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.

1. 1. Feng Q

   (2020) Dryad Digital Repository

   Relative risks of familial cancers in California.

   https://doi.org/10.5061/dryad.80gb5mkq6

1. 1. Applebaum MA
   2. Henderson TO
   3. Lee SM
   4. Pinto N
   5. Volchenboum SL
   6. Cohn SL

   (2015) Second malignancies in patients with neuroblastoma: the effects of risk-based therapy

   Pediatric Blood & Cancer 62:128–133.

   https://doi.org/10.1002/pbc.25249

   • PubMed
   • Google Scholar
2. 1. Baker H

   (2016) Second Cancer risk for hodgkin's lymphoma survivors

   The Lancet Oncology 17:e50.

   https://doi.org/10.1016/S1470-2045(16)00002-4

   • Google Scholar
3. 1. Barrington-Trimis JL
   2. Cockburn M
   3. Metayer C
   4. Gauderman WJ
   5. Wiemels J
   6. McKean-Cowdin R

   (2017) Trends in childhood leukemia incidence over two decades from 1992 to 2013

   International Journal of Cancer 140:1000–1008.

   https://doi.org/10.1002/ijc.30487

   • Google Scholar
4. 1. Breslow NE
   2. Day NE

   (1987)

   Statistical methods in Cancer research volume II--the design and analysis of cohort studies

   IARC Scientific Publications 82:1–406.

   • Google Scholar
5. 1. Brown AL
   2. Arroyo VM
   3. Agrusa JE
   4. Scheurer ME
   5. Gramatges MM
   6. Lupo PJ

   (2019) Survival disparities for second primary malignancies diagnosed among childhood Cancer survivors: a population-based assessment

   Cancer 125:3623–3630.

   https://doi.org/10.1002/cncr.32356

   • PubMed
   • Google Scholar
6. Website

   1. California Cancer Registry

   (2018) Data for researchers - State of California

   Accessed May 10, 2021.

   https://www.ccrcal.org/retrieve-data/data-for-researchers/
7. 1. Calip GS
   2. Law EH
   3. Ko NY

   (2015) Racial and ethnic differences in risk of second primary cancers among breast Cancer survivors

   Breast Cancer Research and Treatment 151:687–696.

   https://doi.org/10.1007/s10549-015-3439-7

   • PubMed
   • Google Scholar
8. 1. Caswell-Jin JL
   2. Gupta T
   3. Hall E
   4. Petrovchich IM
   5. Mills MA
   6. Kingham KE
   7. Koff R
   8. Chun NM
   9. Levonian P
   10. Lebensohn AP
   11. Ford JM
   12. Kurian AW

   (2018) Racial/ethnic differences in multiple-gene sequencing results for hereditary Cancer risk

   Genetics in Medicine 20:234–239.

   https://doi.org/10.1038/gim.2017.96

   • PubMed
   • Google Scholar
9. 1. Cerhan JR
   2. Slager SL

   (2015) Familial predisposition and genetic risk factors for lymphoma

   Blood 126:2265–2273.

   https://doi.org/10.1182/blood-2015-04-537498

   • PubMed
   • Google Scholar
10. 1. Chattopadhyay S
    2. Sud A
    3. Zheng G
    4. Yu H
    5. Sundquist K
    6. Sundquist J
    7. Försti A
    8. Houlston R
    9. Hemminki A
    10. Hemminki K

    (2018) Second primary cancers in non‐hodgkin lymphoma: bidirectional analyses suggesting role for immune dysfunction

    International Journal of Cancer 143:2449–2457.

    https://doi.org/10.1002/ijc.31801

    • Google Scholar
11. 1. Churpek JE
    2. Marquez R
    3. Neistadt B
    4. Claussen K
    5. Lee MK
    6. Churpek MM
    7. Huo D
    8. Weiner H
    9. Bannerjee M
    10. Godley LA
    11. Le Beau MM
    12. Pritchard CC
    13. Walsh T
    14. King M-C
    15. Olopade OI
    16. Larson RA

    (2016) Inherited mutations in Cancer susceptibility genes are common among survivors of breast Cancer who develop therapy-related leukemia

    Cancer 122:304–311.

    https://doi.org/10.1002/cncr.29615

    • Google Scholar
12. 1. Couldwell WT
    2. Cannon-Albright LA

    (2017) A description of familial clustering of meningiomas in the utah population

    Neuro-Oncology 19:1683–1687.

    https://doi.org/10.1093/neuonc/nox127

    • PubMed
    • Google Scholar
13. 1. Crump C
    2. Sundquist J
    3. Sieh W
    4. Winkleby MA
    5. Sundquist K

    (2015) Perinatal and familial risk factors for brain tumors in childhood through young adulthood

    Cancer Research 75:576–583.

    https://doi.org/10.1158/0008-5472.CAN-14-2285

    • PubMed
    • Google Scholar
14. 1. Curtin K
    2. Smith KR
    3. Fraser A
    4. Pimentel R
    5. Kohlmann W
    6. Schiffman JD

    (2013) Familial risk of childhood Cancer and tumors in the Li-Fraumeni spectrum in the utah population database: implications for genetic evaluation in pediatric practice

    International Journal of Cancer 133:2444–2453.

    https://doi.org/10.1002/ijc.28266

    • PubMed
    • Google Scholar
15. 1. Fallah M
    2. Kharazmi E
    3. Pukkala E
    4. Tretli S
    5. Olsen JH
    6. Tryggvadottir L
    7. Sundquist K
    8. Hemminki K

    (2016) Familial risk of non-Hodgkin lymphoma by sex, relationship, age at diagnosis and histology: a joint study from five nordic countries

    Leukemia 30:373–378.

    https://doi.org/10.1038/leu.2015.272

    • PubMed
    • Google Scholar
16. 1. Fantus RJ
    2. Helfand BT

    (2019) Germline genetics of prostate Cancer: time to incorporate genetics into early detection tools

    Clinical Chemistry 65:74–79.

    https://doi.org/10.1373/clinchem.2018.286658

    • Google Scholar
17. 1. Feng Q
    2. de Smith AJ

    (2020) Trends in acute lymphoblastic leukemia incidence in the US from 2000-2016: an increased risk in latinos across all age groups

    American Journal of Epidemiology 190:519–527.

    https://doi.org/10.1093/aje/kwaa215

    • Google Scholar
18. 1. Frank C
    2. Sundquist J
    3. Hemminki A
    4. Hemminki K

    (2017) Risk of other cancers in families with melanoma: novel familial links

    Scientific Reports 7:42601.

    https://doi.org/10.1038/srep42601

    • PubMed
    • Google Scholar
19. 1. Goldgar DE
    2. Easton DF
    3. Cannon-Albright LA
    4. Skolnick MH

    (1994) Systematic Population-Based assessment of Cancer risk in First-Degree relatives of Cancer probands

    JNCI: Journal of the National Cancer Institute 86:1600–1608.

    https://doi.org/10.1093/jnci/86.21.1600

    • Google Scholar
20. Website

    1. Guidelines for Using Confidence Intervals for Public Health Assessment

    (2012) Washington state department of health

    Accessed March 29, 2021.

    https://www.doh.wa.gov/Portals/1/Documents/1500/ConfIntGuide.pdf
21. 1. Heath JA
    2. Smibert E
    3. Algar EM
    4. Dite GS
    5. Hopper JL

    (2014) Cancer risks for relatives of children with Cancer

    Journal of Cancer Epidemiology 2014:1–4.

    https://doi.org/10.1155/2014/806076

    • Google Scholar
22. 1. Hemminki K
    2. Czene K

    (2002)

    Attributable risks of familial Cancer from the Family-Cancer database

    Cancer Epidemiology, Biomarkers & Prevention 11:1638–1644.

    • PubMed
    • Google Scholar
23. 1. Kamihara J
    2. Bourdeaut F
    3. Foulkes WD
    4. Molenaar JJ
    5. Mossé YP
    6. Nakagawara A
    7. Parareda A
    8. Scollon SR
    9. Schneider KW
    10. Skalet AH
    11. States LJ
    12. Walsh MF
    13. Diller LR
    14. Brodeur GM

    (2017) Retinoblastoma and neuroblastoma predisposition and surveillance

    Clinical Cancer Research 23:e98–e106.

    https://doi.org/10.1158/1078-0432.CCR-17-0652

    • PubMed
    • Google Scholar
24. 1. Kharazmi E
    2. Fallah M
    3. Sundquist K
    4. Hemminki K

    (2012) Familial risk of early and late onset Cancer: nationwide prospective cohort study

    BMJ 345:e8076.

    https://doi.org/10.1136/bmj.e8076

    • Google Scholar
25. 1. Kohli DR
    2. Smith KR
    3. Wong J
    4. Yu Z
    5. Boucher K
    6. Faigel DO
    7. Pannala R
    8. Burt RW
    9. Curtin K
    10. Samadder NJ

    (2019) Familial pancreatic Cancer risk: a population-based study in utah

    Journal of Gastroenterology 54:1106–1112.

    https://doi.org/10.1007/s00535-019-01597-3

    • PubMed
    • Google Scholar
26. 1. Landero-Huerta DA
    2. Vigueras-Villasenor RM
    3. Yokoyama-Rebollar E
    4. Arechaga-Ocampo E
    5. Rojas-Castaneda JC
    6. Jimenez-Trejo F
    7. Chavez-Saldana M

    (2017) Epigenetic and risk factors of testicular germ cell tumors: a brief review

    Frontiers in Bioscience 22:1073–1098.

    https://doi.org/10.2741/4534

    • Google Scholar
27. 1. Lindo J
    2. Huerta-Sánchez E
    3. Nakagome S
    4. Rasmussen M
    5. Petzelt B
    6. Mitchell J
    7. Cybulski JS
    8. Willerslev E
    9. DeGiorgio M
    10. Malhi RS

    (2016) A time transect of exomes from a native american population before and after european contact

    Nature Communications 7:13175.

    https://doi.org/10.1038/ncomms13175

    • PubMed
    • Google Scholar
28. 1. Lynch HT
    2. Deters CA
    3. Hogg D
    4. Lynch JF
    5. Kinarsky Y
    6. Gatalica Z

    (2003) Familial sarcoma: challenging pedigrees

    Cancer 98:1947–1957.

    https://doi.org/10.1002/cncr.11743

    • PubMed
    • Google Scholar
29. 1. Madanat‐Harjuoja L‐M
    2. Pitkäniemi J
    3. Hirvonen E
    4. Malila N
    5. Diller LR

    (2020) Linking population‐based registries to identify familial Cancer risk in childhood Cancer

    Cancer 126:3076–3083.

    https://doi.org/10.1002/cncr.32882

    • Google Scholar
30. 1. Marees T
    2. Moll AC
    3. Imhof SM
    4. de Boer MR
    5. Ringens PJ
    6. van Leeuwen FE

    (2008) Risk of second malignancies in survivors of retinoblastoma: more than 40 years of Follow-up

    JNCI: Journal of the National Cancer Institute 100:1771–1779.

    https://doi.org/10.1093/jnci/djn394

    • Google Scholar
31. 1. Mazonakis M
    2. Kachris S
    3. Damilakis J

    (2018) Second Cancer risk from radiation therapy for common solid tumors diagnosed in reproductive-aged females

    Radiation Protection Dosimetry 182:208–214.

    https://doi.org/10.1093/rpd/ncy050

    • PubMed
    • Google Scholar
32. 1. McNerney ME
    2. Godley LA
    3. Le Beau MM

    (2017) Therapy-related myeloid neoplasms: when genetics and environment collide

    Nature Reviews Cancer 17:513–527.

    https://doi.org/10.1038/nrc.2017.60

    • PubMed
    • Google Scholar
33. 1. Molica S

    (2005) Second neoplasms in chronic lymphocytic leukemia: incidence and pathogenesis with emphasis on the role of different therapies

    Leukemia & Lymphoma 46:49–54.

    https://doi.org/10.1080/10428190400007524

    • PubMed
    • Google Scholar
34. 1. Moriyama T
    2. Relling MV
    3. Yang JJ

    (2015) Inherited genetic variation in childhood acute lymphoblastic leukemia

    Blood 125:3988–3995.

    https://doi.org/10.1182/blood-2014-12-580001

    • PubMed
    • Google Scholar
35. 1. O'Fallon BD
    2. Fehren-Schmitz L

    (2011) Native americans experienced a strong population bottleneck coincident with european contact

    PNAS 108:20444–20448.

    https://doi.org/10.1073/pnas.1112563108

    • PubMed
    • Google Scholar
36. 1. Ostrom QT
    2. Adel Fahmideh M
    3. Cote DJ
    4. Muskens IS
    5. Schraw JM
    6. Scheurer ME
    7. Bondy ML

    (2019) Risk factors for childhood and adult primary brain tumors

    Neuro-Oncology 21:1357–1375.

    https://doi.org/10.1093/neuonc/noz123

    • PubMed
    • Google Scholar
37. 1. Plon SE
    2. Lupo PJ

    (2019) Genetic predisposition to childhood Cancer in the genomic era

    Annual Review of Genomics and Human Genetics 20:241–263.

    https://doi.org/10.1146/annurev-genom-083118-015415

    • PubMed
    • Google Scholar
38. 1. Reynolds AW
    2. Mata-Míguez J
    3. Miró-Herrans A
    4. Briggs-Cloud M
    5. Sylestine A
    6. Barajas-Olmos F
    7. Garcia-Ortiz H
    8. Rzhetskaya M
    9. Orozco L
    10. Raff JA
    11. Hayes MG
    12. Bolnick DA

    (2019) Comparing signals of natural selection between three indigenous north american populations

    PNAS 116:9312–9317.

    https://doi.org/10.1073/pnas.1819467116

    • PubMed
    • Google Scholar
39. 1. Ricker C
    2. Culver JO
    3. Lowstuter K
    4. Sturgeon D
    5. Sturgeon JD
    6. Chanock CR
    7. Gauderman WJ
    8. McDonnell KJ
    9. Idos GE
    10. Gruber SB

    (2016) Increased yield of actionable mutations using multi-gene panels to assess hereditary Cancer susceptibility in an ethnically diverse clinical cohort

    Cancer Genetics 209:130–137.

    https://doi.org/10.1016/j.cancergen.2015.12.013

    • PubMed
    • Google Scholar
40. 1. Ripperger T
    2. Bielack SS
    3. Borkhardt A
    4. Brecht IB
    5. Burkhardt B
    6. Calaminus G
    7. Debatin KM
    8. Deubzer H
    9. Dirksen U
    10. Eckert C
    11. Eggert A
    12. Erlacher M
    13. Fleischhack G
    14. Frühwald MC
    15. Gnekow A
    16. Goehring G
    17. Graf N
    18. Hanenberg H
    19. Hauer J
    20. Hero B
    21. Hettmer S
    22. von Hoff K
    23. Horstmann M
    24. Hoyer J
    25. Illig T
    26. Kaatsch P
    27. Kappler R
    28. Kerl K
    29. Klingebiel T
    30. Kontny U
    31. Kordes U
    32. Körholz D
    33. Koscielniak E
    34. Kramm CM
    35. Kuhlen M
    36. Kulozik AE
    37. Lamottke B
    38. Leuschner I
    39. Lohmann DR
    40. Meinhardt A
    41. Metzler M
    42. Meyer LH
    43. Moser O
    44. Nathrath M
    45. Niemeyer CM
    46. Nustede R
    47. Pajtler KW
    48. Paret C
    49. Rasche M
    50. Reinhardt D
    51. Rieß O
    52. Russo A
    53. Rutkowski S
    54. Schlegelberger B
    55. Schneider D
    56. Schneppenheim R
    57. Schrappe M
    58. Schroeder C
    59. von Schweinitz D
    60. Simon T
    61. Sparber-Sauer M
    62. Spix C
    63. Stanulla M
    64. Steinemann D
    65. Strahm B
    66. Temming P
    67. Thomay K
    68. von Bueren AO
    69. Vorwerk P
    70. Witt O
    71. Wlodarski M
    72. Wössmann W
    73. Zenker M
    74. Zimmermann S
    75. Pfister SM
    76. Kratz CP

    (2017) Childhood Cancer predisposition syndromes-A concise review and recommendations by the Cancer predisposition working group of the society for pediatric oncology and hematology

    American Journal of Medical Genetics Part A 173:1017–1037.

    https://doi.org/10.1002/ajmg.a.38142

    • PubMed
    • Google Scholar
41. 1. Saletta F
    2. Dalla Pozza L
    3. Byrne JA

    (2015) Genetic causes of Cancer predisposition in children and adolescents

    Translational Pediatrics 4:67–75.

    https://doi.org/10.3978/j.issn.2224-4336.2015.04.08

    • PubMed
    • Google Scholar
42. 1. Samadder NJ
    2. Curtin K
    3. Wong J
    4. Tuohy TM
    5. Mineau GP
    6. Smith KR
    7. Pimentel R
    8. Pappas L
    9. Boucher K
    10. Garrido-Laguna I
    11. Provenzale D
    12. Burt RW

    (2014) Epidemiology and familial risk of synchronous and metachronous colorectal Cancer: a population-based study in utah

    Clinical Gastroenterology and Hepatology 12:2078–2084.

    https://doi.org/10.1016/j.cgh.2014.04.017

    • PubMed
    • Google Scholar
43. 1. Sasaki K
    2. Kantarjian HM
    3. O'Brien S
    4. Ravandi F
    5. Konopleva M
    6. Borthakur G
    7. Garcia-Manero G
    8. Wierda WG
    9. Daver N
    10. Ferrajoli A
    11. Takahashi K
    12. Jain P
    13. Rios MB
    14. Pierce SA
    15. Jabbour EJ
    16. Cortes JE

    (2019) Incidence of second malignancies in patients with chronic myeloid leukemia in the era of tyrosine kinase inhibitors

    International Journal of Hematology 109:545–552.

    https://doi.org/10.1007/s12185-019-02620-2

    • PubMed
    • Google Scholar
44. 1. Sud A
    2. Chattopadhyay S
    3. Thomsen H
    4. Sundquist K
    5. Sundquist J
    6. Houlston RS
    7. Hemminki K

    (2019) Analysis of 153 115 patients with hematological malignancies refines the spectrum of familial risk

    Blood 134:960–969.

    https://doi.org/10.1182/blood.2019001362

    • PubMed
    • Google Scholar
45. 1. Turcotte LM
    2. Liu Q
    3. Yasui Y
    4. Henderson TO
    5. Gibson TM
    6. Leisenring W
    7. Arnold MA
    8. Howell RM
    9. Green DM
    10. Armstrong GT
    11. Robison LL
    12. Neglia JP

    (2019) Chemotherapy and risk of subsequent malignant neoplasms in the childhood Cancer survivor study cohort

    Journal of Clinical Oncology 37:3310–3319.

    https://doi.org/10.1200/JCO.19.00129

    • PubMed
    • Google Scholar
46. 1. Valdez JM
    2. Nichols KE
    3. Kesserwan C

    (2017) Li-Fraumeni syndrome: a paradigm for the understanding of hereditary Cancer predisposition

    British Journal of Haematology 176:539–552.

    https://doi.org/10.1111/bjh.14461

    • PubMed
    • Google Scholar
47. 1. Vatsiou AI
    2. Bazin E
    3. Gaggiotti OE

    (2016) Changes in selective pressures associated with human population expansion may explain metabolic and immune related pathways enriched for signatures of positive selection

    BMC Genomics 17:504.

    https://doi.org/10.1186/s12864-016-2783-2

    • Google Scholar
48. 1. Walsh KM
    2. Chokkalingam AP
    3. Hsu LI
    4. Metayer C
    5. de Smith AJ
    6. Jacobs DI
    7. Dahl GV
    8. Loh ML
    9. Smirnov IV
    10. Bartley K
    11. Ma X
    12. Wiencke JK
    13. Barcellos LF
    14. Wiemels JL
    15. Buffler PA

    (2013) Associations between genome-wide native american ancestry, known risk alleles and B-cell ALL risk in hispanic children

    Leukemia 27:2416–2419.

    https://doi.org/10.1038/leu.2013.130

    • PubMed
    • Google Scholar
49. 1. Wang X
    2. Harmon J
    3. Zabrieskie N
    4. Chen Y
    5. Grob S
    6. Williams B
    7. Lee C
    8. Kasuga D
    9. Shaw PX
    10. Buehler J
    11. Wang N
    12. Zhang K

    (2010) Using the utah population database to assess familial risk of primary open angle Glaucoma

    Vision Research 50:2391–2395.

    https://doi.org/10.1016/j.visres.2010.09.018

    • PubMed
    • Google Scholar
50. 1. Yadav S
    2. Couch FJ

    (2019) Germline genetic testing for breast Cancer risk: the past, present, and future

    American Society of Clinical Oncology Educational Book 39:61–74.

    https://doi.org/10.1200/EDBK_238987

    • PubMed
    • Google Scholar
51. 1. Zhang J
    2. Walsh MF
    3. Wu G
    4. Edmonson MN
    5. Gruber TA
    6. Easton J
    7. Hedges D
    8. Ma X
    9. Zhou X
    10. Yergeau DA
    11. Wilkinson MR
    12. Vadodaria B
    13. Chen X
    14. McGee RB
    15. Hines-Dowell S
    16. Nuccio R
    17. Quinn E
    18. Shurtleff SA
    19. Rusch M
    20. Patel A
    21. Becksfort JB
    22. Wang S
    23. Weaver MS
    24. Ding L
    25. Mardis ER
    26. Wilson RK
    27. Gajjar A
    28. Ellison DW
    29. Pappo AS
    30. Pui C-H
    31. Nichols KE
    32. Downing JR

    (2015) Germline mutations in predisposition genes in pediatric Cancer

    New England Journal of Medicine 373:2336–2346.

    https://doi.org/10.1056/NEJMoa1508054

    • Google Scholar

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

• 792

  views

• 77

  downloads

• 2

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.