Haematological malignancies are a group of diverse conditions that originate from cells of the bone marrow and the lymphatic system [1]. There are three major groups of haematological malignancies: leukaemia, lymphoma, and plasma cell neoplasms [1]. The exact cause of most cases of haematological malignancies is not known although acquired factors like DNA-damaging agents and infections have been implicated [1,2]. Haematological malignancies have been linked to rare inherited syndromes and rare germline mutations in Mendelian disorders (ANKRD26, CEBPA, DDX41, ELANE, ETV6, GATA2, HAX1, RUNX1, SAMD9, SAMD9L, SRP72, and LSD1) [2]. Familial aggregation has been reported for several haematological malignancies [2]. Using aggregated whole exome sequencing (WES) data from UK biobank provided by two public portals (https://azphewas.com/ and https://app.genebass.org/) we have summarized the genetic contribution to haematological malignancies in UK biobank [3,4].
Wang et al reported the relationships between rare protein-coding variants and 17,361 binary phenotypes using WES data from 269,171 UK Biobank participants (https://azphewas.com/) [3], and Karczewski et al determined gene-based association investigating 4529 phenotypes in 394,841 UK biobank exomes (https://app.genebass.org/) [4]. AstraZeneca portals includes both variant-level and gene-level association tests (https://azphewas.com/) [3]. Gene-level association tests used collapsing analyses [3]. The proportion of cases with a qualifying variant was compared with the proportion of controls with a qualifying variant in each gene [3]. Twelve different sets of qualifying variant filters (models) were analyzed [3]. Genebass also includes single-variant tests and gene collapsing tests with gene-based burden (mean), SKAT (variance), and SKAT-O (hybrid variance/mean) tests (https://app.genebass.org/) [4]. Only candidate genes with p-values < 2.5 × 10–6 and candidate variants with p-values < 5 × 10–8 are shown in Table 1. To be comparable we only show the union of the ICD-10 codes (International Classification of Diseases 10th Revision) [3,4]. In Table 1 the genes with genome-wide significant results are shown with p-values for the best significant model. The involvement of rare variation using gene collapsing analysis was observed for 16 malignant phenotypes studied. Totally, thirty-one genes were linked to hematologic malignancies using gene collapsing analysis. Involvement of twenty-two specific coding variants in eighteen genes were observed for 14 malignant phenotypes. Many of the genes and variants are known to be involved in haematological malignancies as somatic mutations [5]. However, the data from UK biobank shows that germline inheritance of rare variants and specific coding variants are risk factors for hematologic malignancies (https://azphewas.com/ and https://app.genebass.org/) [3,4]. This is in line with two recent reports of patients with hematologic malignancies [6,7]. Some of the genes and variants are shared between different malignancies and others are unique (Table 1). There were associations to two syndromic genes associated with hematologic malignancies [2]: ETV6 (myelodysplastic syndrome) and RUNX1 (myeloid leukaemia). There were association between CHEK2 and myeloid leukaemia and ATM and lymphoid leukaemia (Table 1) in agreement with previous studies [8]. Aberrations in CHEK2 and ATM genes are known to increase the risk for all types of malignancies including hematologic malignancies [6]. Rare POT1 (Protection Of Telomeres 1) mutations were associated with multiple myeloma and malignant plasma cell neoplasms and chronic lymphocytic leukaemia (Table 1). Mutations in POT1 are known to be associated with malignant melanoma and glioma (https://www.genecards.org/). The data from UK biobank reported by Wang et al [3] and Karczewski et al [4] may give the molecular explanation for previously observed familial aggregation of haematological malignancies [2].
Table 1.
Results of gene collapsing analysis of rare variants and individual coding variants for haematological malignancies according to ICD-10* codes (https://azphewas.com/ and https://app.genebass.org/).3,4 Union was used to define phenotypes for https://azphewas.com. Only candidate genes with p-values < 2.5 × 10–6 and candidate variants with p-values < 5 × 10–8 are shown.
| Genebass LOF | Genebass missense/LC | Genebass variant level | Astra Zeneca gene collapsing | Astra Zeneca variant level | |
|---|---|---|---|---|---|
| C81 Hodgkin's disease | NS | ELF3 7.09e-9 | ELF3 Asn104Ser 3.51e-8 | NS | NS |
| XRRA1 Gly772Asp 4.66e-8 | |||||
| C82 Follicular [nodular] non-Hodgkin's lymphoma | NS | NS | HLA-DRB5 Asp66Ala 1.26e-8 | NS | CRYBG3 Thr1114Thr 3.71e-8 |
| HLA-DRB5 Lys14ThrfsTer27 3.23e-8 | |||||
| C83 Diffuse non-Hodgkin's lymphoma | NS | CCND1 1.22e-6 | NS | NS | TAF6 7-100113586-C-T 8.24e-9 |
| C84 Peripheral and cutaneous T-cell lymphomas | NS | NS | NS | NS | NS |
| C85 Other and unspecified types of non-Hodgkin's lymphoma | NS | MYD88 1.68e-8 | MYD88 Ter147ArgextTer8 3.8e-9 | TET2 5.24e-7 | NOTCH4 Leu15_Leu16dup 4.32e-8 |
| SCAMP1 1.29e-6 | |||||
| C86 Other specified types of T|NK-cell lymphoma | ND | ND | ND | NS | NS |
| C88 Malignant immunoproliferative diseases | NS | OR1J1 1.54e-6 | NS | NS | NS |
| IWS1 1.77e-6 | |||||
| C90 Multiple myeloma and malignant plasma cell neoplasms | NS | MKRN2 1.76e-7 | NS | POT1 2.80e-7 | NS |
| TET2 2.42e-6 | |||||
| C91 Lymphoid leukaemia | NOTCH1 1e-18 | MYD88 1.6e-14 | MYD88 Ter147ArgextTer8 1.5e-16 | NOTCH1 6.32e-15 | NS |
| CHD2 3.25e-12 | SF3B1 9.43e-12 | IGLL5 Glu15Asp 3.47e-14 | IGLL5 3.79e-14 | ||
| IGLL5 1.71e-12 | NOTCH1 Pro2514ArgfsTer4 5.52e-14 | CHD2 1.50e-13 | |||
| IGHV4-39 1.53e-8 | SF3B1 Lys700Glu 1.5e-11 | SF3B1 9.93e-9 | |||
| GIP 6.92e-7 | ATM 4.34e-7 | ||||
| POT1 4.57e-7 | |||||
| CHEK2 5.41e-7 | |||||
| KMT2D 1.48e-6 | |||||
| C91.0 Acute lymphoblastic leukaemia | ND | ND | ND | NS | NS |
| C91.1 Chronic lymphocytic leukaemia | ND | ND | ND | IGLL5 3.08e-16 | BCL2L11 Ile95Ile 3.32e-8 |
| NOTCH1 2.08e-15 | |||||
| CHD2 9.60e-14 | |||||
| SF3B1 1.34e-10 | |||||
| POT1 2.21e-7 | |||||
| KMT2D 6.08e-7 | |||||
| C91.4 Hairy-cell leukaemia | ND | ND | ND | NS | NS |
| C92 Myeloid leukaemia | TET2 2.45e-20 | JAK2 6.29e-12 | IDH2 Arg140Gln 3.33e-21 | SRSF2 1.43e-58 | SRSF2 Pro95His 7.13e-42 |
| EZH2 1.53e-8 | DNMT3A 2.58e-7 | JAK2 Val617Phe 2.54e-16 | TET2 5.83e-41 | JAK2 Val617Phe 1.09e-24 | |
| CHEK2 5.78e-7 | SRSF2 Pro95_Arg102del 1.45e-8 | ASXL1 5.97e-35 | |||
| DDX41 1.76e-13 | |||||
| RUNX1 2.49e-11 | |||||
| CHEK2 1.65e-10 | |||||
| DNMT3A 8.74e-10 | |||||
| EZH2 2.35e-8 | |||||
| C92.1 Chronic myeloid leukaemia | ND | ND | ND | TET2 4.80e-13 | NS |
| SRSF2 7.22e-13 | |||||
| C93 Monocytic leukaemia | ND | ND | ND | TET2 9.58e-37 | SRSF2 Pro95His 4.57e-10 |
| SRSF2 5.77e-26 | |||||
| ASXL1 1.11e-9 | |||||
| ALX4 2.06e-6 | |||||
| C94 Other leukaemias of specified cell type | ND | ND | ND | TET2 1.43e-8 | JAK2 Val617Phe 5.76e-34 |
| C95 Leukaemia of unspecified cell type | ND | ND | ND | NS | NS |
| C96 Other and unspecified malignant neoplasms of lymphoid| haematopoietic and related tissue | ND | ND | ND | TET2 5.29e-11 | JAK2 Val617Phe 2.05e-31 |
| HIST1H4G 1.77e-7 | |||||
| D45 Polycythaemia vera | ND | ND | ND | JAK2 7.71e-13 | JAK2 Val617Phe 6.79E-255 |
| TET2 5.97e-9 | HFE Cys282Tyr 1.50E-22 | ||||
| JAK2 His163His 5.33E-17 | |||||
| INSL4 Arg63Arg 1.50E-14 | |||||
| JAK2 Leu830Leu 2.14E-11 | |||||
| HIST1H1T Gln178Lys 2.77E-08 | |||||
| HIST1H4C Leu91Leu 3.06E-08 | |||||
| D46 Myelodysplastic syndromes (MDS) | ND | ND | ND | SRSF2 4.98e-34 | SRSF2 Pro95His 4.63e-26 |
| TET2 3.94e-32 | JAK2 Val617Phe 2.91e-25 | ||||
| ASXL1 1.43e-25 | |||||
| SF3B1 8.45e-15 | |||||
| DDX41 1.97e-13 | |||||
| ETV6 2.50e-7 | |||||
| EZH2 7.80e-7 | |||||
| ZNF528 1.35e-6 | |||||
| D47 Other neoplasms of uncertain or unknown behaviour of lymphoid| haematopoietic and related tissue | ND | ND | ND | TET2 1.61e-28 | JAK2 Val617Phe 1.83e-272 |
| SRSF2 2.38e-17 | SRSF2 Pro95His 9.72e-12 | ||||
| ASXL1 2.36e-16 | JAK2 His163His 6.47e-9 | ||||
| CALR 3.58e-12 | JAK2 Leu830Leu 3.49e-8 | ||||
| JAK2 4.23e-10 | |||||
| SF3B1 1.97e-7 | |||||
| DNMT3A 3.06e-7 | |||||
| D47.2 Monoclonal gammopathy | ND | ND | ND | NS | NS |
| D47.3 Essential (haemorrhagic) thrombocythaemia | ND | ND | ND | TET2 5.41e-13 | JAK2 Val617Phe 4.35e-93 |
| CALR 1.52e-10 | |||||
| ZIC5 9.14e-7 |
*ICD-10=International Classification of Diseases 10th Revision; NS=no genome wide significant gene; ND=not determined; Significance threshold was p-values < 0.05/20,000 genes = 2.5 × 10–6 commonly used for WES studies and p < 5 × 10–8 for variant level. For Genebass portal (https://app.genebass.org) the highest p-value for SKAT, SKAT-O or burden test is shown.4 For Astra Zeneca portal (https://azphewas.com) the highest p-value of the 12 tested models is shown.3
LoF: High-confidence Loss of function variants indicated by LOFTEE.4 Missense/LC: Missense variants are grouped with in-frame insertions and deletions, as well as low-confidence LoF variants filtered out by LOFTEE.4 The latter have a frequency spectrum consistent with missense variation and affect a set of amino acids in a similar way.4
There were several mutations found in genes [DNMT3A, TET2, ASXL1, JAK2, SF3B1, SRSF2] associated with clonal haematopoiesis of indeterminate potential (CHIP) [9,10] (Table 1), suggesting that inherited germline mutations may predispose individuals for hematologic malignancies. However, a recent report by Ariste et al suggests these mutations should be interpreted with caution especially when they occur in elder individuals [10]. They could represent somatic alterations and not germline variants [10]. Determination of these mutations in other cell types, such as skin fibroblasts, could be assessed to avoid misclassification of variants in cancer-predisposition genes as inherited [10]. Thus, this will be an important research task to determine whether these mutations are somatic or germline.
In conclusion, rare variation and specific coding variants are risk factors for haematological malignancies. These findings provide the molecular fundament for the previously reported familial aggregation of haematological malignancies.
Declaration of Competing Interest
None
Acknowledgments
This work was supported by a grant awarded to Dr Bengt Zöller by ALF-funding from Region Skåne, Sparbanken Skåne, and by the Swedish Research Council.
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