Aim 1: Identify Cancer Risk Factors and Biomarkers

Our research in Aim 1: Identify cancer risk factors and biomarkers, focuses on:

  • Identifying distribution and determinants of cancer risk, both genetic and nongenetic
  • Defining the biological mechanisms underlying cancer risk
  • Improving risk assessment of precancer and cancer

Major ongoing research efforts related to Aim 1 include:

  • Research in collaboration with Mayo Clinic Comprehensive Cancer Center's Hematologic Malignancies Program to evaluate the interaction between inherited and acquired genetic variants for chronic lymphocytic leukemia (CLL) to improve the understanding of the underlying biology of CLL risk and ultimately enable identification of patients who will benefit from early screening or prevention strategies (R01 CA235026; Susan L. Slager, Ph.D., and other principal investigators).

  • In parallel, a study in collaboration with the Hematologic Malignancies Program to characterize inherited and acquired genetic variants for chronic lymphocytic leukemia among African Americans to understand the underlying biology of CLL risk and prognosis in this understudied population, and ultimately to reduce the health disparity that is observed between African American and Caucasian populations with chronic lymphocytic leukemia (R01 CA254951; Susan L. Slager, Ph.D., and other principal investigators).

  • Research to understand monoclonal B cell lymphocytosis (MBL), a precursor to CLL that affects 10% of people older than age 65. One theme is to identify factors, including genetic biomarkers, that drive progression of monoclonal B cell lymphocytosis to require CLL therapy (R21 CA256648; Susan L. Slager, Ph.D., and other principal investigators). A second theme is to determine the major clinical consequences of monoclonal B cell lymphocytosis with respect to overall survival, risk of infection and nonhematologic malignancy, and to identify the risk factors for developing MBL (R01 AG058266; Susan L. Slager, Ph.D., and other principal investigators).

  • Research in collaboration with Mayo Clinic Comprehensive Cancer Center's Women's Cancer Program that leverages previous discoveries that resulted in a predictive model for benign breast disease diagnosed on surgical biopsies to build a new model for percutaneous biopsies (now the dominant clinical method), and that also incorporates mammographic density, novel tissue biomarkers and machine learning statistical approaches (R01 CA229811; Mark E. Sherman, M.D., and other principal investigators).

  • A project to provide insight about previously unstudied mechanisms that control lobular involution in order to identify why postmenopausal women who have not completed the process of lobular involution are at greater risk of breast cancer (R01 CA237602; Mark E. Sherman, M.D., Derek C. Radisky, Ph.D., and other principal investigators).

  • A study in collaboration with Mayo Clinic Comprehensive Cancer Center's Neuro-Oncology Program to test whether germline genotyping and MRI-based machine learning can better diagnose indeterminate brain mass lesions and predict glioma molecular subtype before surgery and thus better enable personalized treatment (R01 NS113803; Jeanette E. Eckel Passow, Ph.D., and other principal investigators).

  • Development of a novel computational strategy to build a cancer progression model using genomic data obtained from excised tumor tissue samples (static data), with the goal of delineating the dynamic disease process and identifying pivotal molecular events that drive stepwise cancer progression (R01 CA241123; Steven Goodison, Ph.D., principal investigator).

  • A project to broaden the secondary use of electronic health records (EHRs) across the research community by combining innovative privacy-preserving computing techniques and clinical natural language processing (5U01 TR002062; Hongfang Liu, Ph.D., and other principal investigators).

  • Research to advance informatics solutions for cohort discovery and identification using information retrieval and deep representation techniques, which impacts many applications based on electronic health record data (both structured and unstructured), such as learning health care systems, predictive modeling and artificial intelligence in health care (R01 LM011934; Hongfang Liu, Ph.D., and other principal investigators).

  • A Maximizing Investigators Research Award to Daniel J. Schaid, Ph.D., to develop novel statistical and computational methods to integrate different types of data, provide useful software tools to the scientific community, and apply new methods to existing data sets to better understand the genetic basis of human diseases and traits and to predict disease risk (R35 GM140487).

Recently published highlights related to Aim 1 include:

  • Hereditary factors play a key role in the risk of developing several cancers. Identification of a germline predisposition can have important implications for treatment decisions, risk-reducing interventions, cancer screening and germline testing. In a prospective, multicenter cohort study that assessed germline genetic alterations among patients with solid tumor cancer (n~3,000) receiving care at Mayo Clinic cancer clinics and a community practice, pathogenic germline variants were found in >13% of patients, of which ~70% were moderate- and high-penetrance cancer susceptibility genes. Variants of uncertain significance were ~50%, while 6.4% of patients had incremental clinically actionable findings that would not have been detected by phenotype or family history-based testing criteria. (Samadder NJ, et al. Comparison of Universal Genetic Testing vs Guideline-Directed Targeted Testing for Patients With Hereditary Cancer Syndrome. JAMA Oncol. 2021 Feb 1;7(2):230-237.doi:10.1001/jamaoncol.2020.6252.)

  • Chronic lymphocytic leukemia (CLL) has one of the highest familial risks among cancers. Although, the rate of progression to CLL for high-count monoclonal B cell lymphocytosis (clonal B-cell count ≥500/µL) is ∼1% to 5% a year, no low-count MBLs have been reported to progress to date. Investigators in our program and in the Hematological Malignancies Program reported the incidence and natural history of MBL in 1,045 relatives from 310 families with CLL, in which they reported progression from normal-count to low-count MBL to high-count MBL to CLL, demonstrating that low-count MBL precedes progression to CLL. The rate of progression from low-count MBL to CLL was estimated at 1.1% a year, which exceeds the rate in the general population. (Slager, et al. Natural History of Monoclonal B-cell Lymphocytosis Among Relatives in CLL Families. Blood. 2021 Apr 15;137(15):2046-2056. doi:0.1182/blood.2020006322.)

  • Twenty-five germline variants have been associated with adult diffuse glioma, and some of these variants are linked to particular subtypes of glioma. By performing a genome-wide association study by molecular subtype, this study led by our program investigators identified two new regions that were associated with specific molecular subtypes of glioma. Variants in D2HGDH on chromosome 2 were associated with IDH-mutated glioma. A variant near FAM20C on chromosome 7 was associated with gliomas that have IDH mutation, TERT mutation and 1p/19q codeletion. One of the regions, D2HGDH, is a region that is also associated with allergy, asthma and glioma risk factors. (Eckel-Passow, et al. Adult Diffuse Glioma GWAS by Molecular Subtype Identifies Variants in D2HGDH and FAM20C. Neuro Oncol. 2020 Nov 26;22(11):1602-1613. doi:10.1093/neuonc/noaa117.)

  • Tumor mutational burden (TMB) as a biomarker for patient selection to receive immune checkpoint inhibitors (ICIs) therapy without patient-paired germline sequencing may introduce racial bias due to the underrepresentation of minority groups in public databases. Using paired tumor and germline exome sequencing data from 701 patients newly diagnosed with multiple myeloma, including 575 self-reported white patients and 126 self-reported Black patients, our researchers observed that compared with the gold standard of filtering germline variants with patient-paired germline sequencing data, tumor mutational burden estimates were significantly higher in both Black and white patients when using public databases for filtering nonsomatic mutations. However, tumor mutational burden was more significantly inflated in Black patients compared with white patients. (Asmann YW, et al. Inflation of Tumor Mutation Burden by Tumor-Only Sequencing in Under-Represented Groups. NPJ Precis Oncol. 2021 Mar 19;5(1):22. doi:10.1038/s41698-021-00164-5.)

  • Deficient intake of micronutrients involved in one-carbon metabolism (for example, choline, methionine, vitamin B12 and folic acid) leads to hepatocellular carcinoma (HCC) development in rodents, but the role of micronutrients in the etiology of hepatocellular carcinoma in people is understudied. Our investigators studied the association between one-carbon metabolism-related micronutrient intake and hepatocellular carcinoma risk in a prospective cohort of 494,860 participants with 16 years of follow-up in the NIH-AARP study. During the 16-year follow-up period, 647 incident hepatocellular carcinoma cases were diagnosed. The study found that higher vitamin B3 intake was associated with lower HCC risk, whereas higher vitamin B6 intake was associated with increased risk. (Antwi, et al. One-Carbon Metabolism-Related Micronutrients Intake and Risk for Hepatocellular Carcinoma: A Prospective Cohort Study. Int J Cancer. 2020 Oct 15;147(8):2075-2090. doi:10.1002/ijc.33007. Epub 2020 Apr 25.)