Detecting likely germline variants during tumor-based molecular profiling

As the use of molecular profiling of tumors expands, cancer diagnosis, prognosis, and treatment planning increasingly rely on the information it provides. Although primarily designed to detect somatic variants, next-generation sequencing (NGS) tumor-based profiling also identifies germline DNA alterations, necessitating careful clinical interpretation of the data. Traditionally, germline risk testing has depended on prioritizing individuals based on physical exam findings consistent with known hereditary cancer syndromes, tumor-specific features, age at diagnosis, personal history, and family history. As NGS-based molecular profiling is used increasingly to diagnose, prognosticate, and follow cancer progression, DNA variants that are likely to be of germline origin are identified with increased frequency. Because pathogenic/likely pathogenic germline variants are critical biomarkers for risk stratification and treatment planning, consensus guidelines are expanding to recommend comprehensive germline testing for more cancer patients. This Review highlights the nuances of identifying DNA variants of potential germline origin incidentally at the time of NGS-based molecular profiling and emphasizes key differences between comprehensive germline versus tumor-based platforms, sample types, and analytical methodologies. In the growing era of precision oncology, clinicians should be adept at navigating these distinctions to optimize testing strategies and leverage insights regarding germline cancer risk surveillance and management for all people with cancer.

Deleterious germline variants disrupt the function of CSGs that encode components integral to DNA repair, cell cycle regulation, telomere biology, and other essential cellular processes. Defects in homologous recombination repair (HRR) genes, such as ATM, CHEK2, BRCA1, and BRCA2 (22), impair the accurate repair of double-strand DNA breaks. Consequently, cells rely on error-prone DNA repair mechanisms like single-strand annealing (SSA) or non-homologous end joining (NHEJ), leading to increased genomic instability and the accumulation of somatic variants (23, 24) (Figure 1). For instance, SSA promotes chromosomal rearrangements, deletions, and amplifications that drive oncogenesis, as seen in hereditary breast and ovarian cancers (25). Similarly, the upregulation of alternative NHEJ components, including LIG3 and PARP1, contributes to disease progression (26), as observed in chronic myeloid leukemia (27) and lung adenocarcinoma (28), as well as treatment resistance. In multiple myeloma, for example, PARP1 overexpression has been implicated in resistance to melphalan, a chemotherapeutic agent (29). Moreover, in BRCA1-deficient tumors, the loss of LIG3 can revert resistance to poly(ADP-ribose) polymerase (PARP) inhibitors by exposing single-strand DNA gaps, highlighting alternative NHEJ as a mediator of drug resistance (30).

Figure 1

DNA repair pathways and additional mechanisms underlying hereditary cancer risk. (A) Defects in the homologous recombination repair (HRR) genes BRCA1 and BRCA2 impair the accurate repair of double-strand DNA breaks, resulting in error-prone mechanisms like single-strand annealing, alternative end joining, and non-homologous end joining, leading to increased genomic instability and the accumulation of somatic variants. HRR deficiency is associated with several tumor types, including breast, ovarian, prostate, and pancreatic cancers. (B) Defects in the mismatch repair (MMR) genes MLH1, MSH2, MSH6, and PMS2 impair the repair of DNA replication errors, leading to microsatellite instability and genome-wide hypermutation. MMR deficiency is commonly associated with Lynch syndrome, predisposing individuals to a variety of cancers, including colorectal, endometrial, and ovarian cancers. Similar to HRR defects, MMR defects result in a mutator phenotype that drives tumorigenesis by allowing the accumulation of somatic variants. (C) Additional pathways implicated in hereditary cancer risk are shown, highlighting commonly altered cancer susceptibility genes recommended for further germline evaluation by the ACMG and the ESMO PMWG when identified on tumor-based profiling. Corresponding clinical phenotypes and penetrance estimates are detailed in Table 1.

Beyond HRR defects, disruptions in mismatch repair (MMR) pathway genes, like MLH1, MSH2, MSH6, and PMS2, compromise DNA replication error correction, leading to microsatellite instability (MSI), a hallmark of Lynch syndrome–associated cancers, promoting genomic instability and tumorigenesis (31) (Figure 1). Germline alterations in other genes also contribute to cancer predisposition through diverse mechanisms. For instance, loss-of-function mutations in CDH1, which encodes E-cadherin, compromise epithelial integrity and promote invasion, predisposing individuals to hereditary diffuse gastric cancer and lobular breast cancer (32, 33). Similarly, mutations in APC, a regulator of the Wnt signaling pathway, result in unchecked β-catenin activation, driving the development of colorectal adenomas and carcinomas (34). These germline cancer risk alleles predispose individuals to specific cancer types and also shape the mutational landscape of tumors (Table 1).

Table 1

A list of common cancer susceptibility genes recommended by the ACMG and the ESMO PMWG for further testing when detected on tumor-based profiling

The mode by which deleterious germline variants influence tumorigenesis varies considerably. In some cases, these alleles serve as initial driver events, whereas in others, cancer arises sporadically through independent biological pathways (2). Germline and somatic aberrations cooperate to drive cancer initiation and progression through diverse mechanisms dependent on factors such as tumor lineage and penetrance, defined as the proportion of individuals carrying a variant who develop the associated phenotype (35). Penetrance also varies significantly across genes and even within the same gene. High-penetrance variants are associated with a greater likelihood of cancer development, whereas low-penetrance variants confer a more modest risk. Srinivasan et al. identified two major routes by which germline variants influence tumorigenesis based on the analysis of pathogenic variants in 17,512 sequenced patients with cancer (2). In carriers of high-penetrance CSGs with deleterious germline variants, lineage-dependent selective pressure for biallelic inactivation in associated cancer types (e.g., BRCA1/2 in hereditary breast cancer) demonstrated earlier age of cancer onset, fewer somatic drivers, and characteristic somatic features suggestive of dependence on the germline allele for tumor development. In this context, the germline alteration likely served as the initiating oncogenic event, with subsequent somatic events accelerating tumor formation and progression. In contrast, 27% of tumors in carriers of high-penetrance deleterious variants, and most cancers in carriers of lower-penetrance variants, did not show somatic loss of the wild-type allele or indicators of germline dependence, suggesting that the heterozygous germline variant may not have played a significant role in tumor pathogenesis. Interestingly, nearly half of the patients with high-penetrance CSGs carrying deleterious germline alleles developed cancers not typically associated with these genes, regardless of the presence of biallelic inactivation.

Although it remains unclear whether heterozygous deleterious variants foster an environment conducive to tumorigenesis, particularly in non-hereditary cancers, one potential explanation for this phenomenon is haploinsufficiency. In this scenario, a single functional allele fails to produce sufficient gene product to maintain normal cellular function, leading to abnormal phenotypes (36). Haploinsufficiency is often implicated in dosage-sensitive genes, where proper function relies on a precise amount of gene product (36). Examples of haploinsufficient genes implicated in solid and hematopoietic malignancies include those encoding transcription factors, such as CUX1 (37, 38) and Nkx3.1 (39), and tumor suppressors like p53 (40) and CHD5 (41). The degree to which haploinsufficiency contributes to disease in individuals with deleterious germline variants, particularly in cancers exhibiting incomplete penetrance, remains an area of active investigation (42–44).

Importantly, not all germline variants identified are deleterious. Germline pathogenicity is assessed through the integrated evaluation of population frequency, disease phenotype, functional data, familial segregation patterns, and predictive modeling, which together inform clinical significance. Variants are classified using the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) five-tier system as pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), likely benign, or benign (45). Pathogenic variants demonstrate strong evidence of disease association, such as cosegregation with cancer in families, functional studies showing detrimental effects, or a high prevalence in affected individuals. Likely pathogenic variants lack the full spectrum of evidence required for pathogenicity but still exhibit strong indications of disease association. Both P and LP variants are clinically actionable.

To standardize clinical reporting, organizations such as the ACMG and the European Society for Medical Oncology Precision Medicine Working Group (ESMO PMWG) highlight specific CSGs for additional evaluation during tumor-based profiling (46–48). For example, the ACMG recommends reporting findings from at least 28 CSGs as secondary or incidental findings (46), and the ESMO PMWG updated its guidelines in 2022 to include 40 CSGs based on data from over 49,000 tumor-normal paired samples (47, 48). These genes were selected based on their high germline conversion rate (>5% proportion that are of true germline origin), pathogenicity classification (P/LP), and high penetrance, warranting further clinical attention (Table 1).

As our understanding of germline pathogenicity evolves, so too does the process of variant classification. ClinVar (ncbi.nlm.nih.gov/clinvar) is an open-source platform that serves as a centralized repository for variant data (49, 50), where clinical laboratories and others deposit DNA variants identified in individuals along with their classifications. Clinical Genome Resource (ClinGen; https://clinicalgenome.org/) comprises expert panels who develop gene curation rules and then apply those rules to classify variants deposited into ClinVar, providing consistency in variant curation across the world. These curation panels review variants every two years to integrate new scientific literature and update variant classifications accordingly.

Numerous large pan-cancer studies have examined the frequency of incidental germline variant detection in patients who have undergone tumor-based sequencing, reporting a prevalence of 3%–17% across extensive cohort analyses (51–64) (Table 2). The majority of these studies used paired tumor-normal sequencing, in which tumor-based samples are compared with non-malignant tissue from the same patient to determine the true rate of P/LP germline variants. The wide range of reported P/LP germline variants seen across studies is likely due to multiple factors, including differences in study populations, sequencing techniques, and the number of CSGs evaluated. Earlier studies cite frequencies closer to 3%–4% (59–61), but advances in sequencing techniques in addition to larger cohort sizes likely explain the consistently higher prevalence rates observed in more recent analyses. In the largest pan-cancer study of its kind, Tung et al. examined comprehensive genomic profiling data in over 125,000 patients with advanced cancer across a wide range of solid and hematopoietic malignancies and found that 9.7% of patients harbored P/LP germline variants (51). Notably, P/LP germline variants were inferred based on a list of CSGs, ClinVar evidence, and variant allele frequency thresholds, rather than confirmed via paired tumor-normal sequencing. Another notable large pan-cancer study that used paired tumor-normal sequencing found that among 10,389 individuals across 33 cancer types, 8% of patients carried P/LP germline variants with considerable variability by cancer type, ranging in prevalence from 22.9% in pheochromocytoma/paraganglioma and 19.9% in ovarian cancer to 2.2% in cholangiocarcinoma (56). In another recent study, Stadler et al. found the prevalence of P/LP germline variants to be as high as 17%, which may reflect differences in cohort composition, as their study included younger patients (median age at diagnosis, 58 years) and a higher proportion with more advanced disease (81% stage IV malignancies) (53). Taken together, the true frequency of incidental germline variants detected in patients who have undergone tumor-based profiling likely approximates 10%, representing a substantial proportion of patients.

Table 2

Pan-cancer studies reporting the prevalence of pathogenic and likely pathogenic germline variant detection during tumor-based profiling

Notably, Tung et al. found P/LP germline variants in similar proportions between cancers with formal National Comprehensive Cancer Network (NCCN) recommendations for universal testing (e.g., epithelial ovarian cancer, metastatic prostate cancer, pancreatic adenocarcinoma) and all other cancer types (11% and 9%, respectively) (51). The most frequently identified genes with potential P/LP germline variants included BRCA2 (16.9%), MUTYH (15.0%), ATM (13.4%), CHEK2 (11.7%), and BRCA1 (9.8%). Importantly, the majority (64%) of germline variants were discovered in patients who would not have been recommended for germline testing based on current guidelines. For example, P/LP germline variants were detected in over 2,000 (7.1%) patients with lung cancer, a group for whom universal testing is not currently recommended. Similar findings were reported in a study by Yap et al., which found that among 9,279 patients with six cancer types notably lacking hereditary cancer guidelines (e.g., bladder, brain, lung, bile duct, esophageal, and head/neck cancers), 6.5% harbored incidental P/LP germline variants (52).

In addition to pan-tumor studies, others have investigated the prevalence of incidental P/LP germline variants detected in certain subpopulations. Drazer et al. reviewed NGS panels in 360 patients with HMs, focusing on deleterious variants in nine genes associated with hereditary HMs, including DDX41, GATA2, RUNX1, and TP53. Among 52 P/LP variants detected in 44 patients, 12% (6/52) were confirmed as germline through paired tumor–normal analyses of germline tissue (57). In studies investigating the prevalence of germline P/LP variants in pediatric populations with cancer, 8%–12% of pediatric patients were found to harbor such genetic variants (62–64). In one such study by Zhang et al., the CSGs most commonly implicated included TP53, APC, BRCA2, NF1, PMS2, RB1, and RUNX1 (63). Notably, the study found that only 40% of patients with P/LP germline mutations had a positive family history of cancer, comparable to 42% of randomly selected pediatric patients without germline variants, suggesting that family history alone is not a reliable predictor of underlying germline predisposition in pediatric cancer patients. Together, these findings underscore the prevalence of suspected deleterious germline variants detected during tumor-based profiling and support the notion that P/LP germline variants are far more common than has been traditionally thought. Such findings further suggest that expanded germline testing may help to capture at-risk patients and their relatives who would not have met existing screening criteria, with the potential to profoundly inform future genetic counseling, risk stratification, and targeted therapeutics. Moreover, given that tumor-only sequencing has been shown to miss a significant number of germline variants (65, 66) (as discussed below in “Germline versus tumor-based profiling in cancer: current approaches”), the adoption of comprehensive germline testing may reveal an even greater frequency of P/LP variants, particularly in cancers not historically associated with inherited risk.

Because comprehensive germline analysis is central to uncovering novel predisposition genes and identifying new biological pathways, such as epigenetic regulation and tyrosine kinase signaling in heritable oncogenesis (63, 67–69), broader testing could substantially expand our understanding of cancer etiology. These findings have led to a growing reconsideration of what constitutes “hereditary” versus “sporadic” cancer, suggesting that many cases previously labeled as sporadic may, in fact, arise on a continuum of germline susceptibility (2, 70, 71). As such, the broader adoption of germline sequencing not only offers tangible benefits for treatment and prevention but also has the potential to reshape foundational concepts of cancer origin, risk stratification, and classification (2, 56, 70, 71).

Targeted therapeutic management. Proper distinction between somatic and germline variants has important clinical implications for patients in both early- and late-stage cancers. From a therapeutic perspective, the emergence of immune checkpoint inhibitors (ICIs) like pembrolizumab and nivolumab has revolutionized the treatment of microsatellite instability–high (MSI-H) or defective mismatch repair (dMMR) cancers, commonly linked to germline variants in MMR genes (e.g., MLH1, MSH2, MSH6, PMS2) (72, 73). By blocking the PD-1/PD-L1 pathway, ICIs demonstrate durable therapeutic responses in colorectal (74), endometrial (75), non-colorectal digestive (76), and other Lynch syndrome cancers with MSI-H/dMMR phenotypes. Similarly, P/LP germline BRCA1/2 variants confer heightened sensitivity to PARP inhibitors like olaparib, which is FDA-approved in BRCA-mutated breast (77, 78), ovarian (79, 80), pancreatic (81), and prostate cancers (82, 83), in both the early and metastatic settings. Moreover, patients harboring germline HRR alterations, including ATM, CHEK2, BRCA1/2, PALB2, RAD51D, and BAP1, are particularly susceptible to the use of platinum-based chemotherapies, which exploit DNA repair deficiencies conferred by HRR dysfunction (84–87).

Importantly, the efficacy of targeted therapies relies on the deleterious germline variant being the primary “driver” of tumorigenesis. For example, although PARP inhibitors represent a revolutionary advancement in precision medicine, their efficacy in people with germline BRCA1/2 cancer risk alleles with non-BRCA-associated cancer types may be limited (88). Jonsson et al. found that among advanced-cancer patients with deleterious germline BRCA1/2 alleles (2.7%) or somatic loss-of-function alterations (1.8%), selective pressure for biallelic gene inactivation and PARP inhibitor sensitivity was only observed in BRCA-associated cancers (88). Conversely, non-BRCA-associated tumors appeared to develop and evolve independently of the BRCA1/2 germline variant (88). Clinicians should recognize that the presence of a deleterious germline variant in a CSG with traditionally high penetrance does not necessarily portend therapeutic responsiveness, particularly in cancers not associated with the disorder (2).

Surveillance and risk reduction strategies. Individuals with inherited cancer predisposition benefit from variant-specific surveillance and management strategies. For example, it is recommended that germline TP53 carriers undergo annual whole-body and brain MRIs, along with enhanced colorectal cancer screening every 2–5 years, to identify malignancies at localized stages, allowing for potentially curative treatment (89). Further, modifications in radiation therapy protocols may be necessary to reduce the risk of secondary cancers (90). Asymptomatic carriers of germline ERCC6L2 variants should undergo regular hematologic surveillance, including bone marrow testing, cytogenetic analysis, and tumor-based profiling, with a focus on detecting somatic TP53 variants, as these may progress to acute myeloid leukemia (AML) (91). Moreover, detection of somatic TP53 variants in HMs portends inferior responses to conventional chemotherapy. Therapies specifically targeted against acquired variants, such as vemurafenib against BRAF V600E in hairy cell leukemia (92), now exist.

In the case of HMs, identification of deleterious germline variants is critical for donor consideration in allogeneic hematopoietic stem cell transplantation (allo-HSCT), as family members are generally preferred (93). Deleterious variants in genes such as CEBPA, DDX41, GATA2, and RUNX1 have been associated with inferior allo-HSCT outcomes (94–97), including donor-derived malignancies (94–97), failure or delay in engraftment (98, 99), severe graft-versus-host disease (100), impaired immune reconstitution (101), and early relapse (98). Currently, there is no standardized approach for mandated donor germline testing in allo-HSCT, leaving the possibility that matched unrelated donors may also carry germline cancer risk alleles, especially those relatively common in certain populations (102).

Identifying hereditary risk can prompt cascade testing for family members, enabling targeted screening and risk-reducing interventions (103). Cascade testing begins with identification of an individual (proband) diagnosed with a hereditary cancer syndrome and confirmation of a deleterious germline cancer risk allele. At-risk relatives can then be identified, informed, and offered genetic testing. Notably, direct communication from the clinical team to relatives significantly improves the uptake of cascade testing, facilitating earlier detection and implementation of risk reduction strategies (104, 105).

Guideline-based germline testing: challenges of limited gene coverage. Many clinical practice guidelines advocate for more expansive germline testing. Guidelines from the American Society of Clinical Oncology (ASCO) (8, 9, 20, 21, 106) and the NCCN (15, 17, 107–109) recommend universal germline testing in patients with epithelial ovarian cancer, pancreatic adenocarcinoma, metastatic or high-risk prostate cancer, pleural mesothelioma, adrenocortical carcinoma, pheochromocytomas, or paragangliomas, regardless of age, family history, or personal history. For other malignancies, such as breast, colorectal, and endometrial cancers, guidelines support germline testing based on simplified clinical criteria such as age at diagnosis, tumor subtype, or universal MMR tumor screening to identify at-risk individuals, even in the absence of family history (15, 16). However, recommendations for universal testing across many other cancer types, regardless of clinical or familial risk, remain underdeveloped (48, 106, 110, 111). These recommendations, while evolving, leave gaps in testing for patients with cancers outside of high-priority groups, in whom underlying genetic drivers may still play a crucial role in tumorigenesis. Moreover, current guidelines emphasize multi-gene panel testing that targets a subset of high-penetrance genes, such as BRCA1/2, MLH1, and MSH2, associated with well-established hereditary cancer syndromes and P/LP variants with known clinical significance (15, 16, 48, 106, 110). Although effective for identifying common germline predisposition alleles, this strategy may exclude less studied variants in genes that may confer heritable risk, particularly in atypical or rare cancer syndromes. In these cases, a broader, more inclusive testing approach is necessary, but such options are not yet standard in guidelines, which dictate the scope of clinical action and management strategies. Consequently, this limitation may lead to missed diagnoses in patient populations whose genetic risk profiles do not align with currently defined criteria (51, 53).

Given that the prevalence of P/LP germline variants in cancer patients likely approaches 10% at a minimum, we anticipate that recommendations for all cancer patients to undergo comprehensive germline testing will emerge soon. Without recognition of the germline cause of many cancers, there are detrimental consequences of failing to diagnose these conditions for both patients and their families. Given the significant overlap in cancer risk alleles between solid and hematopoietic malignancies, we envision a future in which tumor-agnostic cancer risk testing is recommended for all cancer patients, with increasing ability to identify these alleles from tumor-based NGS assays. Owing to financial and practical limitations, this approach may not be feasible, as accessibility to laboratories with advanced variant detection and curation programs remains constrained in the current landscape of genetic testing (112). Nonetheless, clinicians should advocate for germline testing in patients in whom they suspect germline predisposition or who meet high-risk criteria (outlined below in “Distinguishing germline variants in tumor-based profiling”) (Figure 2).

Figure 2

Proposed algorithm to aid the identification of likely germline variants during tumor-based profiling. In a patient with a probable malignancy (i.e., before pathologic confirmation), a thorough clinical history should be performed to determine risk. High-risk patients are prioritized for paired tumor-normal sequencing to differentiate between somatic and germline alterations. For patients not identified as high-risk who undergo tumor-based profiling, variant allele frequency (VAF) thresholds and pathogenicity classifications should guide confirmatory germline testing. In patients with a VAF less than 0.3 who experience a change in disease status, clinicians should evaluate the appropriateness of additional germline testing based on the clinical context. Confirmed germline variants should prompt targeted clinical action. Shared decision-making is essential, particularly when prognosis is limited or results are unlikely to influence management. In the future (indicated by the dashed line), we anticipate standardized integration of simultaneous germline testing and tumor-based profiling at the time of diagnosis. ^AGuidelines from ASCO and NCCN recommend universal germline testing in patients with epithelial ovarian cancer, pancreatic adenocarcinoma, metastatic or high-risk prostate cancer, pleural mesothelioma, adrenocortical carcinoma, pheochromocytomas, or paragangliomas, regardless of age, family history, or personal history (15, 17, 107–109). ^BGene selection for multi-gene panels should adhere to ASCO guidelines, which consider clinical relevance, actionability, penetrance, and associations with hereditary cancer syndromes (106). ^CFor paired tumor-normal testing, best practice entails using cultured skin for germline analysis. ^DThis VAF threshold is derived from ESMO PMWG, which designated a cutoff of ≥0.3 for single-nucleotide variants and ≥0.2 for indels (48). Notably, VAF alone, particularly when derived from a tumor sample at a single time point, remains insufficient to determine germline origin. ^EVAF may fluctuate across time points because of technical variation, tumor heterogeneity, or biological phenomena such as allelic loss or subclonal architecture. Thus, redemonstration of the same P/LP variant on repeat tumor sequencing, independent of VAF, warrants consideration for germline evaluation (66, 106).

Tumor-based profiling, while informative, has limitations in detecting P/LP germline variants. In a cohort of patients with various cancer types, Terraf et al. found that tumor-only sequencing missed 10.5% of clinically actionable deleterious germline variants, particularly in homologous recombination deficiency (HRD) and MMR genes (65). Similarly, Lincoln et al. reported that 8.1% of deleterious germline variants were missed using tumor-only testing (66). These studies highlight the need for separate, comprehensive germline testing, as tumor-based panels cannot serve as an adequate substitute owing to various factors (65, 66). First, the optimal sample type for each test is distinct. Tumor-based profiling requires malignant cells, whereas germline testing relies ideally on non-malignant cells to avoid confusion with somatic variants (113). In solid tumors, buccal swabs are generally reliable for germline testing, as they provide DNA free from tumor contamination (114). However, in HMs, best practice entails using cultured skin fibroblasts for germline testing (115–117), as hematopoietic tissue (e.g., saliva, peripheral blood) is contaminated with malignant cells and can be confounded by processes like clonal hematopoiesis, and therefore somatic alterations (113, 116, 118). Clonal hematopoiesis, a form of somatic mosaicism, refers to the expansion of hematopoietic stem cell clones, which increases with age (118). As peripheral blood contains all stem cell progeny, deep sequencing can reveal acquired mutations over time that may confound germline analysis (118). Differentiating between somatic and true germline variants in the setting of mosaicism remains a challenge. For instance, somatic alterations in TP53, referred to as “clonal TP53,” can result from aberrant clonal expansion rather than true constitutional mosaicism or germline predisposition, requiring careful analysis (119–121). Somatic reversion, as seen with SAMD9/SAMD9L, occurs when a deleterious germline variant is spontaneously corrected in somatic cells, eliminating the original germline allele and further complicating interpretation (122–124). Although hair follicles and nail clippings can serve as alternative germline sources and may yield reliable results, both are limited by low DNA yield and technical constraints (125–127). Hair follicles exhibit variable DNA degradation and poor genotyping performance (128), while nail clippings are prone to tumor contamination, particularly in myeloid neoplasms (129). Given the prolonged timeline for fibroblast culture, for cases in which rapid results are required for clinical assessment, we recommend buccal swabs as the preferred initial source for germline testing in HMs (125), followed by confirmatory analysis using fibroblast-derived DNA.

Generally, although tumor-based profiling is primarily focused on identifying variants with known clinical implications, comprehensive germline platforms must remain adaptable to accommodate newly identified cancer risk genes. Thus, tumor-based and germline assays require distinct platforms. Targeted panels in tumor profiling focus on exonic regions of genes relevant to oncogenesis and therapeutic targets, given that clinically relevant acquired mutations typically occur in these regions. Because of the inherent heterogeneity of tumor samples, including varying ratios of malignant to non-malignant cells, tumor-based platforms require average coverage depths of at least 1,000-fold to detect low-frequency somatic variants, especially in tissue samples with low tumor cellularity (130). These tests are designed to detect single-nucleotide variants (SNVs) and large copy number variants (CNVs), which have therapeutic and prognostic relevance (65, 131, 132).

In contrast, germline testing offers broader coverage, including both exonic and non-coding regions (e.g., promoters and enhancers), which allows for the identification of many variant types, including small CNVs, that may be missed in tumor-based profiling (65, 131, 132). Comprehensive germline testing can be conducted through augmented whole-exome sequencing (aWES), where primers that capture selected non-coding regions supplement exonic analysis for CNV identification (65, 131, 132). Because approximately 85% of P/LP variants exist within exons (133), aWES is typically favored over whole-genome sequencing given its currently lower costs, but this may change as sequencing costs decline rapidly.

If aWES is not employed, germline multi-gene (multiplex) tests can be used, especially to query multiple high-penetrance genes or particular hereditary cancer syndromes (134). These panels involve disease-targeted exon-capture methods focused on selected genes of interest, offering higher coverage and depth for these genes when compared with aWES (135–137). In contrast to the high coverage depths in tumor-based assays, germline testing requires a minimum depth of 30-fold, as germline variants exist in heterozygous or homozygous states (138, 139). This enhanced resolution improves the detection of variants, including larger deletions or duplications, which may be missed by other methods. For example, multiplex ligation-dependent probe amplification is often used to identify deleterious structural variants in MLH1 and MSH2 for Lynch syndrome, or APC for familial adenomatous polyposis (140). By prioritizing predefined, clinically actionable genes, multi-gene panels provide a cost-effective and efficient approach for identifying hereditary predispositions. However, as noted above, they may miss cancer-associated variants in genes not included in the panel, which can be problematic in rare hereditary syndromes or atypical presentations (106).

Molecular and bioinformatic considerations. The clinical classifications of variants detected by tumor profiling tests are based on their somatic nature. Given that DNA changes are often context dependent, germline alleles may have different effects compared with somatic alleles that are only present in a tumor (141, 142). For this reason, germline and somatic curations are distinct (141, 143). Clinicians should recognize variant alleles that are overwhelmingly likely to be germline to facilitate timely germline testing and confirmation. For instance, DDX41 variants identified in AML/myelodysplastic syndrome (MDS) panels at a variant allele frequency (VAF) greater than 40% are germline in 94% of patients (144). Simon et al. found that a notable proportion (27%) of RUNX1 variants in patients with RUNX1-mutant AML were of germline origin (145), and among those, 16% are deleterious when curated using RUNX1-specific curation rules (116, 146–148). This example underscores that germline status alone does not confer pathogenicity and highlights the importance of careful, variant-specific interpretation to guide appropriate management (116).

The VAF quantifies the proportion of specific variant alleles within a given NGS sample. For germline variants, the VAF ideally approximates 50% for heterozygous variants and 100% for homozygous variants (149). However, underestimations in germline VAF may occur for several reasons, including germline mosaicism, wherein post-zygotic mutations during early embryonic development cause the variant to be present in only a subset of cells, as well as genomic loss of the wild-type allele, CNVs in tumor cells, structural rearrangements, and technical factors such as sequencing artifacts (102, 150, 151). Although VAF alone, particularly when derived from a tumor sample at one single point in time, is not sufficient to determine germline origin, some identification methods use VAF thresholds to inform bioinformatic algorithms in distinguishing germline variants. Kraft et al. analyzed VAFs across sequential tumor samples and observed that VAFs between 0.3 and 0.7, when stable over time, were more likely to represent germline variants (152). Using coefficients of variation to assess VAF changes, variants were graded on a scale from 1 to 5, with grade 1 being the most likely to be germline and grade 5 the least likely. To validate these predictions, they performed aWES on cultured skin fibroblast DNA, confirming 89% (48 of 54) of grade 1 variants as germline. Alternative bioinformatic approaches used to flag potential germline variants compare tumor samples to a “virtual normal,” which is a composite of sequencing data from unrelated healthy individuals, used in place of the patient’s own matched normal sample (153). This approach assumes that most variants are prevalent in the general population, effectively removing over 96% of germline variants in tumor samples, but is otherwise limited in its ability to detect rare pathogenic variants (153). Jalloul et al. introduced a computational method that integrates VAF, tumor purity, and copy number to infer germline status from tumor-only sequencing, achieving 86% accuracy and a 3% false omission rate (154). Unlike threshold-based or population-filtering approaches, this gene-agnostic model provides systematic, variant-level inference without requiring matched normal tissue. Complementing these bioinformatic methods, integration of clinical genetics into tumor sequencing workflows has been shown to enhance germline variant detection; in one study, incorporation of clinical genetics during molecular tumor board review of tumor-only NGS increased detection from 1.4% to 7.5%, even among patients who did not meet standard testing criteria (155).

Other strategies utilize VAF thresholds as part of a multistep process to screen pathogenic germline variants. To aid clinicians in identifying potential germline variants in high-risk patients during tumor-based profiling, we have outlined an approach that integrates VAF thresholds, ClinVar pathogenicity classifications, and a list of CSGs with reported high rates of germline conversion, guided by ACMG and ESMO PMWG recommendations (46–48) (Figure 2).

Paired tumor-normal analysis. Paired tumor-normal testing is the most common method for confirmation of incidental germline variants detected during tumor-based profiling (65, 156, 157) (Figure 3). This approach involves the simultaneous sequencing of DNA isolated from both tumor tissue and matched normal, or non-malignant, tissue (e.g., cultured skin fibroblasts) in the same patient (157). By comparing the two sequences, paired testing differentiates inherited from acquired variants, thereby identifying heritable cancer risk even in the absence of clear family history while minimizing false-positive somatic variant detection and improving result specificity (61, 158). Although detection of a suspected germline variant through tumor-only sequencing triggers additional confirmatory testing, tumor-normal testing allows for direct discrimination between somatic and germline alterations, thereby reducing test burden (157). Although considered the gold standard, tumor-normal testing is limited by cost and logistical challenges, due in part to the requirement for an additional normal sample (159, 160).

Figure 3

Comparison of germline, tumor-based, and paired tumor-normal sequencing. Paired tumor-normal sequencing, the gold standard for confirming incidental germline findings, involves simultaneous sequencing of tumor and matched normal, non-malignant tissue from the same patient. This approach identifies inherited variants to generate a germline report. Tumor-based profiling analyzes DNA from tumor cells, reporting both somatic alterations and potential germline variants. Germline sequencing should be performed using non-hematopoietic tissue, with cultured skin fibroblasts considered the gold standard. Alternative sources are depicted in descending order based on DNA quality, ease of collection, and interpretive reliability. By subtracting germline variants from the tumor sequence, paired testing differentiates between somatic and germline variants, improving specificity and reducing false-positive somatic calls. Unlike tumor-based profiling, paired tumor-normal sequencing provides direct discrimination between somatic and germline variants.

Patient history: clues for testing. Specific indicators in a patient’s history that should raise suspicion for heritable cancer risk include young age of onset, personal history of two or more cancers, early-onset cancers in multiple relatives, and clinical scenarios that raise concern regarding hereditary cancer syndromes (16, 106). Generally, germline testing should be considered in an individual diagnosed with cancer prior to age 50 years within two generations of the proband (15, 16, 18, 19). Certain populations, such as individuals of Ashkenazi Jewish (AJ) ancestry, have a higher prevalence of founder mutations and should be tested if they have a personal or family history of breast, pancreatic, prostate, ovarian, or uterine cancers (15, 106). Additionally, individuals of AJ ancestry have an increased prevalence of colorectal cancer and renal cancer due to the increased frequency of the APC I1307K polymorphism in this population (161). Germline testing is also recommended for individuals with a first-degree relative with features suggestive of heritable cancer risk, such as early-onset or triple-negative breast cancer, male breast cancer, ovarian cancer, pancreatic cancer, or high-risk prostate cancer, even in the absence of a personal cancer diagnosis (15). Further, tumors with low prevalence in the general population, such as retinoblastoma, pheochromocytoma, or paraganglioma, should prompt evaluation for inherited cancer syndromes (109). It is also important to consider the clinical scenario within which a gene/tumor combination presents itself. For instance, a RET variant found in a sporadic medullary thyroid carcinoma (MTC) in a 75-year-old individual without a family history of endocrine neoplasias is unlikely to be hereditary. In contrast, the same variant in a 25-year-old with MTC and a history of pheochromocytoma may strongly suggest a germline RET variant associated with multiple endocrine neoplasia type 2A (MEN2A). A personal HM history, particularly when coupled with a family history of another HM, prolonged cytopenias, or other hematopoietic abnormalities, should also raise suspicion of a hereditary predisposition (18, 19, 113). In addition, the presence of hypocellular MDS or a new diagnosis of aplastic anemia should prompt further consideration for hereditary myeloid predisposition syndromes (19). Identification of a DNA variant in at least two related individuals defines its germline status, and this approach is occasionally more feasible, particularly in HMs when skin biopsy or fibroblast culture may be difficult to obtain (113).

Both laboratories and ordering clinicians share the responsibility for identifying and managing incidental germline findings. Laboratories conducting tumor-based profiling must implement robust data filtering algorithms to differentiate between germline and somatic variants, conduct downstream testing to confirm potential germline variants, and establish standardized protocols for reporting findings (162). When a potential germline variant is detected, reporting should follow the standards set forth by the joint consensus guidelines by ASCO, AMP, and the College of American Pathologists (149). These guidelines emphasize reporting incidental germline variants that have known clinical impact, including those with therapeutic implications or links to hereditary cancer syndromes or that inform clinical management and surveillance strategies (149) (Table 1). Variant pathogenicity should be assessed using established criteria and determined independently of the interpretation of the cause of disease in a given patient. Variants of uncertain significance (VUS) should also be reported, but should not be used in clinical decision-making (149). Following confirmation of germline status, clinicians are responsible for determining whether identified variants, particularly those classified as P/LP, are clinically actionable within the context of the patient’s presentation. If appropriate, further actions, such as genetic counseling, cascade testing, or targeted management, should be pursued (Figure 2).

Clinicians should obtain appropriate consent through engagement of patients in a discussion of the potential for incidental germline variant detection during tumor-based profiling, explicitly addressing the implications such findings may have for medical management, familial risk assessment, and future clinical decisions. This conversation should also clarify the scope of testing, the limitations of current knowledge, and the possibility that information may evolve with advances in NGS testing.

The genetic architecture of inherited cancer predisposition is complex, shaped by substantial heterogeneity in penetrance, lineage-dependence, and germline-somatic variant interactions that collectively influence cancer susceptibility. As our understanding of cancer predisposition continues to evolve, the role of germline variant detection in guiding targeted management becomes increasingly critical. The cumulative prevalence of incidental P/LP germline variant detection during tumor-based profiling approximates 10%, representing a substantial proportion of patients. Notably, detection occurs at comparable rates among cancers with and without formal guideline recommendations for germline testing. Although tumor-based profiling provides a valuable opportunity to identify clinically actionable germline variants, it does not serve as an appropriate substitute for comprehensive germline testing. Given the potentially detrimental consequences of missed variant detection for both patients and their families, we anticipate a future in which tumor-agnostic germline testing is accessible to all patients with solid and/or hematopoietic malignancies. Until such testing becomes standard, clinicians should advocate for germline testing in patients in whom they suspect germline predisposition or those who meet high-risk criteria. Moreover, with the differential effects that germline variants can exhibit on cancer pathogenesis and progression, clinicians must be equipped with the knowledge to interpret results of comprehensive genetic testing, determine clinical actionability, and appropriately counsel patients. Until the influence of germline predisposition in non-associated cancers is better understood, the emphasis on cancer screenings, genetic counseling, and disease-specific preventative strategies in patients with variants in cancer susceptibility genes should remain a priority.

Conflict of interest: The authors have declared that no conflict of interest exists.

Copyright: © 2025, Jaber et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.

Reference information: J Clin Invest. 2025;135(15):e190264. https://doi.org/10.1172/JCI190264.

1. Rahman N. Realizing the promise of cancer predisposition genes. Nature. 2014;505(7483):302–308.
   View this article via: CrossRef PubMed Google Scholar
2. Srinivasan P, et al. The context-specific role of germline pathogenicity in tumorigenesis. Nat Genet. 2021;53(11):1577–1585.
   View this article via: CrossRef PubMed Google Scholar
3. Garber JE, Offit K. Hereditary cancer predisposition syndromes. J Clin Oncol. 2005;23(2):276–292.
   View this article via: CrossRef PubMed Google Scholar
4. Xu P, et al. CHEK2 deficiency increase the response to PD-1 inhibitors by affecting the tumor immune microenvironment. Cancer Lett. 2024;588:216595.
   View this article via: CrossRef PubMed Google Scholar
5. Vokes NI, et al. ATM mutations associate with distinct co-mutational patterns and therapeutic vulnerabilities in NSCLC. Clin Cancer Res. 2023;29(23):4958–4972.
   View this article via: CrossRef PubMed Google Scholar
6. Ricciuti B, et al. Clinicopathologic, genomic, and immunophenotypic landscape of ATM mutations in non-small cell lung cancer. Clin Cancer Res. 2023;29(13):2540–2550.
   View this article via: CrossRef PubMed Google Scholar
7. Karachaliou N, et al. Association of PALB2 messenger RNA expression with platinum-docetaxel efficacy in advanced non-small cell lung cancer. J Thorac Oncol. 2019;14(2):304–310.
   View this article via: CrossRef PubMed Google Scholar
8. Bedrosian I, et al. Germline testing in patients with breast cancer: ASCO-Society of Surgical Oncology guideline. J Clin Oncol. 2024;42(5):584–604.
   View this article via: CrossRef PubMed Google Scholar
9. Konstantinopoulos PA, et al. Germline and somatic tumor testing in epithelial ovarian cancer: ASCO guideline. J Clin Oncol. 2020;38(11):1222–1245.
   View this article via: CrossRef PubMed Google Scholar
10. Schlegelberger B, et al. Review of guidelines for the identification and clinical care of patients with genetic predisposition for hematological malignancies. Fam Cancer. 2021;20(4):295–303.
    View this article via: CrossRef PubMed Google Scholar
11. Yang F, et al. Identification and prioritization of myeloid malignancy germline variants in a large cohort of adult patients with AML. Blood. 2022;139(8):1208–1221.
    View this article via: CrossRef PubMed Google Scholar
12. Hampel H, Yurgelun MB. Point/counterpoint: is it time for universal germline genetic testing for all GI cancers? J Clin Oncol. 2022;40(24):2681–2692.
    View this article via: CrossRef PubMed Google Scholar
13. Giri VN, et al. Implementation of germline testing for prostate cancer: Philadelphia Prostate Cancer Consensus Conference 2019. J Clin Oncol. 2020;38(24):2798–2811.
    View this article via: CrossRef PubMed Google Scholar
14. Whitworth PW, et al. Clinical utility of universal germline genetic testing for patients with breast cancer. JAMA Netw Open. 2022;5(9):e2232787.
    View this article via: CrossRef PubMed Google Scholar
15. National Comprehensive Cancer Network. Genetic/Familial High-Risk Assessment: Breast, Ovarian, Pancreatic, and Prostate. https://www.nccn.org/guidelines/guidelines-detail?category=2&id=1545 Accessed April 7, 2025.
16. National Comprehensive Cancer Network. Genetic/Familial High-Risk Assessment: Colorectal, Endometrial, and Gastric. https://www.nccn.org/guidelines/guidelines-detail?category=2&id=1544 Accessed April 7, 2025.
17. National Comprehensive Cancer Network. Mesothelioma: Pleural. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1512 Accessed April 7, 2025.
18. National Comprehensive Cancer Network. Acute Myeloid Leukemia. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1411 Accessed April 7, 2025.
19. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Myelodysplastic Syndromes. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1446 Accessed April 7, 2025.
20. Sohal DPS, et al. Metastatic pancreatic cancer: ASCO Guideline update. J Clin Oncol. 2020;38(27):3217–3230.
    View this article via: CrossRef PubMed Google Scholar
21. Kindler HL, et al. Treatment of pleural mesothelioma: ASCO guideline update. J Clin Oncol. 2025;43(8):1006–1038.
    View this article via: CrossRef PubMed Google Scholar
22. Kuchenbaecker KB, et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA. 2017;317(23):2402–2416.
    View this article via: CrossRef PubMed Google Scholar
23. Ceccaldi R, et al. Repair pathway choices and consequences at the double-strand break. Trends Cell Biol. 2016;26(1):52–64.
    View this article via: CrossRef PubMed Google Scholar
24. Kamp JA, et al. BRCA1-associated structural variations are a consequence of polymerase theta-mediated end-joining. Nat Commun. 2020;11(1):3615.
    View this article via: CrossRef PubMed Google Scholar
25. Blasiak J. Single-strand annealing in cancer. Int J Mol Sci. 2021;22(4):2167.
    View this article via: CrossRef PubMed Google Scholar
26. Caracciolo D, et al. Alternative non-homologous end-joining: error-prone DNA repair as cancer’s Achilles’ heel. Cancers (Basel). 2021;13(6):1392.
    View this article via: CrossRef PubMed Google Scholar
27. Sallmyr A, et al. Up-regulation of WRN and DNA ligase IIIalpha in chronic myeloid leukemia: consequences for the repair of DNA double-strand breaks. Blood. 2008;112(4):1413–1423.
    View this article via: CrossRef PubMed Google Scholar
28. Kara A, et al. DNA repair pathways and their roles in drug resistance for lung adenocarcinoma. Mol Biol Rep. 2021;48(4):3813–3825.
    View this article via: CrossRef PubMed Google Scholar
29. Sousa MM, et al. An inverse switch in DNA base excision and strand break repair contributes to melphalan resistance in multiple myeloma cells. PLoS One. 2013;8(2):e55493.
    View this article via: CrossRef PubMed Google Scholar
30. Paes Dias M, et al. Loss of nuclear DNA ligase III reverts PARP inhibitor resistance in BRCA1/53BP1 double-deficient cells by exposing ssDNA gaps. Mol Cell. 2021;81(22):4692–4708.
    View this article via: CrossRef PubMed Google Scholar
31. Lynch HT, de la Chapelle A. Hereditary colorectal cancer. N Engl J Med. 2003;348(10):919–932.
    View this article via: CrossRef PubMed Google Scholar
32. Hansford S, et al. Hereditary diffuse gastric cancer syndrome: CDH1 mutations and beyond. JAMA Oncol. 2015;1(1):23–32.
    View this article via: CrossRef PubMed Google Scholar
33. Roberts ME, et al. Comparison of CDH1 penetrance estimates in clinically ascertained families vs families ascertained for multiple gastric cancers. JAMA Oncol. 2019;5(9):1325–1331.
    View this article via: CrossRef PubMed Google Scholar
34. Hegde M, et al. ACMG technical standards and guidelines for genetic testing for inherited colorectal cancer (Lynch syndrome, familial adenomatous polyposis, and MYH-associated polyposis). Genet Med. 2014;16(1):101–116.
    View this article via: CrossRef PubMed Google Scholar
35. Gordon AS, et al. Consideration of disease penetrance in the selection of secondary findings gene-disease pairs: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2024;26(7):101142.
    View this article via: CrossRef PubMed Google Scholar
36. Johnson AF, et al. Causes and effects of haploinsufficiency. Biol Rev Camb Philos Soc. 2019;94(5):1774–1785.
    View this article via: CrossRef PubMed Google Scholar
37. Arthur RK, et al. The haploinsufficient tumor suppressor, CUX1, acts as an analog transcriptional regulator that controls target genes through distal enhancers that loop to target promoters. Nucleic Acids Res. 2017;45(11):6350–6361.
    View this article via: CrossRef PubMed Google Scholar
38. Ramdzan ZM, Nepveu A. CUX1, a haploinsufficient tumour suppressor gene overexpressed in advanced cancers. Nat Rev Cancer. 2014;14(10):673–682.
    View this article via: CrossRef PubMed Google Scholar
39. Mogal AP, et al. Haploinsufficient prostate tumor suppression by Nkx3.1: a role for chromatin accessibility in dosage-sensitive gene regulation. J Biol Chem. 2007;282(35):25790–25800.
    View this article via: CrossRef PubMed Google Scholar
40. Lynch CJ, Milner J. Loss of one p53 allele results in four-fold reduction of p53 mRNA and protein: a basis for p53 haplo-insufficiency. Oncogene. 2006;25(24):3463–3470.
    View this article via: CrossRef PubMed Google Scholar
41. Henrich KO, et al. 1p36 tumor suppression — a matter of dosage? Cancer Res. 2012;72(23):6079–6088.
    View this article via: CrossRef PubMed Google Scholar
42. Randall MP, et al. BARD1 germline variants induce haploinsufficiency and DNA repair defects in neuroblastoma. J Natl Cancer Inst. 2024;116(1):138–148.
    View this article via: CrossRef PubMed Google Scholar
43. Karaayvaz-Yildirim M, et al. Aneuploidy and a deregulated DNA damage response suggest haploinsufficiency in breast tissues of BRCA2 mutation carriers. Sci Adv. 2020;6(5):eaay2611.
    View this article via: CrossRef PubMed Google Scholar
44. Minello A, Carreira A. BRCA1/2 haploinsufficiency: exploring the impact of losing one allele. J Mol Biol. 2024;436(1):168277.
    View this article via: CrossRef PubMed Google Scholar
45. Rivera-Muñoz EA, et al. ClinGen Variant Curation Expert Panel experiences and standardized processes for disease and gene-level specification of the ACMG/AMP guidelines for sequence variant interpretation. Hum Mutat. 2018;39(11):1614–1622.
    View this article via: CrossRef PubMed Google Scholar
46. Miller DT, et al. ACMG SF v3.1 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2022;24(7):1407–1414.
    View this article via: CrossRef PubMed Google Scholar
47. Mandelker D, et al. Germline-focussed analysis of tumour-only sequencing: recommendations from the ESMO Precision Medicine Working Group. Ann Oncol. 2019;30(8):1221–1231.
    View this article via: CrossRef PubMed Google Scholar
48. Kuzbari Z, et al. Germline-focused analysis of tumour-detected variants in 49,264 cancer patients: ESMO Precision Medicine Working Group recommendations. Ann Oncol. 2023;34(3):215–227.
    View this article via: CrossRef PubMed Google Scholar
49. Landrum MJ, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(database issue):D980–D985.
    View this article via: CrossRef PubMed Google Scholar
50. Rehm HL, et al. ClinGen — the clinical genome resource. N Engl J Med. 2015;372(23):2235–2242.
    View this article via: CrossRef PubMed Google Scholar
51. Tung N, et al. Potential pathogenic germline variant reporting from tumor comprehensive genomic profiling complements classic approaches to germline testing. NPJ Precis Oncol. 2023;7(1):76.
    View this article via: CrossRef PubMed Google Scholar
52. Yap TA, et al. Prevalence of germline findings among tumors from cancer types lacking hereditary testing guidelines. JAMA Netw Open. 2022;5(5):e2213070.
    View this article via: CrossRef PubMed Google Scholar
53. Stadler ZK, et al. Therapeutic implications of germline testing in patients with advanced cancers. J Clin Oncol. 2021;39(24):2698–2709.
    View this article via: CrossRef PubMed Google Scholar
54. Cobain EF, et al. Assessment of clinical benefit of integrative genomic profiling in advanced solid tumors. JAMA Oncol. 2021;7(4):525–533.
    View this article via: PubMed CrossRef Google Scholar
55. Schneider BP, et al. Implications of incidental germline findings identified in the context of clinical whole exome sequencing for guiding cancer therapy. JCO Precis Oncol. 2020;4:1109–1121.
    View this article via: CrossRef PubMed Google Scholar
56. Huang KL, et al. Pathogenic germline variants in 10,389 adult cancers. Cell. 2018;173(2):355–370.
    View this article via: CrossRef PubMed Google Scholar
57. Drazer MW, et al. Prognostic tumor sequencing panels frequently identify germ line variants associated with hereditary hematopoietic malignancies. Blood Adv. 2018;2(2):146–150.
    View this article via: CrossRef PubMed Google Scholar
58. Schrader KA, et al. Germline variants in targeted tumor sequencing using matched normal DNA. JAMA Oncol. 2016;2(1):104–111.
    View this article via: CrossRef PubMed Google Scholar
59. Meric-Bernstam F, et al. Incidental germline variants in 1000 advanced cancers on a prospective somatic genomic profiling protocol. Ann Oncol. 2016;27(5):795–800.
    View this article via: CrossRef PubMed Google Scholar
60. Seifert BA, et al. Germline analysis from tumor-germline sequencing dyads to identify clinically actionable secondary findings. Clin Cancer Res. 2016;22(16):4087–4094.
    View this article via: CrossRef PubMed Google Scholar
61. Jones S, et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci Transl Med. 2015;7(283):283ra53.
    View this article via: CrossRef PubMed Google Scholar
62. Parsons DW, et al. Diagnostic yield of clinical tumor and germline whole-exome sequencing for children with solid tumors. JAMA Oncol. 2016;2(5):616–624.
    View this article via: CrossRef PubMed Google Scholar
63. Zhang J, et al. Germline mutations in predisposition genes in pediatric cancer. N Engl J Med. 2015;373(24):2336–2346.
    View this article via: CrossRef PubMed Google Scholar
64. Mody RJ, et al. Integrative clinical sequencing in the management of refractory or relapsed cancer in youth. JAMA. 2015;314(9):913–925.
    View this article via: CrossRef PubMed Google Scholar
65. Terraf P, et al. Comprehensive assessment of germline pathogenic variant detection in tumor-only sequencing. Ann Oncol. 2022;33(4):426–433.
    View this article via: CrossRef PubMed Google Scholar
66. Lincoln SE, et al. Yield and utility of germline testing following tumor sequencing in patients with cancer. JAMA Netw Open. 2020;3(10):e2019452.
    View this article via: CrossRef PubMed Google Scholar
67. Kellman LN, et al. Functional analysis of cancer-associated germline risk variants. Nat Genet. 2025;57(3):718–728.
    View this article via: CrossRef PubMed Google Scholar
68. Kar SP. A new frontier for cancer genetics: identification of germline-somatic associations. Cancer Res. 2023;83(8):1165–1166.
    View this article via: CrossRef PubMed Google Scholar
69. Houlahan KE, et al. Genome-wide germline correlates of the epigenetic landscape of prostate cancer. Nat Med. 2019;25(10):1615–1626.
    View this article via: CrossRef PubMed Google Scholar
70. Lu Y, et al. Most common ‘sporadic’ cancers have a significant germline genetic component. Hum Mol Genet. 2014;23(22):6112–6118.
    View this article via: CrossRef PubMed Google Scholar
71. Samadder NJ, et al. Comparison of universal genetic testing vs guideline-directed targeted testing for patients with hereditary cancer syndrome. JAMA Oncol. 2021;7(2):230–237.
    View this article via: CrossRef PubMed Google Scholar
72. Bartley AN, et al. Mismatch repair and microsatellite instability testing for immune checkpoint inhibitor therapy: guideline from the College of American Pathologists in collaboration with the Association for Molecular Pathology and Fight Colorectal Cancer. Arch Pathol Lab Med. 2022;146(10):1194–1210.
    View this article via: CrossRef PubMed Google Scholar
73. Shah SM, et al. Therapeutic implications of germline vulnerabilities in DNA repair for precision oncology. Cancer Treat Rev. 2022;104:102337.
    View this article via: CrossRef PubMed Google Scholar
74. Cohen R, et al. Efficacy of immune checkpoint inhibitors for metastatic colorectal cancer with microsatellite instability in second or latter line using synthetic control arms: a non-randomised evaluation. Eur J Cancer. 2024;199:113537.
    View this article via: CrossRef PubMed Google Scholar
75. Abu-Rustum N, et al. Uterine neoplasms, version 1.2023, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2023;21(2):181–209.
    View this article via: CrossRef PubMed Google Scholar
76. Moreau M, et al. A multicenter study evaluating efficacy of immune checkpoint inhibitors in advanced non-colorectal digestive cancers with microsatellite instability. Eur J Cancer. 2024;202:114033.
    View this article via: CrossRef PubMed Google Scholar
77. Tutt ANJ, et al. Adjuvant olaparib for patients with BRCA1- or BRCA2-mutated breast cancer. N Engl J Med. 2021;384(25):2394–2405.
    View this article via: CrossRef PubMed Google Scholar
78. Robson M, et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N Engl J Med. 2017;377(6):523–533.
    View this article via: CrossRef PubMed Google Scholar
79. Banerjee S, et al. Maintenance olaparib for patients with newly diagnosed advanced ovarian cancer and a BRCA mutation (SOLO1/GOG 3004): 5-year follow-up of a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2021;22(12):1721–1731.
    View this article via: CrossRef PubMed Google Scholar
80. Ray-Coquard I, et al. Olaparib plus bevacizumab as first-line maintenance in ovarian cancer. N Engl J Med. 2019;381(25):2416–2428.
    View this article via: CrossRef PubMed Google Scholar
81. Golan T, et al. Maintenance olaparib for germline BRCA-mutated metastatic pancreatic cancer. N Engl J Med. 2019;381(4):317–327.
    View this article via: CrossRef PubMed Google Scholar
82. de Bono J, et al. Olaparib for metastatic castration-resistant prostate cancer. N Engl J Med. 2020;382(22):2091–2102.
    View this article via: CrossRef PubMed Google Scholar
83. Saad F, et al. Olaparib plus abiraterone versus placebo plus abiraterone in metastatic castration-resistant prostate cancer (PROpel): final prespecified overall survival results of a randomised, double-blind, phase 3 trial. Lancet Oncol. 2023;24(10):1094–1108.
    View this article via: CrossRef PubMed Google Scholar
84. Tung NM, et al. Management of hereditary breast cancer: American Society of Clinical Oncology, American Society for Radiation Oncology, and Society of Surgical Oncology Guideline. J Clin Oncol. 2020;38(18):2080–2106.
    View this article via: CrossRef PubMed Google Scholar
85. Pennington KP, et al. Germline and somatic mutations in homologous recombination genes predict platinum response and survival in ovarian, fallopian tube, and peritoneal carcinomas. Clin Cancer Res. 2014;20(3):764–775.
    View this article via: CrossRef PubMed Google Scholar
86. Toh M, Ngeow J. Homologous recombination deficiency: cancer predispositions and treatment implications. Oncologist. 2021;26(9):e1526–e1537.
    View this article via: CrossRef PubMed Google Scholar
87. Hassan R, et al. Inherited predisposition to malignant mesothelioma and overall survival following platinum chemotherapy. Proc Natl Acad Sci U S A. 2019;116(18):9008–9013.
    View this article via: CrossRef PubMed Google Scholar
88. Jonsson P, et al. Tumour lineage shapes BRCA-mediated phenotypes. Nature. 2019;571(7766):576–579.
    View this article via: CrossRef PubMed Google Scholar
89. Villani A, et al. Biochemical and imaging surveillance in germline TP53 mutation carriers with Li-Fraumeni syndrome: 11 year follow-up of a prospective observational study. Lancet Oncol. 2016;17(9):1295–1305.
    View this article via: CrossRef PubMed Google Scholar
90. Petry V, et al. Frequency of radiation therapy-induced malignancies in patients with Li-Fraumeni syndrome and early-stage breast cancer and the influence of radiation therapy technique. Int J Radiat Oncol Biol Phys. 2024;119(4):1086–1091.
    View this article via: CrossRef PubMed Google Scholar
91. Hakkarainen M, et al. The clinical picture of ERCC6L2 disease: from bone marrow failure to acute leukemia. Blood. 2023;141(23):2853–2866.
    View this article via: PubMed CrossRef Google Scholar
92. Dietrich S, et al. BRAF inhibition in hairy cell leukemia with low-dose vemurafenib. Blood. 2016;127(23):2847–2855.
    View this article via: CrossRef PubMed Google Scholar
93. Churpek JE, et al. Correspondence regarding the consensus statement from the worldwide network for blood and marrow transplantation standing committee on donor issues. Biol Blood Marrow Transplant. 2016;22(1):183–184.
    View this article via: CrossRef PubMed Google Scholar
94. Xiao H, et al. First report of multiple CEBPA mutations contributing to donor origin of leukemia relapse after allogeneic hematopoietic stem cell transplantation. Blood. 2011;117(19):5257–5260.
    View this article via: CrossRef PubMed Google Scholar
95. Kobayashi S, et al. Donor cell leukemia arising from preleukemic clones with a novel germline DDX41 mutation after allogenic hematopoietic stem cell transplantation. Leukemia. 2017;31(4):1020–1022.
    View this article via: CrossRef PubMed Google Scholar
96. Galera P, et al. Donor-derived MDS/AML in families with germline GATA2 mutation. Blood. 2018;132(18):1994–1998.
    View this article via: CrossRef PubMed Google Scholar
97. Buijs A, et al. A novel CBFA2 single-nucleotide mutation in familial platelet disorder with propensity to develop myeloid malignancies. Blood. 2001;98(9):2856–2858.
    View this article via: CrossRef PubMed Google Scholar
98. Owen CJ, et al. Five new pedigrees with inherited RUNX1 mutations causing familial platelet disorder with propensity to myeloid malignancy. Blood. 2008;112(12):4639–4645.
    View this article via: CrossRef PubMed Google Scholar
99. Fogarty PF, et al. Late presentation of dyskeratosis congenita as apparently acquired aplastic anaemia due to mutations in telomerase RNA. Lancet. 2003;362(9396):1628–1630.
    View this article via: CrossRef PubMed Google Scholar
100. Saygin C, et al. Allogeneic hematopoietic stem cell transplant outcomes in adults with inherited myeloid malignancies. Blood Adv. 2023;7(4):549–554.
     View this article via: CrossRef PubMed Google Scholar
101. Sakata N, et al. Donor-derived myelodysplastic syndrome after allogeneic stem cell transplantation in a family with germline GATA2 mutation. Int J Hematol. 2021;113(2):290–296.
     View this article via: CrossRef PubMed Google Scholar
102. Kraft IL, Godley LA. Identifying potential germline variants from sequencing hematopoietic malignancies. Blood. 2020;136(22):2498–2506.
     View this article via: CrossRef PubMed Google Scholar
103. Schmidlen TJ, et al. The impact of proband indication for genetic testing on the uptake of cascade testing among relatives. Front Genet. 2022;13:867226.
     View this article via: CrossRef PubMed Google Scholar
104. Frey MK, et al. Prospective feasibility trial of a novel strategy of facilitated cascade genetic testing using telephone counseling. J Clin Oncol. 2020;38(13):1389–1397.
     View this article via: CrossRef PubMed Google Scholar
105. Frey MK, et al. Cascade testing for hereditary cancer syndromes: should we move toward direct relative contact? A systematic review and meta-analysis. J Clin Oncol. 2022;40(35):4129–4143.
     View this article via: CrossRef PubMed Google Scholar
106. Tung N, et al. Selection of germline genetic testing panels in patients with cancer: ASCO guideline. J Clin Oncol. 2024;42(21):2599–2615.
     View this article via: CrossRef PubMed Google Scholar
107. National Comprehensive Cancer Network. Pancreatic Adenocarcinoma. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1455 Accessed April 7, 2025.
108. National Comprehensive Cancer Network. Prostate Cancer. https://www.nccn.org/guidelines/guidelines-detail?id=1459 Accessed April 7, 2025.
109. National Comprehensive Cancer Network. Neuroendocrine and Adrenal Tumors. https://www.nccn.org/guidelines/guidelines-detail?id=1448 Accessed April 7, 2025.
110. Mosele MF, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with advanced cancer in 2024: a report from the ESMO Precision Medicine Working Group. Ann Oncol. 2024;35(7):588–606.
     View this article via: CrossRef PubMed Google Scholar
111. Hampel H, et al. A practice guideline from the American College of Medical Genetics and Genomics and the National Society of Genetic Counselors: referral indications for cancer predisposition assessment. Genet Med. 2015;17(1):70–87.
     View this article via: CrossRef PubMed Google Scholar
112. Matalon DR, et al. Clinical, technical, and environmental biases influencing equitable access to clinical genetics/genomics testing: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2023;25(6):100812.
     View this article via: CrossRef PubMed Google Scholar
113. Godley LA. Prioritization of patients for germline testing based on tumor profiling of hematopoietic malignancies. Front Oncol. 2023;13:1084736.
     View this article via: CrossRef PubMed Google Scholar
114. Church AJ, et al. Section E6.7-6.12 of the American College of Medical Genetics and Genomics (ACMG) Technical Laboratory Standards: cytogenomic studies of acquired chromosomal abnormalities in solid tumors. Genet Med. 2024;26(4):101070.
     View this article via: CrossRef PubMed Google Scholar
115. Singhal D, et al. Targeted gene panels identify a high frequency of pathogenic germline variants in patients diagnosed with a hematological malignancy and at least one other independent cancer. Leukemia. 2021;35(11):3245–3256.
     View this article via: CrossRef PubMed Google Scholar
116. Feurstein S, et al. Germline variants drive myelodysplastic syndrome in young adults. Leukemia. 2021;35(8):2439–2444.
     View this article via: CrossRef PubMed Google Scholar
117. University of Chicago Hematopoietic Malignancies Cancer Risk Team. How I diagnose and manage individuals at risk for inherited myeloid malignancies. Blood. 2016;128(14):1800–1813.
     View this article via: CrossRef PubMed Google Scholar
118. Cobaleda C, et al. Insights into the molecular mechanisms of genetic predisposition to hematopoietic malignancies: the importance of gene-environment interactions. Cancer Discov. 2024;14(3):396–405.
     View this article via: CrossRef PubMed Google Scholar
119. Schwartz AN, et al. Evaluation of TP53 variants detected on peripheral blood or saliva testing: discerning germline from somatic TP53 variants. JCO Precis Oncol. 2021;5:1677–1686.
     View this article via: CrossRef PubMed Google Scholar
120. Weitzel JN, et al. Somatic TP53 variants frequently confound germ-line testing results. Genet Med. 2018;20(8):809–816.
     View this article via: CrossRef PubMed Google Scholar
121. Coffee B, et al. A substantial proportion of apparently heterozygous TP53 pathogenic variants detected with a next-generation sequencing hereditary pan-cancer panel are acquired somatically. Hum Mutat. 2020;41(1):203–211.
     View this article via: CrossRef PubMed Google Scholar
122. Buonocore F, et al. Somatic mutations and progressive monosomy modify SAMD9-related phenotypes in humans. J Clin Invest. 2017;127(5):1700–1713.
     View this article via: JCI CrossRef PubMed Google Scholar
123. Shima H, et al. Two patients with MIRAGE syndrome lacking haematological features: role of somatic second-site reversion SAMD9 mutations. J Med Genet. 2018;55(2):81–85.
     View this article via: CrossRef PubMed Google Scholar
124. Pillay BA, et al. Somatic reversion of pathogenic DOCK8 variants alters lymphocyte differentiation and function to effectively cure DOCK8 deficiency. J Clin Invest. 2021;131(3):e142434.
     View this article via: JCI CrossRef PubMed Google Scholar
125. Padron E, et al. Germ line tissues for optimal detection of somatic variants in myelodysplastic syndromes. Blood. 2018;131(21):2402–2405.
     View this article via: CrossRef PubMed Google Scholar
126. Ceyhan-Birsoy O, et al. Universal germline genetic testing in patients with hematologic malignancies using DNA isolated from nail clippings. Haematologica. 2024;109(10):3383–3390.
     View this article via: CrossRef PubMed Google Scholar
127. Kakadia PM, et al. Efficient identification of somatic mutations in acute myeloid leukaemia using whole exome sequencing of fingernail derived DNA as germline control. Sci Rep. 2018;8(1):13751.
     View this article via: CrossRef PubMed Google Scholar
128. Agalliu I, et al. Illumina DNA test panel-based genotyping of whole genome amplified-DNA extracted from hair samples: performance and agreement with genotyping results from genomic DNA from buccal cells. Clin Chem Lab Med. 2009;47(5):516–522.
     View this article via: CrossRef PubMed Google Scholar
129. Krystel-Whittemore M, et al. Cell-free DNA from nail clippings as source of normal control for genomic studies in hematologic malignancies. Haematologica. 2024;109(10):3269–3281.
     View this article via: CrossRef PubMed Google Scholar
130. Jennings LJ, et al. Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diagn. 2017;19(3):341–365.
     View this article via: CrossRef PubMed Google Scholar
131. Gray PN, et al. TumorNext: a comprehensive tumor profiling assay that incorporates high resolution copy number analysis and germline status to improve testing accuracy. Oncotarget. 2016;7(42):68206–68228.
     View this article via: CrossRef PubMed Google Scholar
132. Chakravarty D, et al. Somatic genomic testing in patients with metastatic or advanced cancer: ASCO Provisional Clinical Opinion. J Clin Oncol. 2022;40(11):1231–1258.
     View this article via: CrossRef PubMed Google Scholar
133. Rabbani B, et al. The promise of whole-exome sequencing in medical genetics. J Hum Genet. 2014;59(1):5–15.
     View this article via: CrossRef PubMed Google Scholar
134. Kurian AW, et al. Clinical evaluation of a multiple-gene sequencing panel for hereditary cancer risk assessment. J Clin Oncol. 2014;32(19):2001–2009.
     View this article via: CrossRef PubMed Google Scholar
135. Summerer D, et al. Targeted high throughput sequencing of a cancer-related exome subset by specific sequence capture with a fully automated microarray platform. Genomics. 2010;95(4):241–246.
     View this article via: CrossRef PubMed Google Scholar
136. Feliubadaló L, et al. Benchmarking of whole exome sequencing and ad hoc designed panels for genetic testing of hereditary cancer. Sci Rep. 2017;7:37984.
     View this article via: CrossRef PubMed Google Scholar
137. Baert-Desurmont S, et al. Optimization of the diagnosis of inherited colorectal cancer using NGS and capture of exonic and intronic sequences of panel genes. Eur J Hum Genet. 2018;26(11):1597–1602.
     View this article via: CrossRef PubMed Google Scholar
138. Asan , et al. Comprehensive comparison of three commercial human whole-exome capture platforms. Genome Biol. 2011;12(9):R95.
     View this article via: CrossRef PubMed Google Scholar
139. Parla JS, et al. A comparative analysis of exome capture. Genome Biol. 2011;12(9):R97.
     View this article via: CrossRef PubMed Google Scholar
140. Bunyan DJ, et al. Dosage analysis of cancer predisposition genes by multiplex ligation-dependent probe amplification. Br J Cancer. 2004;91(6):1155–1159.
     View this article via: CrossRef PubMed Google Scholar
141. Walsh MF, et al. Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes. Hum Mutat. 2018;39(11):1542–1552.
     View this article via: CrossRef PubMed Google Scholar
142. Krysiak K, et al. CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase. Nucleic Acids Res. 2023;51(d1):1230–1241.
     View this article via: CrossRef PubMed Google Scholar
143. Ritter DI, et al. A case for expert curation: an overview of cancer curation in the Clinical Genome Resource (ClinGen). Cold Spring Harb Mol Case Stud. 2019;5(5):a004739.
     View this article via: CrossRef PubMed Google Scholar
144. Bannon SA, et al. Next-generation sequencing of DDX41 in myeloid neoplasms leads to increased detection of germline alterations. Front Oncol. 2020;10:582213.
     View this article via: CrossRef PubMed Google Scholar
145. Simon L, et al. High frequency of germline RUNX1 mutations in patients with RUNX1-mutated AML. Blood. 2020;135(21):1882–1886.
     View this article via: CrossRef PubMed Google Scholar
146. Feurstein S, et al. Accurate germline RUNX1 variant interpretation and its clinical significance. Blood Adv. 2020;4(24):6199–6203.
     View this article via: CrossRef PubMed Google Scholar
147. Wu D, et al. How I curate: applying American Society of Hematology-Clinical Genome Resource Myeloid Malignancy Variant Curation Expert Panel rules for RUNX1 variant curation for germline predisposition to myeloid malignancies. Haematologica. 2020;105(4):870–887.
     View this article via: CrossRef PubMed Google Scholar
148. Luo X, et al. ClinGen Myeloid Malignancy Variant Curation Expert Panel recommendations for germline RUNX1 variants. Blood Adv. 2019;3(20):2962–2979.
     View this article via: CrossRef PubMed Google Scholar
149. Li MM, et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017;19(1):4–23.
     View this article via: CrossRef PubMed Google Scholar
150. Evans DG, et al. Differential rates of germline heterozygote and mosaic variants in NF2 may show varying propensity for meiotic or mitotic mutation. J Med Genet. 2023;60(9):838–841.
     View this article via: CrossRef PubMed Google Scholar
151. Hayashi H, et al. Germline BRCA2 variant with low variant allele frequency detected in tumor-only comprehensive genomic profiling. Cancer Sci. 2024;115(2):682–686.
     View this article via: CrossRef PubMed Google Scholar
152. Kraft IL, et al. Sequential tumor molecular profiling identifies likely germline variants. Genet Med. 2024;26(3):101037.
     View this article via: CrossRef PubMed Google Scholar
153. Hiltemann S, et al. Discriminating somatic and germline mutations in tumor DNA samples without matching normals. Genome Res. 2015;25(9):1382–1390.
     View this article via: CrossRef PubMed Google Scholar
154. Jalloul N, et al. Germline testing data validate inferences of mutational status for variants detected from tumor-only sequencing. JCO Precis Oncol. 2021;5:PO.21.00279.
     View this article via: PubMed CrossRef Google Scholar
155. Klek S, et al. Genetic counseling and germline testing in the era of tumor sequencing: a cohort study. JNCI Cancer Spectr. 2020;4(3):pkaa018.
     View this article via: CrossRef PubMed Google Scholar
156. Roggia C, et al. Germline findings in patients with advanced malignancies screened with paired blood-tumour testing for personalised treatment approaches. Eur J Cancer. 2023;179:48–55.
     View this article via: CrossRef PubMed Google Scholar
157. Mandelker D, Ceyhan-Birsoy O. Evolving significance of tumor-normal sequencing in cancer care. Trends Cancer. 2020;6(1):31–39.
     View this article via: CrossRef PubMed Google Scholar
158. Rabizadeh S, et al. Comprehensive genomic transcriptomic tumor-normal gene panel analysis for enhanced precision in patients with lung cancer. Oncotarget. 2018;9(27):19223–19232.
     View this article via: CrossRef PubMed Google Scholar
159. Gordon LG, et al. Estimating the costs of genomic sequencing in cancer control. BMC Health Serv Res. 2020;20(1):492.
     View this article via: CrossRef PubMed Google Scholar
160. Mandelker D, Zhang L. The emerging significance of secondary germline testing in cancer genomics. J Pathol. 2018;244(5):610–615.
     View this article via: CrossRef PubMed Google Scholar
161. Forkosh E, et al. Ashkenazi Jewish and other white APC I1307K carriers are at higher risk for multiple cancers. Cancers (Basel). 2022;14(23):5875.
     View this article via: CrossRef PubMed Google Scholar
162. Raymond VM, et al. Germline findings in tumor-only sequencing: points to consider for clinicians and laboratories. J Natl Cancer Inst. 2016;108(4):djv351.
     View this article via: CrossRef PubMed Google Scholar