Mutational landscape of chronic myeloid leukemia: more than a single oncogene leukemia

Figures & data

Figure 1. Frequency of mutations in cancer-associated genes at diagnosis (CP) and BP-CML. The data used to build the figure were derived from 27 studies of CP and/or BP [38–54,61,63–71] and included genes that were reported to be mutated in more than one patient and in more than one study. *The frequency of patients with mutated genes was calculated in relation to the number of patients screened for each individual gene, which was highly variable between different genes.

Table 1. Studies of the genetic events at different phases of CML in the TKI era.

Study	Patients						Sequencing
	CP	BP		Matched samples		Total	Method	No. of patients**	No. of genes***	Targeted genes****
		No.	Phenotype*	CP/ follow-up	CP/BP
Corm et al. [38]	1				1	1	Sanger	1	1	RUNX1
Roche-Lestienne et al. [39]	1	12	My-BP:8 Ly-BP:1 AP:3	–	–	13	Sanger	13	1	RUNX1
Roche-Lestienne et al. [40]	91			71	20	91	Sanger	91	4	ASXL1, TET2, IDH1/2
Menezes et al. [41]	14			8	6	14	WES	1	–	ASXL1, TP53, IKZF3
							Sanger	13	3
Valikhani et al. [42]	66					66	Sanger	66	2	ASXL1, JAK2
Soverini et al. [43]	50	120	My-BP:75 Ly-BP:31 Amb-BP:9 AP:5		1	170	RNA seq	1	–	IDH1/2
							Sanger	169	2
Schmidt et al. [44]	29			15		29	Targeted	29	25
Kim et al.^† [45]	100			92	8	100	Targeted	100	92
Nteliopoulos et al.^† [46]	124			11	3	124	Targeted	124	71
Ernst et al. [47]	21			1		21	Targeted	21	30
Adnan Awad et al.^† [48]	43	19	My-BP:10 Ly-BP:4 AP:3	25	3	62	WES	17	–
							RNA	4	–
							Targeted	41	578
Togasaki et al.^† [49]	24					24	WES	24
Mitani et al. [50]	20			20		20	WES	20
Mologni et al.^† [51]	19					19	WES	19
Branford et al.^† [52]	51	39	My-BP:19 Ly-BP:20		25	65	WES	38
							RNA	59
Ko et al. [53]	13	52	My-BP: 27 Ly-BP:23 AP: 2		13	52	WGS	13
							WES	39
Kim et al. [54]	5	8	BP:4 AP:4			13	WES
Makishima et al. [61]	14	40	My-BP:20 Ly-BP/ Amb-BP:6 AP:14			54	Sanger	54	7	JAK2, CBL, CBLB, ASXL1, TET2, IDH1/2
Zhang et al. [63]		85	BP:57 AP:28			85	Sanger	85	2	RUNX1, GATA2
Yamamoto et al. [64]		13				13	Sanger	13	1	RUNX1
Boultwood et al. [65]	32	21	My-BP:10 Ly-BP:2		12	41	Sanger	41		ASXL1, TP53, IKZF1
							SNP array
Magistroni et al. [66]	41	24	My-BP:5 Ly-BP:4 ns:1		10	55	WES	10	–	UBE2A
							Amplicon	45	1
Grossmann et al. [67]		39	My-BP:24 Ly-BP:10 ns:5			39	Amplicon	39	11
Sklarz et al. [68]	1	1	My-BP		1	1	WES	1
Huang et al. [69]	1	1			1	1	WES	1
Adnan Awad et al. [70]	2	8	My-BP:5 Ly-BP:2 Amb-BP:1		2	8	WES	1
Mullighan et al. [71]	19	20	My-BP:12 Ly-BP:3 AP:5		16	23	SNP array	22

^†Study that primarily investigated the clinical significance of mutations on treatment outcomes.

*Phenotype of blasts from BP patients as reported in the included studies. My: myeloid, Ly: lymphoid, Amb: ambiguous, ns: not specified.

**Refers to the number of patients sequenced with the specified sequencing method.

***Refers to the number of genes the panel sequenced by Sanger, amplicon or targeted sequencing.

****Only genes in panels including less than 10 genes are listed.

Figure 2. Frequency of the most common CHIP mutations in elderly healthy individuals and different leukemias. The frequency of DNMT3A, TET2, and ASXL1 mutations were calculated based on pivotal studies of elderly healthy individuals (>55 years) [143], AML [144,145], and MDS [146].

Figure 3. Algorithm of suggested future directions of CML management integrating genetic screening in risk stratification and drug selection. In BP, in case of non-fit patients ineligible for chemotherapy or allogenic hematopoietic stem cell transplantation (allo-HSCT), targeted therapy based on mutation profile can be considered.

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