Both genetic and environmental factors play a role in the causes of early-onset cancer. Several well-defined genetic syndromes contribute to early-onset cancer risk, along with a wider array of common alleles that influence risk marginally and detected at the population level. As an example of the former, Li–Fraumeni syndrome caused by mutations in the tumor suppressor gene TP53 is associated with an increased risk of a spectrum of cancers diagnosed at early ages (Saletta et al., 2015). An example of low penetrance common genetic variations associated with cancer risk includes IKZF1 and ARID5B genes in pediatric acute lymphoblastic leukemia (ALL) (Moriyama et al., 2015). Both of these classes of variants may vary in frequency by race/ethnic group and cluster by families (Caswell-Jin et al., 2018; Walsh et al., 2013; Ricker et al., 2016). Examination of cancer predisposition requires investigation in ethnic strata particularly where cancer incidence rates are known to differ as they do for many pediatric cancer types, such as leukemia (Feng and de Smith, 2020) and brain cancer (Ostrom et al., 2019).

In addition to primary cancers, incidence patterns of second independent malignancies may also provide a perspective of underlying genetic predisposition. Among childhood cancer survivors, more second primary malignancy (SPM) cases are observed among non-Latino Whites (NLW) than Latino subjects (Brown et al., 2019). This is also reflected in adult cancers, where Latino breast cancer survivors had lower risk of second cancers than NLW and NL Black women (Calip et al., 2015).

Germline pathogenic/likely pathogenic variants in cancer predisposition genes are found in approximately 10% of pediatric cancer patients (Saletta et al., 2015; Zhang et al., 2015; Plon and Lupo, 2019; Ripperger et al., 2017), and may be inherited or arise de novo. Highly penetrant inherited variants will contribute to clustering of cancer cases within the family. Shared environments within the family unit may also be considered alongside genetic risk as potential causes for family-based cancer concordance.

Familial concordance of a wide variety of cancers has been assessed using the Swedish Family-Cancer Database, leading to a deep understanding of familial relative risks (Sud et al., 2019; Kharazmi et al., 2012; Hemminki and Czene, 2002). The Victorian Paediatric Cancer Family Study in Australia also explored the cancer risks for relatives of children with cancer in a small population (Heath et al., 2014). In the United States, the Utah Population database may be the best-known population for studying familial risk (Curtin et al., 2013; Kohli et al., 2019; Samadder et al., 2014; Wang et al., 2010; Goldgar et al., 1994). Importantly, these studies largely comprise families of European ancestry, and therefore have not examined potential ethnic-specific familial risks. Here, we utilized linked population registries with over 64,000 individuals to quantify the familial risks (siblings and mothers) and the risks of early-onset SPMs in the highly diverse and large population of California. Risk patterns suggest that race/ethnic minority subjects in the United States may harbor a higher burden of familial risks for some types of cancers compared with the majority population – NLW.

To our knowledge, this is the first study to quantify the familial clustering risks and risks of SPMs among early-onset cancer patients with an emphasis on racial/ethnic differences. Using linked population registry data in the California population, we found that the risk for a sibling child/AYA or mother to have early-onset cancer was elevated once a proband was identified with an early-onset cancer. Likewise, the relative risks for SPMs were elevated among children/AYAs who contracted a first primary cancer. Due to the rarity of childhood cancers, the absolute risk of early-onset cancer is very small, but still higher among young siblings and mothers in the current study (0.074%) compared to general population (0.023%, calculated from SEER) of the same age group. The findings were consistent across race/ethnic groups; however, the magnitude was different. Latinos and non-Latino Blacks had higher sibling/maternal relative risks compared to NLWs for solid cancers, and APIs exhibited a higher risk of hematologic neoplasms.

Consistent with our results, a rich literature with a primary focus on European ancestry populations has reported excessive familial risks of hematologic malignancies (Sud et al., 2019), lymphomas (Cerhan and Slager, 2015; Fallah et al., 2016; Madanat‐Harjuoja et al., 2020), brain tumors (Couldwell and Cannon-Albright, 2017; Crump et al., 2015), neuroblastomas (Kamihara et al., 2017), retinoblastomas (Madanat‐Harjuoja et al., 2020; Kamihara et al., 2017), germ cell tumors (Landero-Huerta et al., 2017), sarcomas (Lynch et al., 2003), and melanomas (Frank et al., 2017). In terms of secondary cancers, studies have reported excessive risks of SPMs among of survivors of hereditary retinoblastoma (Marees et al., 2008), chronic myeloid leukemia (Sasaki et al., 2019), chronic lymphocytic leukemia (Molica, 2005), Hodgkin’s lymphoma (Baker, 2016), non-Hodgkin’s lymphoma (Chattopadhyay et al., 2018), and neuroblastoma (Applebaum et al., 2015). The excessive familial risks of certain cancers are highly likely to be associated with genetic predisposition. The archetypic examples are germline loss-of-function mutations in RB1, which are found in ~40% of retinoblastoma cases (Kamihara et al., 2017), and adrenal cortical cancer, with germline TP53 mutations accounting for most familial cases (Kamihara et al., 2017). Low penetrance common genetic variations, for instance in CEBPE, IKZF1, and ARID5B genes in ALL, are associated with cancer risk and may also contribute to familial concordance as combinations of low-frequency alleles or ‘polygenic risk scores’ have been shown to be as impactful as single strong predisposition mutations in adult cancers (Fantus and Helfand, 2019; Yadav and Couch, 2019); however, their contribution to cancer clustering among children and their families has not yet been studied.

Our data demonstrate a higher degree of familial-based clustering of solid cancers among Latinos and non-Latino Blacks compared to non-Latino Whites, and hematologic cancers among Asian/Pacific Islanders. This familial concordance is likely due to both shared genetic and environmental causes and appears to affect some cancer types differentially by race/ethnicity. Latinos are an admixed population, comprising an ancestral mixture from Native American, European, and African sources; likewise, non-Latino Blacks are admixed with African and smaller levels of European ancestry. Asian/Pacific Islanders constitute a particularly diverse group in California with origins in multiple countries. California Latinos, particularly the youth population, are largely from Mexico and harbor a higher risk of certain cancers particularly pediatric leukemias, the most common cancer in children (Barrington-Trimis et al., 2017); however, this higher risk is partially accounted for by a higher frequency of common risk alleles, which do not address strong familial predisposition loci (Walsh et al., 2013) and obviously cannot account for their higher familial risk of solid tumors. Clearly, this higher risk identified in relation to the family units in US minorities has not been systematically studied, and our results here beg for an analysis of comparative sources of genetic and environmental risk that contribute to the higher risk and familial clustering of certain cancers in Latinos, Blacks, and Asian/Pacific Islanders.

Therapy of the first primary cancer is a major factor in the induction of secondary independent malignancies (McNerney et al., 2017; Mazonakis et al., 2018; Turcotte et al., 2019). Multiple primary cancer diagnoses are considered a key feature of hereditary cancer predisposition syndromes (Valdez et al., 2017). As such secondary cancers are rare, genetics are still likely to play a strong role (Churpek et al., 2016), and our overall SPM results here emphasize a similar patterning as cancer clustering in FPMs. Of note, our analysis was not designed to distinguish risk influences from therapies for the primary cancers on secondary cancers.

For some tumor types, the germline predisposition was readily noted in this cohort, for example 10 of the 14 affected relatives who had a proband with retinoblastoma were diagnosed with the same cancer, an unsurprising finding given that germline RB1 mutations account for a significant proportion of retinoblastoma are highly penetrant and those tumors tend to be diagnosed young. We also observed increased relative risks for sarcomas given a proband with leukemias, suggesting the presence of families with Li–Fraumeni syndrome, which is characterized by a spectrum of childhood and adult onset cancers including adrenocortical carcinoma, breast cancer, CNS tumors, sarcomas, and leukemia (Valdez et al., 2017).

Population-level selection pressures are thought to influence the relative frequencies of alleles. For instance, genetic adaptations that shaped the Native American genome to cold and warm environments (Reynolds et al., 2019), and immune response following colonization by Europeans (Lindo et al., 2016). Our result suggests that some adaptive selection pressures, or simply genetic drift, in specific ethnic groups may differentially influence familial cancer clustering as it does for immune and metabolic phenotypes among several ethnic groups (O'Fallon and Fehren-Schmitz, 2011; Vatsiou et al., 2016). If replicated in other study settings, this contrast between genetic risk of child and adult onset cancers by ethnicity should be studied further for a fuller understanding of familial risks.

Our analyses capitalized on the highly diverse population in California, allowing us to quantify the relative risks across different ethnic groups. Moreover, the utilization of linked population-based registries in California enabled us to minimize the selection and information biases introduced by a case–control study design or other strategies that only sample portions of the population. There are also some limitations of our study. Despite the large number of total cancer cases, the number of affected siblings and second primaries is very small for some cancer types, thus limiting the power to detect significant relative risks. Also, we are unable to track cancer incidence for affected siblings, maternal cancers, and SPMs that may have been diagnosed outside of California. In addition, the follow-up time of 26 years is not enough for a comprehensive detection of SPMs in the probands, nor for cancers arising in proband mothers at older ages. These insufficient follow-up time and loss-to follow-up issues have limited our ability to quantify the relative risks among mothers with cancer onset at older ages (>40 years). Furthermore, it is likely that the low number of mothers with cancer is a result of bias against some very strong cancer predispositions, so the patients could not survive long enough or be healthy enough to reproduce. Lastly, the lack of records on fathers reduces our ability to quantify the relative risks among other first-degree relatives and may reduce the appreciation of the potential contribution of high-risk cancer predisposition syndromes which can be inherited from either parent.

Accepting those limitations with the current dataset, our study has several important implications that may open windows to future research. First, the genetic predispositions driving the excessive early-onset cancer risks among the Latino, non-Latino Black, and Asian populations, whether from higher frequencies of known cancer predisposition syndromes or mutations in novel genes or from a higher burden of common or rare genetic risk alleles, warrant further investigation. Second, the comparative attributable fraction of familial risk based on environmental risk factors interacting with genetic predispositions warrants further investigation. Lastly, descriptive studies on familial and secondary cancer risks among race/ethnic groups other than NLW may provide additional insights into cancer incidence variation leading to incidence disparities by race/ethnicity and provide critical information for tailoring appropriate messages for familial genetic counseling.

We have uploaded a blinded version of datasets used to generate the figures in this study to Dryad. https://doi.org/10.5061/dryad.80gb5mkq6.

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Author details

1. Qianxi Feng

   Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4888-4629

2. Eric Nickels

   1. Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States
   2. Children's Hospital Los Angeles, Los Angeles, United States

   Contribution

   Data curation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Ivo S Muskens

   Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States

   Contribution

   Supervision, Visualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Adam J de Smith

   Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States

   Contribution

   Conceptualization, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

5. W James Gauderman

   Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States

   Contribution

   Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Amy C Yee

   Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States

   Contribution

   Resources, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

7. Charite Ricker

   Norris Comprehensive Cancer Center, USC Keck School of Medicine, Los Angeles, United States

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

8. Thomas Mack

   Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

9. Andrew D Leavitt

   Departments of Medicine and Laboratory Medicine, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Resources, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

10. Lucy A Godley

    Departments of Medicine and Human Genetics, The University of Chicago, Chicago, United States

    Contribution

    Conceptualization, Resources, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

11. Joseph L Wiemels

    Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing - review and editing

    For correspondence

    wiemels@usc.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4838-9951

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