Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Charles G Mullighan
 + 3153 word(s) 3153 2021-08-31 05:40:23 |
2 format correction
Peter Tang
 Meta information modification 3153 2021-09-09 04:52:43 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Mullighan, C.; Iacobucci, I. Genomic Alterations in ALL. Encyclopedia. Available online: https://encyclopedia.pub/entry/14022 (accessed on 20 November 2024).
Mullighan C, Iacobucci I. Genomic Alterations in ALL. Encyclopedia. Available at: https://encyclopedia.pub/entry/14022. Accessed November 20, 2024.
Mullighan, Charles, Ilaria Iacobucci. "Genomic Alterations in ALL" Encyclopedia, https://encyclopedia.pub/entry/14022 (accessed November 20, 2024).
Mullighan, C., & Iacobucci, I. (2021, September 09). Genomic Alterations in ALL. In Encyclopedia. https://encyclopedia.pub/entry/14022
Mullighan, Charles and Ilaria Iacobucci. "Genomic Alterations in ALL." Encyclopedia. Web. 09 September, 2021.
Genomic Alterations in ALL
Edit
This entry is adapted from the peer-reviewed paper 10.3390/jcm10173792

Acute lymphoblastic leukemia (ALL) is the most successful paradigm of how risk-adapted therapy and detailed understanding of the genetic alterations driving leukemogenesis and therapeutic response may dramatically improve treatment outcomes, with cure rates now exceeding 90% in children. 

B-ALL DUX4 IKZF1 PAX5 Ph-like ZNF384 NUTM1 T-ALL NOTCH1 BCL11B transcriptome genome

1. Introduction

Acute lymphoblastic leukemia (ALL) is the most frequent childhood tumor and despite cure rates now exceeding 90% in children, outcomes for older children and adults remain poor with cure rates below 40% in those over the age of 40 [1][2][3], despite pediatric-inspired chemotherapy regimens [4]. This discrepancy is in part attributable to the different prevalence of genetic alterations across age. ALL may be of B- (B-ALL) or T-lymphoid (T-ALL) lineage, and comprises over thirty distinct subtypes characterized by germline and somatic genetic alterations that converge on distinct gene expression profiles [5][6][7][8][9][10][11][12]. These subtypes are defined by disease-initiating recurrent chromosomal gains and losses (hyper- and hypodiploidy, and complex intrachromosomal amplification of chromosome 21); chromosomal rearrangements that deregulate oncogenes or encode chimeric fusion oncoproteins, importantly often including cryptic rearrangements not identifiable by conventional cytogenetic approaches, such as DUX4 and EPOR rearrangements; subtypes defined by single point mutations (e.g., PAX5 P80R or IKZF1 N159Y); subtypes defined by enhancer hijacking (e.g., BCL11B-rearrangements in T-ALL and lineage ambiguous leukemia) [5]; and subtypes that “phenocopy” established subtypes, with similar gene expression profile but different founding alterations (e.g., BCR-ABL1-like ALL and ETV6-RUNX1-like ALL) [7][13][14][15]. Secondary somatic DNA copy number alterations and sequence mutations are also important in leukemogenesis and treatment response, and their nature and prevalence vary according to the ALL subtype [6]. Multiple genes are associated with predisposition to ALL, including polymorphic variants in ARID5B, BAK1, CDKN2A, CDKN2B, CEBPE, ELK3, ERG, GATA3, IGF2BP1, IKZF1, IKZF3, LHPP, MYC, PTPRJ, TP63 and the BMI1-PIP4K2A locus or rare mutations in PAX5, TP53, IKZF1 and ETV6 [16]. Several are associated with ALL subtype, for example, variants in GATA3 have been associated with an increased risk of Philadelphia- like (Ph-like) ALL in patients of Hispanic ancestry [17], variants in TP63 and PTPRJ with ETV6-RUNX1 ALL [18] and in ERG with TCF3-PBX1 ALL and African American ancestry [19][20]. A variant in the deubiquitinase gene USP7 has been instead associated with risk of T-lineage ALL [19].

2. B-Cell Precursor Acute Lymphoblastic Leukemia

2.1. Previously Established Subtypes with Recurring Chromosomal Abnormalities

Prior the advent of next generation sequencing (NGS), classification of ALL has been relied on conventional karyotyping, fluorescence in situ hybridization (FISH) and targeted-molecular analyses for the identification of recurring chromosomal abnormalities including aneuploidy, chromosomal rearrangements and/or known gene fusions (Figure 1).
Jcm 10 03792 g001 550
Figure 1. This schematic algorithm for B-ALL subtyping was modified from the figure originally published in Paietta E. et al. Molecular Classification Improves Risk Assessment in Adult BCR-ABL1-negative B-ALL. Blood Prepublished Apr 25 2021; doi:10.1182/blood.2020010144 [21]. This figure describes each B-ALL subtype according to the specific genetic alterations and gene expression profile. Moreover, for each subtype peak prevalence and prognosis are shown. Subtypes are colored according to defining genetic alteration: gross chromosomal abnormalities (purple), transcription factor rearrangements (blue), other transcription factor alterations (blue), and kinase alterations (orange). Abbreviations: AYA, adolescent and young adult; tSNE, t-distributed stochastic neighbor embedding; TBD: to be defined; -R: rearranged.

2.2. Emerging B-ALL Subtypes Defined by Genome Sequencing Studies

NGS approaches, particularly whole transcriptome sequencing (WTS), have enabled several research groups the identification of a large number of novel genetic alterations. These include cryptic rearrangements not identifiable by conventional approaches; novel subtypes that “phenocopy” established subtypes sharing similar gene expression profile but having different founding alterations; and subtypes defined by a single point mutation.

2.3. Prognostic Implications

The frequent and wide use of genomics to profile the landscape of ALL has allowed a tailored refinement of risk in association with standard criteria, such as minimal residual disease (MRD) levels [22] (Figure 1). In childhood B-ALL, ETV6-RUNX1, high-hyperdiploid, and DUX4-rearranged B-ALL are categorized as favorable due the highest overall survival rates and the lowest relapse rates, despite elevated early MRD in DUX4-rearranged cases. BCR-ABL1, BCR-ABL1-like, ETV6-RUNX1-like, KMT2A-rearranged, and MEF2D-rearranged ALL show high levels of MRD and the worst event-free survival rates and thus are categorized to be unfavorable subtypes. The remaining subtypes including TCF3-PBX1, PAX5alt, iAMP21, hypodiploid, ZNF384-rearranged, NUTM1-rearranged, and PAX5 P80R ALL have intermediate risk [23]. These prognostic groups have been mostly confirmed in a historic, non-MRD risk adapted trial (UKALLXII/ECOG-ACRIN E2993, NCT00002514) in adolescents and adult B-ALL cases [21] according to the following risk assignment: standard risk genotypes: DUX4-rearranged, ETV6-RUNX1/-like, TCF3-PBX1, PAX5 P80R, high-hyperdiploid; high-risk genotypes: Ph-like, KMT2A-AFF1, low-hypodiploid/near-haploid, BCL2/MYC-rearranged; and intermediate-risk genotypes: PAX5alt, ZNF384/-like, and MEF2D-rearranged.

3. T-Cell Acute Lymphoblastic Leukemia (T-ALL)

3.1. Genomic Overview of T-ALL

T-ALL leukemic cells express a subset of T-cell makers (CD3, cyCD3, CD2, CD5, CD7, CD8) and arises from immature T-cell progenitors [24][25]. Pediatric T-ALL accounts for 10–15% of newly diagnosed pediatric ALL and is characterized by higher incidence in boys, high initial white blood cell counts, mediastinal mass, central nervous system infiltration, and slightly worse prognosis compared to B-ALL [26]. The majority of T-ALL cases may be subclassified into subtypes according to the aberrant expression and dysregulated pathways of transcription factors and oncogenes induced by leukemia-initiating alterations involving basic helix–loop–helix (bHLH) factors (TAL1, TAL2, LYL1), homeobox genes (TLX1 (HOX11), TLX3 (HOX11L2), NKX2-1, NKX2-5, HOXA), LMO1, LMO2, MYB, BCL11B and SPI1 (Figure 2) [5][27][28]. These subtypes are defined with expression profiles by WTS or microarray, however, almost half of these leukemia-initiating alterations in T-ALL show intergenic breakpoints that can be missed by WTS but rescued by whole genome sequencing (WGS) [27][28]. Epigenomic analyses have also identified novel leukemia-initiating alterations in non-coding regions [5][29][30].
Jcm 10 03792 g002 550
Figure 2. T-cell differentiation and T-ALL subtyping. This schema describes differentiation stages of each T-ALL subtype according to the specific genetic alterations leading to aberrant expression of rearranged or mutated genes. Prevalence and prognosis of each subtype are shown. Subtypes are colored according to corresponding normal T-cell differentiation stage: early T-cell precursor (ETP, red and orange), early stages of cortical thymocytes maturation (green), and late stages of cortical thymocytes maturation (blue). Abbreviations: T-ALL: T-cell acute lymphoblastic leukemia; T/M MPAL: T/myeloid mixed phenotype acute leukemia; -R: rearranged.
NOTCH1 activating mutations and deletion of CDKN2A/CDKN2B loci (9p21) are found in over 70% of T-ALL cases and considered as secondary but core events in leukemogenesis [27][28][31]. Concurrent somatic mutations and copy number alterations are frequently observed in T-ALL leading to dysregulation of several cellular pathways, including JAK-STAT signaling (IL7R, JAK1, JAK3, DNM2), Ras signaling (NRAS, KRAS, and NF1), PI3K-AKT signaling (PTEN, AKT1, PIK3CA PIK3CD), epigenetic regulation (PHF6, SUZ12, EZH2, KDM6A), transcription factors and regulators (ETV6, GATA3, RUNX1, LEF1, WT1, BCL11B), and translation regulators (CNOT3, RPL5, RPL10) [27][28][32][33]. Accumulation of these aberrant expression and dysregulated pathways disrupt the normal T-cell differentiation, proliferation, and survival, and results in T-ALL with unique gene expression signatures reflecting the point of differentiation arrest during T-cell development [25][32]. In addition to expression profiles, DNA methylation signatures are also associated with immunophenotypic profiles and normal T-cell development differentiation stage [34][35].

3.2. T-ALL in Early Stages of Cortical Thymocyte Maturation

T-ALL with CD1a+, CD4+, and CD8+ immunophenotype includes several subgroups, such as rearrangements of TLX1, TLX3, NKX2-1, reflecting a differentiation arrest in early stages of cortical thymocyte maturation and confers a relatively favorable prognosis [36][37]. These subgroups almost commonly harbor NOTCH1 and CDKN2A alterations. Dysregulated expression of HOX transcription factor genes is mostly induced by chromosomal translocations and inversions that juxtapose these genes to enhancers in the TCR and BCL11B regulatory regions [27][38]. Importantly, BCL11B rearrangements (BCL11B-TLX3) in this subgroup are mechanistically distinct from those identified in BCL11B-rearranged lineage ambiguous leukemias, in that in the BCL11B-TLX3 leukemia, the BCL11B enhancer is used for aberrant expression of TLX3 at the cost of the loss of expression of BCL11B, leading to complete difference in expression profiles [5][38][39]. Instead, TLX3 rearranged T-ALL (including BCL11B-TLX3) shares gene expression signatures, DNA methylation profiles, somatic mutations (BCL11B, WT1, PHF6, DNM2), and downstream targets (JAK-STAT, epigenetic regulators) with TLX1 rearranged T-ALL [27][28][34][35][37]. Some of overlapping genomic features with TLX1/TLX3 rearranged T-ALL, including NUP214-ABL1 (TKIs) and JAK-STAT pathway (ruxolitinib, a JAK-STAT inhibitor), can be targetable and have been incorporated into ongoing clinical trials [40].

3.3. TAL1-Driven T-ALL with Late Stages of Cortical Thymocyte Maturation

Deregulation of the TAL1 oncogene is a feature of T-ALL that typically exhibits a late cortical thymocyte immunophenotype (CD4+, CD8+, CD3+) and comprises approximately 40% of T-ALL [27][28]. This T-ALL subtype includes TAL1 and TAL2 rearranged cases and is further classified into two subgroups by expression profiles whose one expresses PTCRA (pre-TCR) suggesting LCK activation that correlated with dasatinib sensitivity [28][37][41]. During normal T-cell differentiation, TAL1 expression is transcriptionally silenced along with T-cell lineage commitment to proceed appropriate TCR rearrangements and differentiation [25]. TAL1 overexpression is induced by several mechanisms: (1) chromosomal translocations with TCRA/D; (2) sub-microscopic interstitial deletion (STIL-TAL1); (3) disruption of insulated neighborhoods by losing CTCF binding sites [42]; and (4) somatic indels in a noncoding intergenic regulatory element upstream of TAL1 to generate aberrant MYB binding site (MuTE) [29]. The latter two mechanisms have benefited of NGS technologies for their identification. Dysregulated TAL1 expression inhibits the function of E-protein dimers by forming TAL1-E-protein heterodimer [43]. Furthermore, TAL1 forms the central node of the core regulatory circuit to coordinately regulate downstream target genes with several hematopoietic transcription factors including GATA3, RUNX1, MYB, and the ETS family genes, which is active in normal hematopoietic stem cell (HSC) and progenitor cells [44][45], and RUNX1 inhibition is reported to impair the growth of T-ALL but not normal hematopoietic cells [46]. However, although TAL1 functions as a master transcription factor related to T-cell differentiation and leukemogenesis of T-ALL, only 30% of transgenic mice develop T-ALL after a latent period, indicating that additional abnormalities are required for leukemogenesis [47]. Expression of Lmo2 accelerates the onset of leukemia in Tal1 transgenic mice, and LMO1/LMO2 are commonly expressed in human TAL1-driven T-ALL [48][49]. Other cooperative genes and noncoding RNAs in TAL1-driven T-ALL include ARID5B, ARIEL, and MYC, driving aberrant expression of TAL1 [50][51]. In addition, PI3K-AKT pathway genes including PTEN are frequently mutated in this subgroup [27][28], which associates with glucocorticoid resistance and can be reversed by the inhibition of this pathway [52]. Several cell cycle regulators including CDK6 and CCND3 are regulated by TAL1 complex [44] and may be potential targets of therapeutic intervention [53].

3.4. Early T-Cell Precursor (ETP) ALL and Mixed Phenotype Acute Leukemia

ETP-ALL is often referred to as a subtype of T-ALL as it exhibits an immunophenotype analogous to the earliest stages of T-cell development (cytoplasmic CD3+, CD7+; CD8−, CD1a−, CD5weak), and with expression of myeloid and/or stem-cell markers [36][54]. However, the genomic alterations and gene expression profile of ETP-ALL are more similar to a hematopoietic stem cell than a T cell precursor, suggesting that ETP-ALL could be included in a subgroup of immature acute leukemias of ambiguous lineage (ALAL), originating from a hematopoietic progenitor at a maturational stage prior to initiation of a definitive program of T cell differentiation. Consistent with this, recent studies have defined a subgroup of BCL11B-deregulated ALAL, that includes one third of ETP-ALL and T/myeloid mixed phenotype acute leukemia (T/M MPAL) cases with a very distinct expression profile [5]. BCL11B-deregulated ALAL is characterized by structural variations of the region containing BCL11B at 14q32 including translocations and high-copy amplification generating a distal neo-enhancer, that each leads to aberrant expression of BCL11B, in the case of the rearrangements by hijacking super-enhancers active in CD34+ hematopoietic stem and progenitor cell (HSPCs) [5][39]. FLT3 activating mutations were found in 80% of BCL11B-deregulated ALAL, and concurrent expression of BCL11B and FLT3-ITD on HSPC exhibited synergistic effects on activating T-cell directed differentiation to express cytoplasmic CD3 while blocking myeloid differentiation [5]. Other genomic features of ETP-ALL include a subgroup of aberrant expression of PU.1 (SPI1 fusions), HOXA genes (rearrangements of HOXA genes, KMT2A rearrangements, PICALM-MLLT10, SET-NUP214) and mutations of multiple cellular pathways (Ras signaling, JAK-STAT signaling, and epigenetic regulators) and transcription factors related to T-cell development [27][28][55]. Especially, T-ALL with SPI1 fusions represents unique expression profiles with high relapse rate [5][28]. Again, several of these genomic mutations were shared with T/M MPAL, including biallelic WT1 alterations, mutations of hematopoietic transcription factors (ETV6, RUNX1, CEBPA) and activating mutations of signaling pathways (JAK-STAT, FLT3, Ras) [56][55], supporting that they are similar entities in the spectrum of immature leukemias and both might have sensitivity to FLT3 and/or JAK inhibition [57].

3.5. NOTCH1 Activating Mutations in T-ALL

NOTCH1 encodes a highly conserved ligand-dependent transcription factor. The NOTCH1 signaling pathway plays an important role in the commitment of T-cell lineage specification and for further T-cell development [25][58]. In T-ALL, NOTCH1 activating mutations are found in more than 70% of cases and is considered an oncogene involved in leukemogenesis [27][28]. Aberrant activation of NOTCH1 pathway in T-ALL is mostly induced by (1) ligand-independent activation (somatic mutations, indels and large deletions that disrupt the negative regulatory region), or (2) impairment of the proteasomal degradation of intracellular domain of NOTCH1 (truncation of the PEST domain, NOTCH1 mutations in 3′ untranslated region, and FBXW7 mutations) [59][60][61][62][63][64]. These two types of NOTCH1 activating mutations have synergistic effects and more than 20% of T-ALL cases harbor both types of alterations [59]. However, most NOTCH1 activating mutations found in human T-ALL are considered as a weak tumor initiator event. Co-existence of both types of NOTCH1 mutations in hematopoietic progenitors tends to induce a transient preleukemic CD4+/CD8+ double positive cells and takes 10 to 15 weeks to fully transform into T-ALL, suggesting that they are alone incompletely leukemogenic [65][66][67]. In addition, more than 40% of T-ALL cases harbor subclonal NOTCH1 activating mutations and their heterogeneity at diagnosis was reported by several studies [27][28][68]. Furthermore, NOTCH1 activating mutations are considered to be acquired as a late secondary event in leukemogenesis [31][68][69].
A key target of NOTCH1 is the MYC oncogene that shares several overlapping target genes with NOTCH1 to promote cell proliferation and dysregulate anabolic pathways in T-ALL [67][70][71]. NOTCH1 controls T-cell-specific distal enhancer of MYC (“NMe”), resulting in the NOTCH1-MYC regulatory circuit [67][70][71]. In addition, pre-TCR signaling also correlates with NOTCH signaling, leading to LCK signaling and robust cell growth at DN3 stage in the T-cell development, which can be targetable by dasatinib [41][72].
Due to the high prevalence and importance of NOTCH1 activating mutations in T-ALL, targeted therapy on NOTCH1 pathway has been a major interest. This includes γ-secretase inhibitors (GSIs), ADAM inhibitors, SERCA inhibitors, and monoclonal antibodies [73]. Among them, GSIs, that block the activation process of NOTCH receptors by inhibiting proteolytic cleavage, have been tested in preclinical and Phase 1 studies [74][75]. However, the usage of GSIs in T-ALL is still in a developing phase due to gastrointestinal toxicity and insufficient antitumor responses that mostly induce transient growth arrest rather than cell death [76][77]. To overcome these problems, combination with other agents have been explored including glucocorticoids that showed synergistic effects by reversing glucocorticoid resistance [78]. Inhibition of mTORC1 signaling and PKCδ signaling are also promising combination strategies to restore GSIs sensitivity in resistant cells [79][80].

4. Implications for Diagnosis

The revolution in genomic characterization of ALL has created important opportunities and challenges for the clinical implementation of sequencing-based approaches for diagnosis and management of ALL (Table 1). This is particularly true for B-ALL, where many of the recently identified subtypes are associated with prognosis (even in the context of MRD-based risk-adapted therapy) [22][21] and where molecular characterization is needed to identify patients suitable for targeted therapy (an exemplar being Ph-like ALL). This is currently less compelling for T-ALL where identification of founding lesions driving T-ALL subtypes are of biological and mechanistic interest but are not typically used to risk stratify or guide therapy, exceptions possibly being kinase inhibition for JAK-STAT alterations and ABL1 rearrangements, identification of alterations in Ras, PTEN, NOTCH1 and/or FBXW7 that have been found to be associated with outcome in some studies [81], and LCK dependence for dasatinib therapy [41]. The challenge is clinical implementation of appropriately comprehensive diagnostic approaches to identify all key genomic features. Despite the mutationally sparse genome of ALL, there is striking diversity of the nature of underlying driver alterations, including sequence mutations, DNA copy number alterations, and structural variations, many of which may involve the non-coding genome. Accurate subtyping is also challenged by the inability of conventional cytogenetic and targeted molecular approaches to identify several types of driver (e.g., DUX4-rearrangement) and the importance of identifying phenocopies (e.g., ETV6-RUNX1-like, and Ph-like ALL). Thus, moving forward, optimal clinical diagnostics require genomic approaches. The choice of approach in part rests on how clinical information will be used. If comprehensive subtyping and identification of all potentially clinically relevant genomic alterations is desirable, a combination of DNA and RNA-based technologies is required. For example, the combination of WGS and WTS enables the identification of sequence mutations, DNA copy number alterations, aneuploidy and structural variants (from WGS) together with identification of fusion chimeras, mutant allele expression, and gene expression profiling (from WTS). The use of one or both approaches is becoming increasingly widely used, and at St Jude Children’s Research Hospital, three platform sequencing (WGS, WTS and exome sequencing) is clinical standard of care, informs clinical decision making in ALL [40], and retrieves more actionable clinical information than any single platform alone [82]. WGS is offered using a paired non-tumor sample to aid identification of somatic variants and provides the opportunity to return clinically relevant germline findings. Moreover, this comprehensive approach enables a more streamlined workflow [83][84][85], provided the demands of analysis and interpretation can be met.
Table 1. Clinical implementation of high-throughput sequencing.

Platform

Capability

Cost

Detectable

Subtypes

Difficult

Subtypes

WTS

(RNAseq)

Fusion chimeras

Gene expression profiling

Mutant allele expression

Alternative splicing analysis

(BCR/TCR rearrangements)

(Sequence mutations)

(Copy number analysis)

Moderate

B-ALL

ETV6-RUNX1; KMT2A; TCF3-PBX1; BCR-ABL1; DUX4; MEF2D; ZNF384/362

NUTM1; HLF; BCL2/MYC; PAX5alt; ZEB2/CEBPE;

-like subtypes

B-ALL

Aneuploidies

T-ALL

HOXA (KMT2A-R, PICALM-MLLT10, SET-NUP214); SPI1; NKX2-1; TAL1 (STIL-TAL1)

T-ALL

BCL11B; TLX1/3; LMO1/2; HOXA (others); TAL1 (others); T-other

WGS

Sequence mutations

Structural variants

Copy number analysis

(BCR/TCR rearrangements)

(GWAS)

High

B-ALL

Aneuploidies; ETV6-RUNX1; KMT2A; TCF3-PBX1; BCR-ABL1; DUX4; MEF2D; ZNF384/362; NUTM1; HLF; BCL2/MYC; PAX5 P80R; IKZF1 N159Y; ZEB2/CEBPE; Sequence and structural alterations in Ph-like ALL

B-ALL

-like subtypes;

Part of PAX5alt

T-ALL

BCL11B; TLX1/3; LMO1/2; HOXA;

SPI1; NKX2-1; TAL1

T-ALL

T-other

WES

Sequence mutations (coding)

Structural variants (coding)

Copy number analysis

Moderate

B-ALL

(Aneuploidies)

PAX5 P80R

IKZF1 N159Y

Sequence mutations in Ph-like ALL (e.g., JAK1/2/3, Ras)

Most of other B-ALL and T-ALL subtypes

Targeted sequencing

(DNA and/or RNA)

Fusion chimeras (targeted)

Gene expression (targeted)

Sequence mutations (targeted)

Structural variants (targeted)

(Copy number analysis)

Low

Targeted alterations

Non-targeted

alterations

The parenthesis in “Capability” indicates analyses in development. Abbreviations: WTS: whole transcriptome sequencing; BCR: B-cell receptor; TCR: T-cell receptor; WGS: whole genome sequencing; GWAS: genome wide association study; WES: whole exome sequencing; -R: rearranged.

References

  1. Tran, T.H.; Hunger, S.P. The genomic landscape of pediatric acute lymphoblastic leukemia and precision medicine opportunities. Semin. Cancer Biol. 2020.
  2. Pui, C.-H. Precision medicine in acute lymphoblastic leukemia. Front. Med. 2020, 14, 689–700.
  3. Inaba, H.; Mullighan, C.G. Pediatric acute lymphoblastic leukemia. Haematologica 2020, 105, 2524.
  4. Stock, W.; Luger, S.M.; Advani, A.S.; Yin, J.; Harvey, R.C.; Mullighan, C.G.; Willman, C.L.; Fulton, N.; Laumann, K.M.; Malnassy, G.; et al. A pediatric regimen for older adolescents and young adults with acute lymphoblastic leukemia: Results of CALGB 10403. Blood 2019, 133, 1548–1559.
  5. Montefiori, L.E.; Bendig, S.; Gu, Z.; Chen, X.; Polonen, P.; Ma, X.; Murison, A.; Zeng, A.; Garcia-Prat, L.; Dickerson, K.; et al. Enhancer hijacking drives oncogenic BCL11B expression in lineage ambiguous stem cell leukemia. Cancer Discov. 2021.
  6. Iacobucci, I.; Mullighan, C.G. Genetic Basis of Acute Lymphoblastic Leukemia. J. Clin. Oncol. 2017, 35, 975–983.
  7. Gu, Z.; Churchman, M.L.; Roberts, K.G.; Moore, I.; Zhou, X.; Nakitandwe, J.; Hagiwara, K.; Pelletier, S.; Gingras, S.; Berns, H.; et al. PAX5-driven subtypes of B-progenitor acute lymphoblastic leukemia. Nat. Genet. 2019, 51, 296–307.
  8. Pui, C.-H.; Nichols, K.E.; Yang, J.J. Somatic and germline genomics in paediatric acute lymphoblastic leukaemia. Nat. Rev. Clin. Oncol. 2018, 16, 227–240.
  9. Schwab, C.; Harrison, C.J. Advances in B-cell Precursor Acute Lymphoblastic Leukemia Genomics. HemaSphere 2018, 2, e53.
  10. Li, J.; Dai, Y.; Wu, L.; Zhang, M.; Ouyang, W.; Huang, J.; Chen, S. Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia. Front. Med. 2021, 15, 347–371.
  11. Mullighan, C.G. How advanced are we in targeting novel subtypes of ALL? Best Pract. Res. Clin. Haematol. 2019, 32, 101095.
  12. Kimura, S.; Mullighan, C.G. Molecular markers in ALL: Clinical implications. Best Pract. Res. Clin. Haematol. 2020, 33, 101193.
  13. Li, J.-F.; Dai, Y.-T.; Lilljebjörn, H.; Shen, S.-H.; Cui, B.-W.; Bai, L.; Liu, Y.-F.; Qian, M.-X.; Kubota, Y.; Kiyoi, H.; et al. Transcriptional landscape of B cell precursor acute lymphoblastic leukemia based on an international study of 1223 cases. Proc. Natl. Acad. Sci. USA 2018, 115, E11711–E11720.
  14. Roberts, K.G.; Morin, R.D.; Zhang, J.; Hirst, M.; Zhao, Y.; Su, X.; Chen, S.-C.; Payne-Turner, D.; Churchman, M.L.; Harvey, R.; et al. Genetic Alterations Activating Kinase and Cytokine Receptor Signaling in High-Risk Acute Lymphoblastic Leukemia. Cancer Cell 2012, 22, 153–166.
  15. Roberts, K.G.; Li, Y.; Payne-Turner, D.; Harvey, R.; Yang, Y.-L.; Pei, D.; McCastlain, K.; Ding, L.; Lu, C.; Song, G.; et al. Targetable Kinase-Activating Lesions in Ph-like Acute Lymphoblastic Leukemia. N. Engl. J. Med. 2014, 371, 1005–1015.
  16. Klco, J.M.; Mullighan, C.G. Advances in germline predisposition to acute leukaemias and myeloid neoplasms. Nat. Rev. Cancer 2020, 21, 122–137.
  17. Perez-Andreu, V.; Roberts, K.G.; Harvey, R.; Yang, W.; Cheng, C.; Pei, D.; Xu, H.; Gastier-Foster, J.; Lim, J.Y.-S.; Chen, I.-M.; et al. Inherited GATA3 variants are associated with Ph-like childhood acute lymphoblastic leukemia and risk of relapse. Nat. Genet. 2013, 45, 1494–1498.
  18. Ellinghaus, E.; Stanulla, M.; Richter, G.; Kronnie, G.T.; Cario, G.; Cazzaniga, G.; Horstmann, M.; Grümayer, R.P.; Cavé, H.; Trka, J.; et al. Identification of germline susceptibility loci in ETV6-RUNX1-rearranged childhood acute lymphoblastic leukemia. Leukemia 2011, 26, 902–909.
  19. Qian, M.; Zhao, X.; Devidas, M.; Yang, W.; Gocho, Y.; Smith, C.; Gastier-Foster, J.M.; Li, Y.; Xu, H.; Zhang, S.; et al. Genome-Wide Association Study of Susceptibility Loci for T-Cell Acute Lymphoblastic Leukemia in Children. J. Natl. Cancer Inst. 2019, 111, 1350–1357.
  20. Qian, M.; Xu, H.; Perez-Andreu, V.; Roberts, K.G.; Zhang, H.; Yang, W.; Zhang, S.; Zhao, X.; Smith, C.; Devidas, M.; et al. Novel susceptibility variants at the ERG locus for childhood acute lymphoblastic leukemia in Hispanics. Blood 2019, 133, 724–729.
  21. Paietta, E.; Roberts, K.G.; Wang, V.; Gu, Z.; Buck, G.; Pei, D.; Cheng, C.; Levine, R.L.; Abdel-Wahab, O.; Cheng, Z.; et al. Molecular Classification Improves Risk Assessment in Adult BCR-ABL1-negative B-ALL. Blood 2021.
  22. Jeha, S.; Choi, J.; Roberts, K.G.; Pei, D.; Coustan-Smith, E.; Inaba, H.; Rubnitz, J.E.; Ribeiro, R.C.; Gruber, T.A.; Raimondi, S.C.; et al. Clinical Significance of Novel Subtypes of Acute Lymphoblastic Leukemia in the Context of Minimal Residual Disease–Directed Therapy. Blood Cancer Discov. 2021, 2, 326–337.
  23. Sirvent, N.; Trassard, M.; Ebran, N.; Attias, R.; Pedeutour, F. Fusion of EWSR1 with the DUX4 facioscapulohumeral muscular dystrophy region resulting from t(4;22)(q35;q12) in a case of embryonal rhabdomyosarcoma. Cancer Genet. Cytogenet. 2009, 195, 12–18.
  24. Aifantis, I.; Raetz, E.A.; Buonamici, S. Molecular pathogenesis of T-cell leukaemia and lymphoma. Nat. Rev. Immunol. 2008, 8, 380–390.
  25. Yui, M.; Rothenberg, E. Developmental gene networks: A triathlon on the course to T cell identity. Nat. Rev. Immunol. 2014, 14, 529–545.
  26. Teachey, D.T.; Pui, C.-H. Comparative features and outcomes between paediatric T-cell and B-cell acute lymphoblastic leukaemia. Lancet Oncol. 2019, 20, e142–e154.
  27. Liu, Y.; Easton, J.; Shao, Y.; Maciaszek, J.; Wang, Z.; Wilkinson, M.R.; McCastlain, K.; Edmonson, M.; Pounds, S.B.; Shi, L.; et al. The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia. Nat. Genet. 2017, 49, 1211–1218.
  28. Seki, M.; Kimura, S.; Isobe, T.; Yoshida, K.; Ueno, H.; Nakajima-Takagi, Y.; Wang, C.; Lin, L.; Kon, A.; Suzuki, H.; et al. Recurrent SPI1 (PU.1) fusions in high-risk pediatric T cell acute lymphoblastic leukemia. Nat. Genet. 2017, 49, 1274–1281.
  29. Mansour, M.; Abraham, B.; Anders, L.; Berezovskaya, A.; Gutierrez, A.; Durbin, A.; Etchin, J.; Lawton, L.; Sallan, S.E.; Silverman, L.B.; et al. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science 2014, 346, 1373–1377.
  30. Hu, S.; Qian, M.; Zhang, H.; Guo, Y.; Yang, J.; Zhao, X.; He, H.; Lu, J.; Pan, J.; Chang, M.; et al. Whole-genome noncoding sequence analysis in T-cell acute lymphoblastic leukemia identifies oncogene enhancer mutations. Blood 2017, 129, 3264–3268.
  31. Mansour, M.R.; Duke, V.; Foroni, L.; Patel, B.; Allen, C.; Ancliff, P.J.; Gale, R.E.; Linch, D.C. Notch-1 Mutations Are Secondary Events in Some Patients with T-Cell Acute Lymphoblastic Leukemia. Clin. Cancer Res. 2007, 13, 6964–6969.
  32. Gianni, F.; Belver, L.; Ferrando, A. The Genetics and Mechanisms of T-Cell Acute Lymphoblastic Leukemia. Cold Spring Harb. Perspect. Med. 2019, 10, a035246.
  33. Belver, L.; Ferrando, L.B.A. The genetics and mechanisms of T cell acute lymphoblastic leukaemia. Nat. Rev. Cancer 2016, 16, 494–507.
  34. Kimura, S.; Seki, M.; Kawai, T.; Goto, H.; Yoshida, K.; Isobe, T.; Sekiguchi, M.; Watanabe, K.; Kubota, Y.; Nannya, Y.; et al. DNA methylation-based classification reveals difference between pediatric T-cell acute lymphoblastic leukemia and normal thymocytes. Leukemia 2019, 34, 1163–1168.
  35. Roels, J.; Thénoz, M.; Szarzyńska, B.; Landfors, M.; De Coninck, S.; Demoen, L.; Provez, L.; Kuchmiy, A.; Strubbe, S.; Reunes, L.; et al. Aging of Preleukemic Thymocytes Drives CpG Island Hypermethylation in T-cell Acute Lymphoblastic Leukemia. Blood Cancer Discov. 2020, 1, 274–289.
  36. Ferrando, A.A.; Neuberg, D.S.; Staunton, J.; Loh, M.L.; Huard, C.; Raimondi, S.C.; Behm, F.G.; Pui, C.-H.; Downing, J.R.; Gilliland, D.; et al. Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell 2002, 1, 75–87.
  37. Soulier, J.; Clappier, E.; Cayuela, J.-M.; Regnault, A.; García-Peydró, M.; Dombret, H.; Baruchel, A.; Toribio, M.L.; Sigaux, F. HOXA genes are included in genetic and biologic networks defining human acute T-cell leukemia (T-ALL). Blood 2005, 106, 274–286.
  38. Dadi, S.; Le Noir, S.; Bornet, D.P.; Lhermitte, L.; Zacarias-Cabeza, J.; Bergeron, J.; Villarèse, P.; Vachez, E.; Dik, W.A.; Millien, C.; et al. TLX Homeodomain Oncogenes Mediate T Cell Maturation Arrest in T-ALL via Interaction with ETS1 and Suppression of TCRα Gene Expression. Cancer Cell 2012, 21, 563–576.
  39. Di Giacomo, D.; La Starza, R.; Gorello, P.; Pellanera, F.; Atak, Z.K.; De Keersmaecker, K.; Pierini, V.; Harrison, C.J.; Arniani, S.; Moretti, M.; et al. 14q32 rearrangements deregulating BCL11B mark a distinct subgroup of T and myeloid immature acute leukemia. Blood 2021.
  40. Inaba, H.; Azzato, E.M.; Mullighan, C.G. Integration of Next-Generation Sequencing to Treat Acute Lymphoblastic Leukemia with Targetable Lesions: The St. Jude Children’s Research Hospital Approach. Front. Pediatr. 2017, 5, 258.
  41. Gocho, Y.; Liu, J.; Hu, J.; Yang, W.; Dharia, N.V.; Zhang, J.; Shi, H.; Du, G.; John, A.; Lin, T.-N.; et al. Network-based systems pharmacology reveals heterogeneity in LCK and BCL2 signaling and therapeutic sensitivity of T-cell acute lymphoblastic leukemia. Nat. Rev. Cancer 2021, 2, 1–16.
  42. Hnisz, D.; Weintraub, A.S.; Day, D.S.; Valton, A.-L.; Bak, R.; Li, C.; Goldmann, J.; Lajoie, B.R.; Fan, Z.P.; Sigova, A.A.; et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 2016, 351, 1454–1458.
  43. Park, S.T.; Sun, X.-H. The Tal1 Oncoprotein Inhibits E47-mediated Transcription. J. Biol. Chem. 1998, 273, 7030–7037.
  44. Sanda, T.; Lawton, L.N.; Barrasa, M.I.; Fan, Z.P.; Kohlhammer, H.; Gutierrez, A.; Ma, W.; Tatarek, J.; Ahn, Y.; Kelliher, M.A.; et al. Core Transcriptional Regulatory Circuit Controlled by the TAL1 Complex in Human T Cell Acute Lymphoblastic Leukemia. Cancer Cell 2012, 22, 209–221.
  45. Nottingham, W.T.; Jarratt, A.; Burgess, M.; Speck, C.L.; Cheng, J.F.; Prabhakar, S.; Rubin, E.M.; Li, P.S.; Sloane-Stanley, J.; Kong, A.S.J.; et al. Runx1-mediated hematopoietic stem-cell emergence is controlled by a Gata/Ets/SCL-regulated enhancer. Blood 2007, 110, 4188–4197.
  46. Choi, A.; Illendula, A.; Pulikkan, J.A.; Roderick, J.E.; Tesell, J.; Yu, J.; Hermance, N.; Zhu, L.J.; Castilla, L.H.; Bushweller, J.H.; et al. RUNX1 is required for oncogenic Myb and Myc enhancer activity in T-cell acute lymphoblastic leukemia. Blood 2017, 130, 1722–1733.
  47. O’Neil, J.; Shank, J.; Cusson, N.; Murre, C.; Kelliher, M. TAL1/SCL induces leukemia by inhibiting the transcriptional activity of E47/HEB. Cancer Cell 2004, 5, 587–596.
  48. Draheim, K.M.; Hermance, N.; Yang, Y.; Arous, E.; Calvo, J.; Kelliher, M.A. A DNA-binding mutant of TAL1 cooperates with LMO2 to cause T cell leukemia in mice. Oncogene 2010, 30, 1252–1260.
  49. Ferrando, A.A.; Look, A.T. Gene expression profiling in T-cell acute lymphoblastic leukemia. Semin. Hematol. 2003, 40, 274–280.
  50. Leong, W.Z.; Tan, S.H.; Ngoc, P.C.T.; Amanda, S.; Yam, A.W.Y.; Liau, W.-S.; Gong, Z.; Lawton, L.N.; Tenen, D.G.; Sanda, T. ARID5B as a critical downstream target of the TAL1 complex that activates the oncogenic transcriptional program and promotes T-cell leukemogenesis. Genes Dev. 2017, 31, 2343–2360.
  51. Tan, S.H.; Leong, W.Z.; Ngoc, P.C.T.; Tan, T.K.; Bertulfo, F.C.; Lim, M.C.; An, O.; Li, Z.; Yeoh, A.E.J.; Fullwood, M.J.; et al. The enhancer RNA ARIEL activates the oncogenic transcriptional program in T-cell acute lymphoblastic leukemia. Blood 2019, 134, 239–251.
  52. Piovan, E.; Yu, J.; Tosello, V.; Herranz, D.; Ambesi-Impiombato, A.; Da Silva, A.C.; Sanchez-Martin, M.; Perez-Garcia, A.; Rigo, I.; Castillo, M.; et al. Direct Reversal of Glucocorticoid Resistance by AKT Inhibition in Acute Lymphoblastic Leukemia. Cancer Cell 2013, 24, 766–776.
  53. Jena, N.; Sheng, J.; Hu, J.K.; Li, W.; Zhou, W.; Lee, G.; Tsichlis, N.; Pathak, A.; Brown, N.; Deshpande, A.; et al. CDK6-mediated repression of CD25 is required for induction and maintenance of Notch1-induced T-cell acute lymphoblastic leukemia. Leukemia 2015, 30, 1033–1043.
  54. Coustan-Smith, E.; Mullighan, C.; Onciu, M.; Behm, F.G.; Raimondi, S.C.; Pei, D.; Cheng, C.; Su, X.; Rubnitz, J.; Basso, G.; et al. Early T-cell precursor leukaemia: A subtype of very high-risk acute lymphoblastic leukaemia. Lancet Oncol. 2009, 10, 147–156.
  55. Zhang, J.; Ding, L.; Holmfeldt, L.; Wu, G.; Heatley, S.; Payne-Turner, D.; Easton, J.; Chen, X.; Wang, J.; Rusch, M.; et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 2012, 481, 157–163.
  56. Alexander, T.B.; Gu, Z.; Iacobucci, I.; Dickerson, K.; Choi, J.K.; Xu, B.; Payne-Turner, D.; Yoshihara, H.; Loh, M.L.; Horan, J.; et al. The genetic basis and cell of origin of mixed phenotype acute leukaemia. Nature 2018, 562, 373–379.
  57. Maude, S.L.; Dolai, S.; Delgado-Martin, C.; Vincent, T.; Robbins, A.; Selvanathan, A.; Ryan, T.; Hall, J.; Wood, A.C.; Tasian, S.K.; et al. Efficacy of JAK/STAT pathway inhibition in murine xenograft models of early T-cell precursor (ETP) acute lymphoblastic leukemia. Blood 2015, 125, 1759–1767.
  58. McCarter, A.C.; Wang, Q.; Chiang, M. Notch in Leukemia. Adv. Exp. Med. Biol. 2018, 1066, 355–394.
  59. Weng, A.; Ferrando, A.A.; Lee, W.; Iv, J.P.M.; Silverman, L.B.; Sanchez-Irizarry, C.; Blacklow, S.C.; Look, A.T.; Aster, J.C. Activating Mutations of NOTCH1 in Human T Cell Acute Lymphoblastic Leukemia. Science 2004, 306, 269–271.
  60. Sulis, M.L.; Williams, O.; Palomero, T.; Tosello, V.; Pallikuppam, S.; Real, P.J.; Barnes, K.; Zuurbier, L.; Meijerink, J.P.; Ferrando, A.A. NOTCH1 extracellular juxtamembrane expansion mutations in T-ALL. Blood 2008, 112, 733–740.
  61. Haydu, J.E.; De Keersmaecker, K.; Duff, M.K.; Paietta, E.; Racevskis, J.; Wiernik, P.H.; Rowe, J.M.; Ferrando, A. An activating intragenic deletion in NOTCH1 in human T-ALL. Blood 2012, 119, 5211–5214.
  62. Thompson, B.J.; Buonamici, S.; Sulis, M.L.; Palomero, T.; Vilimas, T.; Basso, G.; Ferrando, A.; Aifantis, I. The SCFFBW7 ubiquitin ligase complex as a tumor suppressor in T cell leukemia. J. Exp. Med. 2007, 204, 1825–1835.
  63. O’Neil, J.; Grim, J.; Strack, P.; Rao, S.; Tibbitts, D.; Winter, C.; Hardwick, J.; Welcker, M.; Meijerink, J.; Pieters, R.; et al. FBW7 mutations in leukemic cells mediate NOTCH pathway activation and resistance to γ-secretase inhibitors. J. Exp. Med. 2007, 204, 1813–1824.
  64. Suarez-Puente, X.; Beà, S.; Valdés-Mas, R.; Villamor, N.; Gutiérrez-Abril, J.; Martin-Subero, J.I.; Munar, M.; Rubio-Perez, C.; Jares, P.; Aymerich, M.; et al. Non-coding recurrent mutations in chronic lymphocytic leukaemia. Nature 2015, 526, 519–524.
  65. Chiang, M.; Xu, L.; Shestova, O.; Histen, G.; L’Heureux, S.; Romany, C.; Childs, M.E.; Gimotty, P.A.; Aster, J.C.; Pear, W.S. Leukemia-associated NOTCH1 alleles are weak tumor initiators but accelerate K-ras–initiated leukemia. J. Clin. Investig. 2008, 118, 3181–3194.
  66. Wendorff, A.A.; Quinn, S.A.; Rashkovan, M.; Madubata, C.J.; Ambesi-Impiombato, A.; Litzow, M.R.; Tallman, M.S.; Paietta, E.; Paganin, M.; Basso, G.; et al. Phf6 Loss Enhances HSC Self-Renewal Driving Tumor Initiation and Leukemia Stem Cell Activity in T-ALL. Cancer Discov. 2018, 9, 436–451.
  67. Herranz, D.; Ambesi-Impiombato, A.; Palomero, T.; Schnell, S.A.; Belver, L.; Wendorff, A.A.; Xu, L.; Castillo-Martin, M.; Llobet-Navás, D.; Cordon-Cardo, C.; et al. A NOTCH1-driven MYC enhancer promotes T cell development, transformation and acute lymphoblastic leukemia. Nat. Med. 2014, 20, 1130–1137.
  68. Albertí-Servera, L.; Demeyer, S.; Govaerts, I.; Swings, T.; De Bie, J.; Gielen, O.; Brociner, M.; Michaux, L.; Maertens, J.; Uyttebroeck, A.; et al. Single-cell DNA amplicon sequencing reveals clonal heterogeneity and evolution in T-cell acute lymphoblastic leukemia. Blood 2021, 137, 801–811.
  69. De Bie, J.; Demeyer, S.; Alberti-Servera, L.; Geerdens, E.; Segers, H.; Broux, M.; De Keersmaecker, K.; Michaux, L.; Vandenberghe, P.; Voet, T.; et al. Single-cell sequencing reveals the origin and the order of mutation acquisition in T-cell acute lymphoblastic leukemia. Leukemia 2018, 32, 1358–1369.
  70. Yashiro-Ohtani, Y.; Wang, H.; Zang, C.; Arnett, K.L.; Bailis, W.; Ho, Y.; Knoechel, B.; Lanauze, C.; Louis, L.; Forsyth, K.; et al. Long-range enhancer activity determinesMycsensitivity to Notch inhibitors in T cell leukemia. Proc. Natl. Acad. Sci. USA 2014, 111, E4946–E4953.
  71. Sanchez-Martin, M.; Ferrando, A. The NOTCH1-MYC highway toward T-cell acute lymphoblastic leukemia. Blood 2017, 129, 1124–1133.
  72. Reizis, B. Direct induction of T lymphocyte-specific gene expression by the mammalian Notch signaling pathway. Genes Dev. 2002, 16, 295–300.
  73. Zheng, R.; Li, M.; Wang, S.; Liu, Y. Advances of target therapy on NOTCH1 signaling pathway in T-cell acute lymphoblastic leukemia. Exp. Hematol. Oncol. 2020, 9, 31.
  74. Papayannidis, C.; De Angelo, D.J.; Stock, W.; Huang, B.; Shaik, M.N.; Cesari, R.; Zheng, X.; Reynolds, J.M.; English, P.A.; Ozeck, M.; et al. A Phase 1 study of the novel gamma-secretase inhibitor PF-03084014 in patients with T-cell acute lymphoblastic leukemia and T-cell lymphoblastic lymphoma. Blood Cancer J. 2015, 5, e350.
  75. Gavai, A.V.; Quesnelle, C.A.; Norris, D.J.; Han, W.-C.; Gill, P.S.; Shan, W.; Balog, A.; Chen, K.; Tebben, A.J.; Rampulla, R.; et al. Discovery of Clinical Candidate BMS-906024: A Potent Pan-Notch Inhibitor for the Treatment of Leukemia and Solid Tumors. ACS Med. Chem. Lett. 2015, 6, 523–527.
  76. Sanchez-Martin, M.; Ambesi-Impiombato, A.; Qin, Y.; Herranz, D.; Bansal, M.; Girardi, T.; Paietta, E.; Tallman, M.S.; Rowe, J.M.; De Keersmaecker, K.; et al. Synergistic antileukemic therapies inNOTCH1-induced T-ALL. Proc. Natl. Acad. Sci. USA 2017, 114, 2006–2011.
  77. Wei, P.; Walls, M.; Qiu, M.; Ding, R.; Denlinger, R.H.; Wong, A.; Tsaparikos, K.; Jani, J.P.; Hosea, N.A.; Sands, M.; et al. Evaluation of Selective γ-Secretase Inhibitor PF-03084014 for Its Antitumor Efficacy and Gastrointestinal Safety to Guide Optimal Clinical Trial Design. Mol. Cancer Ther. 2010, 9, 1618–1628.
  78. Real, P.; Tosello, V.; Palomero, T.; Castillo, M.; Hernando, E.; De Stanchina, E.; Sulis, M.L.; Barnes, K.; Sawai, C.; Homminga, I.; et al. γ-secretase inhibitors reverse glucocorticoid resistance in T cell acute lymphoblastic leukemia. Nat. Med. 2008, 15, 50–58.
  79. Cullion, K.; Draheim, K.M.; Hermance, N.; Tammam, J.; Sharma, V.M.; Ware, C.; Nikov, G.; Krishnamoorthy, V.; Majumder, P.K.; Kelliher, M.A. Targeting the Notch1 and mTOR pathways in a mouse T-ALL model. Blood 2009, 113, 6172–6181.
  80. Franciosa, G.; Smits, J.G.A.; Minuzzo, S.; Martinez-Val, A.; Indraccolo, S.; Olsen, J.V. Proteomics of resistance to Notch1 inhibition in acute lymphoblastic leukemia reveals targetable kinase signatures. Nat. Commun. 2021, 12, 2507.
  81. Trinquand, A.; Tanguy-Schmidt, A.; Ben Abdelali, R.; Lambert, J.; Beldjord, K.; Lengliné, E.; De Gunzburg, N.; Bornet, D.P.; Lhermitte, L.; Mossafa, H.; et al. Toward a NOTCH1/FBXW7/RAS/PTEN–Based Oncogenetic Risk Classification of Adult T-Cell Acute Lymphoblastic Leukemia: A Group for Research in Adult Acute Lymphoblastic Leukemia Study. J. Clin. Oncol. 2013, 31, 4333–4342.
  82. Newman, S.; Nakitandwe, J.; Kesserwan, C.A.; Azzato, E.M.; Wheeler, D.A.; Rusch, M.; Shurtleff, S.; Hedges, D.J.; Hamilton, K.V.; Foy, S.G.; et al. Genomes for Kids: The scope of pathogenic mutations in pediatric cancer revealed by comprehensive DNA and RNA sequencing. Cancer Discov. 2021.
  83. Jobanputra, V.; Wrzeszczynski, K.O.; Buttner, R.; Caldas, C.; Cuppen, E.; Grimmond, S.; Haferlach, T.; Mullighan, C.; Schuh, A.; Elemento, O. Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology. Semin. Cancer Biol. 2021.
  84. Rosenquist, R.; Cuppen, E.; Buettner, R.; Caldas, C.; Dreau, H.; Elemento, O.; Frederix, G.; Grimmond, S.; Haferlach, T.; Jobanputra, V.; et al. Clinical utility of whole-genome sequencing in precision oncology. Semin. Cancer Biol. 2021.
  85. Meggendorfer, M.; Jobanputra, V.; Wrzeszczynski, K.O.; Roepman, P.; de Bruijn, E.; Cuppen, E.; Buttner, R.; Caldas, C.; Grimmond, S.; Mullighan, C.G.; et al. Analytical demands to use whole-genome sequencing in precision oncology. Semin. Cancer Biol. 2021.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Oncology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Charles Mullighan
,
Ilaria Iacobucci
View Times: 560
Revisions: 2 times (View History)
Update Date: 09 Sep 2021
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback