Abstract

   A hallmark of cancer is a tumor cell’s ability to evade immune
   destruction. Somatic mutations in tumor cells that prevent immune
   destruction have been extensively studied. However, somatic mutations
   in tumor infiltrating immune (TII) cells, to our knowledge, have not
   been previously studied. Understandably so since normal hematopoiesis
   prevents the accumulation of somatic mutations in immune cells.
   However, clonal hematopoiesis does result in the accumulation of
   somatic mutations in immune cells. These mutations cannot “drive” tumor
   growth, however, they may “facilitate” it by inhibiting an effective
   anti-tumor immune response. To identify potential immunosuppressive
   clonal hematopoietic (CH) mutations in TII cells, we analyzed exome and
   RNA sequencing data from matched tumor and normal blood samples, and
   single-cell RNA sequencing data, from breast cancer patients. We
   selected mutations that were somatic, present in TII cells, clonally
   expanded, potentially pathogenic, expressed in TII cells, unlikely to
   be a passenger mutation, and in immune response associated genes. We
   identified eight potential immunosuppressive CH mutations in TII cells.
   This work is a first step towards determining if immunosuppressive CH
   mutations in TII cells can affect the progression of solid tumors.
   Subsequent experimental confirmation could represent a new paradigm in
   the etiology of cancer.

   Subject terms: Breast cancer, Cancer genomics, Tumour immunology,
   Genome informatics, Cancer genomics, Genetic markers, Genome, Genomic
   instability, Genomics, Immunogenetics, Immune evasion, Immunogenetics,
   Diagnostic markers

Introduction

   Although considerable progress has been made in reducing mortality rate
   in cancer patients, it remains a leading cause of death, second only to
   heart disease^[34]1. Clearly, there is an urgent need for more
   effective treatments. Cancer is a complex disease caused by a
   combination of several different factors^[35]2–[36]4. One of the
   hallmarks of cancer is a tumor cell’s ability to avoid immune
   destruction^[37]2. The mechanisms by which tumor cells avoid immune
   destruction have been extensively studied^[38]5–[39]7. These mechanisms
   include mutations in tumor cells that allow them to evade detection by
   immune cells, inhibit immune cell recruitment/infiltrations, induce
   immune cell apoptosis, and produce factors that inhibit immune
   response. However, the potential effect of somatic mutations in tumor
   infiltrating immune (TII) cells, to our knowledge, have not been
   previously studied. This is understandable since somatic mutations do
   not generally accumulate during normal hematopoiesis where immune cells
   are constantly replaced by fresh cells from hematopoietic stem
   cells^[40]8. However, in a relatively recently discovered phenomenon,
   clonal hematopoiesis, mutations in specific genes can lead to the
   clonal proliferation of individual cells resulting in a significant
   clonal population with somatic mutations^[41]9,[42]10.

   Mutations in genes, such as DNA Methyltransferase 3 Alpha (DNMT3A),
   Ten-Eleven Translocation 2 (TET2), and Additional Sex Combs-Like
   Protein 1 (ASXL1), have been shown to cause clonal hematopoiesis, which
   is characterized by these somatic mutations being present in greater
   than 2% of peripheral blood cells^[43]11. Three possible mechanisms
   have been proposed for clonal hematopoiesis. The mutation in a
   hematopoietic stem or progenitor cell causes (1) increased
   self-renewal; (2) increased number of self-renewal cycles required to
   become a committed progenitor, or (3) increased epigenetic or
   transcriptional heterogeneity leading to clonal selection of highly
   proliferative states^[44]10,[45]12,[46]13. Clonal hematopoiesis has
   been implicated in hematologic^[47]14 and cardiovascular^[48]15
   malignancies. Over 10% of adults over age 70 have clonal
   hematopoiesis^[49]16. In addition, mutations associated with clonal
   hematopoiesis frequently occur in TII cells (14–65% of study
   samples)^[50]17–[51]21. A recent study has identified 70 genes that may
   “drive” clonal expansion^[52]22. However, clonal hematopoiesis does not
   by itself result in these malignancies. Secondary mutations are
   required to produce a disease state^[53]9. A longitudinal study of a
   patient who died of secondary acute myeloid leukemia (sAML) exemplifies
   clonal hematopoietic (CH) progression^[54]14. Clonal hematopoiesis due
   to a DNMT3A mutation was detected in blood samples taken at age 64. At
   age 69 the patient was diagnosed with myelodysplastic syndrome (MDS)
   due to a secondary RUNX1 (Runt-Related Transcription Factor 1) mutation
   in a CH clone. At age 72 the patient was diagnosed with sAML, the cause
   of death, due to a secondary FLT3 (Fms-Like Tyrosine Kinase 3) mutation
   in a DNMT3A-RUNX1 mutant subclone. The general mechanism by which a
   secondary pathogenic mutation can accumulate in a clonally expanding
   immune cell population is illustrated in Fig. [55]1.

Figure 1.

   Figure 1
   [56]Open in a new tab

   Clonal expansion of pathogenic mutations in TII cells. Increased cell
   survival and/or growth due to clonal hematopoietic (CH) mutations in
   hematopoietic stem/progenitor cells (HSPC) result in the expansion of a
   clonal population. Secondary pathogenic mutations within this
   population can result in the presence of the mutation in a large enough
   proportion of immune cells for pathogenesis.

   As with clonal hematopoiesis related myeloid malignancies, secondary
   mutations in clonal hematopoietic cells (CH mutations) could also
   result in the accumulation of cells with immunosuppressive mutations
   (Fig. [57]1), thus inhibiting an effective anti-tumor immune response.
   As an intuitive rationale consider the following. Immunodeficiency,
   either due to germline defects or immunosuppressive treatments, is well
   known to increase cancer incidence^[58]23,[59]24. In addition, altering
   the expression of specific immune cell genes can affect their
   anti-tumor immune function^[60]25,[61]26. Similarly, secondary
   immunosuppressive mutations in a significant number of TII cells due to
   clonal hematopoiesis would represent some degree of immunodeficiency,
   which could affect the progression of solid cancers.

   As a first step towards investigating the above hypothesis, in this
   study we identified potential immunosuppressive mutations in TII cells.
   To identify these mutations, we downloaded and analyzed exome and RNA
   sequencing data for 1,064 breast invasive carcinoma (BRCA) samples from
   the cancer genome atlas (TCGA) database^[62]27 and single cell RNA
   sequencing data for 26 breast cancer tumor samples from the gene
   exchange omnibus (GEO) database^[63]28. Mutations were selected based
   on the following seven criteria. The mutations must be (1) somatic, (2)
   in TII cells, (3) clonally expanded in blood (CH), (4) potentially
   pathogenic, (5) in a gene expressed in immune cells, (6) unlikely to be
   a passenger mutation, and (7) potentially immunosuppressive.

Results

   Our approach for identifying potential immunosuppressive CH mutations
   in TII consisted of four stages as shown in Fig. [64]2. (1) We selected
   protein altering mutations, which are more likely to be pathogenic than
   non-coding and synonymous mutations. (2) Clonally expanded somatic
   mutations in TII were identified based on variant allele fraction
   (VAF). (3) Potential pathogenic non-passenger mutations were identified
   based on frequency of occurrence in the genome aggregation database
   (genomAD). In addition, we confirmed that the gene is expressed in
   immune cells in the breast cancer tumor microenvironments using
   single-cell RNA sequencing (scRNA-seq) data. We also excluded potential
   passenger mutations based on several criteria, such as mutations in
   genes that are naturally hypermutated during immune response. (4)
   Finally, we selected potential immunosuppressive mutations based on
   pathways affected by significantly differentially expressed genes and
   published experimental evidence supporting their role in immune
   response. Overall, our selection criteria were designed to reduce the
   possibility of including passenger mutations even at the risk of
   excluding additional potential immunosuppressive CH mutations in TII
   cells. Based on the above criteria, we identified eight potential
   immunosuppressive CH mutations in TII cells (Table [65]1).

Figure 2.

   Figure 2
   [66]Open in a new tab

   Four stage approach for identifying potential immunosuppressive CH
   mutations in TII cells. (1) Select protein altering mutations which are
   more likely to be pathogenic than intergenic, intronic and synonymous
   mutations. (2) Select CH mutations in TII based on VAF in matched blood
   and tumor samples. (3) Select potential pathogenic (non-passenger)
   variants (rare variants occurring frequently in BRCA, predicted to be
   deleterious, and expressed in TII cells). (4) Select variants in genes
   experimentally shown to affect immune function. With these criteria, we
   identified eight potential immuno-suppressive CH mutations in TII
   cells, out of 4.6 million different variants in 1,064 BRCA samples.

Table 1.

   Potential immunosuppressive CH mutations in TII cells. Some of the key
   selection criteria used to select each variant are listed here. Variant
   allele fraction (VAF) shows mean and standard deviation for the samples
   in which the variant is detected. N = 1,064 samples for sample
   fraction. N > 100,000 sequences for gnomAD frequency. For % of TII
   cells in which the gene was expressed, the TII and total cell counts
   from single-cell RNA sequencing of the tumor microenvironment for 26
   breast cancer patients, are shown in parentheses.
   Gene Variant Tumor VAF Blood VAF Sample Fraction gnomAD Freq % of TII
   cells in which gene was expressed
   C1GALT1C1 p.V276G 0.18 ± 0.03 0.14 ± 0.05 0.0705 2E − 5 23%
   (2,810/12,075)
   DPP4 p.V354G 0.18 ± 0.03 0.13 ± 0.06 0.1156 4E − 6 46% (1,110/2,390)
   EIF4EBP1 p.D55H 0.07 ± 0.04 0.05 ± 0.02 0.0855 0 28% (7,596/27,530)
   EIF4EBP1 p.R56W 0.07 ± 0.03 0.05 ± 0.02 0.0902 8E − 6 28%
   (7,596/27,530)
   EIF4EBP1 p.R63W 0.07 ± 0.04 0.05 ± 0.02 0.0770 4E − 6 28%
   (7,596/27,530)
   EIF4EBP1 p.S65L 0.08 ± 0.04 0.05 ± 0.02 0.0761 4E − 6 28%
   (7,596/27,530)
   KIF15 p.T334P 0.17 ± 0.04 0.13 ± 0.05 0.0695 9E − 6 38% (603/1,572)
   UBE2N p.P63A 0.07 ± 0.03 0.08 ± 0.04 0.1137 0 41% (15,806/38,958)
   [67]Open in a new tab

Protein altering variants

   Matched blood and tumor samples from 1,064 breast invasive carcinoma
   (BRCA) patients were used for this study. BRCA cases consisted of 63.6%
   HR + /HER − , 21.6% HR − /HER − , 11.44% HR + /HER2 + , and 3.4%
   HR − /HER2 + (SI Figure [68]S1A), which are significantly different
   from the 87.4%, 13.2%, 12.6%, and 5.1%, respectively in the
   surveillance, epidemiology, and end results (SEER) report for the
   US^[69]29. The median age at diagnosis of 58 for BRCA cases (SI Figure
   [70]S1B), is lower than the median age of 63 in the SEER report. BRCA
   cases consisted of 73.0% Stage I and II cases and 24.5% Stage III and
   IV cases (SI Figure [71]S1C) compared to 65.1% Localized and 33.0%
   Regional/Distant cases in the SEER report. These differences will need
   to be accounted for in any future studies considering the mutations
   identified here as potential risk markers.

   A total of 4,579,609 different mutations were detected in BRCA exome
   sequencing data. The vast majority of these, such as synonymous,
   intergenic and intron mutations, are less likely to be pathogenic.
   Therefore, we excluded these mutations, limiting subsequent analysis to
   protein altering mutations, which are more likely to be pathogenic. In
   addition, for this analysis we did not consider epigenetic
   modifications and copy number variations since their pathogenicity is
   in general unclear. Protein altering mutations included nonsense,
   missense, insertion, deletion, and splice site variants. Of the
   4,579,609 different mutations, 558,470 (12%) were in coding regions. Of
   the coding region variants 445,132 (80%) were protein altering
   mutations (SI Figure [72]S1D).

Clonally expanded somatic mutations in tumor infiltrating immune cells

   Clonally expanded somatic mutations were identified by the variant
   allele fraction (VAF) in matched tumor and blood sample. A lower
   threshold of VAF > 2% is generally accepted as indicative of clonal
   hematopoiesis ^[73]16. With this threshold the estimated false positive
   rate due to sequencing errors is < 1%^[74]30,[75]31. This threshold
   also ensures that the identified mutations are not from circulating
   tumor DNA or cell free DNA, which can comprise up to 1% of blood sample
   DNA^[76]32,[77]33. An upper threshold of VAF < 25% was used to select
   for somatic variants and exclude germline variants. Mutations that
   occur with 2% < VAF < 25% in both blood and tumor samples are
   indicative of CH mutations in TII cells. It is highly improbable that
   the same variant would arise independently in blood and tissue cells,
   therefore it is reasonable to assume that the mutation occurred in
   blood cells and was detected in tumor samples due to the presence of
   infiltrating immune cells in the tumor microenvironment. For example,
   the CH VAF in blood and tumor samples for the Core 1
   Beta3-Galactosyltransferase-Specific Molecular Chaperone (C1GALT1C1)
   p.V276G variant, one of the eight potential immunosuppressive mutations
   in TII cells (Table [78]1), varies from 7.1–23.5% for tumor and
   4.1–21.6% for blood samples (Fig. [79]3A). The VAF for all 8 mutations
   are summarized in Fig. [80]3B and in Table [81]1. Of the 558,470
   protein altering mutations, 47,255 (8.5%) were clonally expanded
   somatic mutations in TII cells based on the above criteria
   (Fig. [82]2).

Figure 3.

   [83]Figure 3
   [84]Open in a new tab

   CH variants in TII cells. (A) Variant allele fraction (VAF) in matched
   blood and tumor samples for C1GALT1C1 p.V276G. (B) Distribution of VAF
   for each of the eight potential immunosuppressive CH variants in TII.
   Center markers are the mean and end markers are the minimum and
   maximum. VAF distributions for normal blood samples are shown in blue
   and for tumor samples in red. VAF 95% confidence intervals for each
   sample is shown in SI Table [85]S2.

Likely pathogenic non-passenger mutations

   To maximize the likelihood that the mutations identified are pathogenic
   and not passenger mutations, we selected mutations that satisfied the
   following additional criteria: rare mutations that frequently occurred
   in BRCA samples, mutations in genes that are expressed in tumor
   infiltrating immune cells based on scRNA-seq data, and deleterious or
   damaging mutations. Again, we emphasize that although the goal is to
   filter out passenger mutations, the mutations identified are not
   “driver” mutations. Since the mutations are in immune cells and not in
   tumor tissue cells, they will not directly drive tumor growth. Instead,
   the mutations identified here could “facilitate” tumor growth by
   inhibiting an effective anti-tumor immune response.

   The genome aggregation database (gnomAD) includes variant information
   from over 100,000 exome sequences. Rare variants in gnomAD (allele
   population frequency < 0.01%) suggests that the variant may have a
   deleterious effect on gene function. In tumor cells, rare mutations can
   be a result of rapidly dividing tumor cells with defective DNA repair
   mechanisms. Many of these mutations may be passenger mutations, with no
   effect on tumor progression. However, non-tumor immune cells generally
   do not contain defective DNA repair mechanisms and are not rapidly
   dividing. Therefore, the relatively frequent occurrence (> 5%) of such
   rare mutations in BRCA blood samples, suggests that the mutation could
   affect cell function^[86]34. For example, the p.V276G variant in
   C1GALT1C1 occurs in 75 (7%) of 1,064 matched BRCA normal blood samples
   (Fig. [87]4A), compared to 2 (0.0019%) of 105,263 sequences in gnomAD.
   The frequency of the eight potential immunosuppressive CH mutations in
   TII cells range from 7.05 – 11.56% in BRCA normal blood samples
   (Fig. [88]4D), compared to the 0 – 0.002% in gnomAD sequences (Table
   [89]1). As noted in the Methods section, we used protected TCGA data
   which includes mutations in blood samples (Fig. [90]4A) that would be
   excluded in open access databases since they are considered potential
   germline mutations that may identify the sample donor (Fig. [91]4C).
   For this analysis we selected CH mutation in TII cells (Fig. [92]4B).
   Figure [93]4A mutations not included in Fig. [94]4B,C are non-CH
   variants in TII (VAF ≤ 2%) or potential germline mutations (VAF ≥ 25%).

Figure 4.

   [95]Figure 4
   [96]Open in a new tab

   Fraction of samples with variants. N = 1,064 samples. (A) All variants
   in C1GALT1C1. (B) C1GALT1C1 CH variants in TII cells. (C) Open access
   C1GALT1C1 variants (tumor sample variants not detected in matched blood
   samples) reported by cBioPortal. (A–C) Missense, frame-shift and
   in-frame mutations are shown by green, black and brown dots,
   respectively. (D) Fraction of samples with each of the eight
   immunosuppressive CH variants in TII cells listed in Table [97]1.

   As an additional filter for potential passenger mutations, we exclude
   mutations in genes that are not expressed in immune cells since these
   will not affect immune function. We used scRNA-seq data from 100,064
   cells from the breast cancer tumor microenvironment
   (Fig. [98]5A)^[99]28 to exclude mutations in genes not expressed in
   immune cells. The number of immune cells in which the genes in Table
   [100]1 are expressed, range from 603 for KIF15 to 15,806 for UBE2N
   (Fig. [101]5B). These genes were expressed primarily in T-cells (55.3%)
   and myeloid cells (35.1%) (Fig. [102]5B).

Figure 5.

   [103]Figure 5
   [104]Open in a new tab

   Genes associated with potential immunosuppressive CH mutations in TII
   cells expressed in immune cells. (A) Distribution of cell types in the
   single-cell RNA sequencing data from N = 100,064 cells from the 26
   breast cancer tumor samples, used to determine if a gene is expressed
   in immune cells. (B) Fraction of immune cell types in which the genes
   associated with the immunosuppressive CH mutations in TII cells, were
   expressed.

   We also excluded mutations in genes that are subject to hypermutation
   or extreme variability as part of the natural immune response or due to
   adaptations to different antigens. These include immunoglobulin,
   immunoglobulin-like receptor, histocompatibility antigen, and T-cell
   receptor genes.

   Lastly, most secondary CH mutations (Fig. [105]1) are not likely to
   have a functional effect on the immune cell, even if protein-altering.
   For example, the effect of a single missense mutation or an in-frame
   insertion/deletion in an unstructured region of the protein will not
   always affect the structure or function of the protein. We used two
   mutation-significance prediction tools – sorting intolerant from
   tolerant (SIFT)^[106]35 and PolyPhen2^[107]36 – to select mutations
   that were predicted to be “deleterious” or “damaging” by both tools.
   SIFT predictions are primarily based on the degree of protein sequence
   conservation across homologous proteins. In addition to sequence
   conservation, PolyPhen2 incorporates the potential effect of the
   mutation on protein structure. Truncating and frame-shift mutations
   were also considered to be deleterious (SI Table [108]S3). The
   pathogenicity prediction of SIFT and PolyPhen2 were consistent with
   predictions by CADD, a tool that integrates the annotations by multiple
   other predictors (SI Table [109]S3)^[110]37. Gene pathway enrichment
   analysis using the Reactome pathway analysis tool^[111]38 identified 32
   significantly over-represented pathways (false discovery rate
   (FDR) < 0.05). Of the 32 pathways, 17 were part of the Disease
   top-level pathway (SI Table [112]S4).

   Together, the above considerations maximize the likelihood that the
   selected mutations are pathogenic and not passenger mutations. Of the
   47,255 CH mutations in TII, 1,710 (3.6%) frequently occurred in BRCA
   samples (> 5% of samples). Of these 1,710 mutations, 567 (33.2%) were
   rare mutations with gnomeAD allele frequency < 0.01%. Of these 567
   mutations, 384 (67.7%) did not occur in genes that are naturally
   hypermutated or highly variable. Finally, of these 384 mutations, 95
   (24.7%) were predicted to be deleterious or damaging by both SIFT and
   PolyPhen2 (SI Table [113]S3) The cancer subtype, stage, and age
   distribution of these mutations (SI Figure [114]S2) is similar to the
   overall distribution for all samples (SI Figure [115]S1).

Potential immunosuppressive mutations

   Although the pathogenic variants identified above may affect cellular
   function, they may not necessarily affect anti-tumor immune response.
   To identify mutations that are likely to affect anti-tumor immune
   response, we considered two factors. First, we conducted a literature
   review to determine if there is in vivo or in vitro experimental
   evidence showing that the gene associated with each variant is involved
   in immune response. Second, we analyzed differentially expressed genes
   to confirm that immune system pathways could be affected by the
   variant. Eight potentially immunosuppressive CH mutations in TII cells
   were identified (Table [116]1). Studies suggesting an immunosuppressive
   effect of mutations in these genes are summarized below.

   Core 1 Beta1,3-Galactosyltransferase 2 (C1GALT1C1, COSMC) encodes a
   chaperone protein required for the proper folding of T-synthase.
   T-synthase is required for the complete glycosylation of membrane
   glycoprotein Core 1 O-Glycan (T antigen)^[117]39. Incomplete
   glycosylation of T antigen results in the Tn antigen which has been
   associated with Tn syndrome, an autoimmune disease, and an immune
   suppressive microenvironment in colorectal cancer^[118]40. An analysis
   of blood cells from two patients with Tn syndrome and 25 healthy donors
   showed that the Tn syndrome was likely a result of somatic variants in
   COSMC which caused it to lose its chaperone function resulting in Tn
   antigens^[119]41. An in vitro study using the Jurkat cell line
   (T-lymphoblast) also showed that variants in COSMC results in the Tn
   positive phenotype^[120]42.

   The dipeptidyl-peptidase IV (DPP4) is a protein found in the
   extracellular domain of CD26 that acts by cleaving N-terminal proline
   or alanine dipeptides at position two. CD26 is expressed as either a
   membrane-bound form that is typically expressed on CD4 + helper/memory
   T cells, or as a soluble form (sCD26) found in serum^[121]43. CD26 was
   also associated and co-expressed with adenosine deaminase (ADA) in
   Jurkat T cell lines, verified with in vitro binding assays
   demonstrating its binding via the extracellular domain of CD26. ADA
   deficiency causes severe combined immunodeficiency disease (SCID) in
   humans and demonstrates one of the immunopathological roles of
   DPP4^[122]44. DPP4 also plays a role in fibrosis and immunoregulation
   and has received increasing attention in autoimmune diseases such as
   systemic lupus erythematosus (SLE) in which clinical evidence showed
   increased CD26 mRNA in SLE patients by 3.6-fold compared to
   controls^[123]45.

   Eukaryotic Translation Initiation Factor 4E Binding Protein 1
   (EIF4EBP1, 4E-BP1) encodes a translation repressor protein that
   directly interacts with eukaryotic translation initiation factor 4E
   (eIF4E). eIF4E is a component of a complex that recruits 40S ribosomal
   subunits to the 5’ end of mRNAs specific to monocytes.
   Dephosphorylation of EIF4EBP1 leads to its interaction with eIF4E
   resulting in the inhibition of cap-dependent translation both in vivo
   and in vitro, playing a key role in human cancer^[124]46. EIF4EBP1
   phosphorylation was shown to regulate protein synthesis required for
   T-cell proliferation^[125]47. Immunohistochemical analysis showed
   increase expression of EIF4EBP1 in subtypes of B-cell lymphoma and
   reactive lymphoid tissue^[126]48. Additionally, EIF4EBP1 has been
   suggested to have a positive regulatory effect on autophagy through its
   regulation of mammalian target of rapamycin complex 1 (mTORC1). A study
   transfected a miR-99a-3p antagomir leading to negative regulation of
   autophagy. Thus, it not only plays a role in cancer, but due to its
   relationship with miR-99a-3p has a role in autoimmune diseases such as
   in SLE^[127]49.

   Interestingly, four separate mutations in EIF4EBP1 were included in our
   list of eight potentially immunosuppressive mutations. In addition,
   there was considerable overlap in the occurrence of EIF4EBP1 mutations
   with 134 of 173 samples (77.46%) containing two or more of the four
   mutations. Since all these variants were detected in both tumor and
   matched blood samples with log odds (LOD) accuracy > 4.0, they are
   unlikely to be artifacts. For example, a sample with both the R63W and
   S65L variants had LODs of 44.02 and 54.99 for the two variants.
   Instead, it was more likely that the frequent occurrence of these
   mutations is because loci for two of the variants (R56W and R63W)
   contain CpG dinucleotides. Mutations in CpG dinucleotides are an order
   of magnitude more frequent than at other sites^[128]50. In addition,
   these four mutations are in the eIF4E binding site (residues
   51–67)^[129]51. Phosphorylation of the protein in response to hormone
   signaling initiates mRNA translation. These mutations could disrupt
   mRNA translation, potentially affecting anti-tumor immune activity of
   the associated cells. Therefore, we speculate that the occurrence of
   these mutations may be correlated with the occurrence of cancer.

   The protein expressed by the Kinesin Family Member 15 (KIF15, NY-BR-62)
   gene is part of a family of proteins that transport various cellular
   components such as organelles, protein complexes, and mRNA along
   microtubules. This protein has been implicated in the progression of
   various cancer types, including breast cancer^[130]52. One study found
   that KIF15 was primarily expressed in inflammatory monocytes in the
   tumor microenvironment and was a prognostic marker for hepatocellular
   carcinoma^[131]53. Expression of KIF15 was also found to increase
   B-cell proliferation in Burkitt lymphoma^[132]54.

   Ubiquitination requires a ubiquitin activating E1 enzyme, an E2
   ubiquitin conjugase, and an E3 ubiquitin ligase^[133]55. UBE2N is a
   K63-Ub-specific E2 enzyme that has been investigated for its role as a
   growth promoter of several human cancers such as breast cancer and
   neuroblastoma^[134]56,[135]57. Gene expression analysis in human acute
   myeloid leukemia, implicated UBE2N as necessary for maintaining
   oncogenic immune signaling states. Suppression of UBE2N decreased
   oncogenic immune signaling, promoting cell death of leukemic
   hematopoietic stem and progenitor cells (HSPC) and ensured normal
   hematopoiesis^[136]58. UBE2N was also found to be essential for RIG-I
   mediated immune signaling in response to viral infection^[137]59.

   Based on in vitro or in vivo experimental evidence described above,
   eight (5.3%) of the 95 potential pathogenic CH mutations in TII cells
   were identified as potentially affecting immune response (Table
   [138]1). As further support for a potential immune system related
   effect, RNA sequencing data for the BRCA samples were used to identify
   differentially expressed genes between samples with and without each
   mutation. For the significantly differentially expressed (SDE) genes
   (false discovery rate < 0.05), we then identified the associated
   top-level pathways in the Reactome pathways database^[139]38. A number
   of the SDE genes for each of the mutations were associated with immune
   system pathways, providing further support for an immunosuppressive
   role for these mutations (Table [140]2).

Table 2.

   Pathways affected by the potential immunosuppressive mutations. For
   each of these mutations, we identified significantly differentially
   expressed (SDE) genes (False Discovery Rate < 0.05). For each of these
   SDE genes we then identified the associated top-level Reactome pathway.
   The following table shows the number of SDE genes associated with each
   pathway. A number of SDE genes for each of the mutations are associated
   with the Immune System pathway, supporting an immunosuppressive role
   for these mutations.
   Reactome top-level pathways C1GALT1C1 V276G DPP4 V354G EIF4EBP2 D55H
   EIF4EBP2 R56W EIF4EBP2 R63W EIF4EBP2 S65L KIF15 T334P UBE2N P63A
   Immune system 52 158 19 28 7 35 72 26
   Signal transduction 61 131 54 64 14 46 142 25
   Metabolism 53 93 35 43 13 35 126 24
   Gene expression 27 74 48 49 13 59 42 15
   Metabolism of proteins 21 51 20 21 10 40 54 10
   Developmental Biology 19 39 15 18 6 18 47 15
   Disease 28 53 11 20 3 16 40 9
   Vesicle-mediated transport 19 29 5 12 2 14 28 5
   Cell cycle 6 17 7 11 2 43 7 2
   Homeostasis 19 36 9 13 1 8 36 12
   Cellular response to stress 8 23 7 6 1 13 14 3
   Organelle biogenesys and maintenance 7 18 2 4 2 11 13 6
   Neuronal systems 13 13 6 8 2 3 19 2
   DNA repair 11 18 3 4 0 12 10 0
   Extracellular matrix organization 7 10 1 3 0 0 27 4
   Chromatin organization 4 11 6 11 0 4 7 2
   Muscle contraction 6 3 2 6 2 1 18 2
   Programmed cell death 4 8 0 1 2 8 6 2
   Cell–cell communication 4 8 3 3 0 0 9 2
   DNA replication 1 4 0 0 1 12 0 0
   Circadian clock 4 3 0 0 1 1 1 1
   Reproduction 0 1 1 2 0 0 3 1
   Mitophagy 0 1 0 0 1 1 0 0
   [141]Open in a new tab

Discussion

   Tumor cell mutations that inhibit anti-tumor immune response have been
   extensively studied^[142]5–[143]7. However, mutations in immune cells
   that could affect anti-tumor immune response, to our knowledge, have
   not been previously studied. There are two reasons for this. One,
   cancer genomics research has been primarily focused on finding
   mutations in tumor cells that “drive” tumor growth^[144]34. Two, under
   normal hematopoiesis immune cells are unlikely to accumulate somatic
   mutations in a significant fraction of immune cells due to constant
   replacement from hematopoietic stem and progenitor cells^[145]8.
   However, a relatively recently discovered mechanism, clonal
   hematopoiesis, does in fact result in the accumulation of somatic
   mutations in a significant proportion (> 2%) of immune
   cells^[146]9,[147]10. The incidence of clonal hematopoiesis increases
   with age (> 10% of population over 70 years)^[148]16, occurs frequently
   in TII cells (14–65% of study samples)^[149]17–[150]21, and has been
   implicated in hematologic^[151]14 and cardiovascular^[152]15
   malignancies. However, clonal hematopoiesis does not by itself result
   in these malignancies. Secondary mutations are required to produce a
   disease state^[153]14, as illustrated in Fig. [154]1. For example,
   mutations in genes associated with Clonal Hematopoiesis of
   Indeterminate Potential (CHIP) (DNMT3A, TET2, etc.) by themselves are
   not immunosuppressive. Additional secondary mutations are required for
   pathogenicity^[155]9. Mutations in the top 10 CHIP genes were not
   included in our set of potentially immunosuppressive mutations because
   they did not occur in at least 5% of the breast cancer cases, one of
   our selection criteria (SI Table [156]S1). There may be as many as 70
   genes that “drive” clonal expansion^[157]22.

   Although CHIP mutations cannot “drive” tumor growth, it is possible
   that secondary mutations in clonally expanding immune cells could
   affect their anti-tumor activity, potentially “facilitating” or
   indirectly increasing the risk of tumor growth. Secondary
   immunosuppressive mutations in a significant number (2–25%) of TII
   cells due to clonal hematopoiesis would represent some degree of
   immunodeficiency, which could affect the progression of solid cancers.
   There were many instances where the VAF in the tumor (or matched blood)
   sample was significantly higher than in the matched blood (or tumor)
   sample (Fig. [158]4A). We offer two possible explanations. The tumor
   microenvironment was enriched (or limited) in the specific immune cell
   subtype containing the clonal hematopoietic mutation, due to the nature
   of the immune response. Alternatively, the tumor microenvironment
   varies significantly between regions of the tumor^[159]60, with vastly
   different immune infiltration in different regions. In stage 3 of our
   approach, we identified ninety-five potential pathogenic non-passenger
   CH mutations in TII cells. Further investigation may reveal that a
   combination of these mutations could represent a polygenic risk marker
   for breast cancer.

   In stage 4 of our approach, we identified a set of eight potential
   immunosuppressive clonal hematopoietic mutations in tumor infiltrating
   immune cells (Table [160]1). Highly restrictive criteria were used to
   select these potential immunosuppressive mutations. It is likely that
   these restrictive criteria resulted in the exclusion of other potential
   immunosuppressive mutations. However, as a first step it was important
   to identify the most likely candidates for subsequent in vitro and in
   vivo experiments required to confirm their effect on immune response.
   Without additional experimental validation the immunosuppressive role
   of these mutations remains speculative. It is possible that the
   mutations identified here are passenger mutations that do not affect
   gene function in the context of tumor growth, despite the multiple
   highly restrictive criteria designed to prevent the selection of
   passenger mutations. However, if an immunosuppressive effect of these
   mutations in TII cells is experimentally confirmed it could represent a
   novel paradigm in our understanding of cancer progression.

Conclusions

   This study is a first step towards investigating the potential role of
   clonal hematopoietic (CH) mutations in tumor infiltrating immune (TII)
   cells, on anti-tumor immune response. Mutations in immune cells, unlike
   mutations in tumor cells, cannot drive tumor growth. However, it is
   possible that mutations, in a significant proportion (2–25%) of TII
   cells, that inhibit an effective immune response could facilitate tumor
   growth. Out of over 4 million different mutations in 1,064 breast
   invasive carcinoma matched tumor and blood samples, we have identified
   a set of eight mutations that were clonally expanded, tumor
   infiltrating, and potentially immunosuppressive. Multiple highly
   restrictive criteria were used to exclude potential passenger mutations
   while selecting for potential immunosuppressive mutations. Further in
   vitro and in vivo investigations are needed to confirm that these
   mutations inhibit anti-tumor immune response. With experimental
   validation, the role of immunosuppressive CH mutations in TII cells
   could represent a novel paradigm in the etiology of cancer.

Methods

   Exome sequencing data for 1,064 breast invasive carcinoma (BRCA)
   samples from the cancer genome atlas (TCGA)^[161]27 were used to
   identify mutations in tumor infiltrating immune (TII) cells. The
   sequencing data includes data from matched tumor and normal blood
   samples. The “protected” or “controlled access” mutation accumulation
   format (MAF) file^[162]61 containing mutations called using the genome
   analysis toolkit (GATK) version 2.4 variant caller^[163]62, was
   downloaded from TCGA. These protected files include mutations that are
   filtered out in publicly accessible databases, such as cBioPortal, to
   prevent donor identification. The protected files include mutations in
   normal blood sample which are considered potential germline mutations
   that could identify specific individuals. However, for this study we
   obtained permission to use the protected data so that we could
   investigate somatic mutations in immune cells. The MAF file includes
   read counts for all alleles, which were used to calculate variant
   allele fractions (VAF). Only somatic variants were considered in this
   study, and all variants were supported by reads in both the tumor
   sample and matched blood sample. For this analysis we selected variants
   with a minimum Log Odds (LOD) ratio of 4.0, corresponding to a 10^4:1
   odds of correctly detecting variants, and a minimum base quality score
   of 10^[164]63. The LOD for selected variants ranged from 4.01–75.10 and
   sequencing depth ranged from 16–725. The filtering criteria used to
   limit the possibility of sequencing artifacts are discussed further in
   the SI Text.

   Gene expression data from RNA sequencing data from the BRCA samples
   were downloaded from TCGA to identify differentially expressed genes.
   Clinical data were also downloaded from TCGA to assess demographic and
   tumor characteristics of the data used.

   Single-cell RNA sequencing (scRNA-seq) data were used to confirm that
   the genes containing mutations in TII cells are expressed in immune
   cells. scRNA-seq data for 26 breast cancer cases were downloaded from
   the genome exchange omnibus (GEO), [165]GSE176078^[166]28. Cell-type
   and barcoded single cell expression data from these datasets were used
   to determine the cell-types in which a given gene is expressed.

   The genome aggregation database (gnomAD v2.1) contains variant
   information from over 100,000 genomes^[167]64 The allele population
   frequency in gnomAD was used to identify potentially pathogenic
   mutations. For this analysis, mutations with allele population
   frequency of less than 0.0001 were considered to be potentially
   pathogenic.

   Two mutation significance prediction tools – SIFT^[168]35 and
   PolyPhen2^[169]36 – were used to determine if a given mutation is
   likely to affect protein function. SIFT predictions are primarily based
   on protein sequence conservation and the differences in characteristics
   of the wild type and mutant amino acid. PolyPhen2 combines multiple
   sequence and structural features to predict the potential effect of
   missense mutations. Structural features are determined by homology
   modeling.

   The python statsmodels v0.13.2 was used to perform all statistical
   analysis. p value calculations for significantly differentially
   expressed genes used the two-sided Welch t test with Satterthwait
   degrees of freedom for independent samples with unequal standard
   deviations. False discovery rate calculations used the
   Benjamini–Hochberg correction for multiple testing with independent
   samples. 95% confidence interval for VAF was calculated using the
   binomial proportion function.

Supplementary Information

   [170]Supplementary Information 1.^ (1MB, docx)
   [171]Supplementary Information 2.^ (141.9KB, xlsx)

Acknowledgements