Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Integrative genomic analyses of APOBEC-mutational signature, expression and germline deletion of APOBEC3 genes, and immunogenicity in multiple cancer types

BMC Medical Genomics volume 12, Article number: 131 (2019) Cite this article

Abstract

Background

Although APOBEC-mutational signature is found in tumor tissues of multiple cancers, how a common germline APOBEC3A/B deletion affects the mutational signature remains unclear.

Methods

Using data from 10 cancer types generated as part of TCGA, we performed integrative genomic and association analyses to assess inter-relationship of expressions for isoforms APOBEC3A and APOBEC3B, APOBEC-mutational signature, germline APOBEC3A/B deletions, neoantigen loads, and tumor infiltration lymphocytes (TILs).

Results

We found that expression level of the isoform uc011aoc transcribed from the APOBEC3A/B chimera was associated with a greater burden of APOBEC-mutational signature only in breast cancer, while germline APOBEC3A/B deletion led to an increased expression level of uc011aoc in multiple cancer types. Furthermore, we found that the deletion was associated with elevated APOBEC-mutational signature, neoantigen loads and relative composition of T cells (CD8+) in TILs only in breast cancer. Additionally, we also found that APOBEC-mutational signature significantly contributed to neoantigen loads and certain immune cell abundances in TILs across cancer types.

Conclusions

These findings reveal new insights into understanding the genetic, biological and immunological mechanisms through which APOBEC genes may be involved in carcinogenesis, and provide potential genetic biomarker for the development of disease prevention and cancer immunotherapy.

Peer Review reports

Background

Somatic mutations are one of the most common causes of carcinogenesis. Recent studies have revealed that somatic mutations can be characterized by several distinct patterns, termed mutational signature. A particular signature mutation has been found to be driven by a sub-family of the human APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) gene. Kataegis, a phenomenon described cluster mutation along genome, are associated with APOBEC enzymatic activity [1]. The APOBEC-mediated mutagenesis, or mutational signature, substantially contribute to the overall mutation burden in the spectrum of human cancers, especially in bladder and breast cancers [2,3,4,5,6,7,8,9,10,11,12]. Two members of the gene family, APOBEC3A and APOBEC3B, have been known to play a role in inducing APOBEC-mutational signature [4, 9, 11, 13]. The APOBEC cytidine deaminase C, within the TCW trinucleotide motif that frequently changes to T or G mutations, have been ubiquitously observed in human cancer [1, 9]. A common germline deletion covering the last intronic of APOBEC3A to the last exon of APOBEC3B (called APOBEC3A/B) is found to increase APOBEC-mutational signature in breast cancer, as a similar pattern driven by the elevated APOBEC3A or APOBEC3B expressions [3]. A further study has shown that a fusion protein that is generated by germline APOBEC3A/B deletion has a higher expression level than the APOBEC3A protein [2]. Previous genome-wide association studies (GWAS) have shown that germline APOBEC3A/B deletion is not associated with breast cancer risk in several studies in European populations [14,15,16,17,18]. However, the association of the deletion with breast cancer risk in European populations has been observed in other studies [19, 20]. In our recent work, using the GWAS identified single nucleotide polymorphisms (SNPs) from The Breast Cancer Association Consortium (BCAC) [21], we found that a risk allele of the GWAS-identified SNP rs12628403 is significantly associated with a decreased expression of the APOBEC3B gene, supporting that germline APOBEC3A/B deletion contributes to breast cancer risk in European populations [22]. In Asian populations, all previous studies, together with our own work, have shown that this germline APOBEC3A/B deletion increased breast cancer risk [23,24,25]. In contrast to the findings in breast cancer, the germline APOBEC3A/B deletion was not observed in other cancer types with known enriched APOBEC-mutational signature, such as bladder cancer [11]. The underlying mechanism for why the specific association occurs only with breast cancer remains unclear.

It is known that neoantigens (or neoepitopes) arise from missense somatic mutations in cancer cells [26]. Neoantigens presented on the cell surface in the context of a major histocompatibility complex (MHC) of tumor tissues could be recognized by T cells as foreign antigens [27]. In a tumor microenvironment, a significant proportion of Tumor-infiltrating lymphocytes (TILs) that are comprised of immune cells, primarily from CD8+ cytotoxic T-cells (CTLs), has been observed in many cancer types, including breast cancer [28]. Thus, we hypothesized that APOBEC-mutational signature may affect cancer immunogenic abilities, such as attracting immune cells in TILs, which is likely mediated by affecting neoantigens. Recent studies have investigated the differentially-expressed genes between samples that were predicted to carry germline APOBEC3A/B deletion and samples that were predicted to have no deletion. The studies have shown that, based on gene set enrichment analyses, these differentially expressed genes significantly enriched the function of immune activation [25, 29]. Another study recently showed that APOBEC mutational signature was significantly correlated with immunotherapy response in non-small cell lung cancer [30]. However, the influence of APOBEC-mutational signature and germline APOBEC3A/B deletion on neoantigens and TILs remains largely unexplored, especially in pan-cancer studies.

The Cancer Genome Atlas (TCGA) has generated massive quantities of high-dimensional genetic and genomic data, including whole genome and exome sequencing, array-based genotypes, and RNA sequencing data, for many cancer types. The data resource provides an unprecedented opportunity to investigate APOBEC-mutational signature and immunogenicity in relation to APOBEC expression and germline APOBEC3A/B deletion. In particular, how APOBEC-mutational signature are affected by the individual isoforms of APOBEC3A and APOBEC3B still remain largely unexplored. Especially, the analysis challenge of the complexity of alterative splicing has been related to the isoform uc011aoc, transcribed from an APOBEC3A/B chimera that is primarily generated by germline APOBEC3A/B deletion. In this study, using data generated from approximately 4000 tumor samples across 10 cancer types from TCGA, we performed integrative genomic and association analyses of the isoform expressions of APOBEC3A and APOBEC3B, APOBEC-mutational signature, germline APOBEC3A/B deletion, neoantigen loads and TILs.

Methods

Data resources

This study utilized the sample resources from TCGA, including a total of 3937 samples with gene expression and somatic mutations data generated for 10 cancer types: bladder (N = 388), breast (N = 961), cervical (N = 185), lung adenocarcinoma (N = 475), lung squamous carcinoma (N = 178), head and neck (N = 498), stomach (N = 368), pancreas (N = 119), thyroid (N = 485) and kidney (N = 280). We included these cancer types for the investigation as they are known to have a significant enrichment of APOBEC-mutational signature [1].

The measurement of isoform expression for APOBEC3A and APOBEC3B

We downloaded the human gene annotation from the Table Browser of the UCSC genome browser (https://genome.ucsc.edu/cgi-bin/hgTables). The six isoforms were investigated, including uc003awn, uc011aob, uc011aoc, uc003awo, uc003awp and uc003awq, transcribed from the genes APOBEC3A and APOBEC3B. The normalized expression levels for each isoform in tumor tissue samples have been measured using RNA-Seq by Expectation Maximization (RSEM) by the group of GDAC. A log2 transform of the RSEM values was applied to fit a better distribution for the downstream analysis.

The data processing of APOBEC-mutational signature

The APOBEC-mutational signature was measured by the total count of TCWs changing to either TTW or TGW mutations from the signature mutation analysis by GDAC [1, 9]. The proportion of APOBEC-mutational signature relative to total mutations was measured as “[tCw_to_G + tCw_to_T]_per_mut”, in the column marked “Mutsig_maf_modified.maf_sorted_sum_all_fisher_Pcorr.txt” in the file. The number of total mutations for each sample was also extracted from the column “mutations” in the file. The number of APOBEC-mutational signature was also extracted from the column “tCw_to_G + tCw_to_T”. We applied a log2 transform to the number of APOBEC-mutational signature to fit a better distribution for the downstream analysis.

The identification of germline APOBEC3A/B deletion

We downloaded the germline deletion data for the samples in TCGA using the whole genome sequencing and whole exome sequencing data from a previous study [3]. The germline deletions for additional samples that did not have sequencing data were identified using array-based genotype data. In our recent deep whole genome sequencing project involving breast cancer, we identified a ~ 29 kb common germline deletion covering from the last exon of the APOBEC3A gene to the last exon of the APOBEC3B gene [31]. The deletion breakpoint was localized on chromosome 22 between 39,358,340 and 39,388,452 (hg19). Using the coordinates of the deletion breakpoint as a reference, we examined the segmented copy number data within the region to determine germline APOBEC3A/B deletion for samples. A median value of segmented copy number signals within the reference region was used to infer the deletion in the APOBEC3A/B gene. Homozygous deletion, heterozygous deletion and diploids were identified using the cutoff values of <− 1, − 0.2 and 0, respectively. For samples that predicted a non-carrying deletion, we additionally filtered samples with < 50 probes (corresponding to the number of segmented copy number signals) that overlapped with the deletion regions. We observed that few samples predicted carrying deletion with a relatively high expression level of the APOBEC3B gene. As such, we filtered the samples that predicted carrying deletion with expression levels of the APOBEC3B gene higher than the median value in samples for each cancer type. Specifically, we removed samples predicted to carry heterozygous deletion in bladder (N = 9), breast (N = 36), cervical (N = 2), lung adenocarcinoma (N = 12), lung squamous carcinoma (N = 1), head and neck (N = 9), stomach (N = 3), pancreas (N = 1), thyroid (N = 8), and kidney (N = 5), while no samples predicted to carry homozygous deletion were filtered. Additionally, somatic copy number alterations for breast cancer were downloaded from the cBioPortal (http://www.cbioportal.org/). The somatic copy number alterations for the APOBEC3A and APOBEC3B genes were extracted from the copy number data. We removed samples identified with somatic copy number alterations in our downstream analysis. Specifically, we removed samples predicted to have somatic APOBEC3A or APOBEC3B copy number alterations in bladder (N = 46), breast (N = 86), cervical (N = 22), lung adenocarcinoma (N = 62), lung squamous carcinoma (N = 77), head and neck (N = 103), stomach (N = 4), pancreas (N = 4), thyroid (N = 1), and kidney (N = 1). In the end, we analyzed a total of 2556 samples that were reliably predicted to carry deletions or to have no deletions in the bladder (N = 286), breast (N = 734), cervical (N = 133), lung adenocarcinoma (N = 363), lung squamous carcinoma (N = 85), head and neck (N = 323), stomach (N = 66), pancreas (N = 90), thyroid (N = 333), and kidney (N = 143).

The analysis of prediction neoantigen loads

Neoantigen load for each sample in TCGA has been characterized by The Cancer Immunome Atlas (TCIA, https://tcia.at/). Charoentong and colleagues characterized the genome-wide Neoantigen landscape for each sample by analyzing RNA-sequencing and whole-exome data from TCGA. In brief, mutational neoantigens were predicted by the use of HLA typing and MHC class I/II binding capabilities. The established neoantigen prediction algorithm NetMHCcons [32] was applied to missense somatic mutations to estimate their binding affinity to the HLA alleles. More detailed analysis processing has been described in previous literature [33]. We downloaded the number of neoantigen loads for each sample from TCIA and applied log2 transfer to fit a better distribution.

Prediction of the abundance of relative immune cell compositions in TILs

To predict the abundance of immune cell compositions in TIL, we used the normalized expression data for each cancer type from GDAC. We applied Cell Type Identification by Estimating Relative Subsets of known RNA Transcripts (CIBERSORT) to estimate the abundance of each of the 22 immune cell types (i.e. T cells CD4 naive, T cells CD4 memory activated and Tregs) in each tumor tissue sample, based on the normalized expression data of 547 genes [34]. This analysis was implemented in a Stanford University server (https://cibersort.stanford.edu/). Only samples inferred with an abundance of immune cell compositions in TILs at P < 0.2 remained for the downstream analysis, as recommended by previous literature [34].

Pathway enrichment analysis

To identify genes co-expressed with the isoform uc011aoc, we performed a correlation analysis using Rank-based Spearman approach for samples predicted to carry with germline APOBEC3A/B deletions for each cancer type. We further performed functional enrichment analysis for the top 100 correlated genes using the Ingenuity Pathway Analysis (IPA) tool (http://www.ingenuity.com/). The top five significant canonical signaling pathways were presented.

Statistical analysis

We first applied the univariate analyses to evaluate the association of APOBEC-mutational signature with expression levels of APOBEC3A and APOBEC3B genes, and their isoforms. We then include all six isoforms as independent variables in the same models for mutual adjustment for each cancer type. Because the distribution of APOBEC-signature mutation is severely right skewed, the ordinal regression models implemented in the ‘orm’ function from the ‘rms’ library of the R package were used. To elucidate whether germline APOBEC3A/B deletion affects APOBEC-signature mutation, the above models were constructed as following: APOBEC-mutational signature ~ germline APOBEC3A/B deletion; APOBEC-mutational signature ~ germline APOBEC3A/B deletion + uc003awn + uc003awo + uc011aoc (Expression levels); proportion of APOBEC-mutational signature ~ germline APOBEC3A/B deletion; and proportion of APOBEC-mutational signature ~ germline APOBEC3A/B deletion + uc003awn + uc003awo + uc011aoc (Expression levels). To investigate the effects of germline APOBEC3A/B deletion on neoantigen loads and immune cell compositions in TILs, linear regression analyses were conducted by cancer type. Additionally, we used the Wilcoxon signed-rank test to compare the differences of immune cell compositions between the samples with and without germline APOBEC3A/B deletion Finally, the association between APOBEC-signature mutation and neoantigen load was analyzed with univariate linear regression models for each cancer type. All statistical analyses were conducted using the R software.

Results

Distinct patterns of the APOBEC3 genes, associated with APOBEC-mutational signature in multiple cancer types

Following the previous analysis of APOBEC-mutational signature, we measured the mutations using the number of deaminase C that were within the TCW trinucleotide motif change to T or G mutations per each sample across 10 cancer types [1, 9] (see Methods). We conducted univariate analyses to evaluate associations of APOBEC-mutational signature with the overall gene expression levels of APOBEC3A and APOBEC3B. We observed that APOBEC3A expression level was positively associated with APOBEC-mutational signature in a total of six cancer types – bladder, breast, cervical, lung adenocarcinoma, head and neck, and thyroid. No significant associations were observed in the remaining cancer types, although the same association directions were observed (Additional file 1: Table S1). Interestingly, we observed that APOBEC3B expression level was positively associated with APOBEC-mutational signature in all cancer types, except for lung squamous carcinoma (Additional file 1: Table S1). In comparison with the associations from APOBEC3A, APOBEC3B was specifically associated with APOBEC-mutational signature in stomach, pancreas and kidney cancers, with a P = 5.2 × 10− 11, P = 2.0 × 10− 3, and P = 1.1 × 10− 4, respectively (Additional file 1: Table S1). These findings were in line with previous studies [4, 9, 11, 13]. In addition, we also evaluated the associations of APOBEC-mutational signature with the gene expression of other APOBEC3 genes: APOBEC3C, APOBEC3D, APOBEC3F, APOBEC3G, and APOBEC3H. Our results showed that expressions of these genes were associated with APOBEC-mutational signature varied in distinct cancer types (Additional file 1: Table S2). For example, the expression APOBEC3C was associated with increased APOBEC-mutational signature in cervical and head and neck cancers, while its expression was associated with decreased APOBEC-mutational signature in stomach cancer. Our result also showed that the expression APOBEC3D, APOBEC3F, and APOBEC3G were associated with increased APOBEC-mutational signature, whereas the result from the association of APOBEC3G was in line with the previous finding [35]. Interestingly, we observed that expression APOBEC3H was associated with increased APOBEC-mutational signature in cervical, but not observed in breast cancer.

Distinct patterns of the isoforms of APOBEC3A and APOBEC3B, associated with APOBEC-mutational signature in multiple cancer types

We further evaluated associations between APOBEC-mutational signature and expression levels of each of the isoforms transcribed from APOBEC3A and APOBEC3B. A total of six isoforms were analyzed, including uc003awn and uc011aob transcribed from APOBEC3A, uc003awo, uc003awp and uc003awq transcribed from APOBEC3B, and another isoform, uc011aoc, derived from a fusion event involved in a region covering the last intronic of APOBEC3A to the last exon of APOBEC3B (Fig. 1a, b). We confirmed that the isoforms uc003awn and uc003awo were primarily transcribed from APOBEC3A and APOBEC3B, respectively (Additional file 1: Figure S1) [11]. As expected, the association direction of both the uc003awn and uc003awo expression levels and APOBEC-mutational signature were consistent with the observations of overall gene expression levels across all cancer types (Fig. 1c, d; Additional file 1: Table S3). Surprisingly, the expression level of uc011aoc (APOBEC3A/B) was positively associated with the APOBEC-mutational signature only in breast cancer (P = 5.0 × 10− 10) (Fig. 1e; Additional file 1: Table S3). However, no associations for the remaining isoforms were observed in any cancer types, except for a weak association observed for uc011aob (APOBEC3A) with head and neck cancer (P = 0.02; Additional file 1: Table S3).

Fig. 1
figure 1

Distinct patterns of the isoforms of APOBEC3A and APOBEC3B associated with APOBEC-mutational signature. a Two isoforms, uc003awn and uc011aob, transcribed from APOBEC3A, and three isoforms, uc003awo, uc003awp and uc003awq, transcribed from APOBEC3B. b Isoform uc011aoc derived from a fusion event covering from the last intronic of APOBEC3A to the last exon of APOBEC3B. c, d, e Distribution plots indicate the association between APOBEC-mutational signature and isoforms, uc003awn (c), uc003awo (d) and uc011aoc (e)

Full size image

Using multiple regression analyses that included all isoforms of both APOBEC3A and APOBEC3B, we found that the expression levels of uc003awn (APOBEC3A) and uc003awo (APOBEC3B) were independently and commonly associated with evaluated APOBEC-mutational signature in multiple cancer types: bladder (a marginal association for uc003awn), breast, cervical, lung adenocarcinoma, and head and neck (Table 1). Likewise, an additional association for uc011awn (APOBEC3A) was observed in thyroid, while associations for uc003awo (APOBEC3B) were observed in stomach, pancreas and kidney cancers. Consistent with the univariate analysis, a striking association for uc011aoc (APOBEC3A/B) with APOBEC-mutational signature was observed in breast cancer (P = 5.3 × 10− 11), and an additional association was also observed in lung adenocarcinoma (P = 0.01) (Table 1). These findings suggest that uc011aoc plays a tissue-specific role in affecting APOBEC-mutational signature primarily in breast cancer, while uc003awn and uc003awo play a ubiquitous but distinct role in the entire spectrum of human cancer. Notably, our additional analysis showed the positive expression correlation between the isoform uc011aoc and APOBEC3A across a major of cancer types, especially in breast cancer (Additional file 1: Table S4).

Table 1 Associations between APOBEC-mutational signature and isoform expression levels of APOBEC3A and APOBEC3B
Full size table

In addition, we performed an association analysis stratified by clinical subtypes in breast cancer. We observed that the associations of the isoforms of uc003awn (APOBEC3A) and uc011aoc (APOBEC3A/B) with APOBEC-mutational signature varied across different clinical subtypes, with the most significant association being observed in the LumA subtype (Additional file 1: Table S5).

Germline APOBEC3A/B deletion affecting expression levels of isoforms of the APOBEC3A and APOBEC3B genes

To investigate how germline APOBEC3A/B deletion affects expression of the isoforms of APOBEC3A and APOBEC3B, we first identified 30 samples predicted to carry homozygous deletion, 239 samples predicted to carry heterozygous deletion and 2287 samples predicted to have no deletion (Additional file 1: Table S6, see Methods). Next, we evaluated associations between germline APOBEC3A/B deletion and the expression levels of each isoform using univariate analysis (see Methods). As expected, we observed that germline APOBEC3A/B deletion was significantly associated with decreased expression levels of the isoform uc003awo (APOBEC3B) across all cancer types at P < 0.05, except for pancreas cancer with a P = 0.14. Significant associations with decreased expression levels of uc003awn (APOBEC3A) were also observed in three cancer types – bladder, breast and thyroid (Fig. 2; Table 2). In contrast, our results showed that germline APOBEC3A/B deletion was significantly associated with an increased expression level of uc011aoc (APOBEC3A/B) across all cancer types, except for stomach, pancreas and kidney cancers. For these, there was no statistical significance but had the same association directions (Fig. 2; Table 2). In particular, head and neck cancer showed the most significant association with P = 3.8 × 10− 65, and breast and bladder cancers showed the significant association with P = 3.0 × 10− 8, and P = 2.9 × 10− 7, respectively. Additionally, we performed the same analysis, stratified by population, and a similar trend was observed in these cancer types (data not shown). Our findings suggest that, in almost all investigated cancer types, germline APOBEC3A/B deletion was significantly associated with decreased expression levels of uc003awn and uc003awo, but there was an increased expression level of uc011aoc.

Fig. 2
figure 2

Associations between the isoform expression levels of APOBEC3A and APOBEC3B and germline APOBEC3A/B deletion. a Isoform uc011aoc transcribed from a fusion region derived from a germline deletion covering from the last intronic of APOBEC3A to the last exon of APOBEC3B. b, c, d Box plots indicate expression levels of isoforms, uc003awn (b), uc003awo (c) and uc011aoc (d), for samples that are predicted to carry homozygous and heterozygous deletion and have no deletion for each cancer type

Full size image
Table 2 Associations between isoform expression levels of APOBEC3A and APOBEC3B and germline APOBEC3A/B deletion
Full size table

Germline APOBEC3A/B deletion influencing APOBEC-mutational signature, neoantigen loads and relative immune cell compositions, specifically in breast cancer

A previous study showed that germline APOBEC3A/B deletion is associated with increased APOBEC-mutational signature in breast cancer, while a similar pattern, but without statistical significance, was observed in many other cancer types, such as bladder [3]. Using univariate analysis to evaluate the overall effects of germline APOBEC3A/B deletion on APOBEC-mutational signature, we found that the deletion was significantly associated with increased APOBEC-mutational signature only in breast cancer (P = 5.6 × 10− 3; Table 3; Fig. 3a). However, an opposite trend was observed in most other cancer types, although most associations were not statistically significant (Table 3). Specifically, in bladder cancer, we observed that germline APOBEC3A/B deletion was significantly associated with decreased APOBEC-mutational signature (P = 1.7 × 10− 3; Table 3). To further elucidate whether the influence of germline APOBEC3A/B deletion on APOBEC-mutational signature is due to its effect on gene expression, we further evaluated associations between APOBEC-mutational signature and the deletion with an adjustment for the expression of isoforms (see Methods). Similarly, our results revealed that germline APOBEC3A/B deletion was significantly associated with increased APOBEC-mutational signature only in breast cancer (P = 2.8 × 10− 6; Table 3; Fig. 3b). A higher effect size (Beta = − 0.620) of the deletion with an adjusted isoform expression was observed when compared to the initial observation of the overall effect (Beta = − 0.281; Table 3). To evaluate whether the germline deletion may contribute to the proportion of APOBEC-mutational signature, we also analyzed a proportion of APOBEC-mutational signature relative to total mutations for each sample (see Methods). Consistent with the initial observation of APOBEC-mutational signature, we observed that the germline deletion was significantly associated with increased proportion of APOBEC-mutational signature only in breast cancer (Beta = − 0.287, P = 4.8 × 10− 3 and Beta = − 0.5, P = 1.4 × 10− 4 for the overall effect and effects with adjusted gene expression; see Table 3). These results indicate that germline APOBEC3A/B deletion, which leads to increased APOBEC-mutational signature, is likely due to it’s the distinct function of uc011aoc transcribed from the deletion, apart from its effect on increased expressions of uc011aoc. It has been reported that APOBEC3H haplotype I (APOBEC3H-I) may majorly contribute to APOBEC-mutational signature for samples carrying germline APOBEC3A/B deletions in breast cancer [36]. We further analyzed the APOBEC3H-I haplotype for a total of 76 samples that were predicted to carry germline APOBEC3A/B deletions (see Methods). Our results showed that APOBEC-mutational signature was not significantly correlated with the APOBEC3H-I haplotype, regardless of the samples predicted to carry homozygous or heterozygous germline APOBEC3A/B deletions (Data not shown). However, our findings are in line with the previous finding that the APOBEC3A/B protein, generated by the deletion, has a higher expression level than the APOBEC3A protein based on the investigation from in vitro functional assays [2].

Table 3 Associations between APOBEC-mutational signature and germline APOBEC3A/B deletion
Full size table
Fig. 3
figure 3

Germline APOBEC3A/B deletion associated with APOBEC-mutational signature, neoantigen loads and relative composition of T cells (CD8+) in breast cancer. A significantly higher absolute (a) and relative (b) APOBEC-mutational signature, neoantigen loads (c) and relative composition of T cells (CD8+) (d) in samples predicted to carry homozygous and heterozygous deletion compared to samples predicted to have no deletion in breast cancer

Full size image

To further explore the distinct function and potential pathways that the isoform uc011aoc expression (transcribed from the APOBEC3A/B deletion) may be involved in, we analyzed genes that were co-expressed with the isoform uc011aoc in the samples predicted to carry germline APOBEC3A/B deletions (see Methods). An enrichment analysis in canonical signaling pathways using IPA revealed that these co-expressed genes were significantly enriched in distinct canonical pathways varied across cancer types. Specifically, we observed that the top enriched pathways were HIPPO signaling for bladder, PTEN signaling for breast, iNOS and Interferon signaling for lung adenocarcinoma, Acyl-CoA Hydrolysis for stomach, and DNA Double-Strand Break Repair and Fatty Acid α-oxidation for pancreas, EIF2 signaling for thyroid and GPCR-Mediated Integration for kidney (P < 0.01 for all; Additional file 1: Table S7). Additionally, a functional enrichment in Cell Death and Survival was commonly observed in multiple cancer types including bladder, breast, cervical, lung adenocarcinoma, and lung carcinoma (P < 0.05 for all).

We further evaluated the association between germline APOBEC3A/B deletion and neoantigen loads. Consistent with the observation of APOBEC-mutational signature, our results showed that germline APOBEC3A/B deletion was significantly associated with increased neoantigen loads only in breast cancer (P = 6.5 × 10− 3; Fig. 3c), while an opposite trend was observed in many other cancer types (Additional file 1: Table S8). Similarly, we found that the germline deletion was marginally associated with the relative abundance of the composition of T cells (CD8+) in TILs, but only in breast cancer (P = 0.08; Fig. 3d). The significant association was detected when we combed samples with both homozygous and heterozygous deletions and compared them to the samples with non-carrying deletions (a Wilcoxon signed-rank test, P < 0.05). However, no association was observed for other immune cells. Our findings showed that germline APOBEC3A/B deletion plays a tissue-specific role in affecting APOBEC-mutational signature and immunogenicity in breast cancer, likely reinforcing the findings in previous genome wide association studies of potential mechanisms for their association with increased breast cancer risk.

APOBEC-mutational signature significantly contributing to neoantigens

To investigate to what extent APOBEC-mutational signature contribute to neoantigens, we analyzed predicted neoantigen loads for each sample collected from a previous study [33] (see Methods). Using univariate analysis, we evaluated associations between APOBEC-mutational signature and neoantigen loads for each cancer type. As expected, APOBEC-mutational signature was positively associated with neoantigen loads in all cancer types (P < 1.0 × 10− 4 for all comparisons), whereas the top significant associations were observed in breast and bladder types with P = 5.1 × 10− 125, and P = 1.5 × 10− 90, respectively (Additional file 1: Table S9). Similarly, an overall positive association trend was observed between predicted neoantigen loads and proportion of APOBEC-mutational signature, with the exception of stomach cancer (Fig. 4; Additional file 1: Table S10). Specifically, the significant associations were observed in multiple cancer types, including bladder, breast, cervical, lung adenocarcinoma, head and neck, and thyroid. In particular, breast and bladder cancer showed the best associations with P = 8.9 × 10− 29, and P = 2.8 × 10− 27, respectively (Additional file 1: Table S10). Our findings suggest that APOBEC-mutational signature play a significant role in contributing to the biogenesis of neoantigens in human cancer.

Fig. 4
figure 4

Associations between predicted neoantigen loads and proportion of APOBEC-mutational signature

Full size image

Associations between relative abundance of immune cell compositions in TILs with neoantigen load and APOBEC-mutational signature

To investigate the relationship between neoantigen load and TILs, we used gene expression data in tumor tissues to measure the abundance of relative cell compositions of each immune cell type, including B cell naïve, B cell memory, T cell CD8 and T cells CD4 memory-activated in TILs (see Methods). Using univariate analysis, we evaluated the association between neoantigen load and the relative abundance of immune cell compositions for each cancer type. We observed that there was a positive association trend between neoantigen loads and both T cell CD8+ and CD4+ memory-activated types in all cancer types except thyroid and kidney (Binomial test P = 0.11 and P = 0.02 for T cell CD8+ and CD4+ memory-activated types, respectively). An opposite pattern was observed for both B cell naïve and memory types across all cancer types, with the exception of lung adenocarcinoma (Binomial test P = 0.02 and P = 2.2 × 10− 16 for B cell naïve and memory types, respectively). In particular, our results showed that neoantigen loads had an association with the relative abundances of both T cell CD8+ and CD4+ memory-activated, and B cell naïve and memory types in bladder cancer. There was an association for both B cell naïve and memory types in breast cancer, and T cell CD4 memory- activated cell types in lung adenocarcinoma, head and neck, and pancreas cancers (Additional file 1: Table S11). As APOBEC-mutational signature significantly contributed to neoantigens, we additionally evaluated the association between the relative abundances of the immune cell compositions in TILs and APOBEC-mutational signature. Consistent with the observation from association analysis of naeoantigen load, a similar pattern was also found for APOBEC-mutational signature (Additional file 1: Table S12). Our findings suggest that APOBEC-mutational signature have an influence on cancer immunogenic abilities, such as attracting certain immune cells in TILs, possibly mediated by the increased neoantigen load.

Discussion

In this pan-cancer study, we systematically analyzed APOBEC-mutational signature in relation to isoform expression and germline APOBEC3/B deletion. Our study showed that the isoforms uc003awn (APOBEC3A) and uc003awo (APOBEC3B) were independently associated with a higher burden of APOBEC-mutational signature in multiple cancer types, while such an association for the uc011aoc (APOBEC3A/B) was only observed in breast cancer. We also found that, across cancer types, the germline APOBEC3A/B deletion led to decreased expression levels of uc003awn and uc003awo but caused an increased expression level of uc011aoc. Furthermore, our results indicate that germline APOBEC3A/B deletion leading to increased APOBEC-mutational signature is likely due to a distinct function of the isoform uc011aoc transcribed from the APOBEC3A/B chimera, apart from its effect on an increased expression of uc011aoc. Our findings provide novel insight into understanding the APOBEC biological mechanisms involved in carcinogenesis.

The investigation of the relationship between germline APOBEC3A/B deletion and APOBEC-mutational signature has been well-studied. For example, Nik-Zainal and colleagues showed that the deletion was associated with increased APOBEC-mutational signature in breast cancer. They also concluded that this pattern may exist ubiquitously in other cancer types (e.g., bladder cancer) [3]. Their findings significantly contributed to the understanding of the association of germline deletion with breast cancer risk [20, 23]. Recently, Middlebrooks and colleagues analyzed the expression level of the APOBEC3A/B deletion isoform as a proxy to evaluate the association between germline APOBEC3A/B deletion and APOBEC-mutational signature in breast and bladder cancer. They suggested that the expression of the deletion isoform (uc011aoc) was associated with evaluated APOBEC-mutational signature in breast cancer, but not in bladder cancer [11]. In our study, we refined the analysis to identify samples predicted to carry deletions, and samples predicted to have no deletions, by integrating the deletion data from the sequencing data of a previous study [3] with an analysis of array-based genotype and gene expression data (see Methods). In comparison to previous analyses, we used a strategy to strictly control data quality and to filter samples with ambiguous deletion calling by introducing additional multiple datasets. In particular, we filtered a few samples that were predicted to carry a deletion with a relatively high expression level of the APOBEC3B gene (uc003awo). On the other hand, we performed multiple regression analyses that included all isoforms in order to evaluate uc011aoc (APOBEC3A/B) with APOBEC-mutational signature, to address the analysis challenge of introducing potential confounders due to the complexity of alternative splicing. Our results indicate that the expression level of uc011aoc derived from germline APOBEC3A/B deletion plays a tissue-specific functional influence on APOBEC-mutational signature in breast cancer. Notably, the expression level of the isoform uc011aoc in tumor tissues may not fully reflect the level in premalignant tissues due to possible factors, such as tumor heterogeneity and potential confounders. In addition, the expression level of the isoform uc011aoc was measured based on RNA-seq data. Future experiment using quantitative PCR (qPCR) is needed to further verify the expression level of isoform uc011aoc from RNA-seq data. Nevertheless, our results showed a strong association between the expression of the isoform and germline APOBEC3A/B deletion in breast and other cancer types, indicating the reliability of these findings. Consistent with this observation, our results further showed that germline APOBEC3A/B deletion led to increased APOBEC-mutational signature in breast cancer, providing an explanation that germline APOBEC3A/B deletion is associated with increased breast cancer risk. These findings provide new insight into the understanding of the deletion that is associated with increased breast cancer risk.

In the analysis of associations between isoforms and mutational signature, we only focused on APOBEC-signature mutation (TCW - > T/G). The background signatures should not affect our analysis, as they are in distinct mutation patterns (i.e. smoking-related mutational signature with A - > C) with APOBEC-mutational signature [6, 37, 38]. Although the statistical power varied across cancer types due to different sample sizes and different proportions of germline APOBEC3A/B deletion carriers, our result showed that the association of uc011aoc (APOBEC3A/B) with APOBEC-mutational signature observed in breast cancer was stood out, which was 1.6 ~ 5 times of the associations compared to other cancer types (Table 1).

Immunotherapies, such as the suppression of immune checkpoints (αPD-1, αPD-L1), have revolutionized the treatment of human cancers [39,40,41,42,43,44,45]. The immune checkpoints (i.e. PD-1, PD-L1 and CTLA-4) and other immune-related and mismatch repair (MMR) genes, together with TILs and neoantigens, play critical roles in anti-cancer immunoreactivity. They have been linked to immunotherapy outcomes. In particular, previous studies have shown that an overall mutation load is highly correlated with neoantigens and TILs [43, 45, 46]. A recent study showed that APOBEC-mutational signature was associated with the increasing of neo-peptide hydrophobicity [47]. In line with these findings, our findings suggest that APOBEC-mutational signature, in relation to expression and germline deletion of APOBEC genes, substantially contribute to neoantigens and, consequently, affect certain immune cell compositions in TILs. In addition, recent studies suggest that APOBEC plays an important role in promoting PD-1, as well as immune activation in multiple cancer types, implying its potential for cancer immunotherapy [25, 29, 30, 48]. Thus, our findings, together with other studies, highlight the importance of the APOBEC genes in immunogenicity and cancer immunotherapy.

Conclusions

In conclusion, our results showed that uc011aoc, primarily generated from germline APOBEC3A/B deletion, plays a tissue-specific role in promoting APOBEC-mutational signature. We further showed that germline APOBEC3A/B deletion influences APOBEC-mutational signature, neoantigen loads and the relative abundance of T cell (CD8+) composition, but only in breast cancer. These functional consequences of the germline deletion are likely due to a distinct function of the isoform uc011aoc transcribed from the APOBEC3A/B chimera, apart from its induced expression level. These findings provide potential mechanisms for understanding the association of germline APOBEC3A/B gene deletion with cancer risk. Our results also showed that APOBEC-mutational signature significantly contribute to neoantigens, and consequently attract certain immune cells in TILs ubiquitously observed in human cancer. This study provides novel insights into understanding the genetic, biological and immunological mechanisms through which APOBEC genes may be involved in carcinogenesis.

Availability of data and materials

Main source R codes that are used in this work are available from Github (https://github.com/XingyiGuo/APOBEC/tree-save/master/Associations/APOBEC-Sig). Completed data sets, which included expressions of genes (RNAseqv2, Level_3, RSEM_genes_normalized) and isoforms (RNAseqv2, Level_3, RSEM_isoforms_ normalized), APOBEC mutational signatures (Mutation_APOBEC, Level_4) and the segmented copy number variations (Merged, genome_wide_snp_6, Level_3, segmentation, hg19) were downloaded from the TCGA using the Broad Institute Genome Data Analysis Center (GDAC) Firehose portal through Firebrowse (stamp data/analyses__2016_01_28, http://gdac.broadinstitute.org). The human gene annotation data set was obtained from the Table Browser of the UCSC genome browser (https://genome.ucsc.edu/cgi-bin/hgTables). The six isoforms that were investigated include uc003awn, uc011aob, uc011aoc, uc003awo, uc003awp and uc003awq, transcribed from the genes APOBEC3A and APOBEC3B.

Abbreviations

APOBEC:

Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like

BCAC:

The Breast Cancer Association Consortium (BCAC)

CIBERSORT:

Estimating Relative Subsets of known RNA Transcripts

CTLs:

Cytotoxic T-cells

GWAS:

Genome-wide association studies

IPA:

Ingenuity Pathway Analysis (IPA)

MHC:

Major histocompatibility complex (MHC)

MMR:

Mismatch repair

RSEM:

RNA-Seq by Expectation Maximization

SNP:

Single nucleotide polymorphisms (SNPs)

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Immunome Atlas

TILs:

Tumor infiltration lymphocytes

References

  1. Nik-Zainal S, Alexandrov LB, Wedge DC, Van Loo P, Greenman CD, Raine K, et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012;149(5):979–93.

    Article  CAS  Google Scholar 

  2. Caval V, Suspene R, Shapira M, Vartanian JP, Wain-Hobson S. A prevalent cancer susceptibility APOBEC3A hybrid allele bearing APOBEC3B 3'UTR enhances chromosomal DNA damage. Nat Commun. 2014;5:5129.

    Article  CAS  Google Scholar 

  3. Nik-Zainal S, Wedge DC, Alexandrov LB, Petljak M, Butler AP, Bolli N, et al. Association of a germline copy number polymorphism of APOBEC3A and APOBEC3B with burden of putative APOBEC-dependent mutations in breast cancer. Nat Genet. 2014;46(5):487–91.

    Article  CAS  Google Scholar 

  4. Burns MB, Temiz NA, Harris RS. Evidence for APOBEC3B mutagenesis in multiple human cancers. Nat Genet. 2013;45(9):977–83.

    Article  CAS  Google Scholar 

  5. Burns MB, Leonard B, Harris RS. APOBEC3B: pathological consequences of an innate immune DNA mutator. Biom J. 2015;38(2):102–10.

    Google Scholar 

  6. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013;500(7463):415–21.

    Article  CAS  Google Scholar 

  7. Thibodeau SN, French AJ, McDonnell SK, Cheville J, Middha S, Tillmans L, et al. Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set. Nat Commun. 2015;6:8653.

    Article  CAS  Google Scholar 

  8. Kuong KJ, Loeb LA. APOBEC3B mutagenesis in cancer. Nat Genet. 2013;45(9):964–5.

    Article  CAS  Google Scholar 

  9. Roberts SA, Lawrence MS, Klimczak LJ, Grimm SA, Fargo D, Stojanov P, et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat Genet. 2013;45(9):970–6.

    Article  CAS  Google Scholar 

  10. Swanton C, McGranahan N, Starrett GJ, Harris RS. APOBEC enzymes: mutagenic fuel for Cancer evolution and heterogeneity. Cancer discovery. 2015;5(7):704–12.

    Article  CAS  Google Scholar 

  11. Middlebrooks CD, Banday AR, Matsuda K, Udquim KI, Onabajo OO, Paquin A, et al. Association of germline variants in the APOBEC3 region with cancer risk and enrichment with APOBEC-signature mutations in tumors. Nat Genet. 2016;48(11):1330–8.

    Article  CAS  Google Scholar 

  12. Taylor BJ, Nik-Zainal S, Wu YL, Stebbings LA, Raine K, Campbell PJ, et al. DNA deaminases induce break-associated mutation showers with implication of APOBEC3B and 3A in breast cancer kataegis. Elife. 2013;2:e00534.

    Article  Google Scholar 

  13. Burns MB, Lackey L, Carpenter MA, Rathore A, Land AM, Leonard B, et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature. 2013;494(7437):366–70.

    Article  CAS  Google Scholar 

  14. Revathidevi S, Manikandan M, Rao AK, Vinothkumar V, Arunkumar G, Rajkumar KS, et al. Analysis of APOBEC3A/3B germline deletion polymorphism in breast, cervical and oral cancers from South India and its impact on miRNA regulation. Tumour Biol. 2016;37(9):11983–90.

    Article  CAS  Google Scholar 

  15. Marouf C, Gohler S, Filho MI, Hajji O, Hemminki K, Nadifi S, et al. Analysis of functional germline variants in APOBEC3 and driver genes on breast cancer risk in Moroccan study population. BMC Cancer. 2016;16:165.

    Article  Google Scholar 

  16. Gohler S, Da Silva Filho MI, Johansson R, Enquist-Olsson K, Henriksson R, Hemminki K, et al. Impact of functional germline variants and a deletion polymorphism in APOBEC3A and APOBEC3B on breast cancer risk and survival in a Swedish study population. J Cancer Res Clin Oncol. 2016;142(1):273–6.

    Article  Google Scholar 

  17. Klonowska K, Kluzniak W, Rusak B, Jakubowska A, Ratajska M, Krawczynska N, et al. The 30 kb deletion in the APOBEC3 cluster decreases APOBEC3A and APOBEC3B expression and creates a transcriptionally active hybrid gene but does not associate with breast cancer in the European population. Oncotarget. 2017;8(44):76357–74.

    Article  Google Scholar 

  18. Gansmo LB, Romundstad P, Hveem K, Vatten L, Nik-Zainal S, Lonning PE, et al. APOBEC3A/B deletion polymorphism and cancer risk. Carcinogenesis. 2018;39(2):118–24.

    Article  CAS  Google Scholar 

  19. Han Y, Qi Q, He Q, Sun M, Wang S, Zhou G, et al. APOBEC3 deletion increases the risk of breast cancer: a meta-analysis. Oncotarget. 2016;7(46):74979–86.

    Article  Google Scholar 

  20. Xuan D, Li G, Cai Q, Deming-Halverson S, Shrubsole MJ, Shu XO, et al. APOBEC3 deletion polymorphism is associated with breast cancer risk among women of European ancestry. Carcinogenesis. 2013;34(10):2240–3.

    Article  CAS  Google Scholar 

  21. Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92–4.

    Article  Google Scholar 

  22. Guo X, Lin W, Bao J, Cai Q, Pan X, Bai M, et al. A comprehensive cis-eQTL analysis revealed target genes in breast Cancer susceptibility loci identified in genome-wide association studies. Am J Hum Genet. 2018;102(5):890–903.

    Article  CAS  Google Scholar 

  23. Long J, Delahanty RJ, Li G, Gao YT, Lu W, Cai Q, et al. A common deletion in the APOBEC3 genes and breast cancer risk. J Natl Cancer Inst. 2013;105(8):573–9.

    Article  CAS  Google Scholar 

  24. Rezaei M, Hashemi M, Hashemi SM, Mashhadi MA, Taheri M. APOBEC3 deletion is associated with breast Cancer risk in a sample of southeast Iranian population. Int J Mol Cell Med. 2015;4(2):103–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Wen WX, Soo JS, Kwan PY, Hong E, Khang TF, Mariapun S, et al. Germline APOBEC3B deletion is associated with breast cancer risk in an Asian multi-ethnic cohort and with immune cell presentation. Breast Cancer Res. 2016;18(1):56.

    Article  Google Scholar 

  26. Chen DS, Mellman I. Oncology meets immunology: the Cancer-immunity cycle. Immunity. 2013;39(1):1–10.

    Article  Google Scholar 

  27. Yarchoan M, Johnson BA 3rd, Lutz ER, Laheru DA, Jaffee EM. Targeting neoantigens to augment antitumour immunity. Nat Rev Cancer. 2017;17(4):209–22.

    Article  CAS  Google Scholar 

  28. Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, et al. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol. 2013;31(7):860–7.

    Article  CAS  Google Scholar 

  29. Cescon DW, Haibe-Kains B, Mak TW. APOBEC3B expression in breast cancer reflects cellular proliferation, while a deletion polymorphism is associated with immune activation. Proc Natl Acad Sci U S A. 2015;112(9):2841–6.

    Article  CAS  Google Scholar 

  30. Wang S, Jia M, He Z, Liu XS. APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer. Oncogene. 2018;37(29):3924–36.

    Article  CAS  Google Scholar 

  31. Guo X, Shi J, Cai Q, Shu XO, He J, Wen W, et al. Use of deep whole genome sequencing data to identify structure risk variants in breast cancer susceptibility genes. Hum Mol Genet. 2018;27(5):853–59.

    Article  CAS  Google Scholar 

  32. Karosiene E, Lundegaard C, Lund O, Nielsen M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics. 2012;64(3):177–86.

    Article  CAS  Google Scholar 

  33. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer Immunogenomic analyses reveal genotype-Immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18(1):248–62.

    Article  CAS  Google Scholar 

  34. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.

    Article  CAS  Google Scholar 

  35. Nowarski R, Wilner OI, Cheshin O, Shahar OD, Kenig E, Baraz L, et al. APOBEC3G enhances lymphoma cell radioresistance by promoting cytidine deaminase-dependent DNA repair. Blood. 2012;120(2):366–75.

    Article  CAS  Google Scholar 

  36. Starrett GJ, Luengas EM, McCann JL, Ebrahimi D, Temiz NA, Love RP, et al. The DNA cytosine deaminase APOBEC3H haplotype I likely contributes to breast and lung cancer mutagenesis. Nat Commun. 2016;7:12918.

    Article  CAS  Google Scholar 

  37. Alexandrov LB, Ju YS, Haase K, Van Loo P, Martincorena I, Nik-Zainal S, et al. Mutational signatures associated with tobacco smoking in human cancer. Science. 2016;354(6312):618–22.

    Article  CAS  Google Scholar 

  38. Helleday T, Eshtad S, Nik-Zainal S. Mechanisms underlying mutational signatures in human cancers. Nat Rev Genet. 2014;15(9):585–98.

    Article  CAS  Google Scholar 

  39. Blankenstein T, Leisegang M, Uckert W, Schreiber H. Targeting cancer-specific mutations by T cell receptor gene therapy. Curr Opin Immunol. 2015;33:112–9.

    Article  CAS  Google Scholar 

  40. Dushyanthen S, Teo ZL, Caramia F, Savas P, Mintoff CP, Virassamy B, et al. Agonist immunotherapy restores T cell function following MEK inhibition improving efficacy in breast cancer. Nat Commun. 2017;8(1):606.

    Article  Google Scholar 

  41. Hendry S, Salgado R, Gevaert T, Russell PA, John T, Thapa B, et al. Assessing tumor-infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the international Immunooncology biomarkers working group: part 1: assessing the host immune response, TILs in invasive breast carcinoma and ductal carcinoma in situ, metastatic tumor deposits and areas for further research. Adv Anat Pathol. 2017;24(5):235–51.

    Article  Google Scholar 

  42. Jacquelot N, Roberti MP, Enot DP, Rusakiewicz S, Ternes N, Jegou S, et al. Predictors of responses to immune checkpoint blockade in advanced melanoma. Nat Commun. 2017;8(1):592.

    Article  CAS  Google Scholar 

  43. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348(6230):124–8.

    Article  CAS  Google Scholar 

  44. Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017;357(6349):409–13.

    Article  CAS  Google Scholar 

  45. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372(26):2509–20.

    Article  CAS  Google Scholar 

  46. Smid M, Rodriguez-Gonzalez FG, Sieuwerts AM, Salgado R, Prager-Van der Smissen WJ, Vlugt-Daane MV, et al. Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration. Nat Commun. 2016;7:12910.

    Article  CAS  Google Scholar 

  47. Boichard A, Pham TV, Yeerna H, Goodman A, Tamayo P, Lippman S, et al. APOBEC-related mutagenesis and neo-peptide hydrophobicity: implications for response to immunotherapy. Oncoimmunology. 2019;8(3):1550341.

    Article  Google Scholar 

  48. Boichard A, Tsigelny IF, Kurzrock R. High expression of PD-1 ligands is associated with kataegis mutational signature and APOBEC3 alterations. Oncoimmunology. 2017;6(3):e1284719.

    Article  Google Scholar 

Download references

Acknowledgements

We thank TCGA for providing valuable data resources for the research. We thank Marshal Younger for assistance with editing and manuscript preparation. The data analyses were conducted using the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University.

Funding

Funding information is not applicable.

Author information

Authors and Affiliations

  1. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA

    Zhishan Chen, Wanqing Wen, Jiandong Bao, Krystle L. Kuhs, Qiuyin Cai, Jirong Long, Xiao-ou Shu, Wei Zheng & Xingyi Guo

Authors
  1. Zhishan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wanqing Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiandong Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krystle L. Kuhs
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiuyin Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jirong Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiao-ou Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xingyi Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and design: XG. Acquisition of data and material support: XG, JB and ZC. Analysis and interpretation of data: XG, WW and ZC. Generation of tables/figures: XG and ZC. Writing, review, and/or revision of the manuscript: XG, ZC, WW, KLK, QC, JL, XS and WZ. Study supervision: XG. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xingyi Guo.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1: Table S1

. Associations between APOBEC-mutational signature and gene expression levels of APOBEC3A and APOBEC3B. Table S2. Associations between APOBEC-mutational signature and gene expression levels of APOBEC3C, APOBEC3D, APOBEC3F, APOBEC3G, and APOBEC3H. Table S3. Associations between APOBEC-mutational signature and each isoform expression level of APOBEC3A and APOBEC3B. Table S4. Expression correlation between APOBEC3A with the isoform uc011aoc for each cancer types. Table S5. Associations between APOBEC-mutational signature and isoform of APOBEC3A and APOBEC3B stratified by clinical subtypes in breast cancer. Table S6. The distribution of deletion genotypes in samples for each cancer type. Table S7. A list of top enriched canonical pathways for genes that were co-expressed with the isoform uc011aoc across cancer types. Table S8. Associations between predicted neoantigen loads and germline APOBEC3A/B deletion. Table S9. Associations between predicted neoantigen loads and APOBEC-mutational signature. Table S10. Associations between predicted neoantigen loads and proportion of APOBEC-mutational signature. Table S11. Associations between abundance of relative immune cell compositions in TILs and neoantigen loads. Table S12. Associations between abundance of relative immune cell compositions in TILs and APOBEC-mutational signature. Figure S1. The expression levels of six isoforms of APOBEC3A and ABOBEC3B for each cancer type.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Wen, W., Bao, J. et al. Integrative genomic analyses of APOBEC-mutational signature, expression and germline deletion of APOBEC3 genes, and immunogenicity in multiple cancer types. BMC Med Genomics 12, 131 (2019). https://doi.org/10.1186/s12920-019-0579-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12920-019-0579-3

Keywords

BMC Medical Genomics

ISSN: 1755-8794

Contact us