A systematic literature review of whole exome and genome sequencing population studies of genetic susceptibility to cancer

Abstract

The application of next generation sequencing (NGS) technologies in cancer research has accelerated the discovery of somatic mutations; however, progress in the identification of germline variation associated with cancer risk is less clear. We conducted a systematic literature review of cancer genetic susceptibility studies that used NGS technologies at an exome/genome-wide scale to obtain a fuller understanding of the research landscape to-date and to inform future studies. The variability across studies on methodologies and reporting was considerable. Most studies sequenced few high-risk (mainly European) families, used a candidate analysis approach, and identified potential cancer-related germline variants or genes in a small fraction of the sequenced cancer cases. This review highlights the importance of establishing consensus on standards for the application and reporting of variants filtering strategies. It also describes the progress in the identification of cancer related germline variation to-date. These findings point to the untapped potential in conducting studies with appropriately sized and racially diverse families and populations, combining results across studies, and expanding beyond a candidate analysis approach to advance the discovery of genetic variation that accounts for the unexplained cancer heritability.

INTRODUCTION

Since 2005, the volume of publications enabled by sequencing approaches has grown at an astonishing rate. Several reviews have described the sequencing technology platforms (1) and advancements made in next-generation sequencing (NGS) over the past decade (2). The widespread availability of NGS technologies—including whole genome sequencing (WGS) and whole exome sequencing (WES)—has not only led to its applications in cancer research, but also for use in the clinical setting (3,4).

Use of NGS has accelerated the discovery of somatic mutations (5) and germline mutations in Mendelian diseases (6). Approximately 60% of Mendelian disease projects have successfully identified disease gene mutations (7) using sequencing technologies, improving upon classical approaches for gene discovery (e.g., linkage analysis). Additionally, the application of NGS technologies is revealing complex somatic mutational signatures associated with different types of cancer, a disease that is, by definition a result of somatic mutations (8, 9).

Given these successes, there is hope that sequencing studies may also aid the identification of genes accounting for the expected cancer heritability. The estimated cancer heritability is 33% for overall cancer (10) and its unexplained component has remained high, where a study by Susswein et al. (11) reported that 91% of cancer cases tested negative for known mutations in a large gene-panel testing study. In addition to the heritability hidden in current array-based studies and likely detectable with larger sample sizes, it has been hypothesized that the missing familial heritability may reside in rare variants of high or moderate/low penetrance that are potentially tractable by NGS technologies (12). As NGS technologies continue to evolve, NGS will play an increasing role in cancer research in the foreseeable future.

We conducted a systematic literature review and evaluated the degree of success and limitations in identifying germline cancer susceptibility variants using NGS technologies at a genome-wide scale, i.e., through WES and WGS, with the goal of learning from past efforts and obtaining a fuller understanding of the NGS-related research landscape to-date. Given the transition of genomic discovery research from candidate genes (historically of limited success) to WES/WGS studies, the high cost of WES/WGS methods, and their specific challenge with sifting through millions of variants, this review focuses on the effectiveness of WES/WGS studies –and not on other NGS gene targeted approaches– to identify novel variants and genes involved in cancer risk. The present review provides selected study-related characteristics, technologies, and methodologic details for 186 WES/WGS-related publications with the goal of informing the design of future studies. We also discuss the research needs and opportunities that could further advance the discovery of cancer susceptibility genes or variants. It should be noted that, although the reviewed articles were not selected based on their focus on rare versus common variants nor on their focus on low versus moderate/high penetrant variants, more cost-efficient approaches, based on genome wide genotyping assays, exist to study common variants, while NGS technologies are necessary to study rare variants.

METHODOLOGY

We followed the methodology for systematic literature review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (13).

Search strategy

For this report, PubMed and Embase were searched for the period between January 1, 2000 through December 31, 2018 using various search terms for exome/genome sequencing and germline susceptibility and cancer (see Supplemental Figure S1). Both searches were restricted to articles written in English. The references of the reviewed articles were also checked for the presence of additional in scope articles that may had been missed by the above key words searches.

Exclusion/inclusion criteria

Articles were included in this literature review if they had used genome-wide (i.e., whole exome or whole genome) sequencing to generate germline DNA data in at least two cancer cases (even if within the same family) with the purpose of identifying cancer susceptibility genes or variants (see exclusion criteria in Supplemental Figure S1). We excluded papers that sequenced only one cancer case because they were often case-reports without a variant/gene identification research focus. Notably, we did not exclude publications that restricted the analysis to a priori or candidate genetic regions (referred in this manuscript as “candidate analysis approach”), due to the large methodological diversity across such studies; instead, we opted to include all studies that used WES/WGS independent of their use of a candidate analytical approach, and captured the prior knowledge used to select variants/genes to allow for sensitivity analysis by candidate analysis approach. The eligibility of each abstract/full-text article was assessed independently in a standardized manner by three reviewers. A fourth reviewer confirmed all inclusions and performed quality control on one third of the exclusions. The exclusion criteria were applied in no particular order, and the first reason noted by the coder was recorded, even if a paper could be excluded for multiple reasons.

Data abstraction

We looked for four broad components or phases in each paper: 1) a required discovery phase, and optional phases of 2) technical validation, 3) independent replication, or 4) functional evaluation.

1. The discovery phase refers to the component of the paper where germline DNA of more than one cancer case (and possibly controls) were sequenced by WES or WGS with the goal of identifying cancer susceptibility variants and genes.

2. The technical validation attempts to confirm some of the variants observed in the discovery phase using an alternative sequencing technology.

3. The independent replication phase attempts to replicate some of the variants or genes observed in the discovery phase in independent cases.

4. The functional evaluation phase characterizes through in silico and/or laboratory functional experiments some of the variants or genes observed in the discovery phase.

Data abstracted (see list and definitions of coding fields in Table 1 and Supplemental Table S2) included publication and general study information, numbers and characteristics of cases, controls, and families used in each phase, sequencing technique, data filtering and analysis methods, in silico and experimental functional assessment, and key conclusions. We extracted information on family history of cancer, early age at diagnosis and/or multiple primaries, which are the National Comprehensive Cancer Network (NCCN) guidelines criteria for genetic cancer risk assessment. Of note, we defined ‘familial studies’ as those studies that sequenced samples from cancer cases (here referred to as ‘familial cases’) belonging to a family in which multiple cancer cases of the studied cancer type had been diagnosed (also referred to in the literature as ‘high-risk families’). We defined ‘unselected studies’ as those studies that sequenced samples from cancer cases (here referred to as ‘unselected cases’) who were unselected for family history of cancer (also referred to in the literature as ‘sporadic cases’). For each authors’ selected variants and genes, we recorded the nomenclature and minor allele frequency (MAF), as reported by the authors, the number of families for which cancer status fully segregated or not with the variant, the number of unselected or familial cases, unaffected relatives or unrelated controls that carried the variant, and the number of families, familial cases, unselected cases, or controls carrying any prioritized variant in the same gene. For quality control purposes, 50% of the papers were also reviewed by a second abstractor and discrepancies between coders were resolved by consensus. Information in the articles that was unclear was coded based on best interpretation of the reviewers.

Table 1.

Coding fields used for reviewing papers.

Topic	Coding fields
Publication	Pubmed ID; Journal; Year; Author; Title; Abstract;
Study general	Goal; Study design; Source of individuals; Cancer type; Ethnicity; Sequencing Center; Data repository;
Number of individuals sequenced in discovery	Number of cases, controls, families, and cases per family sequenced in discovery phase;
Sequencing technique	Samples type; Exome and/or genome; Capture kit; Sequencer; Coverage/depth;
Processing of raw-data	Aligner; Reference genome; Variant caller and calling QC; Annotation software and sources;
Technical validation	Yes/No; Validation technology; Number of cases, controls, families, and cases per family sequenced in validation phase; variants/genes validated;
Independent replication	Yes/No; Replication technology and analysis; Number of cases, controls, families, and cases per family sequenced in replication phase; variants/genes replicated;
Functional validation	In silico functional analyses; Experimental functional study;
Variants and genes data analysis	Candidate analysis approach; Filtering strategy overall; Analytical methods;
Variants and genes identification	Yes/No; Identified variants and genes; Number of cases, controls, families carrying the identified variants and genes;
Authors comments and conclusions	Challenges; Suggested next steps; Conclusions;
Derived filtering criteria/categories shown in Figure 1	f_1: variant passing quality control metrics
	f_2: heterozygous variant
	f_3: homozygous variant
	f_4: variant located in a coding region
	f_5: nonsynonymous or splice variant
	f_6: variant damaging according to in silico algorithms
	f_7: truncating variant
	f_8: variant altering protein properties according to molecular modeling
	f_9: not hypervariable gene
	f_10: variant absent from Minor Allele Frequency (MAF) databases
	f_11: variant rare in MAF databases
	f_12: variant segregating with disease status in the family
	f_13: variant present in multiple families or independent cases
	f_14: variant enriched in cases compared to controls
	f_15: gene mutated in multiple families or independent cases
	f_16: gene enriched in cases compared to controls
	f_17: variant present in disease related databases
	f_18: gene known to be linked to disease
	f_19: genetic region known to be linked to disease
	f_20: biological or molecular pathway known to be linked to disease
	f_21: pathway analysis indicating a gene-disease link
	f_22: variant confirmed through technical validation
	f_23: variant or gene replicating in independent cases
	f_24: variant loss of heterozygosity (LOH) observed in tumor
	f_25: relevant somatic mutations observed in tumor
	f_26: gene-disease link supported by functional experiments
	f_27: variant splicing supported by experiment
	f_28: variant-disease link supported by functional experiment

RESULTS

Article selection

The search yielded a total of 6,339 unique articles (see PRISMA flowchart in Supplemental Figure S1) which were evaluated for inclusion and exclusion criteria (Supplemental Table S1). After full-text review, 186 papers met the inclusion criteria and are listed with the derived coding variables in Supplemental Table S2–S3. The distribution of the 186 reviewed articles by publication year shows an increase from 2 to 40 articles/year between 2011 and 2015, followed by a plateau in 2016-2018 (Supplemental Figure S2).

Study design and population characteristics

In the discovery phase, 86% of articles used familial cases (11% of which were in combination with early-age of onset and/or unselected cases), 12% of studies were conducted in unselected cases, and 2% of articles used early-age of onset cases. Fifty-five percent of the studies included controls in the discovery phase, of which 67% were unaffected relatives of the cancer cases. Only 57% of the reviewed articles also attempted some type of replication in an independent group of cancer cases. However, only 17% of the replication phase used the same study design as the discovery phase (Supplemental Figure S3). For example, 16% of the family-based articles that included a replication phase did not include familial cancer cases. Moreover, controls were included more often in the replication phase (80%) than in the discovery phase (55%). Likewise, unselected cases were used more frequently in the replication phase than in the discovery phase (30% compared to 12%).

One third of the studies sequenced only 2 or 3 cancer cases in the discovery phase and only 28 (15%) of the articles sequenced more than 100 cancer cases. Technical validation of some of the variants was performed in 85% of the articles, although most of these were conducted in studies with small sample size (i.e., less than 50 cases). The replication phase had generally larger sample size, including more than 100 cancer cases. Almost half (n=73) of the 160 family-based studies sequenced a single family and the majority (n=115) sequenced one or two familial cancer cases (Table 2).

Table 2.

Number and percentage of cancer cases and families studied per article and by study phase.

	Number (%) of articles
	Discovery	Independent replication	Technical validation
Number of cancer cases
2-3	62 (33%)	0 (0%)	41 (22%)
4-10	38 (20%)	3 (2%)	48 (26%)
11-50	39 (21%)	17 (9%)	35 (19%)
51-100	19 (10%)	10 (9%)	4 (2%)
101-1000	22 (12%)	43 (23%)	3 (2%)
>1000	6 (3%)	34 (18%)	1 (1%)
Not stated	0 (0%)	0 (0%)	26 (14%)
Total*	186 (100%)	107 (57%)	158 (85%)
Number of high-risk families
1	73 (39%)	3 (2%)
2-10	27 (15%)	6 (3%)
11-50	40 (22%)	23 (12%)
51-100	10 (5%)	8 (4%)
101-1000	6 (3%)	23 (12%)
>1000	2 (1%)	5 (3%)
Not stated	2 (1%)	4 (2%)
Total*	160 (86%)	72 (39%)
Average number of sequenced cases per family
1	49 (26%)	56 (30%)
2	66 (35%)	10 (5%)
3	27 (15%)	1 (1%)
4	10 (5%)	1 (1%)
5	4 (2%)	0 (0%)
6-7	3 (2%)	0 (0%)
Not stated	1 (1%)	5 (2%)
Total*	160 (86%)	72 (39%)

^*Some totals do not add to 100% because: 15% of articles did not perform technical validation; 43% of articles did not perform independent replication; 14% of articles did not include familial cases in the discovery phase; 61% of articles did not perform independent replication in familial cases.

The most commonly studied cancer types were breast cancer (15%), followed by hematological malignancies (15%, which included pediatric cases), colorectal cancer (10%), melanoma (7%), lung cancer (7%), and prostate cancer (5%) (Supplemental Figure S4). Information on race, ethnicity, or country of origin was reported in 85% of the articles reviewed and mostly referred to the region or country of origin (Supplemental Figure S5), with only few studies reporting sequencing derived ancestry. Over half of the studies were conducted in Caucasians or individuals from Europe (59%), followed by individuals from Asia (13%), the Middle East (7%), of African-descent (3%), from Latin America (2%) and Australia (2%).

Sequencing technologies, read alignment, variant calling and annotation

Sequencing was performed in DNA extracted from blood (65%), formalin-fixed paraffin-embedded –generally non-tumor– tissues (8%) and/or saliva (5%). However, a notable proportion did not state the DNA source (23%). Moreover, the amount of DNA used for sequencing (~1-3ug per sample) was reported only in 33% of the articles (Supplemental Table S2).

Ten articles (5%) analyzed WGS only, seven studies (4%) conducted both WGS and WES, and 169 studies (91%) analyzed WES only. The reviewed studies used 28 different capture methods and 14 different sequencing platforms (Supplemental Figure S6), whereas 12% and 8% did not report the capture or sequencer used, respectively. Sequencing coverage information was reported for only 71% of the articles, as average depth (52%) and/or percentage of the target genome covered at 10x or higher thresholds (42%) (Supplemental Figure S7). For most articles it was unclear if the reported coverage statistics referred to the targeted or actual coverage and to pre-QC or post-QC coverage. In addition, there was no correlation between the number of samples sequenced and the reported coverage depth.

Sequencing reads were aligned to human genome references, where 79% of the studies reported using hg19 (also known as NCBI build 37 or GRCh37) and 3% used hg18 (a.k.a., NCBI build 36). The most widely used aligner (52%) was the Burrows-Wheeler Aligner (BWA) (14). Reference genome and alignment algorithm used were not specified in 18% and 12% of articles, respectively. Over half of the articles used Genome Analysis Toolkit (GATK) (15) variant calling algorithms. One fourth of the studies used more than one algorithm to call variants, which can improve call quality, and 8% of papers did not report their variant calling method. Various quality metrics were applied to screen the sequencing reads (e.g., removal of PCR duplicates, unmapped or non-uniquely mapped or out of target reads) and the called variants (e.g., removal of variants with quality or coverage below a preset threshold) (Supplemental Table S2). In addition, 26% of the articles used additional control sequencing datasets generated in-house via the same technology and pipeline as the study dataset to control for technical artifacts.

The annotation software used to annotate the called variants was not specified in 30% of the articles and included ANNOVAR (16) in 36% of the articles. Eighty-nine percent of the articles used allele frequency information from publicly available databases, mainly from the 1000 Genomes Project (17) (64%), the NHLBI Exome Sequencing Project (ESP) (18) (47%), dbSNP (19) (46%), and the Exome Aggregation Consortium (ExAC) (20) (32%). Several other annotation tools and source databases were used (Supplemental Table S2).

Criteria used to filter variants and genes

Figure 1 shows which criteria or filters (see the f_1, …, f_28 variables described in Table 1) were used in each reviewed article. Filtering criteria were generally used to prioritize/select a variant/gene over others and/or to seek evidence in support of a selected variant/gene. Data in Figure 1 show that the criteria used to identify variants and genes with a role in cancer susceptibility are disparate across articles, and consequently, results cannot be directly compared across studies. Below we examine these filtering/selection criteria and summarize their use and outcomes by grouping them into seven broader themes: 1) Variant quality; 2) Variant effect; 3) Variant rarity; 4) Mode of inheritance and genetic-disease association; 5) Candidate analysis approaches; 6) Independent replication; and 7) Functional validation. Two general observations hold true: i) lack of sensitivity analyses to assess variability in results by changes in variant/gene selection strategy, and ii) no or minimal reporting of a justification for the choice of criteria and thresholds used.

Figure 1.

Summary across the 186 reviewed articles of the methods used to conclude if a variant or gene was plausibly involved in a causal pathway to cancer. The y-axis displays the PMID of the 186 coded papers; the x-axis displays 29 broad categories of filtering criteria described in Table 1. White color indicates that the criterion was not used by the authors to identify a variant/gene as possibly linked to the cancer under investigation. Light blue indicates that the criterion was used as a selection filter. Dark blue indicates that the criterion was used as increased evidence of variant/gene-cancer association. Pink color indicates that the criterion was used as decreased evidence of variant/gene-cancer association. Dark red indicates that the information related to that criterion was unclear. The last category on the right “unclear” indicates that some other not clearly stated criteria were used. The order of the papers along the y-axis is based on a computed correlation between values 2 (for supporting evidence), 1 (for used selection criteria), 0 (for not used criteria), −1 (for unsupporting evidence) and −2 (for unclear), i.e., papers using similar filtering criteria sets are shown next to each other.

1). Variant quality (f_1, f_9, f_22).

Approximately half of the reviewed articles explicitly described the use of variant call metrics to exclude low-quality variants from the analyses, e.g., manual inspection using the Integrative Genomics Viewer (21), removing variants in paralogs or repeats regions, and/or with Phred-scaled quality scores or coverage below a given threshold (Supplemental Table S2). Approximately 85% of the articles reported technically validating variants, where most used Sanger sequencing. The technical validation success rate was about 80% for studies that tested over 50 variants versus above 90% for studies that tested fewer variants (Supplemental Figure S8).

2). Variant effect (f_4, f_5, f_6, f_7, f_8).

Most articles (n=168 or 90%) required the variants to be in coding regions, and more specifically, to be non-synonymous or in splice sites or frameshift (n=163 or 88%). A subset of these articles also required the selected variants to be functionally impactful (e.g., ‘deleterious’, ‘damaging’, or ‘pathogenic’) according to various in silico algorithms (n=91 or 49%) or to be truncating (n=38 or 20%). Supplemental Table S2 lists for each article the adopted in silico pathogenicity predictors (e.g., references 22–29) which were often used in combination.

3). Variant rarity (f_10, f_11).

Most articles required the selected variants to be absent/not described (25%) or rare (62%) based on a preset MAF threshold (0-0.1 range, Supplemental Figure S9) in internal or publicly available control datasets, such as 1000 Genomes Project, dbSNP, ESP, or others (Supplemental Table S2).

4). Mode of inheritance (f_2, f_3, f_12) and genetic-disease association (f_13, f_14, f_15, f_16, f21).

Only 10 (5%) and 17 (9%) papers restricted their search to homozygous and heterozygous variants according to a recessive or a dominant mode of inheritance, respectively. Since the remaining papers describe only heterozygous variants in their findings, a dominant inheritance hypothesis can be assumed for all but 10 articles. The majority of articles (n=108 or 58%) required the variant to fully or partially segregate with disease status in at least one family whose members were sequenced in the discovery phase. Only two of the reviewed articles looked for de novo variants. In several studies, the selected genes were required to be mutated in more than one family (n=19 or 10%) or in multiple independent cases (n=5 or 3%), or to be enriched in cases compared to controls according to burden tests (n=18 or 10%). Fewer articles required the same selected variant to be present in more than one family (n=10 or 5%), in multiple independent cases (n=6 or 3%), or to be statistically enriched in cases compared to controls (n=8 or 4%). Finally, 12 articles used pathway analysis techniques to identify biological or molecular functions that were enriched with mutated genes.

5). Candidate analysis approaches (f_17, f_18, f_19, f_20).

Two thirds of the reviewed articles (n=121, 65%) used existing information from the literature and curated databases to restrict the discovery analysis to: variants present in disease-related databases such as ClinVar (30) (n=14 or 7%); genes known to be linked to disease such as those listed in OMIM (31) or reported in the literature (n=59 or 32%); genetic region known to be linked to disease through genome wide association studies (GWAS) or linkage studies (n=14 or 7%); and/or biological or molecular pathways known to be linked to disease, such as DNA repair pathways (n=51 or 27%).

6). Independent replication (f_23).

Only 107 (57%) articles attempted replication of variants/genes in an independent set of cancer cases. Overall, 79 (42%) reported various degrees of confirmatory evidence. In some cases, the authors reported the presence of other pathogenic variants in the same gene, whereas in others, a statistically significant burden test in cases compared to controls for that gene was reported. In a few studies, the exact variant(s) initially found in the discovery phase were found in additional cancer cases in the replication phase.

7). Functional validation (f_24, f_25, f_26, f_28).

In those studies that evaluated function of the identified variants/genes (70% of 186), 60 (32%) tested for loss of heterozygosity in tumor samples (58% of which tested positive); 36 (19%) looked for somatic mutations in the same gene (69% of which were found); 22 (12%) looked for gene/methylation expression changes supporting a link with disease (86% with positive results); 35 (19%) checked for variant splicing (86% of which were verified); and 45 (24%) carried out in vitro experiments or other functional assays on the identified variants, 80% of which showed results consistent with the hypothesized function for these variants.

Variants and genes identified

About 95% (n=176) of reviewed articles indicated that they identified variants or genes (listed in Table 3 with PMIDs by cancer type) with various degrees of certainty. Only eight (4%) articles clearly stated that they were not able to identify variants or genes in the studied cases, and the remaining two (1%) articles pointed to molecular or functional pathways of possible relevance to the studied cancer type. The 176 articles indicated as primary findings (Supplemental Table S3) a total of ~2,095 variants (average 11, median 3, range 1 to 222 per article) and ~1,215 (~954 unique) genes (average 6, median 1, range 1 to <222 per article). An exact count of variants and genes identified was not feasible due to incomplete counts and/or variant nomenclature in some of the articles. For the 99 articles that studied more than one high-risk family, and reported the information, the identified variants/genes accounted on average for 25% of the families evaluated in discovery and replication phases combined (we excluded from this analysis 43 articles that studied only a single family and did not attempt replication, Supplemental Figure S10). Regarding the prevalence of the identified variants among controls, 27 (16% of 176) articles did not sequence these variants in controls, 50 (28%) sequenced only some unaffected relative of the cases, 37 (21%) did not report how many of the sequenced controls carried the investigated variants; in the remaining articles the variants’ frequency in unrelated controls averaged 0.015 with median 0 (Supplemental Table S3).

Table 3.

List and counts of identified genes and PubMed IDs by cancer type.

Cancer Type	Authors’ identified genes± [or pathways]	# genes [unique]	PMID¶ (references) {number of articles}
Biliary	BRCA1, BRCA2, RAD51D, MLH1, MSH2, POLD1, POLE, TP53, ATM;	9 [9]	29360550 (32); {1}
Blood	none; none; ACAN; ATM; CBL; CEBPA; CORO1A; DICER1; ETV6; FAAP24; HAVCR2; ITGB2; KDR; MLL; PAX5; POT1; PRDM9; TP53; TYK2; LAPTM5, HCLS1; SLC26A6, FAM107A; POT1, ACD, TERF2IP; ZXDC, ATN1, LRRC3; FNCP/SLX4, FANCA, GEN1; POLK, PRKCB, ZNF676, PRRC2B, PCDHGB6, GNL3L, TTC36, OTOG, OSGEPL1, RASSF9; MLL3; JAK2, MPL, TYK2, BCL2, BCL3, KIT, SH2B3, MCM3AP, PEAR1, TET2;	52 [50]	23379653; 27141497; 25715982; 28652578; 25939664; 26721895; 23522482; 29708584; 26522332; 27473539; 30374066; 27629550; 27365461; 23457195; 24013638; 29693246; 23222848; 23255406; 27733777; 26903547; 29755658; 27528712; 28427458; 26201965; 30097855; 24301523; 30553997; (33–59) {27}
Bone	ANO5; EXT2; T;	3 [3]	27216912; 27636706; 23064415; (60–62) {3}
Brain & nervous system	CASP9; GALNT14; JMJD1C; PMS2; POT1; SMARCE1; SUFU; TP53, ATRX; TP53, MSH4, LATS1; CNL2, ANKRD65, TAS1R2, PINK1, DCAF6, F5, TNN, CFH, DDX59, IGFN1, CHI3L1, CR1, OR2B11, TRIM58, RRM2, ANKRD53, CAPG, IMMT, THNSL2, PIKFYVE, COL4A3, SUMF1, CEP120, ARAP3, ARHGEF37, TCOF1, FAT2, OR2V2, HIVEP1, PMS2, ADAM22, C7orf62, TRPA1, TG, ART1, TRIM22, IGSF22, CD6, CD5, SLC22A24, CD248, MYEOV, KIAA1731, PIK3C2G, LRMP, OVCH1, TPPP2, RPGRIP1, TTLL5, KCNK10, C14orf159, ACSBG1, ACAN, C15orf42, KIF7, ZNF23, ZNF469, ANKRD11, STARD3, ERBB2, AKAP1, FAM20A, ABCA6, ABCA10, DNAH17, LAMA1, DOPEY2, MYO18B, NEFH, CCDC157, RNF215, SMTN, APOL3;	85 [83]	27935156; 26309160; 24896186; 28805995; 25482530; 23377182; 22958902; 29602769; 25041856; 25537509; (63–72) {10}
Breast	none; none; APOBEC3B; ATM; ERCC3; FANCM; GPRC5A; KAT6B; PALB2; RCC1; RECQL; RECQL; RINT1; RNASEL; XRCC2; BRCA2, STK11; FANCC, BLM; ERCC6, BRCA2, BRCA1; BRCA1, TP53, PTEN, PALB2, RAD51C; XCR1, DLL1, TH, ACCS, SPPL3, CCNF, SRL; BRCA1, BRCA2, PALB2, ATM, CHEK2, BARD1, BRIP1, XRCC2; FANCM, WNT8A, SLBP, CNTROB, AXIN1, TIMP3, PTPRF, UBA3, MAPKAP1, TNFSF8, S1PR3; HSD3B1, CFTR, PBK, ITIH2, MMS19, PABPC3, PPL, DNAH3, LRRC29, CALCOCO2, ZNF677, RASSF2; NOTCH2, DNAH7, RAF1, MST1R, LAMB4, NIN, SLX4, ERCC1, SLC22A16, PTPRD, ARHGEF12, ERBB2; ABCC12, APC, ATM, BRCA1, BRCA2, CDH1, ERCC6, MSH2, POLH, PRF1, SLX4, STK11, TP53; ABCA10, CHST15, GRIP1, LOC100129697, LOC388813, NBPF10, PABPC3, C16orf62, KRTAP21-3, NPIPB11, PDE4DIP, CCDC7, CFAP46, CXorf23, SMIM13, GAGE2A, PHIP, SLC15A5, ZNF750, ATP10B, PIGN, PRR14L; ABL1, ADRA2A, DUX2, GATA3, GPRIN1, JAKMIP3, KAT6B, LIG1, LIG4, NANP, NFKBIZ, NFRKB, NOTCH2, PHKB, PINK1, POLK, POLQ, PPFIA4, PRKCQ, RAD23B, SMG1, TNK2, UBE2L3; ABCC11, ADARB1, ADPRH, ATG4C, ATM, ATXN3L, BANK1, BIRC8, BRIX1, BTN2A3, C10orf68, C14orf38, C17orf57, C1orf168, C20orf186, C2orf63, C5orf52, C7orf46, CA12, CASP5, CAT, CCDC99, CDKL2, CFHR5, CGRRF1, CHEK2, CHRNB3, CIB1, CPAMD8, CRYBG3, CXCL6, DCD, DCHS2, DDX60L, DNMT3A, ECEL1, EEF2K, EIF2B4, ENDOD1, ENTPD4, EPS8L1, EPSTI1, ERCC3, EYS, FAM40B, FAP, FETUB, FILIP1, FLG2, FLOT2, FOLH1B, FTMT, GIMAP6, GPRC5A, GTPBP5, HEATR7B2, IFIT2, IGSF22, IL25, ITGB3, KIAA1919, LOC440563, LOC647020, LRRC69, MAGEF1, MBIP, MCAT, ME1, MLL4, MMP3, MTERF, NDUFA10, NEIL1, NLRP7, NOD2, NOX1, NPL, NUP188, OTOA, OXSM, PAFAH2, PARK2, POLQ, POLR3GL, PPEF2, PRMT7, PRSS3, PRSS7, PTCHD3, RBKS, RPS6KC1, SAMD9L, SERPINI2, SGOL2, SIGLEC1, SLAMF6, SLC26A10, SLC4A1AP, SLC6A5, SLCO1B3, SMARCD2, SPATA4, SSX9, STAP2, TAOK1, TIMD4, TMEM56, TNFAIP6, TNFAIP8, TTC21A, USP45, VPS13B, WFDC8, WRN, ZIM2, ZNF311, ZNF451, ZNF491, ZNF582, ZNF599, ZSCAN29; [cell-cell and cell-extracellular matrix adhesion processes;]	253 [229]	21858661; 23383274; 28062980; 27913932; 27655433; 25288723; 24470238; 23800003; 27648926; 29363114; 25915596; 25945795; 25050558; 29422015; 22464251; 26576347; 23028338; 25923920; 29325031; 26157685; 25330149; 23409019; 29879995; 29868112; 28202063; 28076423; 24969172; 22527104; 26969729; (73–101) {29}
Colorectal	BRF1; FAN1; MUTYH; RPS20; SETD6; CENPE, KIF23; POLE, POLD1; WRN, ERCC6; EMR3, PTPN12, LRP6; TTF2/TRIM45/VTCN1/MAN1A2; BRCA2 (FANCD1), BRIP1 (FANCJ), FANCC, FANCE, REV3L (POLZ); FANCM, LAMB4, LAMC3, PTCHD3, TREX2; IL12RB1, LIMK2, POLE2, POT1, MRE11; CDKN1B, XRCC4, EPHX1, NFKBIZ, SMARCA4, BARD1; ACSL5, ADAMTS4, ARHGAP12, ATM, CYTL1, DONSON, INTS5, MCTP2, ROS1, SYNE1; UACA, SFXN4, TWSG1, PSPH, NUDT7, ZNF490, PRSS37, CCDC18, PRADC1, MRPL3, AKR1C4; DDX20, ZFYVE26, PIK3R3, SLC26A8, ZEB2, TP53INP1, SLC11A1, LRBA, CEBPZ, ETAA1, SEMA3G, IFRD2, FAT1; MLH1, MUTYH, MSH6, MSH2, LRP5, ATM, RYR3, EIF2AK4, PRDM1, RYR2, BUB1, RPS6KB2, DAAM1, TCF7, TSC2, LIG3, MCC, MAX, PARP1, ETV4, FZD10, MAST2, MTOR;	96 [94]	28912018; 26052075; 24691292; 24941021; 28973356; 23637064; 23263490; 26344056; 26901136; 29396139; 27165003; 23585368; 27329137; 25058500; 25749350; 24146633; 29844832; 25892863; (102–119) {18}
Esophageal	KCNJ12/KCNJ18, GPRIN2; PTEN, SMAD7; DNAH9, GKAP1, NFX1, BAG1, FUK, DDOST; NR4A2, IL6ST, FZD3, SBNO2, NOTCH3, STAT3, CD3D, MUC16, LIN28A, STIL, ANK1, CD3E, PARN, GDF15, ITGA6, LCK, THBS2, ALX4, NOD2, KALRN, EGF, DLK2, HDAC2, AKT1, FBXW11, MET, SFRP1;	38 [38]	29405996; 25554686; 28165652; 28459198; (120–123) {4}
Gastric	none; ATP4A; CTNNA1; INSR, FBXO24, DOT1L; PALB2, BRCA1, RAD51C; PALB2, MSH2, ATR, NBN, RECQL5; DZIP1L, PCOLCE2, TAS2R7, TRIOBP, IGSF10, NOTCH1, SF3A1, GAL3ST1, NOTCH1, OR13C8, EPB41L4B, SEC16A;	25 [23]	28875981; 25678551; 23208944; 25576241; 28024868; 29706558; 26872740; (124–130) {7}
Glands	MAX; HOXA11, NCOA4, PCSK7, CENPQ, GALC, GOLGA5, GUCA1C, RHCE, DDX12, FAM22F, FAM71F1, MDN1, MLLT4, SYNE1; IGSF3, STXBP5L, PCDHB12, HAVCR1, SMPD2, OPRM1, GTF21RD2/GTF2IRD2B, TAS2R43, KMT5A, NCOR2, BTBD6, PHBDF1, TSC2, RAMP2, CST2, SHANK3;	32 [32]	21685915; 23707928; 28210977; (131–133) {3}
Head and neck	MLL3; MST1R; EXO1, HLTF, TDP2, RAD52, PER1, MSH2, ERCC5, MSH6, RAD17, PMS2, NBN, DCLRE1C, ALKBH3, LIG4, XRCC3, FANCM, PALB2, BRCA1, BRIP1, TP53, XRCC1, CHEK2, FAAP20, WDR48, FANCD2, FANCA, SHPRH, MMS19, ALKBH3, PARP4, RECQL5, NUDT1, PARP2, POLE2;	36 [35]	26014803; 26951679; 29747023; (134–136) {3}
Intestinal	IPMK; MUTYH;	2 [2]	25865046; 28634180; (137–138) {2}
Kidney	BAP1; CTR9; REST; DICER1;	4 [4]	23684012; 25099282; 26551668; 26566882; (139–142) {4}
Liver	DICER1;	1 [1]	28012864; (143) {1}
Lung	CHEK2; HER2; IBSP; MET; PARK2; YAP1; PROM1, CRTC2; CACNB2, CENPE, LCT, MAST1; ARHGEF5, ANKRD20A2, ZNF595, ZNF812, MYO18B; BPIFB1, CHD4, PARP1, NUDT1, RAD52, MFI2; FANCL, FANCG, FANCC, FANCF, BRCA2, PALB2, FANCA, BRCA1, RAD51C, BRIP1; BAG6, SPEN, WISP3, JAK2, TCEB3C, NELFE, TAF1B, EBLN2, GON4L, NOP58, RBMX, KIAA2018, ZNF311; TP53, BRCA1, BRCA2, ERCC4, EXT1, HNF1A, PTCH1, SMARCB1, ABCC10, ATP7B, CACNA1S, CFTR, CLIP4, COL6A1, COL6A6, GCN1, GJB6, RYR1, SCN7A, SEC24A, SP100, TTN, USH2A;	69 [67]	27900359; 24317180; 26717996; 28294470; 25640678; 26056182; 24484648; 26178433; 29054765; 29667179; 30425093; 24954872; 30032850; (144–156) {13}
Melanoma	none; BAP1; GOLM1; POLE; POT1; POT1; RAD51B; TP53AIP1; VPS41; CDKN2A, BAP1, EBF3; MC1R, MITF, BRCA2, MTAP;	15 [13]	29764119; 23977234; 29659923; 26251183; 24686846; 24686849; 25600502; 29359367; 25303718; 29522175; 29317335; (157–167) {11}
Multiple familial*	ERCC3; BAP1; POT1; MLL3; POLE; POLE; HNRNPA0, WIF1;	8 [7]	28911001; 22889334; 28389767; 23429989; 24788313; 25860647; 25716654; (168–174) {7}
Multiple types**	BRCA1, BRCA2, ATM, BRIP1, PALB2, CNKSR1, EME2, MRE11A, MSH6, PIK3C2G, RAD51C, RAD51D, XRCC2; APC, ATM, BAP1, BRCA1, BRCA2, BUB1B, FH, HFE, MAX, MET, MSH6, NF1, PALB2, PTEN, RET, SDHA, SDHB, SDHD, TP53, VHL, WRN;	34 [29]	26689913; 29625052; (175–176) {2}
Pediatric	APC, DICER1, TP53, BRCA2, CHEK2, BAP1, BUB1, ETV6, ACTB, ARID1A, EP300, EZH2, APOB, SCN2A, SPRED1, KDM3B, TYK2; TP53, APC, BRCA2, NF1, PMS2, RB1, RUNX1, ALK, BRCA1, CDH1, KRAS, MSH2, MSH6, NF2, NRAS, PALB2, PTCH1, RET, SDHA, SDHB, VHL;	39 [36]	29351919; 26580448; (177–178) {2}
Ovarian	FANCM; SMARCA4; RAD51D, ATM, FANCM; BRCA1, TP53, NF1, MAP3K4, CDKN2B, MLL3 and other ~220 genes not stated;	~230	28881617; 24658002; 28591191; 24448499; (179–182) {4}
Ovarian & Breast	ATM, MYC, PLAU, RAD1, RRM2B; ATM, PALB2, CHEK2, MSH6, TP53, RAD51C;	11 [10]	27782108; 30128536; (183–184) {2}
Pancreatic	ATM; BRCA2; ATM, PALB2; FAN1, NEK1, RHNO1; ATM, CDKN2A, PALB2, BRCA2, BUB1B, CPA1, FANCC, FANCG; ATM, BRCA1, BRCA2, CFTR, MSH2, MSH6, PALB2, PMS2, TP53;	24 [17]	22585167; 29074453; 23561644; 26546047; 26658419; 27449771; (185–190) {6}
Prostate	none; BTNL2; BRCA2, ATM, NBN; BRCA2, HOXB13, TRRAP, ATP1A1, BRIP1, FANCA, FGFR3, FLT3, HOXD11, MUTYH, PDGFRA, SMARCA4, TCF3; HOXB13, TANGO2, OR5H14, CHAD; LRCC46, PARP2, BLM, KIF2B, CYP3A43; ATM, ATR, BRCA2, FANCL, MSR1, MUTYH, RB1, TSHR, WRN; CYP3A43, HEATR5B, GPR124, HKR1, PARP2, PCTP, MCRS1, DOK3, ATRIP, PLEKHH3; TET2, CEP63, ELK4, NUBP2, SPRR3, TRBV7-7, BRCA1, LAMB3, MSH6, PARP2, DLEC1, ZSWIM2; ACACA, AKR1C1, ALG13, ALG6, APTX, ASXL1, ATP6V0A2, B3GAT3, BGLAP, BLM, BRCA1, CRISP3, CTBP1, CYP1B1, DDB2, DOLK, EFCAB6, ELAC2, FANCA, FANCL, FBXW7, FLT3, GSTA1, HDAC9, HSD3B1, IDE, KDR, MBD5, MGAT2, MSH3, MYH14, NCOR2, NEIL3, NOTCH2, NRIP1, PALB2, PAPSS2, PIAS3, RAD51D, RAD54L2, RNASEL, SCN11A, SP1, SULT1E1, TGIF1, TLN1, TP53BP1, TSC1, BE2D3, UBE2V2;	107 [95]	26604137; 23833122; 29915322; 27701467; 27902461; 25111073; 27084275; 26585945; 27486019; 26485759; (191–200) {10}
Sarcoma***	POT1; SMARC4; STAT4; CDKN2A, PDGFRA; APC, BLM, BRCA1, BRIP1, ERCC3, EXT2, FANCC, FANCD2, FANCM, FLCN, MITF, PMS2, POLE, PTCH2, PTPN11, RAD51, RAD51D, RET, SLX4, TINF2, TP53, WRAP53;	27 [27]	26403419; 23775540; 25492914; 28592523; 28125078; (201–205) {5}
Testicular	none; PKD1; PLEC, DNAH7, EXO5; DNAAF1, LRRC6, CNTRL, DRC1, DYNC2H1, CEP290, MAP4;	11 [11]	29433971; 27577987; 29761480; 27996046; (206–209) {4}
Thyroid	C14orf93 (RTFC); HABP2; MAP2K5; MET; PARP4; SRRM2; USF3; [extracellular matrix organization and DNA repair;]	7 [7]	27864143; 26222560; 30132833; 29219214; 26699384; 26135620; 28011713; 28402931; (210–217) {8}

^±The definition of “identified” is based on the authors’ choice and emphasis in presenting their results and not on an evaluation of the quality of the results. Bolded gene symbols indicate genes reported in more than one article, in black if only within cancer type, in blue if both within and across cancer types, in red if only across cancer types.

^¶Articles are referenced in the same order as the results separated by semicolons.

^*Single families with multiple cancer types: 28911001 (adrenocortical, breast [LF]); 22889334 (melanoma, paraganglioma); 28389767 (melanoma, thyroid, breast); 23429989 (colon, blood); 24788313 (colon, ovary, endometrium, brain); 25860647 (colon, ovary, pancreas, small intestine); 25716654 (prostate, breast, colon, pancreas, melanoma);

^**Large studies of the genetic landscape across cancer types: 26689913 (across 12 cancer types); 29625052 (across 33 cancer types).

^***Sarcoma types: soft tissue (28592523), Ewing (28125078), cardiac (26403419), Kaposi (25492914), rhabdoid (23775540).

Overall, 106 genes were identified in two or more articles (see bolded gene symbols in Table 3). The five genes reported by more than 10 articles are well-established cancer susceptibility genes (i.e., ATM, BRCA2, BRCA1, TP53, and PALB2 were observed in 12%, 12%, 9%, 9% and 8% of the articles, respectively). When the analysis was restricted to the articles that used a fully agnostic, not candidate, analytical approach, these genes were observed less frequently (6%, 4%, 7%, 0%, 2%, respectively) and other less established genes were more frequently observed (>4%) (i.e., PMS2, IGSF22, ABCA10, ACAN, and PABPC3). We also observed 43 variants in 22 genes which were independently identified in two or three articles (Table 4). While some of the observed pleiotropic effects are well established (e.g., PALB2 and BRCA1/2 for breast, ovarian, prostate and pancreatic cancer), others are potentially novel, such as BRCA2 for melanoma and head and neck cancers, MUTYH for prostate and small intestine cancers, and KDR for prostate cancer and Hodgkin lymphoma.

Table 4.

Germline variants identified in two or more of the 186 reviewed articles.

Gene	Variant	Allele Frequency	Articles’ PMID	Cancer type
PALB2	chr16, c.172_175delTTGT, p.Q60fs, rs1214293842	0.000042	25330149, 30128536	Breast, Ovarian & Breast
PALB2	chr16, c.509_510delGA, p.R170fs, rs863224790	0.000037	25330149, 30128536	Breast, Ovarian & Breast
PALB2	chr16, c.1240C>T, p.R414*, rs180177100	0.000009	27449771, 30128536	Pancreatic, Ovarian & Breast
PALB2	chr16, c.3256C>T, p.R1086*, rs587776527	0.000009	23561644, 30128536	Pancreatic, Ovarian & Breast
PALB2	chr16, c.3004_3007delGAAA, p.E1002Tfs	0	26485759, 30128536	Prostate, Ovarian & Breast
PALB2	chr16, c.3549C>A, p.Y1183*, rs118203998	0.000009	26689913, 30128536	Multiple_12, Ovarian & Breast
PALB2	chr16, c.424A>T, p.K142*	0	26689913, 30128536	Multiple_12, Ovarian & Breast
BRCA2	chr13, c.658_659delGT, p.V220fs, rs876660049	0.000028	26689913, 28202063	Multiple_12, Breast
BRCA2	chr13, c.6275_6276delTT, p.Leu2092fs, rs11571658	0.000065	25330149, 29915322	Breast, Prostate
BRCA2	chr13, c.9246_9247insA, p.T3085fs, rs80359752	0	25330149, 29915322	Breast, Prostate
BRCA2	chr13, c.865A>C, p.N289H, rs766173	0.052597	29317335, 29747023	Melanoma, Head and Neck
BRCA2	chr13, c.9294C>G, p.Y3098*, rs80359200	0.000009	26580448, 29625052	Pediatric, Multiple_33
ATM	chr11, c.5071A>C, p.S1691R, rs1800059	0.001937	28202063, 28652578	Breast, Blood (CLL)
ATM	chr11, c.170G>GA, p.W57*	0	22585167, 30128536	Pancreatic, Ovarian & Breast
ATM	chr11, c.6095G>GA, p.R2032K, rs139770721	0.000027	22585167, 29625052	Pancreatic, Multiple_33
ATM	chr11, c.6100C>T p.R2034*, rs532480170	0.000009	27913932, 30128536	Breast, Ovarian & Breast
ATM	chr11, g.108155008_delG, p.E1267fs	0	28652578, 29625052	Blood (CLL), Multiple_33
BRCA1	chr17, c.1067A>G, p.Q356R, rs1799950	0.045162	25923920, 26485759	Breast, Prostate
BRCA1	chr17, c.4065_4068delTCAA, p.N1355fs, rs886040195	0.000018	26689913, 29625052	Multiple_12, Multiple_33
BRCA1	chr17, c.1054G>T, p.E352*, rs80357472	0.000009	26689913, 29625052	Multiple_12, Multiple_33
BRCA1	chr17, c.68_69delAG, p.E23fs, rs80357410	0.000175	26689913, 29625052	Multiple_12, Multiple_33
TP53	chr17, c.733C>T, p.G245S, rs28934575	0	26580448, 29351919, 29602769	Pediatric, Pediatric, Brain
TP53	chr17, c.524G>A, p.R175H, rs28934578	0	26580448, 30128536	Pediatric, Ovarian & Breast
TP53	chr17, c.743G>A, p.R248Q, rs11540652	0.000009	26580448, 30128536	Pediatric, Ovarian & Breast
FANCM	chr14, c.5101C>T, p.Q1701*, rs147021911	0.001530	25288723, 28881617	Breast, Ovarian
FANCM	chr14, c.5791C>T, p.R1931*, rs144567652	0.001181	28591191, 28881617	Ovarian, Ovarian
KAT6B	chr10, c.4546G>T, p.D1516Y	0	23800003, 24969172	Breast, Breast
KAT6B	chr10, c.4729C>T, p.R1577C	0	23800003, 24969172	Breast, Breast
POT1	chr7, c.1851_1852delTA, p.D617fs, rs758673417	0.000009	25482530, 27329137	Glioma, Colorectal
MSH6	chr2, c.3261delC, p.F1088fs	0	26689913, 30128536	Multiple_12, Ovarian & Breast
CHEK2	chr22, c.1100delC, p.T367fs	0	22527104, 29351919, 30128536	Breast, Pediatric, Breast/Ovarian
RAD51D	chr17, g.33433425G>A, p.R206*, rs387906843	0.000017	26689913, 28591191	Multiple_12, Ovarian
FANCC	chr9, c.C553C>T, p.R185*, rs121917783	0.000064	23028338, 28125078	Breast, Sarcoma (Ewing)
MUTYH	chr1, c.1187G>A, p.G396D	0	27084275, 28634180	Prostate, Intestine (small)
BLM	chr15, c.1933C>T, p.Q645*, rs373525781	0.000018	23028338, 28125078	Breast, Sarcoma (Ewing)
TYK2	chr19, c.2279C>T, p.P760L	0	27733777, 29351919	Blood (ALL), Pediatric
MAX	chr14, c.223C>T, p.R75*	0	21685915, 29625052	Pheochromocytoma, Multiple_33
NOTCH2	chr1, c.3625T>G, p.F1209V, rs147223770	0.003217	26485759, 29868112	Prostate, Breast
XRCC2	chr7, c.96delT, p.F32fs, rs774296079	0.000075	25330149, 26689913	Breast, Multiple_12
RET	chr10, c.2370G>C, p.L790F, rs75030001	0.000009	26580448, 28125078	Pediatric, Sarcoma (Ewing)
GPRC5A	chr12, c.183delG, p.R61fs, rs527915306	0.002113	22527104, 24470238	Breast, Breast
KDR	chr4, c.3193G>A, p.A1065T, rs56302315	0.000324	26485759, 27365461	Prostate, Blood (HL)
MITF	chr3, c.952G>A, p.E318K, rs149617956	0.001330	28125078, 29317335	Sarcoma (Ewing), Melanoma

DISCUSSION

Methodological variability across reviewed articles

One major observation from this review was that the criteria used to identify variants and genes presented by the authors as having a role in cancer susceptibility varied dramatically across studies (Figure 1). In addition, most reviewed articles lacked sensitivity analyses to assess the variability in results by changes in variant/gene selection strategy or a justification for the choice of criteria and thresholds adopted. For example, although restricting the analysis to rare variants is justified in principle by the fact that high-risk/high-penetrance variants are very rare in the general population, no clear justification (e.g., based on disease penetrance estimates) was usually given for the exact choice of MAF thresholds, which can impact both false positives and negatives. In addition, requiring that the selected variants be completely absent from internal or publicly available control datasets (25% of articles) may also lead to false negatives, given that known disease related variants are observed in these datasets. Moreover, differences in methodologies—such as, study design, sequencing technologies, depth of coverage, human genome reference used, annotation software, variant calling methods, in silico prediction tools—have been reported to lead to differences in findings (218–219). Similarly, although the choice of transcript set and annotation software have been quantified to have a substantial effect on variant annotation and impact on the analysis of genome sequencing studies (220), none of the reviewed articles examined or discussed these potential effects. Although this literature review spanned a decade wherein underlying technologies, costs and bioinformatic pipelines evolved significantly, we note that 74% of the reviewed articles were published in the years 2015-2018. When restricting the analyses to this group of recent papers, we observed similar results.

Importance of developing consensus on standards

The observed wide variation and inconsistencies in approaches and strategies underscore the importance of establishing a consensus on standards for filtering strategies and rationale for variant identification (e.g., justification for the criteria and thresholds used). The disadvantage of using different methodologies to identify germline susceptibility genes is that it limits the ability to compare results across studies. While initiatives by the American College of Medical Genetics and Genomics (221) and the National Institutes of Health (222) have developed standards to assign a pathogenicity status to a given variant based on the available literature and annotations, to our knowledge, there has not been an attempt to set standards or a framework for agnostic searches of susceptibility variants or genes. Based on the present systematic review, we would recommend that articles in this field: i) report information for all the relevant components described in Table 1 and Supplemental Table S2; ii) include a complete list of identified variants/genes and a count of the individuals carrying those in a format similar to Supplemental Table S3; iii) report and explain the choice of variants/genes filtering criteria and thresholds, including sensitivity analysis when warranted.

Variants and genes identified in the reviewed articles

Approximately 95% of the reviewed studies reported identifying susceptibility variants or genes in the studied cancer cases. However, this observation may reflect general publication bias. Overall, about 2,000 variants and 1,000 unique genes were reported as primary findings by the authors. Breast cancer studies reported the highest number of genes, possibly reflective of the large proportion of published studies rather than the underlying genetic architecture. Notably, one hundred genes were found in more than one article (see bolded gene symbols in Table 3), indicating that results are recurrent within each cancer type and suggestive of pleiotropic effects across cancer types. Some of these observations may also be due to chance and/or to the wide adoption of variant/gene selection approaches based on known candidate variants, genes, or pathways. Indeed, when restricting the analysis to the articles that used a fully agnostic analysis approach, we observed a decrease in the relative frequency of reporting of these genes and an increase in relative frequency for less established genes. This observation may illustrate that more novel genes could be discovered by using a more expansive analysis approach. In addition, we found that 43 variants (Table 4) were each identified in two or more articles across cancer types. The identified variants/genes accounted on average for 25% of the families evaluated in both discovery and replication, suggesting that the fraction of families explained by the genes identified through exome/genome-wide sequencing may have increased since previous linkage analysis and candidate gene sequencing results (10-25% of families depending on the cancer type (223)). The results collectively show that important progress has been made in the identification of cancer susceptibility genes and that pleiotropy is a common phenomenon in genetic cancer susceptibility. Nevertheless, the progress made to-date is not without caveats.

Challenges limiting progress in variant/gene identification

The present review reveals scientific gaps and challenges in the body of literature. Of note, many (especially rare) cancer types remain understudied (or under published) and over 75% of cancer-prone families remain unexplained. While the lack of identification of mutations for cancer in heavily loaded families could reflect a polygenic or omnigenic architecture in these families, several additional challenges may have limited further progress in identifying germline variants associated with cancer, as indicated by the suspected publication bias (95% articles reported positive findings) and by the limited number of articles identified (only 186 articles across ten years and all cancer types). First, through careful review of the literature, we observed variation in study design and case selection, even within studies. For example, many of the studies included in this review used familial cases in the discovery phase before switching to unselected cases in the replication phase (perhaps due to a lack of additional familial samples or funds), which may introduce etiological heterogeneity (e.g., familial cases may carry different and/or more penetrant variants/genes than unselected cases) and may in part explain the lack of replication for some of the reviewed studies. In addition, including suitable control populations is important to ascertain magnitude of risk, whereas the frequency of the identified variants in such controls was reported only in one third of the articles. Second, focusing exclusively on the exome (only 10 of the articles were WGS) may be a limitation in complex trait genetics for which noncoding genetic variation is believed to play a larger role than in Mendelian genetics (224, 225) – a hypothesis that still needs to be verified for rare variants specifically. A third aspect that may have limited progress is the widespread use of candidate analysis approaches that focus the discovery analysis on known variants or genes or pathways by leveraging relevant existing information to select the resulting variants/genes. Although articles that used a candidate sequencing approach were excluded, the use of candidate analysis approaches was reported in 65% of the reviewed articles. These challenges (lack of genome-wide and agnostic studies) may be due to the fact that researchers do not yet have the tools to examine agnostically the whole exome or genome effectively. Alternatively, the researchers may have had specific reasons to focus on candidate regions of interest. Whatever the origin, an important consequence should be acknowledged: much of the human exome (and genome) remains unexplored or untested for cancer. Lastly, authors frequently stated a need for additional research to replicate their findings in larger and more homogeneous (e.g., by race/ethnicity or cancer histology) study populations. Indeed, although the majority of the reviewed articles used in the discovery phase a familial study design (which does not require cancer case numbers as large as unselected case-control study designs), 39% of articles exome/genome sequenced only a single family and 26% only a single member per family. Increasing the number of sequenced families and of cancer cases within each family may be an important avenue for future studies.

Technical considerations

From a technical point of view, the differences in utilization of the various technologies are dependent on the timing of their development and subsequent replacement by the next capture kit or sequencer version. We showed that the reviewed articles reported a technical validation rate of about 80% for studies that tested over fifty variants versus over 90% for studies that tested fewer variants; the difference may be due to pre-validation manual inspection steps (e.g., through IGV) being more feasibly applied to a limited number of variants. This observation suggests the importance for researchers using current sequencing technologies at a genome-wide scale to technically validate any observed variants. Another technical limitation stems from within study aggregation of samples across multiple sequencing experiments, as this approach can generate biases in variant detection and false positives/negatives in variant-cancer associations, particularly for WES datasets which vary also in capture efficiency. Several strategies to control for biological and technical heterogeneity and to minimize calling discordance and erroneous findings were described in the reviewed articles, including checking for comparable depths and rare variants detection and performing alignment and variant calling of all samples simultaneously. One notable point, reinforced by the observed lack of data sharing for over 80% of the reviewed articles, is the importance of saving, storing and being able to access BAM or CRAM files of the studied datasets for the wider research community, both for publicly and privately sponsored datasets. Advancements in long range sequencing and other new technologies may also help address some of the described technical shortcomings in the future and influence near term approaches to genomic analyses (226).

Importance of functional validation

Finally, most reviewed articles acknowledged the importance of functional validation (e.g., through in vitro and in vivo models) to determine whether the function of the mutated gene product is consistent with the cancer of interest and to inform the interpretation of the reported findings. Even though 65% of the articles did attempt some type of experimental validation for the final selection of variants/genes, the reported functional results were usually not considered definitive by the authors. In fact, most articles described the need for additional functional studies to determine whether the identified genes or variants play a causal role in carcinogenesis and to describe the mechanisms for these variants to impact disease.

Limitation and strengths of this literature review

Limitations of our literature review include: the lack of access to primary data, and consequent inability to systematically evaluate how different filtering choices would lead to different results in these studies; likely publication bias towards non-null results; use of the authors definition of “identified gene or variant” which varied greatly across the reviewed articles; the exclusion of articles in which only a single cancer case was sequenced, which although usually case reports, can also in principle lead to the identification of novel cancer susceptibility genes (e.g., PALB2 (227), or NPAT (228)). Strengths of our review are the systematic inclusion/exclusion approach, the comprehensive key term search, and thorough data abstraction.

Conclusions

In conclusion, the findings from this review indicate a growth in usage of NGS technologies at the exome/genome scale to identify genes associated with cancer risk. Nevertheless, progress has been limited by a range of challenges inherent in the field. The review highlights several important next steps including establishing consensus on standards for use and reporting of filtering strategies, describing rationale for variant identification, developing analytical methods that truly mine the whole exome/genome, improving the accuracy and cross-studies interoperability of current sequencing technologies, sharing of the primary data with the research community, and performing extensive variant functional validation. It also points to the untapped potential in conducting studies with more/larger families and in more diverse populations and cancers types, harmonizing results across studies, and expanding searches beyond a candidate analysis approach.