Comprehensive germline genomic profiles of children, adolescents and young adults with solid tumors

Compared to adult carcinomas, there is a paucity of targeted treatments for solid tumors in children, adolescents, and young adults (C-AYA). The impact of germline genomic signatures has implications for heritability, but its impact on targeted therapies has not been fully appreciated. Performing variant-prioritization analysis on germline DNA of 1,507 C-AYA patients with solid tumors, we show 12% of these patients carrying germline pathogenic and/or likely pathogenic variants (P/LP) in known cancer-predisposing genes (KCPG). An additional 61% have germline pathogenic variants in non-KCPG genes, including PRKN, SMARCAL1, SMAD7, which we refer to as candidate genes. Despite germline variants in a broad gene spectrum, pathway analysis leads to top networks centering around p53. Our drug-target analysis shows 1/3 of patients with germline P/LP variants have at least one druggable alteration, while more than half of them are from our candidate gene group, which would otherwise go unidentified in routine clinical care.

S olid tumors account for half of the malignancies in children, adolescents, and young adults (C-AYA), with a lower burden of somatic variants, and are assumed to have higher frequencies of germline alterations, compared to adults with solid tumors 1,2 . Although there has been substantial advancement in understanding somatic variants in cancers, our knowledge regarding the spectrum, frequency, and implications of germline variants in C-AYA with solid tumors is limited. Recent pancancer studies showed that 7-8% of patients with these malignancies diagnosed <20 years of age have pathogenic or likely pathogenic (P/LP) germline variants in known cancerpredisposing genes, with adrenocortical carcinoma (50%) and high-grade glioma (25%) having the highest percentage of variants among all solid tumors. However, these percentages could be biased for the following reasons: a high proportion of patients with those specific tumor types and the absence of a few other subtypes of pediatric cancers in those studies. In contrast, 1.1% of the control group studied by Zhang et al. [1][2][3][4] , comprised of healthy adult individuals from the 1000 Genomes Project, carry such variants.
The cancer-predisposing genes mainly encode tumor suppressors, which are related to DNA damage to ensure DNA repair processes or oncogenes, which promote growth. In general, pathogenic loss-of-function variants in oncogenes can disrupt normal cellular processes, and predispose to cancer development 5 ; moreover, multiple genes can have both types of variants with different functional and phenotypic effects. It is well established that there is an overlap between germline cancer-predisposing genes and somatic tumor-driver genes: there are many examples showing identical genes having roles in somatic oncogenesis and susceptibility to cancer, respectively 6 .
In pediatric and AYA clinics, family history is essentially the primary means used to recognize patients with possible heritable cancer 7 . This is despite prior studies showing that a family history of cancer could only be obtained in about 40% of patients with P/LP mutations due to multiple limitations 2 . It is now confirmed, as well, that there is a remarkably elevated risk of secondary primary neoplasms in C-AYA cancer survivors who carry a germline P/LP mutation in cancer-predisposing genes compared to those who do not 8 . In addition to implications for heritability and second primary neoplasms, germline (in every single cell of the body) variants can also provide novel therapeutic targets. The clonal nature of germline variants compared to the heterogeneous somatic pattern of tumors make them potentially a suitable biomarker and therapeutic target, both of which are lacking for C-AYA malignancies, compared to adult malignancies 9 . Here, we address this gap by investigating germline genomic signatures of 1507 patients with solid tumors diagnosed under 29 years of age.

Results
Germline alterations in Cleveland Clinic patient series. We evaluated 50 prospectively enrolled C-AYA patients at the Cleveland Clinic (CCF), with a broad range of solid tumors diagnosed under 29 years of age. The series had a median age of 12 ± 7.1 years (range 0.  and consisted of 31 children (52% females, median age of 8 ± 4.2 years), 12 adolescents (66.7% males, median age of 18 ± 1.2 years), and 7 young adults (all males, median age of 22 ± 3.6 years). Collectively, these patients had 14 different tumor types, with bone and soft tissue sarcomas being the predominant cancer types (Supplementary Table 1; Supplementary Data 1).
First, we analyzed 204 known cancer-predisposing genes (KCPG), curated using previously established cancer-predisposing genes in addition to the newly proposed genes from recent publications (Supplementary Data 2; Supplementary Fig. 1). We found three pathogenic germline variants (Methods), one nonsense mutation in TP53, and two frameshift indels in BRCA2 and GJB2 genes in two patients with osteosarcoma, which were further confirmed by Sanger sequencing (Table 1; Fig. 1a, b; Supplementary Data 3; Supplementary Fig. 2). The average mean depth was 258× (range 45×-444×) for the CCF P/LP KCPG variants. Assessing germline copy number variations (CNVs), using exome coverage data, we found five genes with germline duplications, including DDX10 and SUZ12 (Supplementary Fig. 3; Supplementary Data 4). There were no known CNVs in the identified regions in the database of genomic variants (DGV). In a rare circumstance, a 27year-old male with multiple primary sarcomas was found to have two pathogenic KCPG variants, one in BRCA2 (paternally inherited) and the other in TP53 (maternally inherited), the latter confirming a Li-Fraumeni syndrome diagnosis (Table 1). Both parents are in their 50s with no history of cancer. Our second representative case was a female patient with osteosarcoma, diagnosed at 10, who carried a pathogenic variant in GJB2 in addition to a germline duplication of DDX10, the latter, a known marker somatically associated with poor prognosis for osteosarcoma (Table 1; Fig. 1c; Supplementary Fig. 3a). Overall, 2 out of 50 C-AYA CCF patients with solid tumors carried a germline pathogenic KCPG variant, and 3 other C-AYA CCF patients harbored a germline CNV.
Evaluation of pedigrees to obtain family histories revealed the existence of a positive family history of cancer in about 40% of the remaining 42 (84%) patients. This suggests the existence of yet-tobe-identified predisposing genes in KCPG mutation-negative patients. Hence, we extended our analyses to explore other P/LP variants from non-KCPG, which we will refer to as candidate genes. Our variant classification based on the ACMG guidelines identified 59 predicted pathogenic and 37 predicted likely pathogenic variants in 89 candidate genes (Fig. 1a,   each type of C-AYA solid tumor had its own well-known associated germline KCPGs, we reported unexpected KCPGs for many tumor types (Figs. 2c and 3a; Supplementary Data 14-15). For example, while TP53, PMS2, and RET are already reported as genes with germline alterations in individuals with Ewing sarcoma, we identified germline P/LP variants in ATM (1 patient), (1), and POLE (1) genes in cases with Ewing sarcoma (Supplementary Fig. 6). (1), and COL7A1 (4) in patients with Wilms tumor. Although RB1 was mutated in about one-third of our retinoblastoma cases, RB1 mutation-negative retinoblastoma patients had germline P/LP variants in other known cancer predisposition genes like BRCA1 (2), EGFR (1), and MSH6 (1) (Fig. 3a, b). Beyond P/LP KCPG variants, we demonstrated an interesting signature of germline P/LP variants in our candidate gene group (Fig. 3c, d). For example, we found germline P/LP variants in TMPRSS3, a member of the serine protease family, in patients with CNS tumors (5), retinoblastoma (3), and soft tissue sarcoma (3) (Fig. 3c, d). Another example is the detection of germline P/LP variants in MCPH1, which encodes a DNA damage a b c d Fig. 3 Germline genomic signatures of children, adolescents, and young adults (C-AYA) with 12 types of solid tumors. a, b Germline gene cloud signatures of the C-AYA patients with solid tumors based on their altered known cancer-predisposing genes (KCPG) (a) or candidate genes (b). The size of the genes is proportional to their pathogenic/likely pathogenic (P/LP) variant frequency in that tumor type, colors do not specify any meaning. c, d Heat maps of top altered KCPGs (c) or candidate genes (d). Two-sided Fisher´s Exact test implemented in R statistical software. P values were adjusted for multiple testing with Bonferroni correction considering 593 tests. FDR threshold of 0.05 considered a significant event. Scale refers to log10 (frequency of P/LP variants in specified genes in each tumor type). Blue rectangles specify significant corrected P values in comparison to non-TCGA ExAC database. ACT adrenocortical carcinoma, CNS central nervous system, EWS Ewing sarcoma, GCT germ cell tumor, HGG high-grade glioma, LGG low-grade glioma, NBL neuroblastoma, OS osteosarcoma, RB retinoblastoma, RHB rhabdomyosarcoma, STS soft tissue sarcoma, WLM Wilms tumor. response protein in three of our Ewing sarcoma patients. Germline P/LP variants in SMARCAL1, another gene related to the DNA damage pathway, was detected in three patients with osteosarcoma. Germline P/LP variants in SMAD7, a gene associated with colorectal and breast cancers, were found in six of our high-grade glioma patients. Germline loss of function of the PRKN gene was frequently found in our patients with Wilms (6), CNS (5), neuroblastoma (4), and osteosarcoma (3) tumors (Fig. 3c, d). Our top proposed candidate genes selected based on their known connection with cancers, and their predicted mechanisms of function can be found in Table 3.
Finally, in order to investigate if C-AYA patients with solid tumors have any germline alteration enrichment in genes related to other common non-cancerous congenital syndromes, we crossmatched our data with 185 congenital heart defect (CHD)-related genes and found 67 (4.4%) of our patients carried at least one P/ LP variant in one autosomal-dominant CHD-related gene (7 KCPG and 19 candidate genes). An additional 72 (4.8%) patients carried heterozygous variants in autosomal-recessive CHDrelated genes (2 KCPG and 31 candidate genes), which we did not include in our analysis due to their mode of inheritance. C-AYA patients with CNS (32 variants), neuroblastoma (21), rhabdomyosarcoma (15), and Wilms (15) tumors had the highest number of CHD-related gene variants, and patients with retinoblastoma (2) and osteosarcoma (2)  Pathway analysis across all solid tumors. Since we noticed a very broad spectrum of both KCPG and candidate genes involved in C-AYA solid tumors, we next sought to determine if they converged on any common pathways. The p53 pathway with fraction affected of 0.5 (3 out of 6 genes) was the most affected, followed by receptor tyrosine kinases and Ras (RTK-RAS) pathway with fraction affected of 0.14 (12 out of 85 genes), Hippo pathway with fraction affected of 0.13 (5 out of 38 genes) ( Fig. 4a; Supplementary Data 17). Candidate genes were remarkably involved in the affected pathways. For example, in the RTK-RAS pathway, 3 out of 12 mutated genes were from the candidate gene group, including SHC1, ERBB3, and FLT3 (Fig. 4b). The Hippo pathway had four affected candidate genes, CRB1, CRB2, HMCN1, and LATS1. In the Wnt pathway, we had one recently recognized KCPG, LZTR1, and the other six affected genes were from the candidate gene group: AXIN1, WNT10A, CHD8, FZD6, LRP5, RSPO1. Although only two genes belonged to the Cell Cycle Pathway, RB1, one of those two, was the gene with the highest variant frequencies in our dataset with 32 cases (24 variants) (Supplementary Fig. 8).
Other than the direct effect on each pathway, we investigated the interactions between our mutated genes through Ingenuity Pathway Analysis (IPA) (Supplementary Data 18). Only genes with at least four variants in the dataset were used to generate our networks. The connection between our target genes and molecules in the IPA knowledge database formed the basis of this network construction. Our IPA-predicted top network comprised 26 of our genes (10 KCPG and 16 candidate genes) centering around p53 (right-tailed Fisher's exact test P = 1 × 10 −42 ; Fig. 4c). Top diseases and functions predicted to be affected by this network were metabolic diseases, organismal injury and abnormalities, and cancer. Our analysis showed the top anticipated canonical pathways affected by our target genes were DNA double-strand break repair by homologous recombination (28.6% overlap, Benjamini-Hochberg (B-H) corrected P = 2.08 × 10 −3 ), role of BRCA1 in DNA damage response (11.2% overlap, B-H corrected P = 5.82 × 10 −5 ), and role of CHK proteins in cell cycle checkpoint control (8.8% overlap, B-H corrected P = 2.48 × 10 −2 ). Using our network analysis, we could predict that Eukaryotic Translation Initiation Factor 4 Gamma 1 (EIF4G1, B-H corrected P = 1.39 × 10 −3 ) and I kappa b kinase (IκB kinase, B-H corrected P = 1.39 × 10 −3 ) could act as master upstream regulators for the altered genes and potentially control the expression of those altered genes (Fig. 4d).  We expanded this evaluation to almost double the number of individuals with solid tumors (1507 C-AYA) and evaluated 204 known cancer-predisposing genes. Notably, we also expanded our assessment with an agnostic approach to evaluate new (not previously known to predispose to cancer) candidate genes, and affected pathways which added to the germline signatures of C-AYA solid tumors. To our knowledge, this study provides the largest evaluation of germline mutations in C-AYA patients with solid tumors. Here, we performed variant-prioritization analysis on germline exome data of 1507 C-AYA patients with solid tumors, while focusing not only on the well-known germline mutations in KCPGs but also any P/LP germline alterations in genes previously unknown to be associated with cancer predisposition. Starting with our prospectively-recruited CCF series, we showed that 10% of our cases harbored P/LP germline alterations, either a truncating mutation in a KCPG and/or a larger CNV in NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16067-1 ARTICLE cancer-related genes, consistent with the previous studies 1-3,12 . One of our CCF osteosarcoma cases presented with a germline truncating mutation in GJB2 and duplication of DDX10. GJB2, which encodes an epithelial gap junction protein, is mostly known for being associated with syndromic hearing loss, for example, keratitis-ichthyosis-deafness (KID). It has been reported that these KID patients with germline GJB2 mutation have increased risks of developing epithelial malignancies, for example, 19% occurrence of squamous cell carcinoma of the skin and oral mucosa compared to the normal population 13 . In total, combined with StJ cases, we detected seven GJB2 germline P/LP variants in C-AYA patients with CNS tumors (3 patients), osteosarcoma (2), Ewing sarcoma (1), and rhabdomyosarcoma (1). DDX10, the second altered gene, in this case, is a known cancer-related gene, and its somatic overexpression has been recently reported to be associated with a lower survival rate in osteosarcoma patients 14 .
Shi and Hao 14 showed that silencing of DDX10 could potentially be therapeutic, and inhibit proliferation, invasion, and migration of the tumor cells, by inhibiting MAPK pathway.
Validating our findings with a larger dataset, focusing on single-nucleotide variations (SNVs) and small indels, we confirmed that 12% of C-AYA patients with solid tumors harbored at least one germline P/LP variant in the KCPGs, with an additional 61% of our cases carrying pathogenic variants in other candidate genes. As expected, about one-third of these KCPGs and candidate genes, each with four and more P/LP variants, were enriched and had statistically significant higher P/LP variant allele frequencies in our C-AYA patients with solid tumor dataset compared to the control group from non-TCGA ExAC dataset. Overrepresentation of germline P/LP variants of those genes in our dataset compared to the control dataset verified the nonincidental nature of those findings. Recently, Wang et al. 15 , with the same approach, proposed that having a germline heterozygous BRCA2 mutation predisposes to pediatric or adolescent non-Hodgkin lymphoma, by showing an overrepresentation of the BRCA2 mutations in the target group compared to a control population without cancer (odds ratio, 3.3; 95% CI, 1.7-5.8) 15 .
On the other hand, there are multiple pieces of evidence connecting a portion of these bioinformatically predicted candidate genes to cancer (Table 3). For example, somatic overexpression of TMPRSS3, a transmembrane serine protease, mostly known for its association with non-syndromic hearing loss, was previously reported to be associated with breast, ovarian, and pancreatic cancers [16][17][18] . TMPRSS3 can mediate cancer progression, using its proteolytic activities, by helping the malignant cells to proliferate, migrate, and survive, via regulation of the ERK1/2 and PI3K/AKT pathways 19 . In another example, somatic depletion of PRKN, a component of a multiprotein E3 ubiquitin ligase complex, and a known gene associated with Parkinson's disease, was also reported in ovarian and lung cancers [20][21][22][23] . PRKN can act as a tumor suppressor gene, and its loss of function can activate the PI3K/AKT pathway via inactivation of PTEN 24 . Here, we reported multiple instances of predicted PRKN loss of function in patients with Wilms (6), CNS (5), neuroblastoma (4), and osteosarcoma (3) tumors (Fig. 3c, d). We also reported germline loss of function of COL7A1 in four C-AYA patients with Wilms tumor. Interestingly, RNA expression data, comparing tumor and normal tissue from gene expression profiling interactive analysis (GEPIA) 25 , confirmed the lower expression of COL7A1 in adult kidney-related tumors as well (Supplementary Fig. 9).
There have been several epidemiologic studies associated with childhood congenital malformations with cancer risk. In a recent study from the Swedish Patient Register, e.g., Mandalenakis et al. 26 showed that C-AYA patients with any kind of CHD had increased risk of developing cancer (hazard ratio = 2.24, 95% CI, 2.01-2.48) compared to their matched controls, from Total Population Register in Sweden, who did not have CHD (2% vs. 0.9%). Here, we also showed that 67 (4.4%) of our C-AYA patients with solid tumors carried at least one germline P/LP variant in a CHD-related gene (7 KCPG and 19 candidate genes) ( Supplementary Fig. 7), confirming the importance of evaluating both KCPGs and candidate genes. As examples, germline NF1 and PTPN11 P/LP variants were found in 24 of our C-AYA patients with solid tumors (Supplementary Data 5): both of these KCPGs predispose to CNS-related tumors, are also strongly correlated with CHDs, via up-regulation of the RAS pathway [27][28][29][30] . NOTCH1 germline P/LP variants in two of our cases (Supplementary Data 16) is another example of a candidate gene associated with both CHD and cancer via varied mechanisms, including downregulation of the TGF-beta signaling pathway affecting epithelial-to-mesenchymal transformation (EMT) [31][32][33] . Together, the data to date re-emphasize the need for referring all C-AYA cancer patients for genetic consultation and further clinical evaluation.
C-AYA patients with solid tumors have a lower burden of somatic mutations while carrying a higher number of germline alterations, compared to their adult counterparts 1,2,34 . Thus, our study here reveals the germline as a therapeutic consideration. Our pathway analysis showed that not only point mutations, and deletion of TP53 itself are important in cancer predisposition in C-AYA patients, but also that P/LP germline mutations of other, seemingly disparate, genes point to a final common disruption of the p53 pathway. Thus, the p53 signaling pathway appears to be a crucial final common pathway in cancer predisposition in C-AYA. Relatedly, we showed also that DNA damage response (DDR) and checkpoint control pathways are the top canonical pathways in this group. While it is routine to target somatic mutations in solid tumors, these observations suggest that germline mutations can also be effectively targeted in those with malignancies. The prime example is using poly (ADP-ribose) polymerase (PARP) inhibitors for the treatment of adults with advanced breast, ovarian, and prostate cancers in the context of germline mutations in DDR genes, such as BRCA1, BRCA2, ATM, or PALB2 [35][36][37] . Although targeting mutated genes in the germline setting is challenging owing to possible toxicity to non-cancerous tissues, we speculate that appropriate drug dose thresholds could lend a high therapeutic index. Moreover, because cancer cells often have a complex network of disrupted genes and pathways (including somatic aberrations absent from normal cells), we would expect varied sensitivity to therapeutic targeting between malignant and normal cells. Our drug-target network analysis opens a new window on potentially druggable genes and possible repurposable drugs for currently considered undruggable tumor targets. Thus, further preclinical and clinical studies are warranted before translation to the routine clinical armamentarium. Acquiring and combining the data for both somatic and germline alterations, and their subsequent affected pathways, can be crucial and rudimentary in selecting the treatment strategy with the highest therapeutic index, and which may even mitigate the late effects in the C-AYA population. Towards these ends, the latest efforts by the National Cancer Institute to establish the childhood cancer data initiative (CCDI), accompanied by ongoing clinical trials such as the comprehensive omics analysis of pediatric solid tumors (NCT01109394), should collectively provide a relevant infrastructure for C-AYA solid tumors which are currently considered difficult to treat because of non-druggable targets.
Our study has several limitations, including lack of matched tumor or RNAseq data for many of our cases. Our series were not population-based cohorts; and only 5-year survivors were included in the St. Jude series. Therefore, the prioritized variants here for cancer risk may be challenged by survivor bias. Future studies (including from the St. Jude side) should fulfill these gaps.

Methods
Patients enrollment/sample selection. Patients' data for this project were obtained from two sources: Variant analysis in the control population. Case-control analysis, using 13 noncancer patients from CCF and 340 non-cancer samples from the StJ dataset as controls, was performed to exclude pipeline alignment errors in our IVA analysis. Also, we extracted germline exome data, for all the KCPG and candidate genes found in our study, and independently performed another IVA analysis, with the same parameters, on 53,105 individuals from non-TCGA ExAC database to compare the frequency of P/LP allele variants between C-AYA patients with solid tumors and this non-cancer control population.
CNV analysis. We used VarSeq™ v2.1.0 (Golden Helix, Inc., Bozeman, MT, www. goldenhelix.com) to detect CNVs in our CCF dataset, using depth of coverage following the manufacturer's instructions (https://link.springer.com/protocol/ 10.1007%2F978-1-4939-8666-8_9). Principle component analysis (PCA) and reference sample normalization were used to normalize the data. We used two metrics to detect a CNV event: (1) Z-score, which is the number of standard deviations from the reference sample mean and (2) ratio, which is then normalized read depth for the sample of interest divided by the normalized mean depth over the reference samples. Both metrics are computed from normalized coverage. We used ratio ≤ 0.75 and Z-score ≤ −2.5 for screening of heterozygous deletion and ratio ≥ 1.25 and Z-score ≥ 2.5 for primary detection of the duplications. 1000 Genomes, ExAC, ClinVar, and Database of Genomic Variants (DGV) were used for data annotation. The Z-scores were used to compute P values for each called event. eXome Hidden Markov Model (XHMM) algorithm 47 was used with default parameters to confirm our CNV findings. The mean per-target depth of coverage for detected CNVs was 149.
Pathway analysis. We used the OncogenicPathways function of maftools 48 to check for the enrichment of known oncogenic signaling pathways in our dataset. To calculate the fraction affected, we divided the number of genes affected in each pathway to the total number of genes within that pathway. Next, we used the Qiagen IPA 49 to generate networks for our target genes. We included only genes with four or more P/LP variants in our dataset, in KCPG and candidate genes, after optimization. Each gene ID was mapped to its corresponding object in Ingenuity's knowledge base. These genes served as seeds for generating our networks. Networks were then algorithmically generated based on the connectivity of our genes of interest with other molecules existing in the Ingenuity's knowledge base.
Reconstruction of the drug-target network. We collected physical drug-target interactions for FDA-approved drugs from seven commonly used data sources. Specifically, drug-target interactions were acquired from the DrugBank 50 , the Therapeutic Target Database 51 , the PharmGKB 52 , and DrugCentral 53 . Bioactivity data of drug-target pairs were collected from three commonly used databases: ChEMBL 54 , BindingDB 55 , and IUPHAR/BPS Guide to Pharmacology 56 . Herein, we defined a physical drug-target interaction using the reported binding affinity/ inhibitory data: inhibition constant/potency (K i ), dissociation constant (K d ), median effective concentration (EC 50 ), or median inhibitory concentration (IC 50 ), each ≤10 µM. After extracting the bioactivity data related to the drugs from the prepared bioactivity databases, only those items meeting the following four criteria were retained: (i) binding affinities, including K i , K d , IC 50 , or EC 50 , ≤10 μM; (ii) proteins represented by unique UniProt accession number; (iii) proteins marked as "reviewed" in the UniProt database 57 , and (iv) proteins of human origin. In total, we collected 13,567 drug-target pairs connecting 2248 targets and 1703 US FDAapproved drugs (December 2018). We defined the therapeutic drug families based on the Anatomical Therapeutic Chemical (ATC) classification codes downloaded from DrugBank 50 and DrugCentral 53 . For example, we defined the antineoplastic and immunomodulating agents based on the first level of the ATC code as L. To select druggable genes, we cross-matched our prioritized gene list with the reconstructed drug-target network and used the Sankey diagram (R package-net-workD3) for the visualization.
Statistical analysis. In pathway analysis, the network scores were created based on the hypergeometric distribution and were calculated with the right-tailed Fisher's exact test. B-H P value correction was used to reduce the FDR. In our case-control comparison analysis, we calculated the P values, ORs, and 95% CIs with a twosided Fisher´s exact test implemented in R statistical software. P values were adjusted, when we compared the frequency of P/LP variants in C-AYA with solid tumors versus non-TCGA ExAC control population, for multiple testing with Bonferroni correction considering 593 tests. FDR threshold of 0.05 considered a significant event. We used an independent dataset from St. Jude Children's Research hospital to reproduce the data from our pilot study on Cleveland Clinic series. All the analysis of this study performed multiple time to ensure the reproducibility of the findings.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability
The whole-exome data for C-AYA cases with solid tumors from Cleveland Clinic have been deposited in the NCBI Sequence Read Archive (SRA) database under the accession code PRJNA559601. Whole-exome data for C-AYA cases with solid tumors from St. Jude Children's Research hospital is accessible at https://www.stjude.cloud/ website. The non-TCGA data referenced during the study are available in a public repository from Broad Institute website at ftp://ftp.broadinstitute.org/pub/ExAC_release/release0.3.1/subsets/. All the other data supporting the findings of this study are available within the article and its Supplementary Information files and from the corresponding author upon reasonable request. A reporting summary for this article is available as a Supplementary Information file.

Code availability
All data analysis, visualization, and codes related to this study are available at the following GitHub link: https://github.com/EngLabGMI/ germline_caya_solidtumor_analysis.