or population. Centering on 0 indicated that the comprehensive polygenomic risk score surprisingly provided identical risk estimates for all racial groups and populations.

[0179] As shown in Table 5, a comprehensive polygenomic risk estimate for breast cancer was therefore demonstrated to provide a breast cancer risk discrimination for all patients and for all subcohorts defined by self-reported ancestry.

[0180] [Table 5]

[0181] FIG. 4 shows a diagram of an embodiment of the invention in which the distribution of comprehensive polygenic risk scores for breast cancer risk is centered around 0 for Hispanic patients who do not carry the 6q25 SNP (rs140068132).

[0182] As shown in Figure 4, the comprehensive polygenic risk estimates for breast cancer were centered around 0 for all ancestrally disease-free patients, except for Hispanic carriers of the 6q25 SNP (rs140068132), who were treated independently to preserve the protective effect. The mean comprehensive polygenic risk estimates for breast cancer in Figure 4 are shown in Table 6.

[0183] [Table 6]

[0184] FIG. 5 shows a comparison of ancestry-specific differences in the distribution of polygenic risk scores based on different breast cancer allele frequencies.

[0185] As shown in Figure 5, for Hispanics, the comprehensive polygenic risk estimates for breast cancer were centered around 0 for Hispanic patients who did not carry the 6q25 SNP (rs140068132) and shifted to a lower risk for 6q25 (rs140068132) carriers.

[0186] FIG. 6 shows a plot of historical breast cancer incidence by ancestry.

[0187] [Example 3] Use of the 86-SNP polygenomic risk estimate in comparison The 86-SNP polygenic risk score was assessed separately for carriers and noncarriers of pathogenic variants in BRCA1, BRCA2, CHEK2, ATM, and PALB2. Use of the 86-SNP polygenomic risk estimate without other markers and factors was the comparative method.

[0188] The IRB-approved study included 152,012 women of European ancestry who were clinically tested for genetic cancer risk using a multigene panel. The 86-SNP polygenic risk score was assessed separately for carriers of pathogenic variants in BRCA1 (N=2,249), BRCA2 (N=2,638), CHEK2 (N=2,564), ATM (N=1,445), and PALB2 (N=906), as well as non-carriers (N=141,160). Multivariate logistic regression was used to examine the association of the 86-SNP score with invasive breast cancer after accounting for age and family history of cancer. Effect sizes, expressed as standardized odds ratios (ORs) with 95% confidence intervals (CIs), were assessed for carriers and non-carriers of each gene. The 86-SNP score was strongly correlated with breast cancer risk in BRCA1, BRCA2, CHEK2, ATM, and PALB2 carrier populations (p=10 -4 ), but different effect sizes for different genes made further interpretation difficult.

[0189] The polygenic risk score is defined as a linear combination of the centered risk alleles:

[0190]

number

[0191] Associations with invasive breast cancer were assessed with p-values ​​and ORs with 95% confidence intervals (CIs) from multivariate logistic regression models constructed using R version 3.4.4 or higher (R Foundation for Statistical Computing, Vienna, Austria). ORs were recorded per unit standard deviation of the polygenic risk score (PRS) in disease-free controls. p-values ​​were calculated from the likelihood ratio chi-squared test statistic and recorded as two-sided. The use of multivariate logistic regression addresses potential bias in genetic testing cohorts in which patients were selected because of eligibility factors, BC diagnosis or family history. Adjustment for factors associated with ascertainment in the clinical testing population may allow derivation of unbiased risk estimates.

[0192] All models included independent variables for age at first invasive breast cancer (BC) diagnosis or age at genetic testing if disease-free, personal history of non-BC, family history of any cancer, and ancestry, European and / or Ashkenazi Jewish. Cases were women diagnosed with invasive breast cancer with or without ductal carcinoma in situ (DCIS). Controls did not have BC cancer at the time of testing. Women diagnosed with DCIS were excluded as controls. To test for the relationship between PRS and age, multivariate models included interaction terms for PRS and age. Interaction tests were also performed for PRS and carrier status to test for differences in PRS performance by gene. In the models, categorical variables represented carrier status, noncarrier, BRCA1 pathogenic variant, BRCA2 pathogenic variant, etc., PRS was standardized within each carrier group, and interaction terms for PRS and carrier status were included.

[0193] The models included clinical variables for age, personal history of cancer, family history of cancer, and ancestry. Data were obtained from testing applications submitted for inherited genetic testing. Clinical variables were also used to define eligibility for the study cohort, so only women with complete clinical data were included in the study.

[0194] Age was coded in years as a continuous variable. For diseased patients, age at first diagnosis of invasive breast cancer was used, and for disease-free patients, age at time of genetic testing was used. Personal cancer history variables were coded as dichotomous: ever had or not had. Separate variables were coded for uterine / endometrial cancer, ovarian cancer, pancreatic cancer, gastric cancer, nonpolyposis colorectal cancer, and adenomatous polyposis patients with 20 or more polyps.

[0195] All patients were tested for germline mutations in the following genes: APC, ATM, BARD1, BMPR1A, BRCA1, BRCA2, BRIP1, CDH1, CDK4, CDKN2A (p14ARF, p16), CHEK2, EPCAM, MLH1, MSH2, MSH6, MYH, NBN, PALB2, PMS2, PTEN, RAD51C, RAD51D, SMAD4, STK11, and TP53. Library preparation included custom-designed targeted next-generation sequencing (NGS) reagents for both exonic fragments and additional DNA fragments harboring breast cancer (BC) informative single nucleotide polymorphisms (SNPs). Pseudogene sequences were excluded by applying long-range PCR and nested PCR to portions of the CHEK2 gene. Sequencing on a HiSeq2500 or MiSeq instrument (Illumina Inc., San Diego, CA) identified both sequence variants and large-scale rearrangements (deletions and duplications).

[0196] Primary analyses examined the association of the 86-SNP score with invasive BC in each gene carrier group. Exploratory analyses compared the performance of the 86-SNP score in carriers of CHEK2 1100delC or other CHEK2 PVs. To test for interactions with family history, either a dichotomous variable (presence or absence of a diseased first-degree relative) or the sum of relatives affected with invasive BC with weighted relative counts was used. To test for interactions with gene carrier status, categorical variables for non-carriers or gene-specific carrier status were created.

[0197] Familial cancers were coded as the number of diagnoses and weighted according to the degree of relatedness. A weighting of 0.5 was used for each first-degree relative, and a weighting of 0.25 was used for each second-degree relative. Variables included invasive ductal carcinoma, lobular invasive breast cancer (LCIS), DCIS, male breast cancer, prostate cancer, and each individual's cancer type as listed above. Anthropogenesis was coded as a quantitative variable representing the proportion of ancestry reported. For example, patients who listed only Ashkenazi ancestry were coded with an Ashkenazi value of 1.0 and those who listed European ancestry were coded with a 0. Patients who reported both European and Ashkenazi ancestry were coded with a European and Ashkenazi value of 0.5.

[0198] To examine relative risk by percentile of 86-SNP score, the non-carrier and BRCA1, BRCA2, CHEK2, and ATM PV-positive cohorts were each binned into quintiles based on the 86-SNP score. The PALB2 cohort was binned into tertiles because it consisted of a smaller sample size. The median percentile bin (33rd to 66th percentile tertiles for PALB2, 40th to 60th percentile quintiles for all others) was set as the reference group in models that also included the covariates mentioned above.

[0199] Absolute lifetime risks of developing BC were calculated for disease-free study participants by combining risks based on the 86-SNP score with previously published gene-specific risk estimates (for PV carriers) or lifetime BC risk estimates from Surveillance, Epidemiology, and End Results (SEER) 2009–2014 data (for non-carriers).

[0200] Lifetime breast cancer risk as a probability density function against the background of absolute risk estimates to age 80 years for carriers of pathogenic variants (PV) in breast cancer-associated genes, as corrected by the 86-SNP score method, can be determined. For women with pathogenic variants (PV) in the moderate-risk breast cancer genes CHEK2, ATM, and PALB2, the point estimates were higher than for BRCA1 / 2 carriers. The interaction between the 86-SNP score and the type of gene carrier was significant. The most significant risk discrimination was observed for CHEK2 carriers, with effect sizes comparable to the odds ratios observed in noncarriers and for the general population.

[0201] A summary of the clinical characteristics and demographic data of the study cohort is presented in Table 7 .

[0202] [Table 7] TIFF2024507542000023.tif33136

[0203] The ORs for developing breast cancer with the continuous 86-SNP score in carriers of CHEK2 1100delC and other CHEK2 PVs are shown in Table 8.

[0204] [Table 8]

[0205] The ORs for developing breast cancer for the continuous 86-SNP score by age bin and carriage status of PVs in BC-associated genes are shown in Table 9.

[0206] [Table 9] TIFF2024507542000026.tif85163

[0207] The ORs for developing breast cancer according to BC status of first-degree relatives and carriage status of PVs in BC-associated genes are shown in Table 10.

[0208] [Table 10]

[0209] A summary of the clinical characteristics and demographic data of the study cohort is presented in Table 11.

[0210] [Table 11] TIFF2024507542000029.tif111168

[0211] The modified risk of developing breast cancer by the 86-SNP polygenic risk score in carriers of pathogenic variants in the five BC-associated genes is shown in Table 12.

[0212] [Table 12]

[0213] The percentiles of the 86-SNP PRS and odds ratios for developing breast cancer according to carrier status of pathogenic variants in BC-associated genes are shown in Tables 13 and 14.

[0214] [Table 13]

[0215] [Table 14] TIFF2024507542000033.tif148161

[0216] Estimated lifetime breast cancer risks to age 80 years and adjustments by the 86-SNP PRS are shown in Table 15.

[0217] [Table 15]

Claims

1. 1. A method for assessing the ancestry of a subject, said method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; and Calculating the racial ratios of genotypes of interest for each of the different racial populations based on the plurality of ancestry-informative SNP markers. The method includes:

2. 2. The method of claim 1, wherein the ancestry-informative SNP markers have different frequencies in three or more different ethnic populations, or have different frequencies in African, European, and East Asian ethnic populations.

3. 2. The method of claim 1, wherein the plurality of ancestry-informative SNP markers is between 10 and 50,000 SNP markers, or between 10 and 56 SNP markers.

4. 1. A method for assessing risk of a trait in a subject, the method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; Obtaining trait-associated SNP markers; and calculating a comprehensive polygenic risk score for risk of the trait in the subject based on the plurality of ancestry-informative SNP markers and trait-associated SNP markers. The method includes:

5. 5. The method of claim 4, further comprising calculating a comprehensive polygenic risk score for the risk of the trait in the subject together with additional clinical variables of the subject, the additional clinical variables being the subject's age, personal medical history, and family medical history.

6. 5. The method of claim 4, wherein the trait is the subject's risk of cancer.

7. The method of claim 4, wherein the plurality of ancestry-informative SNP markers is between 10 and 50,000 SNP markers, or between 10 and 56 SNP markers.

8. The method of claim 4 , wherein the trait-associated SNP markers are a plurality of cancer-associated SNP markers.

9. The method of claim 4, wherein the trait-associated SNP markers are a plurality of, between 10 and 50,000 breast cancer-associated SNP markers, or between 10 and 93 breast cancer-associated SNP markers.

10. 5. The method of claim 4, wherein the step of calculating a comprehensive polygenic risk score for the risk of the trait in the subject is performed in conjunction with training of clinical data of a reference group or in conjunction with validation of clinical data of a reference group.

11. The method of claim 4, wherein the subject's genotype is determined by NGS or using a sequencing chip.

12. 5. The method of claim 4, wherein the plurality of ancestry-informative SNP markers determines the racial ratio of the subject's genotype for each of three or more different racial populations, or for each of African, European, and East Asian racial populations.

13. A comprehensive polygenic risk score for the risk of a trait of interest is For subjects of three or more different racial groups, even if racial groups were self-reported, or For subjects of African, European, and East Asian ethnic groups, even when ethnic group was self-reported: The method of claim 4, wherein the method is accurate.

14. A comprehensive polygenic risk score for the risk of a trait of interest is for subjects from three or more different racial groups, so that the risk of the trait is not overestimated in any one racial group, or To avoid overestimating the risk of the trait in any one ethnic group, subjects of African, European, and East Asian ethnic groups were The method of claim 4, wherein the calibration is performed.

15. A comprehensive polygenic risk score for the risk of a trait of interest is Distinguishing between low and high risk for subjects from three or more different racial groups; or 5. The method of claim 4, which distinguishes between low risk and high risk for subjects of African, European, and East Asian ethnic groups.

16. The method of any one of claims 13 to 15, wherein the trait is the subject's risk of cancer.

17. 5. The method of claim 4, wherein calculating the comprehensive polygenic risk score comprises using a clinical cohort of women of self-reported African ancestry, self-reported East Asian ancestry, and self-reported European ancestry.

18. 5. The method of claim 4, wherein the step of calculating the comprehensive polygenic risk score comprises using a sum of ancestry-specific polygenic risk scores weighted according to the proportion of ancestry makeup.

19. 5. The method of claim 4, wherein the comprehensive polygenic risk score is strongly associated with breast cancer in the reference cohort and in subcohorts defined by self-reported ancestry.

20. 5. The method of claim 4, wherein the comprehensive polygenic risk score is combined with clinical and / or biological risk factors for accurate risk stratification for all women of all ancestry.

21. the step of calculating the comprehensive polygenic risk score comprises a linear combination of the risk alleles according to formula III: [0010] where N is the total number of selected SNPs; Coefficient b k is the allele-specific log OR for the trait association of the kth SNP estimated from the incident cohort; x k is the number of alleles of the kth SNP carried by an individual patient, which is 0, 1, or 2; u k 5. The method of claim 4, wherein k is the average number of alleles of the kth SNP reported for individuals included in a large general population study.

22. 1. A method for diagnosing or prognosing a subject having a disease, the method comprising: The following criteria: The SNP markers span virtually the entire human genome; the SNP markers each have a genomic frequency of at least 1%; and SNP markers have different frequencies in different ethnic populations; selecting a plurality of ancestry-informative SNP markers based on the following criteria: determining the genotype of the subject; Obtaining disease-associated SNP markers; and calculating a comprehensive polygenic risk score for the subject's risk of the disease based on the plurality of ancestry-informative SNP markers and the disease-associated SNP markers, where the score is indicative of a diagnosis or prognosis for the subject. Including, The method wherein the disease is cancer.

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