Integrated Genomic Analysis Identifies ANKRD36 Gene as a Novel and Common Biomarker of Disease Progression in Chronic Myeloid Leukemia

Iqbal, Zafar; Absar, Muhammad; Akhtar, Tanveer; Aleem, Aamer; Jameel, Abid; Basit, Sulman; Ullah, Anhar; Afzal, Sibtain; Ramzan, Khushnooda; Rasool, Mahmood; Karim, Sajjad; Mirza, Zeenat; Iqbal, Mudassar; AlMajed, Maryam; AlShehab, Buthinah; AlMukhaylid, Sarah; AlMutairi, Nouf; Alanazi, Nawaf; Sabar, Muhammad Farooq; Arshad, Muhammad; Asif, Muhammad; Shammas, Masood A.; Mahmood, Amer

doi:10.3390/biology10111182

Cited by 13 publications

(24 citation statements)

References 63 publications

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“…Since IDRs could drive liquid-liquid phase separation 17 (LLPS) and the dysregulation of LLPS was a key event in the initiation and/or evolution of cancer 18 , the identification of recoding DMs in regions encoding predicted IDRs of ANKRD gene loci suggested the interplay of dysregulated LLPS by mutated ANKRD genes along AML pathogenesis. Together with a recent finding of ANKRD36 as a biomarker of Chronic Myeloid Leukemia 19 (CML), these results strongly suggested the possible association of enriched recoding DMs in ANKRD genes with AML.…”

Section: Resultssupporting

confidence: 54%

A deep learning model embedded framework to distinguish DNA and RNA mutations directly from RNA-seq

Fu,

Gao,

Nan

et al. 2023

Preprint

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We develop a stepwise computational framework, called DEMINING, to directly detect expressed DNA and RNA mutations in RNA deep sequencing data. DEMINING incorporates a deep learning model named DeepDDR, which facilitates the separation of expressed DNA mutations from RNA mutations after RNA-seq read mapping and pileup. When applied in RNA-seq of acute myeloid leukemia patients, DEMINING uncovered previously-underappreciated DNA and RNA mutations, some associated with the upregulated expression of host genes or the production of neoantigens. Finally, we demonstrate that DEMINING could precisely classify DNA and RNA mutations in RNA-seq data from non-primate species through the utilization of transfer learning.

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Section: Resultssupporting

confidence: 54%

A deep learning model embedded framework to distinguish DNA and RNA mutations directly from RNA-seq

Fu,

Gao,

Nan

et al. 2023

Preprint

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“…The first one, a 24.2Kb inversion in chromosome 2 which affects the immunoglobulin kappa variable gene IGKV3-11 ( Supplementary Figure 14 ) which expression has been observed in myelomas (https://www.proteinatlas.org/ENSG00000241351-IGKV3-11/pathology. The second, a 8.9Kb deletion also in chromosome 2 which affects the ankyrin repeat domain gn ANKRD36 ( Supplementary Figure 15 ) which has been used as a biomarker of disease progression in Leukemia (Iqbal et al 2021). Upon further inspection, we detected a larger deletion in the same region (12kb) with some reads supporting the SV in the control.…”

Section: Resultsmentioning

confidence: 99%

The benefit of a complete reference genome for cancer structural variant analysis

Paulin,

Fan,

O’Neill

et al. 2024

Preprint

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The complexities of cancer genomes are becoming more easily interpreted due to advancements in sequencing technologies and improved bioinformatic analysis. Structural variants (SVs) represent an important subset of somatic events in tumors. While detection of SVs has been markedly improved by the development of long-read sequencing, somatic variant identification and annotation remains challenging. We hypothesized that use of a completed human reference genome (CHM13-T2T) would improve somatic SV calling. Our findings in a tumour/normal matched benchmark sample and two patient samples show that the CHM13-T2T improves SV detection and prioritization accuracy compared to GRCh38, with a notable reduction in false positive calls. We also overcame the lack of annotation resources for CHM13-T2T by lifting over CHM13-T2T-aligned reads to the GRCh38 genome, therefore combining both improved alignment and advanced annotations. In this process, we assessed the current SV benchmark set for COLO829/COLO829BL across four replicates sequenced at different centers with different long-read technologies. We discovered instability of this cell line across these replicates; 346 SVs (1.13%) were only discoverable in a single replicate. We identify 49 somatic SVs, which appear to be stable as they are consistently present across the four replicates. As such, we propose this consensus set as an updated benchmark for somatic SV calling and include both GRCh38 and CHM13-T2T coordinates in our benchmark. The benchmark is available at: 10.5281/zenodo.10819636 Our work demonstrates new approaches to optimize somatic SV prioritization in cancer with potential improvements in other genetic diseases.

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“…Moreover, the identification of ANKRd36 mutations exclusively with AP-/BC-CML suggests that it is a novel molecular biomarker that can be used to monitor CML progression, early determination of patients at risk of CML progression, particularly blast crisis, and carry out timely therapeutic interventions of at-risk patients to delay or minimize blast crisis development, that can lead to considerable improvement in overall survival of high risk BC-CML patients (30). Given its highest expression in myeloid cells of the bone marrow, ANKRD36 holds potential as a potential novel drug target, particularly for patients with advanced phases of CML (13,31).…”

Section: Discussionmentioning

confidence: 99%

“…In our previous report, we discovered a novel biomarker associated with the progression of Chronic Myeloid Leukemia (CML) (13). The current study focuses on the clinical validation of progression.…”

Section: Discussionmentioning

confidence: 99%

Investigations on Clinical Validation of mutated ANKRD36 as a Novel Molecular Biomarker for Monitoring Early Progression and Timely Therapeutic Interventions in Blast Crisis CML

Alanazi,

Siyal,

Absar

et al. 2023

Preprint

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BackgroundChronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression.Materials and MethodsThe study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2020 and September 2023. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved by scientific and ethical review committees of all participating centers.Sanger sequencing was employed to detect ANKRD36 mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36. Data was analyzed using SPSS version 26.ResultsDuring our study, 17% of CML patients progressed to advanced phases AP-CML n= 11 (8.9%) and BC-CML n=10 (8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c.1183_1184 delGC and c.1187_1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML.ConclusionsThe study confirms ANKRD36 as a novel genomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells and correlation with high WBC count. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

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Integrated Genomic Analysis Identifies ANKRD36 Gene as a Novel and Common Biomarker of Disease Progression in Chronic Myeloid Leukemia

Cited by 13 publications

References 63 publications

A deep learning model embedded framework to distinguish DNA and RNA mutations directly from RNA-seq

A deep learning model embedded framework to distinguish DNA and RNA mutations directly from RNA-seq

The benefit of a complete reference genome for cancer structural variant analysis

Investigations on Clinical Validation of mutated ANKRD36 as a Novel Molecular Biomarker for Monitoring Early Progression and Timely Therapeutic Interventions in Blast Crisis CML

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