Utilizing the PLCO Biospecimens Resource to Bridge Gaps in Cancer Etiology and Early Detection Research

Testing for prostate-specific antigen (PSA) in blood has enabled early detection of prostate cancer and reduced metastasis and death from disease—but also contributed to overdetection of low-risk cancers. Although no PSA concentration confers zero risk of finding cancer at prostate biopsy, a single PSA measurement at midlife is a remarkably strong predictor of the risk of developing lethal prostate cancer decades later. PSA is a proteolytic enzyme that is non-catalytic in blood, and it occurs in multiple forms. A statistical model based on four kallikrein (4K) markers (free, total, and intact PSA, plus human kallikrein-related peptidase-2 [hK2]) improves specificity in detecting high-grade prostate cancer among men with elevated PSA (reducing unnecessary biopsies) and is also a strong predictor of the risk of lethal prostate cancer decades later. While intra-individual fluctuations in PSA levels are common, an excessive degree of variability is highly problematic, as temporary “false positive” elevations reduce the specificity of PSA as a cancer marker, attenuate the diagnostic value of PSA kinetics, and lead to the use of unnecessary antibiotics. Less studied but similarly abundant in prostatic fluid as PSA, the concentration of microseminoprotein-ß (MSP, MSMB) in blood is inversely associated with prostate cancer risk, and a single nucleotide polymorphism (SNP, rs10993994) in the promoter region of the MSMB gene is also associated with prostate cancer risk, but the role of these markers in clinical decision-making is unclear. Similarly, a SNP in the SERPINA3 gene is significantly associated with blood levels of PSA, and the encoded protein, alpha-1-antichymotrypsin (ACT), is the predominant stable complexing ligand to PSA in the blood. However, the clinical value of these makers is undetermined, and it remains unclear whether ACT levels in blood influence the predictive value of a baseline PSA value or affect intra-individual variation in PSA. Additionally, the intra-individual variation of the 4K-panel is currently unknown but could be determined using high-quality serial samples. As the role of these different molecular markers in combined risk-prediction models of aggressive prostate cancer is not well understood, we plan to delineate the influence of intra-individual variability in serial screening samples on clinical decisionmaking for risk stratification and biopsy by a single PSA value and additional markers. Using blood samples from the PLCO, Göteborg-1 & -2 trials, and Multiethnic Cohort (MEC), we plan to: 1) quantify the patterns of variation in the 4K markers + MSP in serial measurements; 2) determine the relationship between a statistical model based on 4K markers + MSP and subsequent risk of lethal prostate cancer, then independently validate the clinical utility of the markers in decision-making and risk stratification before treatment decisions in a randomized trial of prostate cancer treatments (ProtecT); and 3) compare head-to-head the clinical utility of pre-biopsy biomarkers versus magnetic resonance imaging on cancer detection rates. The resulting insights will shed light on how to improve the specificity of prostate cancer screening and early detection.

Multiple Myeloma (MM) is almost always preceded by early precursor conditions: monoclonal gammopathy of undetermined significance (MGUS) and smoldering myeloma (SMM). About 3% of the population >50 years have MGUS, making it a very common precursor condition. The risk is 2-3 times higher in people with a family history of MM or who are Black/African American (AA). Here, we believe that instead of defining risk by race and familial history only, we will define risk as specific genomic signatures, some of which are related to race. By screening at-risk populations for MGUS, one can develop early prevention and interception strategies for patients who would benefit from early therapeutic interventions. Our preliminary data identified an MGUS prevalence of ~13% in high-risk populations; the data came from two sources: our prospective cohort study (the PROMISE study) that is screening 30,000 participants at-risk of developing MM and a large retrospective tissue banking study, the Mass-General Brigham (MGB) biobank, with 123,000 subjects. However, what is lacking is the identification of biological cancer risk mechanisms in MM and translating these discoveries into cancer interception and early therapeutic interventions. This approach will allow the field to transition from a purely demographic definition of risk to a biological one. We believe that samples from the PLCO study, along with our current study cohorts, can help define the mechanistic underpinnings of the carcinogenesis process leading to MGUS/MM. Our overarching hypothesis is that defining the risk of developing MM precursors at the genomic level can more precisely identify specific populations at risk than demographic attributes and define focused strategies for early interception. In Specific Aim 1, we define the prevalence of monoclonal gammopathies in high-risk participants in the PLCO study along with MGB/PROMISE cohorts and characterize their impact on long-term health outcomes. In Specific Aim 2, we identify germline variants that predispose to developing MGUS/MM. We aim to characterize the genetic underpinnings of risk related to race and family history of disease. We expect that this approach will allow us to move past using self-reported race status for risk stratification. In Specific Aim 3, we assess the role of immune aging in developing MGUS/MM. MM is traditionally thought of as a disease of the elderly, but the risk may be better explained by the "aging tissue" of origin rather than chronological age. This approach will allow us to transition from a purely demographic definition of risk to a biological one.

This project aims to discover and validate plasma biomarkers for the early detection of ovarian cancer. A hallmark of cancer is uncontrolled cell division, leading to a doubling time of the tumor. This exponential growth stands in stark contrast to the stable or slowly changing profile of plasma proteins in almost all other diseases or in healthy subjects. This project will leverage this unique hallmark to discover and validate plasma protein biomarkers for the early detection of ovarian cancer. We will discover early detection (ED) plasma protein biomarkers by identifying the proteins that significantly rise over time in an exponential fashion in a substantial fraction of cases and yet remain relatively stable over time in most controls. This requires plasma assays over a large suite of proteins with CVs lower than the protein's biological variation over time which can be as low as a CV of 10%. Furthermore, a low volume requirement is essential for access to precious biospecimens formed from long-term large early detection trials. Olink AB has developed proximity extension assays (PEAs) for a suite of ~1,500 proteins with CVs ranging from 6-12% and with a minimal volume requirement of 3 µL. Applying the Olink proteomic assays to serial pre-diagnostic plasma from subjects in the PLCO who were diagnosed with ovarian cancer during the study (cases n=50) and to serial plasma samples from a 4:1 matched control (n=200) : case (n=50) cohort will provide longitudinal data on ~1,500 plasma proteins from cases and controls by which to identify ED candidate biomarkers. Prior to cancer developing in each case, a biomarker will be stable over time, while after cancer inception the biomarker will rise exponentially reflecting tumor doubling. This behavior is represented by a change-point model in cases while the same biomarker in women without ovarian cancer (controls) will have a flat profile. ED biomarkers will be the proteins which have a change-point in a substantial fraction of cases while remaining stable in most (98%) controls. We will identify the top 20 ED biomarkers where the criteria for inclusion is a combination of fraction of cases, complementarity to proteins already selected, and time of rise with earlier risers having priority. After identification of the 20 ED biomarkers, Olink will develop a custom panel of 20 ED markers with absolute quantification. The custom panel will assay the same PLCO plasma samples as used in discovery. These data will be analyzed with a multivariate longitudinal change-point model to form a multiple marker longitudinal algorithm for ED. This classifier will be locked down. The classifier will be validated by assaying the custom panel of 20 ED biomarkers on an independent PLCO serial plasma sample set, from cases (n=50) and 10:1 matched controls (n=500). From these data the classifier will be assessed for two dimensions of sensitivity for early detection: (i) the number of months prior to detection in PLCO, and (ii) proportion of cases detected, while (iii) maintaining a high specificity goal of 98% - or a false positive rate of 2%. This low false positive rate requires a large number of controls (n=500) for its accurate assessment.

Breast cancer is the most common cancer among women worldwide, as the incidence of breast cancer has increased annually by 3.1% over the past three decades. Though mammography may detect breast cancer early, its application is often limited by overdiagnosis and increased cost. To overcome the limitations, risk prediction and population stratification are needed to identify women likely to benefit from breast cancer mammography screening. Thus, identifying sensitive yet robust biologically relevant risk markers for risk prediction and population stratification is a pressing need to ultimately reduce breast cancer disease burden. An intact DNA repair is essential in breast tissue due to the extensive remodeling of the tissue throughout a woman's life. Experimental evidence provides strong support for homologous recombination repair (HRR), a major DNA repair pathway responsible for repairing DNA double-strand breaks, in guarding against mammary cell tumorigenesis. A classic example is that major high- and moderate-penetrance breast cancer susceptibility genes (e.g., BRCA1, BRCA2, CHEK2, ATM, PALB2, and RAD51D) are key players in HRR. Therefore, suboptimal HRR capacity may lead to an increased accumulation of DNA damage and an elevated risk of breast cancer. However, due to the lack of tools to measure HRR capacity non-invasive, such assumption has not been tested in the non-familial or unselected setting. Recently, we developed a phenotypic assay to measure HRR capacity in peripheral blood lymphocytes (PBLs). Our assay can provide a readout of the efficiency of the multiple steps of HRR in surrogate tissue, which is critically needed for population studies. In our preliminary breast cancer study, we found that HRR capacity was significantly lower in cases than in controls (P<0.001), and decreased HRR capacity was associated with an increased risk of breast cancer. Our primary goal is to fully assess the role of HRR in PBLs in breast cancer development by taking advantage of the rich resources from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) cohort. We will first carry out a nested case-control study to validate HRR capacity in PBLs as a breast cancer risk factor overall and by subtypes. Then, we will evaluate the impact of suboptimal HRR in PBLs on breast tumorigenesis by evaluating suboptimal HRR capacity in PBLs as a predictive biomarker for the mutational signature of HRR in breast tumors. As shown in previous studies, HRR deficiency in tumors has genetic determinants. However, whether HRR in PBLs is correlated with HRR phenotype in breast tumor tissue is unknown. Lastly, to dissect the genetic determinants of HRR in PBLs, we will develop a polygenetic risk score (PRS) for HRR capacity in PBLs and further assess the association of the PRS with breast cancer risk and tumor mutational signature utilizing existing large-scale genetic and genomic datasets.