Background:

Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high- and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB.

Methods:

Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families.

Results:

Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [P = 1.6 × 10−8, logarithm of the odds (LOD) equivalent 6.64]. In the region, IL1A and IL1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers.

Conclusions:

Our results suggest pedigree-informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high-risk breast cancer pedigrees.

Impact:

Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.

See all articles in this CEBP Focus section, “Modernizing Population Science.”

The use of the family study design, and high-risk pedigrees in particular, was instrumental in the discovery of germline breast cancer susceptibility genes and our understanding of their pleiotropies (1, 2). However, breast cancers, like other complex diseases, have many sources of heterogeneity that can hinder gene discovery. Efforts to identify additional etiologic risk factors are hampered by these complexities and new methods to identify and reduce sources of heterogeneities are needed to identify novel disease loci. Deconstructing within-site heterogeneity and identification of across-site pleiotropies will require large multisource data resources and computational techniques to mine them. Many large multisource data resources are currently under development throughout the United States and the world (3–9), providing potential opportunities for a new wave of discoveries. In Utah, an established statewide multisource database (the Utah Population Database, UPDB) with linked biobank resources exists. Here, we will describe two different novel family-based approaches using the UPDB, designed to address heterogeneity and identify pleiotropies, to illustrate the broad utility of multisource databases.

Fundamentally necessary to family studies are data for relationship structure and disease, as well as knowledge of population expectations of disease. The former is critical for defining phenotypes that cluster in families and therefore has potential power for genetic discovery. The UPDB is currently the only statewide resource in the United States that links statewide genealogies (5 million records that span 3–18 generations) with a statewide Surveillance, Epidemiology, and End Results (SEER) Program cancer registry [Utah Cancer Registry (UCR), since 1966]. Hence, it allows for both family construction and designation of significant clustering of disease. Other data sources are also linked to the UPDB (https://uofuhealth.utah.edu/huntsman/utah-population-database/data/), including: electronic medical records (1996–present); historical census data (1880; 1900–1940); vital statistics (1905–present); residential histories (back to 1900); linkages to environmental measures (geographic based); and biobanks. This multisource database is unique and can be harnessed for many designs to study cancer risk and survivorship across the lifespan and across generations (10–14).

Breast cancer is a prime example of a common, complex disease. Substantial etiologic heterogeneity exists both within and across breast cancers in high-risk pedigrees. Reducing heterogeneity is an important design issue in family-based genetic research. For example, even within high-risk pedigrees, the discovery of BRCA1 and BRCA2 (BRCA1/2) required restriction to early-onset disease to clarify segregation (15, 16). It is well-established that gene expression varies across tumors, and hence tumor expression phenotypes may hold promise for deconstructing heterogeneity. In breast cancer, tumor gene expression has been shown to differentiate tumors into intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, and Basal-like; refs. 17, 18), of which Basal-like has increased BRCA1 susceptibility (19). The first approach we describe integrates tumor expression phenotypes with gene mapping in high-risk pedigrees. This approach was made possible by record linkages between genealogy, cancer diagnoses, hospital medical records, and biobanks, all available via the UPDB. We previously defined quantitative tumor expression phenotypes associated with high-risk pedigrees not attributed to BRCA1/2, and illustrated power for mapping breast cancer loci in one large pedigree (20). Here we apply the same approach to a second large, high-risk breast cancer pedigree.

Cancer pleiotropies are a well-accepted phenomenon, and crucial to genetic counselling for accurate risk predictions. In breast cancer, pleiotropies are known to vary by the risk gene involved (Fig. 1). Hence, characterizing families by their patterns of familial cancer risk could provide new opportunities to identify families with similar genetic risk factors. Gene mapping focusing on multi-cancer patterns could also elucidate molecular factors that underlie pleiotropies. For example, Basal-like breast tumors show more gene expression similarities to high-grade serous ovarian cancer than other breast tumor types (21, 22). The multiple linked data sources in the UPDB provide a platform to describe multi-cancer patterns of familial risk. Furthermore, links to biorepositories could support investigations into the molecular factors underlying pleiotropies, and links to environmental data investigations to shared exposures. In the second approach, we illustrated how data-driven methods make it possible to uncover familial multi-cancer signatures. We recently introduced this novel multi-cancer clustering technique and defined four familial multi-cancer signatures in high-risk bladder cancer families (23). Here, we focus on multi-cancer signatures for high-risk breast cancer families.

Figure 1.

Familial multiphenograms for BRCA1 (A) and BRCA2 (B). BR, breast cancer; CRC, colorectal cancer; FA, Fanconi anemia; MEL, melanoma; OV, ovarian cancer; PA, pancreatic cancer; PR, prostate cancer. Source: https://ghr.nlm.nih.gov.

Figure 1.

Familial multiphenograms for BRCA1 (A) and BRCA2 (B). BR, breast cancer; CRC, colorectal cancer; FA, Fanconi anemia; MEL, melanoma; OV, ovarian cancer; PA, pancreatic cancer; PR, prostate cancer. Source: https://ghr.nlm.nih.gov.

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The UPDB

The vast majority of individuals residing in Utah are represented in the UPDB (24–27). Core to the UPDB is an immense genealogy that is record-linked to many other statewide datasets (including the UCR), with annual updates. The full genealogic dataset contains nearly 5 million people with 28 million records and the linking of multiple distinct records for a specific person allows the UPDB to depict the life history of an individual based on medical and administrative data. There are currently 336,000 cancer records from the UCR with diagnoses beginning in 1966 that are linked to the UPDB. The UPDB is linked to the pathology records of two healthcare systems (University of Utah, Salt Lake City, UT and Intermountain Healthcare, Salt Lake City, UT) that together serve over 85% of the state, and facilitates access to over 4 million formalin-fixed, paraffin-embedded (FFPE) tissue blocks linked to clinical data. It is also linked to external data repositories using a statewide federated ID, including approximately 85% of outpatient claims in the state of Utah (1996–present).

The data contained in the UPDB may be used for biomedical and health-related research. It is a rich and unique resource for cancer research that can support genetic, epidemiologic, public health, and healthcare delivery studies. Overseeing ethical approvals for use of the UPDB data for research is the Resource for Genetic and Epidemiological Research (RGE) body, which was established by Executive Order of the Governor of Utah in 1982. RGE administers access to UPDB through a formal review process to ensure the protection of privacy and confidentiality of the persons and data held in UPDB, and protects the interests of the data contributors (28). A summary list of data contributors can be found in Supplementary Table S1.

Approach 1: Reducing heterogeneity: Breast cancer gene mapping using a tumor expression phenotype

Breast cancer pedigrees were identified in the UPDB using record linkage between the 18-generation genealogy and statewide cancer records from the UCR. High-risk status was defined as a statistical excess of breast cancer–compared UPDB internal rates (P < 0.05). Pedigrees known to be attributable to BRCA1/2 from previous Utah studies were removed (i.e., screen positive or linked to chromosomes 17q21 or 13q13). Record linkage between the UPDB and pathology records in the University of Utah (Salt Lake City, UT) and Intermountain Healthcare Systems (Salt Lake City, UT) allowed identification of pathology records and archived tissue blocks. We pursued matched tumor and GU FFPE tissues for 25 high-risk pedigrees. GU refers to tissue that is histologically determined to contain 0% tumor. In the absence of peripheral blood, DNA extracted from GU tissue can be used for germline (inherited) DNA (see Supplementary Materials and Methods for more detail). Eleven of the 25 pedigrees contained at least 15 cases for whom tumor blocks were available. These 11 pedigrees were selected for tumor and germline experiments. Tumor RNA was used for gene expression and GU DNA for germline genotyping. Tumor gene expression was measured using the PAM50 RT-qPCR research assay (29). We used the OmniExpress high-density SNP array for germline genotyping. Quality control included: duplicate check, sex check, SNP call-rate (95%), sample call rate (90%), and failure of Hardy–Weinberg equilibrium (P ≤ 1 × 10−5). All women were of European ancestry. Ethical approvals for the study were governed by RGE and Institutional Review Boards (IRB) at the University of Utah (IRB_00096990; Salt Lake City, UT) and Intermountain Healthcare (IRB_1015580; Salt Lake City, UT).

We previously used a set of population-based breast tumors (30) and identified five principal components from the 50 PAM50 classifier genes, referred to as dimensions PC1—PC5 (31). PC3 and PC5 were shown to be significantly different between the population and the pedigree tumors and hence potentially powerful phenotypes for gene mapping in pedigrees. Here we concentrate on high-risk pedigree 1822 (Fig. 2) and dimension PC3 as the phenotype of interest. Tumors in pedigree 1822 were identified as the most significantly different of all 11 pedigrees to population tumors for PC3 (P = 4.0 × 10−5; ref. 20). Germline DNA was available for 46 breast cancer cases and tumor RNA for 31. As described previously (20), we considered breast cancer cases with tumors in the top decile of PC3 in the population as “extreme,” resulting in 10 PC3-extreme breast cancer cases for gene mapping in pedigree 1822.

Figure 2.

Pedigree 1822. A, Forty-six breast cancer cases with germline DNA available (colored black). B, Thirty-one breast cancer cases with tumor expression data (colored green, light/dark blue, purple, or red). Gray color indicates no tumor data. “PC3” indicates cases whose tumors were established to be extreme for PC3. C, The reduced pedigree structure for 1822 based on the 10 PC3-extreme breast cancer cases. These 10 were the focus of the SGS gene mapping

Figure 2.

Pedigree 1822. A, Forty-six breast cancer cases with germline DNA available (colored black). B, Thirty-one breast cancer cases with tumor expression data (colored green, light/dark blue, purple, or red). Gray color indicates no tumor data. “PC3” indicates cases whose tumors were established to be extreme for PC3. C, The reduced pedigree structure for 1822 based on the 10 PC3-extreme breast cancer cases. These 10 were the focus of the SGS gene mapping

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We used Shared Genomic Segment (SGS) analysis (32), a single-pedigree method which identifies chromosomal identity-by-state (IBS) sharing at consecutive SNPs. Segregation from a common ancestor is implied if the observed IBS sharing is significantly longer than expected by chance (33, 34). To address any residual heterogeneity, sharing evidence is assessed over all possible subsets. Statistical significance was determined empirically using a gene-drop approach. Briefly, a gene-drop assigns haplotypes randomly to pedigree founders under the null hypothesis [i.e., according to a population distribution, we used 1000Genomes Project (ref. 35) data for our linkage disequilibrium model; ref. 36]. Mendelian segregation and recombination are simulated through the pedigree structure (37) to generate genotypes for all pedigree members. We used the established Rutgers genetic map (38) for simulating recombination events. For each simulated configuration of genotypes in the pedigree, shared segments are assessed and result in one genome-wide expectation of sharing under the null hypothesis. The gene-drop procedure was repeated to generate a null distribution of sharing from which an empirical estimate of significance for the observed sharing was made. For accurate interpretation, a genome-wide significance threshold was established, which corrects for the subsets within the pedigree and the whole-genome framework. After 1 million simulations, a gamma distribution was fit to the observed P values across the genome. The genome-wide significance threshold was derived from this distribution using the theory of large deviations (39).

Approach 2: Identifying pleiotropic patterns—multi-cancer signatures for familial breast cancer

High-risk breast cancer families were the focus of the clustering to identify multi-cancer pleiotropies. Linked genealogic, demographic, and cancer data from the UPDB were used. First, all individuals with breast cancer (“probands”) and their first- (FDR), second- (SDR), and third-degree relatives (TDR) were identified using the UPDB. Only family members known to reside in Utah for at least 1 year from 1966–2017 were included. We identified 27,635 probands with at least one TDR and 1,696,913 family members. Second, this set was reduced to only families with at least 10 relatives to allow for family risk assessment. Familial risk for a cancer type was measured using standardized incidence risk (SIR) ratios accounting for the sex, age, birth-cohort, and person-years of the pedigree members (for a detailed description of SIR calculations, see Supplementary Materials and Methods). Person-years were calculated using the minimum of the first year residing in Utah or 1966 to the year of first cancer diagnosis, last year of residence in Utah (due to death or migration), or 2017. Finally, a total of 5,045 families (including 326,024 family members) were determined as high risk for breast cancer, defined as a statistical excess of cases compared with the age- and sex-adjusted internal rates of the UPDB (P < 0.05). These were the basis of our study. This study was approved by IRBs at the University of Utah (IRB_00088870 and IRB_00079328).

Each of the 5,045 high-risk breast cancer families were further characterized by risk for 25 additional cancer types (26 total, including breast cancer). Other cancers were selected on the basis of SEER site codes and frequency (see Supplementary Table S2 for detailed information; ref. 40).

Two risk metrics were used to capture a family's multi-cancer signature. First, wSIR, the SIR weighted by the P value. This incorporated both the magnitude and significance of the familial risk, and was calculated using the following equation. This metric allowed us to include, but down-weight, SIR values that were not significantly different than the overall population.

formula

Where p is the P value, i is the family, and j is the cancer type.

For robustness, and to avoid bias due to large SIRs (especially for rare cancers), we imposed a maximum value such that any wSIR values larger than the 90th percentile were set to the 90th percentile value across all families for the cancer type.

formula

where 90 indicates the 90th percentile for cancer j.

Second, we included a dichotomous indicator of risk (ISIR). Families were considered to have “high risk” status for a cancer type (ISIR = 1) if the SIR was statistically significant (P < 0.05) and “population risk” (ISIR = 0) otherwise. As all families were selected to be high risk for breast cancer by design, we substituted the ISIR for breast with an indicator variable for male breast cancer. Our final matrix included 52 risk metrics per family (26 wSIR and 26 ISIR).

Clustering was performed on the 5,045 × 52 data matrix (families × risk metrics). A Gower general coefficient (ade4 R package) was used as the distance metric for clustering as it allows for the simultaneous use of our two risk metric types (wSIR continuous and ISIR categorical; detailed information can be found in the Supplementary Data). We used partitioning around medoids (PAM or K-medoids clustering package in R; ref. 41) to measure similarities between the multi-cancer risk signatures of families. K was selected by running a series of iterative models from k = 2 to k = 20 and using Silhouette (Supplementary Fig. S1) and elbow plots to identify the point of diminishing improvement in average Silhouette width.

Bootstrapping was used to evaluate the reproducibility of the clustering (clustboot function in R) with 200 random draws. Results from each draw were transformed into a consensus matrix using the ward linkage algorithm and the (consensusmatrix function in R) and then plotted in a heatmap used for visualization. The results for k = 5 were stable (Supplementary Fig. S2).

Each cluster in the matrix represents a familial multi-cancer configuration (FMC) signature for high-risk breast cancer families. To describe and compare these clusters (FMCs), we used Cox proportional hazard models to estimate cluster-specific differences in cancer incidence and their 95% confidence intervals (CI) using the R package survival. All models controlled for birth year and sex.

Approach 1: Reducing heterogeneity: Breast cancer gene mapping using a tumor expression phenotype

Figure 2 illustrates pedigree 1822, showing the 46 breast cancer cases with germline DNA available (Fig. 2A) and the subset of 31 with tumor expression data (Fig. 2B). Their intrinsic subtype (the usual purpose of the PAM50) is also indicated for comparison. The 10 PC3-extreme breast cancer cases used in the SGS analyses are shown in Fig. 2C. The SGS genome-wide significance threshold for 1822 was determined to be \alpha \ $= 2.0 × 10−8, and one 0.6 Mb region at chromosome 2q13 surpassed this (P = 1.6 × 10−8, from 113.2 to 113.8 Mb). This segment was shared by eight of the 10 extreme PC3 breast cancer cases and was inherited through 38 meioses (Fig. 2C). Ten genes are contained in the 2q13 locus: TTL; POLR1B; CHCHD5; SLC20A1; NT5DC4; CKAP2L; IL1A; IL1B; IL37; and IL36G.

We explored fine-mapping of the 2q13 locus within the pedigree by assessing the possibility that the shared haplotype inherited to others. We defined the eight SGS sharers as “core sharers” and ranked all other breast cancer cases with genotype data based on their IBS sharing with them at this locus. We sequentially added these breast cancer cases to core sharers based on their ranking, and reassessed SGS sharing across the full set after each addition. Figure 3 shows how the possible sharing narrows as cases are added. As a post hoc analysis, this cannot be formally tested for significance, but it indicates there may be an additional 15 cases who inherit the same 120,567 bp region. This reduced region contains only NT5DC4, CKAP2L, IL1A, and IL1B.

Figure 3.

The left-hand side y-axis indicates the number of individuals sharing. From the established 8 “core sharers,” individuals are added, thus the range is 9 to 46 individuals (total cases with DNA). The right-hand side y-axis indicates the number of meioses separating the set of sharers on each row. The x-axis indicates the SNP markers across 2q13. Each horizontal rectangle is a shared segment, with the color indicating the P value for the segment (green, highly significant, to red, not significant). The number in the white box on the green rectangles indicates the number of simulations more extreme than the observed segment. At 23 sharers (15 additional to the core 8), significance disappears and returns to that expected.

Figure 3.

The left-hand side y-axis indicates the number of individuals sharing. From the established 8 “core sharers,” individuals are added, thus the range is 9 to 46 individuals (total cases with DNA). The right-hand side y-axis indicates the number of meioses separating the set of sharers on each row. The x-axis indicates the SNP markers across 2q13. Each horizontal rectangle is a shared segment, with the color indicating the P value for the segment (green, highly significant, to red, not significant). The number in the white box on the green rectangles indicates the number of simulations more extreme than the observed segment. At 23 sharers (15 additional to the core 8), significance disappears and returns to that expected.

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Approach 2: Identifying pleiotropic patterns—multi-cancer signatures for familial breast cancer

The 5,045 high-risk breast cancer families in the UPDB ranged in size from 10 to 284 relatives (FDR, SDR, and TDR). Figure 4 shows the hazard rate ratios (HRR) for all 5,045 familial breast cancer families relative to the Utah population and for each familial multi-cancer configuration (FMC1–5). The clustering algorithm identified five family types based on their multi-cancer risks: FMC1 (2,159 families, 42.8%), FMC2 (657, 13.0%), FMC3 (625, 12.4%), FMC4 (1,004, 19.9%), and FMC5 (600, 11.9%). While, by definition, all clusters contained a statistical excess of breast cancer, the magnitude of breast cancer risk varied across clusters (see Table 1): FMC1 HRR = 3.05 (95% CI, 2.98–3.12), FMC2 HRR = 4.32 (4.14–4.50), FMC3 HRR = 3.79 (3.64–3.94), FMC4 HRR = 6.16 (5.96–6.37), and FMC5 HRR = 3.24 (3.12–3.37).

Figure 4.

Familial multiphenograms illustrating the patterns of familial cancer risk across the five high-risk FMCs. The “Overall” column shows the fold difference in risk for all familial breast cancer families relative to the control population. The x-axis is truncated at 2.5, and values larger than 2.5 are noted within the horizontal bars on the chart. Columns FMC1–FMC5 show the unique familial cancer patterns by FMC. These patterns differ significantly from one another and the overall pattern of cancer clustering in familial breast cancer families. NHL, non-Hodgkin lymphoma.

Figure 4.

Familial multiphenograms illustrating the patterns of familial cancer risk across the five high-risk FMCs. The “Overall” column shows the fold difference in risk for all familial breast cancer families relative to the control population. The x-axis is truncated at 2.5, and values larger than 2.5 are noted within the horizontal bars on the chart. Columns FMC1–FMC5 show the unique familial cancer patterns by FMC. These patterns differ significantly from one another and the overall pattern of cancer clustering in familial breast cancer families. NHL, non-Hodgkin lymphoma.

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Table 1.

The HRR of cancer diagnosis by cancer site. Results are displayed for all familial breast cancer families (N = 5,045) combined and by FMC.

OverallFMC1FMC2FMC3FMC4FMC5
HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)
Breast 3.64 (3.57–3.70) 3.05 (2.98–3.12) 4.32 (4.14–4.50) 3.79 (3.64–3.94) 6.16 (5.96–6.37) 3.24 (3.12–3.37) 
Ovary 1.17 (1.09–1.26) 0.19 (0.15–0.24) 0.61 (0.46–0.82) 0.72 (0.57–0.92) 0.17 (0.11–0.28) 6.10 (5.64–6.61) 
Larynx 0.99 (0.93–1.05) 0.41 (0.37–0.46) 0.69 (0.56–0.85) 4.93 (4.58–5.31) 0.19 (0.14–0.27) 0.75 (0.64–0.88) 
Melanoma of the skin 1.09 (1.05–1.13) 0.76 (0.72–0.80) 4.17 (3.95–4.40) 0.83 (0.75–0.92) 0.59 (0.53–0.67) 0.94 (0.86–1.02) 
Prostate 1.09 (1.06–1.11) 1.07 (1.03–1.10) 1.08 (1.01–1.16) 1.05 (0.99–1.12) 1.20 (1.13–1.27) 1.11 (1.05–1.17) 
Acute myeloid leukemia 1.00 (0.91–1.10) 0.87 (0.77–0.99) 1.30 (1.02–1.65) 0.97 (0.75–1.24) 1.34 (1.08–1.65) 1.00 (0.80–1.24) 
Acute lymphocytic leukemia 1.06 (0.97–1.15) 1.14 (1.02–1.27) 1.57 (1.28–1.93) 0.85 (0.66–1.09) 0.92 (0.73–1.17) 0.74 (0.58–0.94) 
Hodgkin—nodal 1.04 (0.91–1.19) 0.95 (0.79–1.14) 1.20 (0.83–1.72) 1.53 (1.15–2.04) 1.14 (0.83–1.57) 0.75 (0.51–1.08) 
NHL—nodal 1.06 (1.00–1.11) 1.05 (0.98–1.12) 1.20 (1.05–1.39) 0.91 (0.79–1.05) 1.20 (1.06–1.36) 0.98 (0.87–1.11) 
Colon 1.00 (0.97–1.03) 0.96 (0.91–1.00) 1.06 (0.96–1.16) 1.02 (0.93–1.11) 1.10 (1.01–1.20) 1.03 (0.95–1.11) 
Thyroid 1.01 (0.95–1.07) 1.03 (0.95–1.12) 1.23 (1.04–1.45) 0.78 (0.65–0.94) 0.93 (0.79–1.10) 1.04 (0.89–1.20) 
Cervical 0.80 (0.74–0.86) 0.76 (0.69–0.84) 0.98 (0.80–1.19) 1.02 (0.86–1.21) 0.84 (0.70–1.01) 0.60 (0.49–0.74) 
Uterine 1.11 (1.05–1.17) 1.05 (0.97–1.12) 1.08 (0.93–1.27) 1.17 (1.02–1.34) 1.39 (1.22–1.57) 1.06 (0.93–1.20) 
Lung and bronchus 0.84 (0.80–0.88) 0.77 (0.72–0.82) 0.92 (0.81–1.05) 1.06 (0.95–1.18) 0.94 (0.84–1.06) 0.77 (0.68–0.86) 
Stomach 0.92 (0.84–1.01) 0.87 (0.76–0.98) 1.25 (0.99–1.58) 0.92 (0.73–1.17) 0.98 (0.78–1.24) 0.87 (0.70–1.08) 
Soft tissue including heart 1.02 (0.90–1.15) 1.03 (0.87–1.21) 1.31 (0.94–1.81) 1.03 (0.75–1.43) 1.14 (0.84–1.55) 0.69 (0.48–0.98) 
Kidney and renal pelvis 0.89 (0.83–0.96) 0.83 (0.75–0.91) 0.99 (0.80–1.22) 1.00 (0.83–1.20) 0.87 (0.72–1.06) 0.97 (0.82–1.15) 
Testis 1.05 (0.92–1.19) 1.00 (0.84–1.20) 1.55 (1.13–2.12) 1.04 (0.74–1.47) 1.09 (0.79–1.50) 0.80 (0.56–1.15) 
Pancreas 1.04 (0.97–1.11) 0.98 (0.89–1.07) 1.12 (0.93–1.36) 1.06 (0.89–1.26) 1.24 (1.05–1.46) 1.03 (0.88–1.21) 
Esophagus 0.88 (0.77–1.01) 0.74 (0.61–0.90) 0.79 (0.51–1.21) 1.16 (0.85–1.60) 1.31 (0.98–1.76) 0.84 (0.60–1.16) 
Liver 0.83 (0.71–0.96) 0.68 (0.55–0.85) 0.58 (0.34–1.01) 0.89 (0.60–1.32) 1.14 (0.81–1.61) 1.17 (0.85–1.59) 
Brain 0.98 (0.90–1.06) 0.90 (0.80–1.02) 1.13 (0.89–1.43) 1.01 (0.81–1.25) 1.03 (0.83–1.27) 1.05 (0.86–1.27) 
CNS 0.94 (0.86–1.03) 0.89 (0.79–1.01) 0.92 (0.70–1.21) 1.05 (0.84–1.32) 0.99 (0.79–1.25) 1.00 (0.81–1.24) 
Myeloma 1.03 (0.95–1.13) 0.98 (0.87–1.10) 1.26 (0.99–1.60) 1.08 (0.86–1.36) 1.07 (0.85–1.34) 1.02 (0.83–1.26) 
Small intestine 1.01 (0.87–1.17) 0.97 (0.79–1.19) 1.22 (0.82–1.83) 0.97 (0.65–1.45) 0.95 (0.63–1.42) 1.07 (0.75–1.51) 
Urinary bladder 0.99 (0.94–1.04) 0.96 (0.90–1.03) 1.04 (0.90–1.22) 1.02 (0.89–1.17) 1.05 (0.92–1.20) 0.94 (0.83–1.07) 
OverallFMC1FMC2FMC3FMC4FMC5
HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)
Breast 3.64 (3.57–3.70) 3.05 (2.98–3.12) 4.32 (4.14–4.50) 3.79 (3.64–3.94) 6.16 (5.96–6.37) 3.24 (3.12–3.37) 
Ovary 1.17 (1.09–1.26) 0.19 (0.15–0.24) 0.61 (0.46–0.82) 0.72 (0.57–0.92) 0.17 (0.11–0.28) 6.10 (5.64–6.61) 
Larynx 0.99 (0.93–1.05) 0.41 (0.37–0.46) 0.69 (0.56–0.85) 4.93 (4.58–5.31) 0.19 (0.14–0.27) 0.75 (0.64–0.88) 
Melanoma of the skin 1.09 (1.05–1.13) 0.76 (0.72–0.80) 4.17 (3.95–4.40) 0.83 (0.75–0.92) 0.59 (0.53–0.67) 0.94 (0.86–1.02) 
Prostate 1.09 (1.06–1.11) 1.07 (1.03–1.10) 1.08 (1.01–1.16) 1.05 (0.99–1.12) 1.20 (1.13–1.27) 1.11 (1.05–1.17) 
Acute myeloid leukemia 1.00 (0.91–1.10) 0.87 (0.77–0.99) 1.30 (1.02–1.65) 0.97 (0.75–1.24) 1.34 (1.08–1.65) 1.00 (0.80–1.24) 
Acute lymphocytic leukemia 1.06 (0.97–1.15) 1.14 (1.02–1.27) 1.57 (1.28–1.93) 0.85 (0.66–1.09) 0.92 (0.73–1.17) 0.74 (0.58–0.94) 
Hodgkin—nodal 1.04 (0.91–1.19) 0.95 (0.79–1.14) 1.20 (0.83–1.72) 1.53 (1.15–2.04) 1.14 (0.83–1.57) 0.75 (0.51–1.08) 
NHL—nodal 1.06 (1.00–1.11) 1.05 (0.98–1.12) 1.20 (1.05–1.39) 0.91 (0.79–1.05) 1.20 (1.06–1.36) 0.98 (0.87–1.11) 
Colon 1.00 (0.97–1.03) 0.96 (0.91–1.00) 1.06 (0.96–1.16) 1.02 (0.93–1.11) 1.10 (1.01–1.20) 1.03 (0.95–1.11) 
Thyroid 1.01 (0.95–1.07) 1.03 (0.95–1.12) 1.23 (1.04–1.45) 0.78 (0.65–0.94) 0.93 (0.79–1.10) 1.04 (0.89–1.20) 
Cervical 0.80 (0.74–0.86) 0.76 (0.69–0.84) 0.98 (0.80–1.19) 1.02 (0.86–1.21) 0.84 (0.70–1.01) 0.60 (0.49–0.74) 
Uterine 1.11 (1.05–1.17) 1.05 (0.97–1.12) 1.08 (0.93–1.27) 1.17 (1.02–1.34) 1.39 (1.22–1.57) 1.06 (0.93–1.20) 
Lung and bronchus 0.84 (0.80–0.88) 0.77 (0.72–0.82) 0.92 (0.81–1.05) 1.06 (0.95–1.18) 0.94 (0.84–1.06) 0.77 (0.68–0.86) 
Stomach 0.92 (0.84–1.01) 0.87 (0.76–0.98) 1.25 (0.99–1.58) 0.92 (0.73–1.17) 0.98 (0.78–1.24) 0.87 (0.70–1.08) 
Soft tissue including heart 1.02 (0.90–1.15) 1.03 (0.87–1.21) 1.31 (0.94–1.81) 1.03 (0.75–1.43) 1.14 (0.84–1.55) 0.69 (0.48–0.98) 
Kidney and renal pelvis 0.89 (0.83–0.96) 0.83 (0.75–0.91) 0.99 (0.80–1.22) 1.00 (0.83–1.20) 0.87 (0.72–1.06) 0.97 (0.82–1.15) 
Testis 1.05 (0.92–1.19) 1.00 (0.84–1.20) 1.55 (1.13–2.12) 1.04 (0.74–1.47) 1.09 (0.79–1.50) 0.80 (0.56–1.15) 
Pancreas 1.04 (0.97–1.11) 0.98 (0.89–1.07) 1.12 (0.93–1.36) 1.06 (0.89–1.26) 1.24 (1.05–1.46) 1.03 (0.88–1.21) 
Esophagus 0.88 (0.77–1.01) 0.74 (0.61–0.90) 0.79 (0.51–1.21) 1.16 (0.85–1.60) 1.31 (0.98–1.76) 0.84 (0.60–1.16) 
Liver 0.83 (0.71–0.96) 0.68 (0.55–0.85) 0.58 (0.34–1.01) 0.89 (0.60–1.32) 1.14 (0.81–1.61) 1.17 (0.85–1.59) 
Brain 0.98 (0.90–1.06) 0.90 (0.80–1.02) 1.13 (0.89–1.43) 1.01 (0.81–1.25) 1.03 (0.83–1.27) 1.05 (0.86–1.27) 
CNS 0.94 (0.86–1.03) 0.89 (0.79–1.01) 0.92 (0.70–1.21) 1.05 (0.84–1.32) 0.99 (0.79–1.25) 1.00 (0.81–1.24) 
Myeloma 1.03 (0.95–1.13) 0.98 (0.87–1.10) 1.26 (0.99–1.60) 1.08 (0.86–1.36) 1.07 (0.85–1.34) 1.02 (0.83–1.26) 
Small intestine 1.01 (0.87–1.17) 0.97 (0.79–1.19) 1.22 (0.82–1.83) 0.97 (0.65–1.45) 0.95 (0.63–1.42) 1.07 (0.75–1.51) 
Urinary bladder 0.99 (0.94–1.04) 0.96 (0.90–1.03) 1.04 (0.90–1.22) 1.02 (0.89–1.17) 1.05 (0.92–1.20) 0.94 (0.83–1.07) 

Note: The overall estimates and 95% CIs are displayed in column 2. The FMC configuration (FMC1–5)-specific HRRs are reported in columns 3–7.

Abbreviations: CNS, cranial nerves, other nervous system; NHL, non-Hodgkin lymphoma.

Separating high-risk breast cancer families into clusters with similar patterns of multi-cancer risk uncovered many differences in effect sizes of cancer risks (including opposing directions), and identified previously undiscovered pleiotropic associations (Table 1; Fig. 4; Supplementary Fig. S3). We found that the risk of ovarian cancer, an established coaggregation with breast cancer for known risk genes, varied widely by cluster. Ovarian cancer risk for each of the five FMCs was significantly different than the risk estimated from all families together (overall HRR = 1.17; 95% CI, 1.09–1.26; Table 1). FMC5 captured extreme increased risk (HRR = 6.10; 95% CI, 5.64–6.61, while the remaining four FMCs showed negative associations (significant decreased risk; Table 1; Fig. 4). Melanoma, another established cancer associated with breast cancer, was found to vary widely across clusters (Table 1; Fig. 4). Novel coaggregations were also evident. There was neither established association for larynx cancer, nor a signal for risk to larynx cancer when all high-risk breast cancer families were considered together. However, significant risks (increased and decreased) were seen for larynx cancer in all five FMCs [e.g., FMC3 HRR = 4.93 (95% CI, 4.58–5.31) and FMC4 HRR = 0.19 (95% CI, 0.14–0.27); Table 1].

Prostate cancer risk was consistent and modest (1.05–1.20) across all clusters, significantly elevated in four of the FMCs, and borderline in the fifth. Some cancers were consistently absent: bladder, brain, cranial nerves and other nervous system (central nervous system), myeloma, and small intestine. The remaining cancers provided patterns that differentiated FMCs. Families in FMC1 were at moderately increased risk for prostate cancer and acute lymphocytic leukemia (ALL) and had decreased risk for 11 cancers (Fig. 4; Table 1), with notable decreases in ovarian (HRR = 0.19; 95% CI, 0.15–0.24) and cancer of the larynx (HRR = 0.41; 95% CI, 0.37–0.46). The FMC2 cluster alone showed strong coaggregation of melanoma (HRR = 4.17; 95% CI, 3.95–4.40) and moderate increases in risk for cancers that are usually seen in adolescents, such as testicular, thyroid, non-Hodgkin lymphoma, acute lymphocytic leukemia, and acute myeloid leukemia (Fig. 4; Table 1). This cluster had increased risk for eight cancer sites, the highest of the FMCs, and decreased risk for two sites, the lowest of the FMCs. FMC3 was the only cluster to exhibit substantial and significant risk for cancer of the larynx (HRR = 4.93; 95% CI, 4.58–5.31) and Hodgkin lymphoma (HRR = 1.53; 95% CI, 1.15–2.04). Families in FMC4 had an increased risk of uterine cancer (HRR = 1.39; 95% CI, 1.22–1.57), and the lowest risk of cancer of the larynx (HRR = 0.19; 95% CI, 0.14–0.27) and ovary (HRR = 0.17; 95% CI, 0.11–0.28). Finally, the FMC5 cluster was the only to capture strong coaggregation with ovarian cancer (HRR = 6.10; 95% CI, 5.64–6.61).

Large multisource database resources are being developed in several healthcare systems across the United States and country-wide initiatives are becoming more common across the world (42–44). Each of these immense resources has its particular strength and together these resources hold the potential for paradigm-shifting opportunities in Population Science research. However, these will only be realized with consummate advances in computational approaches to interrogate the data. In Utah, a strength of the UPDB is an immense genealogy linked to a statewide health data. Here, we have described two different novel approaches that focus on high-risk pedigrees to understand and address etiologic heterogeneity and define pleiotropic patterns. Both rely on the UPDB to provide the necessary linked databases of genealogy, cancer data, demographic, and medical/clinical information. These data are available on nearly the entire population of Utah starting with the original European settlers of Utah in the 1800s (the earliest records) and extending to current residents of the state (where all sources of records are represented). The UPDB is a dynamic resource that continues to expand as the population grows and as linked data sources develop. For example, a recent SEER-funded pilot project by the UCR illustrated a 73.6% success rate for identifying FFPE tumor blocks for breast cancers diagnosed from 2000 to 2015 across the state. Such streamlining of tumor acquisition by the UCR would further benefit UPDB studies.

The techniques and findings here rely on a large multisource population database and cannot easily be replicated. However, the Statistics Sweden Multigeneration Register, which has been used extensively to identify familial associations between concordant and discordant cancers (45, 46), is one of the potential data source that can be used to test the reproducibility of our findings. Notably, previous genetic discoveries using UPDB have proven generalizable, such as for breast cancer (BRCA1/BRCA2), neurofibromatosis type I (NF1), familial adenomatous polyposis coli (APC), and melanoma (CDKN2A). Once other large databases become ready, the methods described here may enable and accelerate the path to discovery elsewhere. Conversely, our methods also have the potential to be broadened, for example, to explore genetic pleiotropy through multiple primaries (22, 47).

In Approach 1, we highlighted a strategy for reducing heterogeneity, and utilized a novel tumor expression phenotype, PC3, previously shown to be increased in high-risk pedigrees in the UPDB (20). We performed gene mapping in a large high-risk pedigree that contained an unusual number of breast cancer cases whose tumors were extreme for PC3. Using SGS, a method specifically designed for identifying segregating haplotypes in very large families (32, 34), we identified a 0.6 Mb genome-wide significant segment in pedigree 1822 at 2q13 (P = 1.6 × 10−8, LOD equivalent 6.64). A post hoc search for additional carriers (not restricted to those with tumor data) indicates the region may only be 120 kb. Only 4 genes are contained in the smaller region, and of particular interest are IL1A and IL1B. ILs are key regulators of inflammation and immune response with roles in cell growth, angiogenesis, and regulation of inflammatory process, and therefore strong candidate genes for breast cancer risk and mortality. In case–control studies, IL1B SNPs have been associated with breast cancer risk (48, 49). IL1B has also been studied as a candidate for metastatic progression, particularly with respect to invasiveness and the epithelial–mesenchymal transition (50–56), as well as resistance to therapy (57). IL1A has been shown to play a role in chronic inflammation driving tumorigenesis and chemotherapy resistance (58). With these compelling candidates, the natural next step will be to sequence the shared haplotype for functional variants.

In Approach 2, we highlighted the ability to identify pleiotropies and described five FMCs for high-risk breast cancer families. This novel, network-inspired approach simultaneously considered risk of multiple cancer types to classify families into clusters with similar patterns of familial cancer risk. Several cancer types that have previously been shown to coaggregate with breast cancer were identified in the signatures of our agnostic clustering approach (prostate, ovary, uterine, and melanoma; refs. 59–61). However, we show that these risks may vary widely across clusters (ovarian and melanoma, in particular). New coaggregations were also identified. Notably, risk for larynx cancer (FMC3 HRR = 4.93) and lymphomas (FMC3 Hodgkin HRR = 1.53 and FMC2 ALL HRR = 1.57). These findings improve resolution and our understanding of cancer family risks and have potential implications for screening and prevention. Also, while it is common for familial studies to focus only on increased risk, we also considered cancers with decreased risk. Isolating patterns of extreme decrease in risk, such as the multiple cancers at decreased risk in FMC1, could aid in the discovery of etiologic factors that have opposing pleiotropic effects (i.e., a genetic mutation that increases risk for one cancer but is protective for others) or are single cause–single phenotype relationships. Another interesting pattern that may provide avenues to better understand etiology was identified in FMC2, which showed increased risk for several cancers often seen in adolescent and young adults. Other studies have shown similar clustering patterns: Hodgkin lymphoma and other lymphoid neoplasms; (10, 62–64), testicular and non-Hodgkin lymphoma (65); and testicular, breast, and melanoma (66). Our multi-cancer signatures of risk have the potential to improve characterization of different subtypes of breast cancer and provide new avenues to explore common etiologic pathways including gene–environment factors. Subtypes provide the potential to reduce heterogeneity and increase power. The method could also be extended to noncancer phenotypes that may have an underlying genetic link to cancer, such as Parkinson disease (60). Cancer is a complex phenotype and by embracing large multisource databases and computational tools, such as machine learning, it will be possible to seek out important combinations, beyond individual factors, to further our knowledge of the disease.

The goal of both approaches was to increase homogeneity to improve genetic studies, the first by defining cases within a pedigree that are similar and second by selecting groups of pedigrees that are similar (and indicative of genetics, rather than environment). It is important to note that findings from both approaches are sensitive to parameters of the methods. In Approach 1, the phenotype used to select cases is critical to power (extreme-PC3, previously shown to cluster in pedigrees). Without restriction, there is no signal at 2q13, or elsewhere in the genome. We note that sharing in the eight cases in 1822 (P = 1.6 × 10−8) compares in significance with the best single-BRCA1 pedigree published (equivalent P = 6.2 × 10−8; ref. 67) or best BRCA2 pedigree (P = 1.8 × 10−5; ref. 2). In Approach 2, as with all clustering techniques, the clusters are sensitive to the distance metrics and weighing scheme used. This is important to consider when interpreting findings. To improve authenticity and generalizability and reduce spurious patterns, these parameters can be grounded with domain-specific knowledge or logical theories.

Large, population-based, multi-faceted databases, such as the UPDB, represent a new era for Population Sciences. Together with novel approaches, such as we have described here, these will play a critical role in advancing knowledge of cancer risk, elucidating the interplay between factors at the molecular level to individual interactions with the environment, and determine how these factors vary between people. Datasets that link family structure will also allow for important questions about the transgenerational nature of disease. We have illustrated that tumor phenotypes identified using high-risk status can map genes for breast cancer, and that various different cancer pleiotropies exist in high-risk breast cancer pedigrees. These types of discoveries will offer new avenues for defining germline susceptibilities, cancer prevention, and multi-cancer risk management.

P.S. Bernard has ownership interest (including patents) in Bioclassifier LLC. No potential conflicts of interest were disclosed by the other authors.

Conception and design: H.A. Hanson, C.L. Leiser, N.J. Camp

Development of methodology: H.A. Hanson, C.L. Leiser, M.J. Madsen, J. Gardner, S. Knight, N.J. Camp

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): H.A. Hanson, S. Knight, M. Cessna, C. Sweeney, K.R. Smith, P.S. Bernard

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): H.A. Hanson, C.L. Leiser, M.J. Madsen, J. Gardner, K.R. Smith, P.S. Bernard, N.J. Camp

Writing, review, and/or revision of the manuscript: H.A. Hanson, C.L. Leiser, S. Knight, M. Cessna, C. Sweeney, J.A. Doherty, K.R. Smith, P.S. Bernard, N.J. Camp

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): H.A. Hanson, P.S. Bernard

Study supervision: H.A. Hanson, K.R. Smith, N.J. Camp

Research reported in this article was supported by the NIH K12 Award 1K12HD085852-01, NIH K07 Award 1K07CA230150-01, and Huntsman Cancer Institute Cancer Center Support Grant (grant number P30CA042014; all to H.A. Hanson). The Utah Cancer Registry is funded by the NCI's SEER Program, contract no. HHSN261201800016I, and the U.S. Centers for Disease Control and Prevention National Program of Cancer Registries, cooperative agreement no. NU58DP0063200, with additional support from the University of Utah and Huntsman Cancer Foundation.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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