A complex MLL rearrangement identified five years after initial MDS diagnosis results in out-of-frame fusions without progression to acute leukemia

The KMT2A recombinome of acute leukemias in 2023

Chromosomal rearrangements of the human KMT2A/MLL gene are associated with de novo as well as therapy-induced infant, pediatric, and adult acute leukemias. Here, we present the data obtained from 3401 acute leukemia patients that have been analyzed between 2003 and 2022. Genomic breakpoints within the KMT2A gene and the involved translocation partner genes (TPGs) and KMT2A-partial tandem duplications (PTDs) were determined. Including the published data from the literature, a total of 107 in-frame KMT2A gene fusions have been identified so far. Further 16 rearrangements were out-of-frame fusions, 18 patients had no partner gene fused to 5'-KMT2A, two patients had a 5'-KMT2A deletion, and one ETV6::RUNX1 patient had an KMT2A insertion at the breakpoint. The seven most frequent TPGs and PTDs account for more than 90% of all recombinations of the KMT2A, 37 occur recurrently and 63 were identified so far only once. This study provides a comprehensive analysis of the KMT2A recombinome in acute leukemia patients. Besides the scientific gain of information, genomic breakpoint sequences of these patients were used to monitor minimal residual disease (MRD). Thus, this work may be directly translated from the bench to the bedside of patients and meet the clinical needs to improve patient survival.

Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation

Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.

The MLL recombinome of acute leukemias in 2017

Chromosomal rearrangements of the human MLL/KMT2A gene are associated with infant, pediatric, adult and therapy-induced acute leukemias. Here we present the data obtained from 2345 acute leukemia patients. Genomic breakpoints within the MLL gene and the involved translocation partner genes (TPGs) were determined and 11 novel TPGs were identified. Thus, a total of 135 different MLL rearrangements have been identified so far, of which 94 TPGs are now characterized at the molecular level. In all, 35 out of these 94 TPGs occur recurrently, but only 9 specific gene fusions account for more than 90% of all illegitimate recombinations of the MLL gene. We observed an age-dependent breakpoint shift with breakpoints localizing within MLL intron 11 associated with acute lymphoblastic leukemia and younger patients, while breakpoints in MLL intron 9 predominate in AML or older patients. The molecular characterization of MLL breakpoints suggests different etiologies in the different age groups and allows the correlation of functional domains of the MLL gene with clinical outcome. This study provides a comprehensive analysis of the MLL recombinome in acute leukemia and demonstrates that the establishment of patient-specific chromosomal fusion sites allows the design of specific PCR primers for minimal residual disease analyses for all patients.

t(15;21) translocations leading to the concurrent downregulation of RUNX1 and its transcription factor partner genes SIN3A and TCF12 in myeloid disorders

Through a combined approach integrating RNA-Seq, SNP-array, FISH and PCR techniques, we identified two novel t(15;21) translocations leading to the inactivation of RUNX1 and its partners SIN3A and TCF12. One is a complex t(15;21)(q24;q22), with both breakpoints mapped at the nucleotide level, joining RUNX1 to SIN3A and UBL7-AS1 in a patient with myelodysplasia. The other is a recurrent t(15;21)(q21;q22), juxtaposing RUNX1 and TCF12, with an opposite transcriptional orientation, in three myeloid leukemia cases. Since our transcriptome analysis indicated a significant number of differentially expressed genes associated with both translocations, we speculate an important pathogenetic role for these alterations involving RUNX1.

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data

MOTIVATION

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data.

RESULTS

We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes.

CONCLUSION

We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

Cancer cytogenetics: methodology revisited

The Philadelphia chromosome was the first genetic abnormality discovered in cancer (in 1960), and it was found to be consistently associated with CML. The description of the Philadelphia chromosome ushered in a new era in the field of cancer cytogenetics. Accumulating genetic data have been shown to be intimately associated with the diagnosis and prognosis of neoplasms; thus, karyotyping is now considered a mandatory investigation for all newly diagnosed leukemias. The development of FISH in the 1980s overcame many of the drawbacks of assessing the genetic alterations in cancer cells by karyotyping. Karyotyping of cancer cells remains the gold standard since it provides a global analysis of the abnormalities in the entire genome of a single cell. However, subsequent methodological advances in molecular cytogenetics based on the principle of FISH that were initiated in the early 1990s have greatly enhanced the efficiency and accuracy of karyotype analysis by marrying conventional cytogenetics with molecular technologies. In this review, the development, current utilization, and technical pitfalls of both the conventional and molecular cytogenetics approaches used for cancer diagnosis over the past five decades will be discussed.

Analyzing acute leukemia patients with complex MLL rearrangements by a sequential LDI-PCR approach

Translocations involving MLL gene are common among children with acute leukemias. Most importantly, the presence of a given MLL fusion partner dictates the outcome of patients. Patients with complex MLL rearrangements, e.g. three-way translocations could be related to a poor clinical outcome. For this purpose, we characterize 5 childhood patients with three-way translocations involving MLL gene. By LDI-PCR we identified 15 out of 17 fusion alleles and determined the localization of these breakpoints. In all cases at least one functional MLL fusion allele was present. In addition, patients displayed a remaining 3'-MLL allele that allow in principle the expression of the MLL* protein variant.

The role of FISH in hematologic cancer

SUMMARY Cytogenetic analysis is now considered a mandatory investigation in the diagnostic work-up of hematologic malignancies. Recurring structural aberrations serve as powerful markers not only for diagnosis and prognosis of these conditions, but also guide the selection of targeted drugs for personalized oncology. The FISH approach is established as an indispensable tool to complement conventional cytogenetics, in addition to basic and clinical research applications. FISH is used to identify specific chromosomal aberrations through the detection of target DNA sequences by fluorescently labeled DNA probes. Multicolor FISH analysis allows the accurate identification of recurring translocations in neoplastic cells by means of genomic probes that flank the breakpoints. This review summarizes the panel of FISH probes for selection and the current utilization of these FISH techniques in unraveling chromosomal aberrations. The niche of FISH analysis is also highlighted. Variant signal patterns of the clinically useful FISH probes for hematologic oncology illustrated here provide useful interpretative reference for molecular pathology laboratories. In addition, the recent application of FISH tests in contributing information on drug targets at the genomic level to support personalized oncology will also be discussed.