Co-occurrence of congenital anomalies and childhood brain tumors in 22 million live births

Thanh T Hoang; Jeremy M Schraw; Charles Shumate; Tania A Desrosiers; Wendy N Nembhard; Mahsa Yazdy; Eirini Nestoridi; Amanda E Janitz; Russell S Kirby; Jason L Salemi; Jean Paul Tanner; Tiffany M Chambers; Michael D Taylor; Chad D Huff; Sharon E Plon; Philip J Lupo; Michael E Scheurer

doi:10.1093/neuonc/noaf087

. 2025 Mar 26;27(7):1910–1922. doi: 10.1093/neuonc/noaf087

Co-occurrence of congenital anomalies and childhood brain tumors in 22 million live births

Thanh T Hoang ^1,^2,^3,^✉, Jeremy M Schraw ^4,^5,⁶, Charles Shumate ⁷, Tania A Desrosiers ⁸, Wendy N Nembhard ⁹, Mahsa Yazdy ¹⁰, Eirini Nestoridi ¹¹, Amanda E Janitz ¹², Russell S Kirby ¹³, Jason L Salemi ¹⁴, Jean Paul Tanner ¹⁵, Tiffany M Chambers ^16,¹⁷, Michael D Taylor ^18,^19,²⁰, Chad D Huff ²¹, Sharon E Plon ^22,^23,^24,^25,²⁶, Philip J Lupo ^27,^28,²⁹, Michael E Scheurer ^30,^31,³²

¹ Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA

² Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA

³ The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA

⁴ Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA

⁵ Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA

⁶ The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA

⁷ Birth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas, USA

⁸ Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

⁹ Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences and Arkansas Center for Birth Defects Research and Prevention, Little Rock, Arkansas, USA

¹⁰ Massachusetts Center for Birth Defects Research and Prevention, Division for Surveillance, Research, and Promotion of Perinatal Health, Massachusetts Department of Public Health, Boston, Massachusetts, USA

¹¹ Massachusetts Center for Birth Defects Research and Prevention, Division for Surveillance, Research, and Promotion of Perinatal Health, Massachusetts Department of Public Health, Boston, Massachusetts, USA

¹² Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences, Oklahoma City, Oklahoma, USA

¹³ Chiles Center, College of Public Health, University of South Florida, Tampa, Florida, USA

¹⁴ Chiles Center, College of Public Health, University of South Florida, Tampa, Florida, USA

¹⁵ Chiles Center, College of Public Health, University of South Florida, Tampa, Florida, USA

¹⁶ Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA

¹⁷ Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA

¹⁸ Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA

¹⁹ Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA

²⁰ The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA

²¹ Division of Cancer Prevention and Population Sciences, Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA

²² Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA

²³ Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA

²⁴ The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA

²⁵ Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA

²⁶ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA

²⁷ Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA

²⁸ Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA

²⁹ The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA

³⁰ Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA

³¹ Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA

³² The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA

^✉

Corresponding Author: Thanh T. Hoang, Department of Pediatrics Baylor College of Medicine, One Baylor Plaza MS: BCM 622, Houston, TX 77030, USA (thanh.hoang@bcm.edu).

PMCID: PMC12417818 PMID: 40138258

Abstract

Background

Children born with a congenital anomaly have a higher risk of developing a brain tumor during childhood or adolescence, but the co-occurrence between specific types of congenital anomalies and specific types of childhood brain tumors (CBTs) is not well described. This study characterized the associations between specific congenital anomalies and CBTs.

Methods

We leveraged a population-based registry linkage study of births (1990–2018), congenital anomalies, and cancer from 9 states (n = 22,599,099 births). Congenital anomalies were classified as major structural without a known chromosomal or genetic syndrome, chromosomal, neurofibromatosis, and/or tuberous sclerosis complex. CBT classification was based on the International Classification of Childhood Cancer for children diagnosed < 20 years. Cox regression analyses were conducted separately by congenital anomaly for anomaly-CBT combinations with at least 5 co-occurring cases. We conducted analyses for any CBT and separately for astrocytoma, atypical teratoid/rhabdoid tumor, ependymoma, medulloblastoma, mixed and unspecified gliomas, and primitive neuroectodermal tumors.

Results

There were 6,247 children diagnosed with a CBT. Having any major structural anomaly was associated with risk of any CBT and across all subgroups (aHR range: 1.48–3.69) except ependymoma, particularly among children diagnosed with a tumor by 1 year of age. Of the 66 anomaly-CBT combinations analyzed, 42 were significant (P < .05), including 25 in an earlier version of this study and 16 novel associations (aHR range: 1.46–525). Anomaly–CBT associations also differed by astrocytoma histology.

Conclusions

We observed consistent evidence that having a structural congenital anomaly increases risk of developing a CBT, particularly in infancy, which may provide insights into etiology.

Keywords: birth defects, congenital malformations, childhood cancer, epidemiology, pediatric brain tumor

Key Points.

Having a major structural anomaly increased the risk of developing a CBT before age one.
Forty-two positive congenital anomaly-CBT associations were significant, and 16 were novel.
Congenital anomaly and CBT co-occurrences differed by astrocytoma type.

Importance of the Study.

While having a congenital anomaly increases the risk of developing a childhood brain tumor (CBT), co-occurrence patterns have not been well described for specific anomalies or histologic type of CBT. This large population-based study of over 22 million live births provided population-based risk estimates for CBTs overall and 6 histologic types (ie, astrocytoma, atypical teratoid/rhabdoid tumor [ATRT], ependymoma, medulloblastoma, mixed and unspecified gliomas, and primitive neuroectodermal tumors [PNET]) among children with specific structural congenital anomalies, chromosomal anomalies, neurofibromatosis, and/or tuberous sclerosis complex. Of the 66 congenital anomaly-CBT associations examined, 42 were statistically significant, of which 16 were novel. Associations differed by pilocytic and diffuse astrocytoma. The patterns of co-occurrences in our study suggest that certain congenital anomalies and CBTs may have shared biological pathways, providing insights into the etiology of CBTs.

Childhood brain tumors (CBTs) are the most common solid tumor observed in children and adolescents.¹ In the United States, CBTs account for more cancer-related deaths in this age group than other types of childhood cancers.² Aside from ionizing radiation and certain genetic syndromes (eg, neurofibromatosis, tuberous sclerosis complex), risk factors for CBTs are not well characterized.^3,4 There is growing evidence that children born with congenital anomalies are at an increased risk of developing a brain tumor during childhood or adolescence,^5–8 particularly within the first year of life.^9–11 Furthermore, the risk of CBT increases with the number of structural anomalies present.⁶

Worldwide, an estimated 7.9 million or 6% of births are born with a serious congenital anomaly.¹² Congenital anomalies can broadly be classified as structural anomalies (eg, congenital heart defect, neural tube defect) without a known genetic diagnosis or those with genetic syndrome. Genetic syndrome can be further subclassified as chromosomal anomalies (eg, Down syndrome or trisomy 18) or single gene disorders (eg, neurofibromatosis or tuberous sclerosis complex). Unlike genetic syndromes, for which the cause of the congenital anomaly is known, structural congenital anomalies without a known genetic diagnosis most likely arise from a complex interaction of genetic and environmental factors.¹² Some known risk factors of congenital anomalies include inadequate folic acid intake, cigarette smoking during pregnancy, and maternal health conditions (eg, diabetes, obesity, infections).¹³

Associations between congenital anomalies and CBTs suggest underlying mechanisms may include shared genetic factors, shared environmental risk factors, and/or exposure to radiation from imaging or therapeutic procedures related to the treatment of the congenital anomaly. Congenital anomalies are heterogenous in terms of the body system affected (eg, brain, heart or circulatory, musculoskeletal) or known genetic syndrome (eg, Down syndrome, neurofibromatosis, tuberous sclerosis complex). Due to the rarity of both congenital anomalies and CBTs, most prior studies were limited to examining associations using broad categories of anomalies (eg, any congenital anomaly [including genetic syndromes], structural anomaly, chromosomal anomaly). This has contributed to a lack of knowledge concerning associations between specific anomalies and CBT,⁵ which is compounded by a lack of data concerning the numerous CBT histologic types (eg, astrocytoma, medulloblastoma, ependymoma). The ability to examine specific combinations of individual congenital anomalies and tumor types may shed more insights into etiology.

To overcome these limitations, the Genetic Overlap Between Anomalies and Cancer in Kids (GOBACK) study previously conducted a population-based registry linkage of 10 million live births across 4 states and has reported associations by specific congenital anomaly and more common histologic types of CBTs.⁶ Since its publication in 2019, GOBACK has expanded to include additional years and additional states, more than doubling the number of live births. This extended dataset allows us to examine histologic types of CBTs that were not included in the previous publication, as well as the previous combinations of specific congenital anomalies and histologic types of CBTs with improved precision. Specifically, this study examined the association between major structural congenital anomalies in those children without a chromosomal or genetic diagnosis and central nervous system (CNS) tumors overall and by 6 histologic types, stratifying by age at tumor diagnosis and number of co-occurring major structural congenital anomalies. We also conducted analyses for 69 specific congenital anomalies (including those with genetic syndromes) and histologic types of CBT.

Materials and Methods

Study Population

The GOBACK study is a population-based registry linkage study that collects data on all cases with congenital anomalies and cancer in participating states.⁶ The GOBACK study has expanded to include 22,599,099 live births from 9 states ranging from 1990-2018: Arkansas (AR), Florida (FL), Massachusetts (MA), Michigan (MI), New Jersey (NJ), North Carolina (NC), Oklahoma (OK), South Carolina (SC), and Texas (TX). Details of the birth years covered and number of live births by state are provided in Table 1. Briefly, each state performed the following linkages: (1) birth defects registry to birth records, (2) cancer registry to birth records, and (3) birth defects registry to cancer registry. The study was approved by the institutional review boards of Baylor College of Medicine and each of the participating institutions. As this study used existing de-identified public health data, all review boards granted a waiver of consent.

Table 1.

Summary of live births, congenital anomalies, and childhood brain tumors by state

	AR	FL	MA	MI	NJ	NC	OK	SC	TX
Birth years included	1995–1999 2001–2011	1998–2013	2000–2017	1992–2018	1990–2018	2003–2012	1997–2012	2008–2018	1999–2017
Date of last follow-up	12/31/2011	12/31/2013	12/31/2017	12/31/2018	12/31/2018	12/31/2012	12/31/2012	12/31/2018	12/31/2017
Birth Defect Surveillance System	Active	Passive, with case confirmation	Active	Passive, with case confirmation	Passive, with case confirmation	Active	Active	Active	Active
Age cutoff for congenital anomaly diagnosis (y)	2	1	1	2	5	1	2	2	1
# Live Births	629,119	3,237,743	1,345,280	3,385,360	3,314,008	1,960,230	803,550	601,794	7,322,015
# Live births with a major structural congenital anomaly	15,793 (2.5%)	134,646 (4.2%)	16,246 (1.2%)	114,770 (3.4%)	83,655 (2.5%)	45,724 (2.3%)	21,400 (2.7%)	7,339 (1.2%)	212,778 (2.9%)
# Live births with any chromosomal anomaly	943	5,554	3,027	7,596	4,693	5,498	1,773	492	16,236
# Live births with neurofibromatosis	-	36	<5¹	1,029	83	10	110	-	267
# Live births with tuberous sclerosis complex	-	45	56	307	134	74	52	-	246
# CNS tumors	202	814	405	1,194	974	354	158	96	2,050

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AR: Arkansas; CNS: central nervous system; FL: Florida; MA: Massachusetts; MI: Michigan; NJ: New Jersey; NC: North Carolina; OK: Oklahoma; SC: South Carolina; TX: Texas.

¹Data are suppressed for confidentiality.

Congenital Anomaly Ascertainment and Classification

Each birth defects registry operated either an active (AR, MA, NC, OK, SC, and TX) or passive case-finding statewide surveillance system with case confirmation (FL, MI, and NJ). All registries recorded congenital anomalies diagnosed up to 1 or 2 years of age except for NJ, which collected diagnoses up to 5 years of age. Congenital anomalies were coded using the U.S. Centers for Disease Control and Prevention (CDC) modification of the British Paediatric Association Classification of Diseases (BPA), the World Health Organization’s International Classification of Diseases (ICD), Ninth Revision, Clinical Modification, or the ICD, Tenth Revision, Clinical Modification. GOBACK’s initial publication included all congenital anomalies broadly, some of which may not have been ascertained consistently across the participating birth defects registries. Thus, for this study, we focused primarily on major structural congenital anomalies (ie, anomalies that can impact an infant’s life expectancy, health status, physical or social functioning) as recommended for surveillance by the National Birth Defects Prevention Network (NBDPN)¹⁴ and genetic syndromes (ie, a health condition caused by a genetic disorder). Recorded congenital anomalies not on the NBDPN’s list were classified as minor structural defects. Genetic syndromes we focused on were chromosomal anomalies, as well as neurofibromatosis and tuberous sclerosis complex given their known associations with gliomas.^15,16 The classification of anomalies is provided in Supplementary Table 1.

Childhood Brain Tumor Ascertainment and Classification

Each cancer registry followed the standards of the CDC’s National Program of Cancer Registries and was certified by the North American Association of Central Cancer Registries for the completeness, timeliness, and quality of their data.¹⁷ Each registry collected information on cancer site, behavior code, and date of diagnosis. The reported International Classification of Diseases for Oncology, third edition (ICD-O-3) codes were utilized to classify cancer based on the International Classification of Childhood Cancer, third edition (Supplementary Table 2). This study focused on any primary malignant central nervous system (CNS) tumor diagnosed up to age 19 years and the following histologic types: astrocytoma, atypical teratoid/rhabdoid tumor (ATRT), ependymoma, medulloblastoma, mixed and unspecified gliomas, and primitive neuroectodermal tumor (PNET). Secondary analyses were conducted for 4 types of astrocytoma (i.e. pilocytic astrocytoma, diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma).

Statistical Analysis

Descriptive information was provided using counts and frequencies. To examine the association between having a congenital anomaly and risk of CBT, analyses were conducted for any CNS tumor and separately for each histologic type, using Cox proportional hazards regression in both unadjusted and adjusted models. The proportional hazards assumption was met using Schoenfeld residuals or visual inspection. The adjusted models included a priori potential confounders^3,5 obtained from birth records: maternal age at delivery (< 20, 20–34, ≥ 35 years), maternal education (less than high school, high school, greater than high school), maternal race and ethnicity (non-Hispanic White, Hispanic, non-Hispanic Black, non-Hispanic Asian, other/unknown), child’s sex (male, female), and state of birth.

Because some structural anomalies are more common in children with genetic syndromes (eg, heart or circulatory anomalies and Down syndrome), live births with any genetic syndrome were excluded from analyses focused on major structural anomalies. Thus, analyses were conducted separately for children with major structural congenital anomalies only, as well as any chromosomal anomalies, neurofibromatosis, and tuberous sclerosis complex independent of the presence of a structural anomaly. Children could be included in more than one analysis if they had more than one genetic syndrome (eg, any chromosomal anomaly and neurofibromatosis). Children without congenital anomalies or genetic syndromes were used as the referent group for all analyses. For major structural congenital anomalies, we conducted analyses with any major structural defect (yes/no) and then stratified by the number of major structural defects (for any CNS tumor: 0, 1, 2, 3, 4, and 5+; for specific histologic types: 0, 1, 2+) and age at tumor diagnosis (< 1, 1–4, 5–9, 10–19 years). Analyses were also repeated for congenital anomalies based on major body system (eg, CNS anomaly, genitourinary anomaly) and specific anomalies (eg, hydrocephaly without spina bifida, hypospadias) for congenital anomaly-CBT combinations with at least 5 co-occurring cases. For analyses with hypospadias, the study population was restricted to males. For analyses with neurofibromatosis and tuberous sclerosis complex, live births from Arkansas and South Carolina were excluded, as neither state collected these two genetic syndromes. Significance was determined at P-value less than .05. Brain tumors can lead to the development of hydrocephalus, which could be recorded in the state’s birth defect registry.^18,19 Thus, we conducted sensitivity analyses, restricting to CBTs diagnosed after each state’s age cutoff for reporting congenital anomalies for analyses with any major structural congenital anomalies, any major CNS anomaly, and hydrocephalus without spina bifida (ie, CBTs diagnosed after age 1 year for FL, MA, NC, TX; after age 2 for AR, MI, OK, SC; and after age 5 for NJ).

Results

Details of the birth years, number of live births, information about the birth defects registry, and number of CBT diagnosed by state are provided in Table 1. Characteristics of the study population according to congenital anomaly status are provided in Table 2. Of the 22,599,099 live births, we excluded 476,383 children with only a minor structural congenital anomaly and did not have a recorded genetic syndrome. Of the remaining 22,122,716 live births, we also excluded the following: 587,484 with missing covariate information on maternal age at delivery, maternal education, child’s sex, date of birth, or date of cancer diagnosis or follow-up timeand 1,796 with an unspecified genetic syndrome. For those with missing covariate, Supplementary Table 3 provides the distribution of CNS tumor excluded by congenital anomaly status.

Table 2.

Characteristics of study population by congenital anomaly

Characteristic	No congenital anomaly (n = 21,420,344)	Any major structural congenital anomaly^a (n = 652,351)	Any chromosomal anomaly^b (n = 45,812)	Neurofibromatosis^c (n = 1,539)	Tuberous sclerosis complex^c (n = 914)
Maternal age at delivery (y)
< 20	2,140,137 (10.0)	64,929 (10.0)	3,100 (6.8)	149 (9.7)	71 (7.8)
20-34	16,097,928 (75.2)	481,492 (73.8)	25,554 (55.8)	1,194 (77.6)	694 (75.9)
≥ 35	3,053,162 (14.3)	102,617 (15.7)	16,960 (37.0)	194 (12.6)	144 (15.8)
Unknown	129,117 (0.6)	3,313 (0.6)	198 (0.4)	2 (0.1)	5 (0.5)
Maternal education
Less than high school	4,652,201 (21.7)	142,242 (21.8)	10,275 (22.4)	354 (23.0)	194 (21.2)
High school	6,025,631 (28.1)	190,889 (29.3)	12,442 (27.2)	526 (34.2)	253 (27.7)
Greater than high school	10,288,323 (48.0)	307,694 (47.2)	21,975 (48.0)	645 (41.9)	443 (48.5)
Unknown	454,189 (2.1)	11,526 (1.8)	1,120 (2.4)	14 (0.9)	24 (2.6)
Maternal race and ethnicity
Non-Hispanic White	11,532,564 (53.8)	355,569 (54.5)	24,103 (52.6)	946 (61.5)	528 (57.8)
Hispanic	5,033,494 (23.5)	149,718 (23.0)	12,575 (27.4)	189 (12.3)	190 (20.8)
Non-Hispanic Black	3,375,582 (15.8)	110,247 (16.9)	6,540 (14.3)	333 (21.6)	134 (14.7)
Non-Hispanic Asian	866,460 (4.1)	20,199 (3.1)	1,477 (3.2)	23 (1.5)	30 (3.3)
Other/Unknown	612,244 (2.9)	16,618 (2.6)	1,117 (2.4)	48 (3.1)	32 (3.5)
Plurality
Singleton	20,705,162 (96.7)	607,525 (93.1)	44,072 (96.2)	1,472 (95.6)	874 (95.6)
Multiple	692,102 (96.7)	43,874 (6.7)	1,640 (3.6)	66 (4.3)	39 (4.3)
Unknown	23,080 (0.1)	952 (0.1)	100 (0.2)	1 (0.07)	1 (0.1)
Sex
Male	10,872,956 (50.8)	380,386 (58.3)	23,115 (50.5)	819 (53.2)	492 (53.8)
Female	10,547,145 (49.2)	271,931 (41.7)	22,688 (49.5)	720 (46.8)	422 (46.2)
Unknown	243 (0.0)	34 (0.01)	9 (0.02)	0	0
Incidence of anomaly^d
Arkansas	-	25.2	1.5	-	-
Florida	-	41.6	1.7	1.1	1.4
Massachusetts	-	12.0	2.2	0.3	4.2
Michigan	-	33.9	2.2	30.4	9.1
New Jersey	-	25.2	1.7	2.5	4.0
North Carolina	-	23.3	2.4	0.5	3.8
Oklahoma	-	26.6	2.2	13.7	16.7
South Carolina	-	12.2	0.8	-	-
Texas	-	29.1	2.2	3.6	3.4
CNS Tumor
Yes	5,591 (0.03)	301 (0.05)	18 (0.04)	108 (7.0)	15 (1.6)
No	21,414,753 (99.97)	652,050 (99.95)	45,770 (99.96)	1,431 (93.0)	899 (98.4)

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^aExcludes children with any chromosomal anomaly, neurofibromatosis, or tuberous sclerosis complex.

^bIncludes trisomy 13, trisomy 18, trisomy 21, Turner syndrome, 13q deletion, 22q deletion. 24 also have neurofibromatosis, and 14 also have tuberous sclerosis complex. These 38 are also included in the single gene disorders.

^cInformation not collected in Arkansas or South Carolina. < 5 children with neurofibromatosis also have tuberous sclerosis complex and are included in both groups.

^dPer 1,000 live births for any major structural congenital anomaly and any chromosomal anomaly and per 100,000 live births for neurofibromatosis and tuberous sclerosis complex.

Due to small cells counts when stratified by age at CBT diagnosis, only unadjusted analyses were conducted. For all other analyses, the results for the unadjusted and adjusted models were similar, thus, the findings from the adjusted models were presented below. Unadjusted results are available in the supplemental tables.

Major Structural Congenital Anomaly and CBTs

Any major structural congenital anomaly

Using Cox regression, having any major structural congenital anomaly without a genetic syndrome was significantly associated with risk of developing any CBT (aHR: 1.86, 95% CI: 1.65–2.10) (Figure 1, Supplementary Table 4). When examining the relationship by histology, significant associations were observed with 5 histologic types: astrocytoma, ATRT, medulloblastoma, mixed or unspecified glioma, and PNET (aHR range: 1.48–3.69; Figure 1, Supplementary Table 4).

The adjusted hazard ratio and its 95% confidence interval are displayed in a forest plot for CNS tumor, astroctyoma, ATRT, ependymoma, medulloblastoma, mixed and unspecified glioma, and PNET. For CNS tumor, the results for number of major structural anomalies are presented for any, 1, 2, 3, 4, and 5+. For each of the histologic type, results are presented for any, 1, and 2+ major structural anomalies. — Adjusted hazard ratio of the association between number of major structural congenital anomalies and childhood brain tumor. aHR: adjusted hazard ratio; ATRT: atypical teratoid/rhabdoid tumor; CI: confidence interval; CNS: central nervous system; PNET: primitive neuroectodermal tumor.

Number of major structural congenital anomalies

For CNS tumors, we counted the number of major specific structural congenital anomalies and categorized children as having 0, 1, 2, 3, 4, or 5 + anomalies. A stronger magnitude of association was observed with 3, 4, or 5 + congenital anomalies and risk of CNS tumors (aHR range: 2.56–3.56) compared to having only one congenital anomaly (aHR: 1.86), but the confidence intervals all overlapped (Figure 1, Supplementary Table 5). Due to smaller numbers by histologic type, the number of major structural congenital anomalies were categorized as 0, 1, or 2+. The magnitude of association did not differ appreciably by number of major structural congenital anomalies across the 6 histologic types (Figure 1, Supplementary Table 5).

Any major structural congenital anomaly stratified by age at tumor diagnosis

When stratifying by age at tumor diagnosis, the association was strongest in children diagnosed before age 1 year for any CNS tumor, astrocytoma, ATRT, medulloblastoma, mixed and unspecified glioma, and PNET (HR range: 3.52- 6.42) (Figure 2, Supplementary Table 6).

The hazard ratio and its 95% confidence interval are displayed in a forest plot for CNS tumor, astroctyoma, ATRT, ependymoma, medulloblastoma, mixed and unspecified glioma, and PNET. Age categories were <1 year, 1-4 years, 5-9 years, and 10+ years. — Hazard ratio of the association between any major structural congenital anomaly and childhood brain tumor by age at tumor diagnosis. ATRT: atypical teratoid/rhabdoid tumor; CI: confidence interval; CNS: central nervous system; HR: hazard ratio; PNET: primitive neuroectodermal tumor. Due to limited sample size, these analyses were unadjusted for potential confounders. The red dotted line indicates a hazard ratio of 1.

Specific major structural congenital anomaly and CBT combinations

We evaluated 59 specific major structural congenital anomaly-CBT combinations, of which 37 were significant at P < .05 (Figure 3, Supplementary Table 7). After accounting for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR),²⁰ 31 combinations remained significant at FDR < 0.05 (Supplementary Table 7).

The columns of the heatmap display the childhood brain tumor (i.e., CNS tumor, astrocytoma, ATRT, ependymoma, medulloblastoma, mixed and unspecified glioma, and PNET). The rows of the heatmap display the specific major structural congenital anomaly. Cells are shaded based on strength of the association. — Heatmap of the association between major structural congenital anomaly and childhood brain tumor. aHR: adjusted hazard ratio; ATRT: atypical teratoid/rhabdoid tumor; CNS: central nervous system; PNET: primitive neuroectodermal tumor. Number in the cell reflects the number that have both the major structural congenital anomaly and tumor.

Sensitivity analysis

After restricting to CBTs diagnosed after each state’s birth defect surveillance period, the associations with any major structural defect attenuated but remained significant for CNS tumor, ATRT, medulloblastoma, and mixed or unspecified glioma (Supplementary Figure 1, Supplementary Table 4). For CNS tumor and the 5 histologic types for which we observed a significant association with CNS anomalies or hydrocephalus (aHR range: 3.93–100.50), the magnitude of the association attenuated but remained significant (aHR range: 3.01–45.78) (Supplementary Table 7). We also repeated analyses adjusting for birth year (ie, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014, 2015–2018). Results did not appreciably change (data not shown) except the association between astrocytoma and atrial septal defect (aHR: 1.37, 95% CI: 0.95–1.97) and astrocytoma and major genitourinary anomaly (aHR: 1.46, 95% CI: 0.96–2.23) were no longer significant.

Genetic Syndromes and CBTs

Of the 45,812 children who had a chromosomal anomaly, 56.9% had Down syndrome and 0.04% developed a CBT. An elevated but non-significant association was observed with CNS tumor (aHR: 1.54, 95% CI: 0.96–2.48) (Figure 4, Supplementary Table 8). Astrocytoma was the only histopathology diagnosed in at least 5 children with a chromosomal anomaly. Diagnosis of a chromosomal anomaly was not associated with the risk of astrocytoma (aHR: 1.52, 95% CI: 0.76–3.04).

In our study population, 7% of the 1,539 children with neurofibromatosis developed CBT, most of which were either astrocytoma or mixed and unspecified glioma. More specifically, ~5% of children with neurofibromatosis who developed astrocytoma or mixed and unspecified glioma had a tumor that originated in the optic nerve. There was no record of ATRT, medulloblastoma, or PNET in children with neurofibromatosis, and less than 10 with neurofibromatosis had a recorded ependymoma. In Cox regression analyses, neurofibromatosis elevated the risk of CNS tumor (aHR: 297, 95% CI: 244–361), astrocytoma (aHR: 525, 95% CI: 419–659), and mixed and unspecified gliomas (aHR: 351, 95% CI: 214–575) (Figure 4, Supplementary Table 9).

Of the 914 children with tuberous sclerosis complex, 1.6% developed a CBT. Having tuberous sclerosis complex was associated with risk of developing a CNS tumor (aHR: 63, 95% CI: 37–107). All but one developed astrocytoma. The magnitude of the association strengthened when restricting astrocytoma (aHR: 125, 95% CI: 72–216) (Figure 4, Supplementary Table 10).

In sensitivity analyses adjusting for birth year, results were similar with any chromosomal anomaly and tuberous sclerosis complex, while the magnitude of associations with neurofibromatosis attenuated (aHR range: 278–492) but remained significant (data not shown).

Secondary Analyses with Astrocytoma Types

For any major structural congenital anomaly, we were able to conduct analyses with the 4 astrocytoma types. A significant association was observed with diffuse astrocytoma (aHR: 2.71, 95% CI: 1.80–4.08) but not with pilocytic astrocytoma, anaplastic astrocytoma, or glioblastoma (Supplementary Table 4). Only pilocytic and diffuse astrocytoma had sufficient co-occurrences for remaining analyses. There was no evidence of a dose–response with the number of major structural defects and risk of developing pilocytic or diffuse astrocytoma (Supplementary Table 5). After stratifying by age at tumor diagnosis, no pattern was observed with pilocytic astrocytoma (Supplementary Table 6). For diffuse astrocytoma, an association was observed among those less than 1 year of age at cancer diagnosis (HR: 3.57, 95% CI: 1.27–10.01), 5–9 years at diagnosis (HR: 3.02, 95% CI: 1.47–6.22), and ≥ 10 years at diagnosis (HR: 2.94, 95% CI: 1.59–5.43).

Fourteen specific structural congenital anomaly analyses were conducted with pilocytic or diffuse astrocytoma (Supplementary Table 7, Supplementary Figure 2), of which 6 significant at P < .05 and were also significant at FDR < 0.05. While having a CNS anomaly was associated with both pilocytic astrocytoma (aHR: 3.17, 95% CI: 1.65–6.11) and diffuse astrocytoma (aHR: 8.53, 95% CI: 3.80–19.31), the 4 other associations were associated with one type of astrocytoma but not the other.

Diagnosis of a chromosomal anomaly was not associated with pilocytic astrocytoma, and analyses were not conducted separately for diffuse astrocytoma (Supplementary Table 8, Supplementary Figure 3). Neurofibromatosis was associated with both pilocytic astrocytoma (aHR: 266, 95% CI: 180–393) and diffuse astrocytoma (aHR: 218, 95% CI: 96–497) (Supplementary Table 9, Figure 3). Analysis with tuberous sclerosis complex was not conducted for pilocytic astrocytoma, but an association was observed with diffuse astrocytoma (aHR: 656, 95% CI: 359–1.199) (Supplementary Table 10, Supplementary Figure 3).

Discussion

In this population-based birth cohort of over 22 million live births in 9 U.S. states, we observed that children born with any major structural congenital anomaly had an increased risk of developing a CNS tumor, specifically astrocytoma, ATRT, medulloblastoma, mixed and unspecified glioma, and PNET. The magnitude of the observed associations was strongest among children with any major CNS anomaly, and among children diagnosed with a CBT during infancy. Compared to our initial assessment of 10 million U.S. live births,⁶ we were able to evaluate an additional 29 congenital anomaly-CBT combinations, of which 16 (55%) were significant, suggesting that disruptions during development may elevate risk of CBTs. Notable novel associations included ATRT and atrial septal defect, medulloblastoma and limb reduction deformities, and PNET and ventricular septal defect. While the risk of glioma in children with neurofibromatosis is well documented,^15,16 this large study provided population-based estimates. Stratifying by astrocytoma type also identified unique specific congenital anomaly and CBT associations.

Major Structural Congenital Anomalies and CBTs

In the initial GOBACK publication, Lupo et al. reported congenital anomaly-CBT associations for CNS tumors, astrocytoma, medulloblastoma, and ependymoma.⁶ In this larger dataset, we had adequate sample size to expand the analyses to include ATRT, mixed and unspecified glioma, and PNET. Our findings with any major structural congenital anomaly and histologic types of CBT confirm the results a large linkage study of 4 Nordic countries.²¹

We expanded on previous findings that children with a structural congenital anomaly are more likely to develop a CBT before age one^9,10,22 and showed that this association is applicable for astrocytoma, ATRT, medulloblastoma, mixed and unspecified glioma, and PNET. However, we cannot rule out the possibility that the stronger association before age one for ATRT, mixed and unspecified gliomas, and PNET may be driven by hydrocephalus that develops because of the tumor. Given the findings in Lupo et al.,⁶ we expected to observe an increasing risk of CBT among children with multiple major anomalies. However, while there was some suggestive evidence of an elevated risk with at least 3 major structural congenital anomalies with CNS tumors overall, our results by histology did not support this hypothesis. Differences in how structural congenital anomalies were defined across the 2 publications may have impacted our results, as this study emphasized a limited set of specific major anomalies that are believed to be ascertained consistently across the participating birth defects registries. It is possible that certain minor anomalies may share disrupted biological pathways with CBTs, but excluding those defects from our analyses precluded us from exploring that possibility and may underestimate some associations (eg, number of structural anomalies).

In specific major structural congenital anomaly-CBT analyses, 34 of our 59 combinations overlapped with those evaluated in Lupo et al., and the results were concordant for 28 (82%) (Supplementary Table 6). Differences in how CBTs overall and congenital anomalies classified by body system (eg, heart or circulatory, musculoskeletal, genitourinary) may partially explain the discordant results. Lupo et al. excluded unspecified intracranial and intraspinal neoplasms from their CBT definition and included congenital anomalies that may not be systematically documented across birth defects registries (eg, other or unspecified heart or circulatory anomaly, minor anomalies that have little or no impact on health or function). Differences in the classification system may limit the ability to homogeneously classify congenital anomalies. For example, compared to the ICD-10 or CDC BPA codes, the ICD-9 system did not have a specific code for craniosynostosis, grouping those defects with other anomalies of skull and face bones.¹⁴

In the United States, the most prevalent congenital anomalies include clubfoot, cleft lip with and without cleft palate, and pulmonary valve atresia and stenosis.^23,24 Interestingly, for these 3 anomalies, no association was observed with any CNS tumors. In fact, aside from the null association between pulmonary valve atresia and stenosis and astrocytoma, all other co-occurrence combinations did not have at least 5 cases. This suggests that the underlying biological mechanisms of these 3 congenital anomalies are not involved in the development of CBTs.

Because some reported cases of hydrocephalus without spina bifida may be a sequela of the tumor, we performed a sensitivity analysis, restricting to tumors diagnosed after the age cutoff for ascertainment used by each birth defects registry. Associations with any major birth defect, major CNS anomalies, and hydrocephalus without spina bifida were attenuated across CNS tumors overall and all histologies except for ependymoma, which showed no association regardless of the timing issue. Despite the attenuation, results with major CNS anomalies and hydrocephaly without spina bifida remained significant for any CNS tumor, astrocytoma, ATRT, medulloblastoma (for CNS anomalies only), mixed and unspecified glioma, and PNET, suggesting that these associations are not due to reverse causation.

While the pathogenesis of CBTs has not been fully elucidated, our results support a couple of hypotheses to consider in future research. During gastrulation, the endoderm is formed first, followed by the mesoderm, and then remaining cells become the ectoderm.²⁵ The mesoderm later gives rise to the musculoskeletal system, circulatory system, kidneys, and internal sex organs, and the ectoderm forms the nervous system.²⁵ In our study, we observed an increased risk of medulloblastoma or mixed or unspecified glioma with pyloric stenosis or limb deficiencies, suggesting there was a biological disruption in during gastrulation before the distinct formation of the mesoderm and ectoderm. Later during neurulation, neural crest cells begin to form. These cells are multipotent progenitor cells that can migrate and contribute to the development of the brain, bone and cartilage of the head and face, heart development, enteric nervous system of the gastrointestinal tract, and peripheral nervous system.²⁶ Dysregulation of the neural crest cell may be partially explained by the increased risk of CBTs in children with a CNS or heart or circulatory anomaly, as has been hypothesized with neuroblastoma.²⁷

Genetic Syndromes and CBTs

The literature on the association between chromosomal anomalies and CBT is mixed. Previously, Daltveit et al. reported an elevated risk of CBT in children with any chromosomal anomaly,²¹ but two other studies reported no association.^7,28 In our study, we observed an elevated and marginally significant association between any chromosomal anomaly and CBT. Stratifying by histology suggests that our results were driven by astrocytoma, as the magnitude of association was similar, and no other brain tumor had at least 5 with any chromosomal anomaly. Down syndrome is the most common chromosomal anomaly, yet we were unable to conduct analyses specifically with this genetic syndrome.^23,24 Overall, our findings suggest that common chromosomal anomalies, including Down syndrome, are not a risk factor of CBTs.

Because neurofibromatosis and tuberous sclerosis complex were not on the list of reportable conditions across all states, there was non-differential misclassification of these two genetic syndromes, which might have biased our results toward the null. The incidence of neurofibromatosis and tuberous sclerosis complex was highest in Michigan and Oklahoma because these two genetic syndromes are required to be reported to their state’s birth defects registry. Despite this limitation, we wanted to include them in our study given their established association with CNS tumors.^15,29–32 We recognize that there are 2 major types of neurofibromatosis (NF1 and NF2), which are genetically and clinically distinct. The types can be distinguished using the ICD-9 or ICD-10 codes but not CDC-BPA code. Of the 75% of neurofibromatosis with an ICD-9 or ICD-10 code, 65% had NF1, 2% had NF2, and 33% were unspecified or other, suggesting results our results are driven by children with NF1. NF1 is an established risk factor for glioma. Previous studies with fewer than 250 patients followed up to 1993^29–31 estimated that 1.5–7.5% of children with NF1 will develop a symptomatic optic pathway glioma.^16,33 In our study with 1,539 neurofibromatosis cases, 5% developed a glioma located in the optic nerve by age 5, confirming these previous estimates. While subependymal giant cell astrocytoma (SEGAs), a benign tumor, was excluded from our analyses, we found 1.6% of tuberous sclerosis complex cases developed astrocytoma. This estimate is much lower than the 6–25% reported in other studies of tuberous sclerosis complex which typically include SEGAs.^15,32 Interestingly, in our analysis, the association of tuberous sclerosis complex with astrocytoma was driven specifically by diffuse astrocytoma.

Limitations

We were unable to identify children who moved away from their state of birth, may have passed away during the follow-up period, or were diagnosed with a CBT in another state. Because we could not appropriately censor these children in our analysis, their follow-up time was calculated to the end of the follow-up period, likely biasing our results toward the null. Next, unlike cancer registries, which are more standardized across states, there is some variability in how congenital anomalies are collected by state (ie, active versus passive surveillance, age cutoff for recording congenital anomalies), which may lead to potential exposure misclassification. Despite these differences, previous work has shown that results are consistent between states with a passive versus active surveillance system.⁶ Some misclassification of congenital anomalies is possible as both active and passive surveillance systems may differentially miss some cases^34,35 and some congenital anomalies may be detected after the surveillance period (eg, heart and circulatory anomalies, genitourinary anomalies).³⁶ Despite the large sample size, our study was underpowered to detect some specific congenital anomaly-CNS tumor associations. The further expansion of GOBACK will improve power to confirm our findings. In our study population, children with a CBT were diagnosed earlier if they had any major structural congenital anomaly or genetic syndrome than those without (median age of 5.6 years vs 7.2 years), suggesting that medical surveillance may influence age of diagnosis. However, cancer surveillance is not routinely recommended for children with structural congenital anomalies, and prior evidence has shown that frequency of an in situ/local CNS tumor is greater children without a congenital anomaly than those with an anomaly, suggesting that differential medical care or screening by congenital anomaly does not fully explain the younger age at diagnosis.³⁷ While this may have biased our associations stratified by age at diagnosis, misclassification of a CBT (yes/no) is likely to be non-differential. Finally, beyond standard histopathology, brain tumors can be further characterized based on certain molecular features. Therefore, it is possible that some co-occurrence patterns may be more common among certain molecular subtypes and represent differing biological mechanisms underlying those tumors. However, many of these subtypes were not defined during the entire study period and the requisite molecular data are lacking in population-based cancer registries.³⁸ Time is needed for cancer registries to fully integrate collection of these molecular data on a scale to fully address this question.

Conclusions

Using data from a large, diverse population-based linkage study, we reported 26 significant congenital anomaly-CBT associations, including 11 that are novel, and provide population-based estimates for children with 3 genetic disorders known to be associated with gliomas. Further research is needed to elucidate the biological mechanisms including disruption of developmental pathways underlying any positive associations and to confirm the novel associations. Our findings suggest novel potential risk factors for CBTs and may contribute to clinical guidelines regarding the surveillance of children with congenital anomalies.

Supplementary material

Supplementary material is available online at Neuro-Oncology (https://academic.oup.com/neuro-oncology).

noaf087_suppl_Supplementary_Tables

noaf087_suppl_supplementary_tables.xlsx^{(54.7KB, xlsx)}

noaf087_suppl_Supplementary_Figures

noaf087_suppl_supplementary_figures.docx^{(555.1KB, docx)}

Acknowledgments

This work would not have been possible without the following state agencies: the Texas Birth Defects Registry, Texas Cancer Registry, Texas Center for Health Statistics, Texas Department of State Health Services, Arkansas Reproductive Health Monitoring System, Arkansas Cancer Registry, Massachusetts Birth Defects Monitoring Program, Massachusetts Cancer Registry, Michigan Department of Health and Human Services, North Carolina Central Cancer Registry, North Carolina Birth Defects Monitoring Program, North Carolina State Center for Health Statistics, North Carolina Division of Public Health, Florida Birth Defects Registry, Florida Cancer Data System, and Oklahoma State Department of Health. Results from this study have been presented at the 2024 Brain Tumor Epidemiology Consortium Annual Meeting, the 2024 International Society of Paediatric Oncology Annual Meeting, and the 2024 Society for Neuro-Oncology Annual Meeting.

Contributor Information

Thanh T Hoang, Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA; Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA; The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.

Jeremy M Schraw, Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA; Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA; The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.

Charles Shumate, Birth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas, USA.

Tania A Desrosiers, Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Wendy N Nembhard, Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences and Arkansas Center for Birth Defects Research and Prevention, Little Rock, Arkansas, USA.

Mahsa Yazdy, Massachusetts Center for Birth Defects Research and Prevention, Division for Surveillance, Research, and Promotion of Perinatal Health, Massachusetts Department of Public Health, Boston, Massachusetts, USA.

Eirini Nestoridi, Massachusetts Center for Birth Defects Research and Prevention, Division for Surveillance, Research, and Promotion of Perinatal Health, Massachusetts Department of Public Health, Boston, Massachusetts, USA.

Amanda E Janitz, Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences, Oklahoma City, Oklahoma, USA.

Russell S Kirby, Chiles Center, College of Public Health, University of South Florida, Tampa, Florida, USA.

Jason L Salemi, Chiles Center, College of Public Health, University of South Florida, Tampa, Florida, USA.

Jean Paul Tanner, Chiles Center, College of Public Health, University of South Florida, Tampa, Florida, USA.

Tiffany M Chambers, Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA; Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA.

Michael D Taylor, Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA; Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA; The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.

Chad D Huff, Division of Cancer Prevention and Population Sciences, Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Sharon E Plon, Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA; Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA; The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

Philip J Lupo, Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA; Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA; The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.

Michael E Scheurer, Department of Pediatrics, Division of Hematology and Oncology, Baylor College of Medicine, Houston, Texas, USA; Texas Children's Cancer and Hematology Center, Texas Children's Hospital, Houston, Texas, USA; The Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.

Funding

This study was supported by the National Cancer Institute (R03CA272955, R01CA284531) and National Institute of Child Health and Human Development (R01HD112081) at the National Institutes of Health; Department of Defense (W81XWH‐20‐1‐0567); Rally Foundation (Career Development Award 23CDN05 to JMS).

Conflict of Interest

None declared.

Authorship Statement

Conceptualization: MDT, CDH, SEP, MES, PJL; Methodology: PJL; Formal analysis: TTH; Investigation: TAD, CS, WNN, MY, EN, AEJ, RSK, JLS, JPT, TMC; Data Curation: TTH, JMS; Writing – Original Draft: TTH; Writing – Review & Editing: all coauthors; Visualization: TTH; Supervision: MES, PJL; Project administration: JMS, TMC, PJL; Funding acquisition: CDH, PJL.

Data Availability

The data underlying this article cannot be shared publicly because of state restrictions and privacy laws. Upon reasonable request to the corresponding author, the data will be shared with appropriate approvals by each state’s institutional review board.

References

1. Steliarova-Foucher E, Colombet M, Ries LAG, et al. International incidence of childhood cancer, 2001-10: a population-based registry study. Lancet Oncol. 2017;18(6):719–731. Epub 20170411 [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Siegel DA, Richardson LC, Henley SJ, et al. Pediatric cancer mortality and survival in the United States, 2001-2016. Cancer. 2020;126(19):4379–4389. Epub 20200729 [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Hoang TT, Whitcomb E, Reardon EE, et al. Environmental Risk Factors for Childhood Central Nervous System Tumors: an Umbrella Review. Curr Epidemiol Rep. 2022;9(4):338–360. [Google Scholar]
4. Onyije FM, Dolatkhah R, Olsson A, et al. Risk factors for childhood brain tumours: A systematic review and meta-analysis of observational studies from 1976 to 2022. Cancer Epidemiol. 2024;88:102510. Epub 20231205 [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Johnson KJ, Lee JM, Ahsan K, et al. Pediatric cancer risk in association with birth defects: A systematic review. PLoS One. 2017;12(7):e0181246. Epub 20170727 [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Lupo PJ, Schraw JM, Desrosiers TA, et al. Association between birth defects and cancer risk among children and adolescents in a population-based assessment of 10 million live births. JAMA Oncol. 2019;5(8):1150–1158. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Norwood MS, Lupo PJ, Chow EJ, et al. Childhood cancer risk in those with chromosomal and non-chromosomal congenital anomalies in Washington State: 1984-2013. PLoS One. 2017;12(6):e0179006. Epub 20170608 [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Daltveit DS, Klungsoyr K, Engeland A, et al. Sex differences in childhood cancer risk among children with major birth defects: a Nordic population-based nested case-control study. Int J Epidemiol. 2023;52(2):450–465. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Janitz AE, Neas BR, Campbell JE, et al. Childhood cancer in children with congenital anomalies in Oklahoma, 1997 to 2009. Birth Defects Res A Clin Mol Teratol. 2016;106(7):633–642. Epub 20160304 [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Sun Y, Overvad K, Olsen J. Cancer risks in children with congenital malformations in the nervous and circulatory system-A population based cohort study. Cancer Epidemiol. 2014;38(4):393–400. Epub 20140505 [DOI] [PubMed] [Google Scholar]
11. Agha MM, Williams JI, Marrett L, et al. Congenital abnormalities and childhood cancer. Cancer. 2005;103(9):1939–1948. [DOI] [PubMed] [Google Scholar]
12. Christianson A, Howson CP, Modell B. Global Report on Birth Defects: The Hidden Toll of Dying and Disabled Children. White Plains, NY: March of Dimes; 2006. [Google Scholar]
13. Lee KS, Choi YJ, Cho J, et al. Environmental and Genetic Risk Factors of Congenital Anomalies: an Umbrella Review of Systematic Reviews and Meta-Analyses. J Korean Med Sci. 2021;36(28):e183. Epub 20210719 [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Mai CT, Cassell CH, Meyer RE, et al. Birth defects data from population-based birth defects surveillance programs in the United States, 2007 to 2011: highlighting orofacial clefts. Birth Defects Res A Clin Mol Teratol. 2014;100(11):895–904. Epub 20141114 [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Jansen AC, Belousova E, Benedik MP, et al. Clinical Characteristics of Subependymal Giant Cell Astrocytoma in Tuberous Sclerosis Complex. Front Neurol. 2019;10:705. Epub 20190703 [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Listernick R, Charrow J, Gutmann DH. Intracranial gliomas in neurofibromatosis type 1. Am J Med Genet. 1999;89(1):38–44. [DOI] [PubMed] [Google Scholar]
17. NAACCR. Certification Criteria: North American Association of Central Cancer Registries; 2021 [cited 2024]. Available from: https://www.naaccr.org/certification-criteria/. [Google Scholar]
18. Bjorge T, Cnattingius S, Lie RT, Tretli S, Engeland A. Cancer risk in children with birth defects and in their families: a population based cohort study of 5.2 million children from Norway and Sweden. Cancer Epidemiol Biomarkers Prev. 2008;17(3):500–506. Epub 20080222 [DOI] [PubMed] [Google Scholar]
19. Botto LD, Flood T, Little J, et al. Cancer risk in children and adolescents with birth defects: a population-based cohort study. PLoS One. 2013;8(7):e69077. Epub 20130717 [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1995;57(1):289–300. [Google Scholar]
21. Daltveit DS, Klungsoyr K, Engeland A, et al. Cancer risk in the siblings of individuals with major birth defects: a large Nordic population-based case-control study. Int J Epidemiol. 2023;52(6):1826–1835. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Partap S, MacLean J, Von Behren J, Reynolds P, Fisher PG. Birth anomalies and obstetric history as risks for childhood tumors of the central nervous system. Pediatrics. 2011;128(3):e652–e657. Epub 20110808 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Mai CT, Isenburg JL, Canfield MA, et al. National population-based estimates for major birth defects, 2010-2014. Birth Defects Res. 2019;111(18):1420–1435. Epub 20191003 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Stallings EB, Isenburg JL, Rutkowski RE, et al. National population-based estimates for major birth defects, 2016-2020. Birth Defects Res. 2024;116(1):e2301. [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Muhr J, Arbor TC, Ackerman KM. Embryology, Gastrulation Treasure Island (FL): StatPearls Publishing; 2025 [updated 2023 Apr 23; cited 2025]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554394/. [PubMed] [Google Scholar]
26. Dash S, Trainor PA. The development, patterning and evolution of neural crest cell differentiation into cartilage and bone. Bone. 2020;137:115409. Epub 20200514 [DOI] [PubMed] [Google Scholar]
27. Testori A, Lasorsa VA, Cimmino F, et al. Exploring shared susceptibility between two neural crest cells originating conditions: neuroblastoma and congenital heart disease. Genes (Basel). 2019;10(9):663. Epub 20190830 [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Fisher PG, Reynolds P, Von Behren J, et al. Cancer in children with nonchromosomal birth defects. J Pediatr. 2012;160(6):978–983. Epub 20120111 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Huson SM, Harper PS, Compston DA. Von Recklinghausen neurofibromatosis. A clinical and population study in south-east Wales. Brain. 1988;111 ( Pt 6)(Pt 6):1355–1381. [DOI] [PubMed] [Google Scholar]
30. Listernick R, Charrow J, Greenwald M, Mets M. Natural history of optic pathway tumors in children with neurofibromatosis type 1: a longitudinal study. J Pediatr. 1994;125(1):63–66. [DOI] [PubMed] [Google Scholar]
31. Listernick R, Charrow J, Greenwald MJ, Esterly NB. Optic gliomas in children with neurofibromatosis type 1. J Pediatr. 1989;114(5):788–792. [DOI] [PubMed] [Google Scholar]
32. Adriaensen ME, Schaefer-Prokop CM, Stijnen T, et al. Prevalence of subependymal giant cell tumors in patients with tuberous sclerosis and a review of the literature. Eur J Neurol. 2009;16(6):691–696. Epub 20090219 [DOI] [PubMed] [Google Scholar]
33. Listernick R, Louis DN, Packer RJ, Gutmann DH. Optic pathway gliomas in children with neurofibromatosis 1: consensus statement from the NF1 Optic Pathway Glioma Task Force. Ann Neurol. 1997;41(2):143–149. [DOI] [PubMed] [Google Scholar]
34. Howley MM, Williford E, St Louis AM, et al. A comparison of active and passive surveillance strategies for selected birth defects in New York. Birth Defects Res. 2024;116(9):e2399. [DOI] [PubMed] [Google Scholar]
35. Salemi JL, Tanner JP, Kennedy S, et al. A comparison of two surveillance strategies for selected birth defects in Florida. Public Health Rep. 2012;127(4):391–400. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Bower C, Rudy E, Callaghan A, Quick J, Nassar N. Age at diagnosis of birth defects. Birth Defects Res A Clin Mol Teratol. 2010;88(4):251–255. [DOI] [PubMed] [Google Scholar]
37. Schraw JM, Desrosiers TA, Nembhard WN, et al. Cancer diagnostic profile in children with structural birth defects: An assessment in 15,000 childhood cancer cases. Cancer. 2020;126(15):3483–3492. Epub 20200529 [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Louis DN, Perry A, Reifenberger G, et al. 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–820. Epub 20160509 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

noaf087_suppl_Supplementary_Tables

noaf087_suppl_supplementary_tables.xlsx^{(54.7KB, xlsx)}

noaf087_suppl_Supplementary_Figures

noaf087_suppl_supplementary_figures.docx^{(555.1KB, docx)}

Data Availability Statement

[CIT0001] 1. Steliarova-Foucher E, Colombet M, Ries LAG, et al. International incidence of childhood cancer, 2001-10: a population-based registry study. Lancet Oncol. 2017;18(6):719–731. Epub 20170411 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0002] 2. Siegel DA, Richardson LC, Henley SJ, et al. Pediatric cancer mortality and survival in the United States, 2001-2016. Cancer. 2020;126(19):4379–4389. Epub 20200729 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0003] 3. Hoang TT, Whitcomb E, Reardon EE, et al. Environmental Risk Factors for Childhood Central Nervous System Tumors: an Umbrella Review. Curr Epidemiol Rep. 2022;9(4):338–360. [Google Scholar]

[CIT0004] 4. Onyije FM, Dolatkhah R, Olsson A, et al. Risk factors for childhood brain tumours: A systematic review and meta-analysis of observational studies from 1976 to 2022. Cancer Epidemiol. 2024;88:102510. Epub 20231205 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0005] 5. Johnson KJ, Lee JM, Ahsan K, et al. Pediatric cancer risk in association with birth defects: A systematic review. PLoS One. 2017;12(7):e0181246. Epub 20170727 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0006] 6. Lupo PJ, Schraw JM, Desrosiers TA, et al. Association between birth defects and cancer risk among children and adolescents in a population-based assessment of 10 million live births. JAMA Oncol. 2019;5(8):1150–1158. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0007] 7. Norwood MS, Lupo PJ, Chow EJ, et al. Childhood cancer risk in those with chromosomal and non-chromosomal congenital anomalies in Washington State: 1984-2013. PLoS One. 2017;12(6):e0179006. Epub 20170608 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0008] 8. Daltveit DS, Klungsoyr K, Engeland A, et al. Sex differences in childhood cancer risk among children with major birth defects: a Nordic population-based nested case-control study. Int J Epidemiol. 2023;52(2):450–465. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0009] 9. Janitz AE, Neas BR, Campbell JE, et al. Childhood cancer in children with congenital anomalies in Oklahoma, 1997 to 2009. Birth Defects Res A Clin Mol Teratol. 2016;106(7):633–642. Epub 20160304 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0010] 10. Sun Y, Overvad K, Olsen J. Cancer risks in children with congenital malformations in the nervous and circulatory system-A population based cohort study. Cancer Epidemiol. 2014;38(4):393–400. Epub 20140505 [DOI] [PubMed] [Google Scholar]

[CIT0011] 11. Agha MM, Williams JI, Marrett L, et al. Congenital abnormalities and childhood cancer. Cancer. 2005;103(9):1939–1948. [DOI] [PubMed] [Google Scholar]

[CIT0012] 12. Christianson A, Howson CP, Modell B. Global Report on Birth Defects: The Hidden Toll of Dying and Disabled Children. White Plains, NY: March of Dimes; 2006. [Google Scholar]

[CIT0013] 13. Lee KS, Choi YJ, Cho J, et al. Environmental and Genetic Risk Factors of Congenital Anomalies: an Umbrella Review of Systematic Reviews and Meta-Analyses. J Korean Med Sci. 2021;36(28):e183. Epub 20210719 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0014] 14. Mai CT, Cassell CH, Meyer RE, et al. Birth defects data from population-based birth defects surveillance programs in the United States, 2007 to 2011: highlighting orofacial clefts. Birth Defects Res A Clin Mol Teratol. 2014;100(11):895–904. Epub 20141114 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0015] 15. Jansen AC, Belousova E, Benedik MP, et al. Clinical Characteristics of Subependymal Giant Cell Astrocytoma in Tuberous Sclerosis Complex. Front Neurol. 2019;10:705. Epub 20190703 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0016] 16. Listernick R, Charrow J, Gutmann DH. Intracranial gliomas in neurofibromatosis type 1. Am J Med Genet. 1999;89(1):38–44. [DOI] [PubMed] [Google Scholar]

[CIT0017] 17. NAACCR. Certification Criteria: North American Association of Central Cancer Registries; 2021 [cited 2024]. Available from: https://www.naaccr.org/certification-criteria/. [Google Scholar]

[CIT0018] 18. Bjorge T, Cnattingius S, Lie RT, Tretli S, Engeland A. Cancer risk in children with birth defects and in their families: a population based cohort study of 5.2 million children from Norway and Sweden. Cancer Epidemiol Biomarkers Prev. 2008;17(3):500–506. Epub 20080222 [DOI] [PubMed] [Google Scholar]

[CIT0019] 19. Botto LD, Flood T, Little J, et al. Cancer risk in children and adolescents with birth defects: a population-based cohort study. PLoS One. 2013;8(7):e69077. Epub 20130717 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0020] 20. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1995;57(1):289–300. [Google Scholar]

[CIT0021] 21. Daltveit DS, Klungsoyr K, Engeland A, et al. Cancer risk in the siblings of individuals with major birth defects: a large Nordic population-based case-control study. Int J Epidemiol. 2023;52(6):1826–1835. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0022] 22. Partap S, MacLean J, Von Behren J, Reynolds P, Fisher PG. Birth anomalies and obstetric history as risks for childhood tumors of the central nervous system. Pediatrics. 2011;128(3):e652–e657. Epub 20110808 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0023] 23. Mai CT, Isenburg JL, Canfield MA, et al. National population-based estimates for major birth defects, 2010-2014. Birth Defects Res. 2019;111(18):1420–1435. Epub 20191003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0024] 24. Stallings EB, Isenburg JL, Rutkowski RE, et al. National population-based estimates for major birth defects, 2016-2020. Birth Defects Res. 2024;116(1):e2301. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0025] 25. Muhr J, Arbor TC, Ackerman KM. Embryology, Gastrulation Treasure Island (FL): StatPearls Publishing; 2025 [updated 2023 Apr 23; cited 2025]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554394/. [PubMed] [Google Scholar]

[CIT0026] 26. Dash S, Trainor PA. The development, patterning and evolution of neural crest cell differentiation into cartilage and bone. Bone. 2020;137:115409. Epub 20200514 [DOI] [PubMed] [Google Scholar]

[CIT0027] 27. Testori A, Lasorsa VA, Cimmino F, et al. Exploring shared susceptibility between two neural crest cells originating conditions: neuroblastoma and congenital heart disease. Genes (Basel). 2019;10(9):663. Epub 20190830 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0028] 28. Fisher PG, Reynolds P, Von Behren J, et al. Cancer in children with nonchromosomal birth defects. J Pediatr. 2012;160(6):978–983. Epub 20120111 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0029] 29. Huson SM, Harper PS, Compston DA. Von Recklinghausen neurofibromatosis. A clinical and population study in south-east Wales. Brain. 1988;111 ( Pt 6)(Pt 6):1355–1381. [DOI] [PubMed] [Google Scholar]

[CIT0030] 30. Listernick R, Charrow J, Greenwald M, Mets M. Natural history of optic pathway tumors in children with neurofibromatosis type 1: a longitudinal study. J Pediatr. 1994;125(1):63–66. [DOI] [PubMed] [Google Scholar]

[CIT0031] 31. Listernick R, Charrow J, Greenwald MJ, Esterly NB. Optic gliomas in children with neurofibromatosis type 1. J Pediatr. 1989;114(5):788–792. [DOI] [PubMed] [Google Scholar]

[CIT0032] 32. Adriaensen ME, Schaefer-Prokop CM, Stijnen T, et al. Prevalence of subependymal giant cell tumors in patients with tuberous sclerosis and a review of the literature. Eur J Neurol. 2009;16(6):691–696. Epub 20090219 [DOI] [PubMed] [Google Scholar]

[CIT0033] 33. Listernick R, Louis DN, Packer RJ, Gutmann DH. Optic pathway gliomas in children with neurofibromatosis 1: consensus statement from the NF1 Optic Pathway Glioma Task Force. Ann Neurol. 1997;41(2):143–149. [DOI] [PubMed] [Google Scholar]

[CIT0034] 34. Howley MM, Williford E, St Louis AM, et al. A comparison of active and passive surveillance strategies for selected birth defects in New York. Birth Defects Res. 2024;116(9):e2399. [DOI] [PubMed] [Google Scholar]

[CIT0035] 35. Salemi JL, Tanner JP, Kennedy S, et al. A comparison of two surveillance strategies for selected birth defects in Florida. Public Health Rep. 2012;127(4):391–400. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0036] 36. Bower C, Rudy E, Callaghan A, Quick J, Nassar N. Age at diagnosis of birth defects. Birth Defects Res A Clin Mol Teratol. 2010;88(4):251–255. [DOI] [PubMed] [Google Scholar]

[CIT0037] 37. Schraw JM, Desrosiers TA, Nembhard WN, et al. Cancer diagnostic profile in children with structural birth defects: An assessment in 15,000 childhood cancer cases. Cancer. 2020;126(15):3483–3492. Epub 20200529 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0038] 38. Louis DN, Perry A, Reifenberger G, et al. 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803–820. Epub 20160509 [DOI] [PubMed] [Google Scholar]

PERMALINK

Co-occurrence of congenital anomalies and childhood brain tumors in 22 million live births

Thanh T Hoang

Jeremy M Schraw

Charles Shumate

Tania A Desrosiers

Wendy N Nembhard

Mahsa Yazdy

Eirini Nestoridi

Amanda E Janitz

Russell S Kirby

Jason L Salemi

Jean Paul Tanner

Tiffany M Chambers

Michael D Taylor

Chad D Huff

Sharon E Plon

Philip J Lupo

Michael E Scheurer

Abstract

Background

Methods

Results

Conclusions

Key Points.

Importance of the Study.

Materials and Methods

Study Population

Table 1.

Congenital Anomaly Ascertainment and Classification

Childhood Brain Tumor Ascertainment and Classification

Statistical Analysis

Results

Table 2.

Major Structural Congenital Anomaly and CBTs

Any major structural congenital anomaly

Figure 1.

Number of major structural congenital anomalies

Any major structural congenital anomaly stratified by age at tumor diagnosis

Figure 2.

Specific major structural congenital anomaly and CBT combinations

Figure 3.

Sensitivity analysis

Genetic Syndromes and CBTs

Figure 4.

Secondary Analyses with Astrocytoma Types

Discussion

Major Structural Congenital Anomalies and CBTs

Genetic Syndromes and CBTs

Limitations

Conclusions

Supplementary material

Acknowledgments

Contributor Information

Funding

Conflict of Interest

Authorship Statement

Data Availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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