Integrative Genomic Analysis Reveals Cancer-Associated Gene Mutations in Chronic Myeloid Leukemia Patients with Resistance or Intolerance to Tyrosine Kinase Inhibitor

PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization

Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.

Frequency and prognostic influence of ASXL1 mutations and its potential association with BCR-ABL1 transcript type and smoke in chronic myeloid leukemia patients

BACKGROUND

Heterogeneous response to tyrosine kinase inhibitors (TKIs) and progress to advance phases, still is a significant clinical problem. These are attributed to additional mutations in mutated non-ABL1 genes. we aimed to determine prognostic effects of ASXL1 mutations as a biomarker for diverse treatment response and disease progression to aid clinical management.

METHODS

We performed ASXL1 gene mutational screening in 80 Ph+CML patients at different phases and 10 healthy control by direct sequencing method. Multiplex and qRT-PCR, standard chromosome banding analysis were used to determine BCR-ABL1 transcript type, molecular and cytogenetic responses respectively.

RESULTS

overall, four type mutations were identified in 11.25% of the patients. There was significant difference regarding mutation frequency between chronic and advance phases (P = 0.0002), sokal risk score (P = 0.0001), smoking (P = 0.02) and mean of during time of imatinib treatment (P = 0.009) between patients with and without ASXL1 mutations. ASXL1 mutations frequency had a bias toward younger than older and women than men, but no significant (P > 0.05). ASXL1 mutations were found more recurrently in patients carrying ABL1 KD mutations (P = 0.003). The risk of increasing resistance and disease progression in patients with ASXL1 mutations was 32 and 63 fold higher than those without mutations respectively (P = 0.01; P = 0.0002). The risk of ASXL1 mutations presence in patients with b2a2 transcript type was much higher than b3a2 type (P = 0.02, OR = 10).

CONCLUSION

Our findings suggest that ASXL1 mutations may be favorable predictive biomarkers to determine the best TKI for each patient, and to prevent CML progression.

Genetic Landscape of Chronic Myeloid Leukemia and a Novel Targeted Drug for Overcoming Resistance

Tyrosine kinase inhibitors (TKIs) exemplify the success of molecular targeted therapy for chronic myeloid leukemia (CML). However, some patients do not respond to TKI therapy. Mutations in the kinase domain of BCR::ABL1 are the most extensively studied mechanism of TKI resistance in CML, but BCR::ABL1-independent mechanisms are involved in some cases. There are two known types of mechanisms that contribute to resistance: mutations in known cancer-related genes; and Philadelphia-associated rearrangements, a novel mechanism of genomic heterogeneity that occurs at the time of the Philadelphia chromosome formation. Most chronic-phase and accelerated-phase CML patients who were treated with the third-generation TKI for drug resistance harbored one or more cancer gene mutations. Cancer gene mutations and additional chromosomal abnormalities were found to be independently associated with progression-free survival. The novel agent asciminib specifically inhibits the ABL myristoyl pocket (STAMP) and shows better efficacy and less toxicity than other TKIs due to its high target specificity. In the future, pooled analyses of various studies should address whether additional genetic analyses could guide risk-adapted therapy and lead to a final cure for CML.

Genomic Mechanisms Influencing Outcome in Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) represents the disease prototype of genetically based diagnosis and management. Tyrosine kinase inhibitors (TKIs), that target the causal BCR::ABL1 fusion protein, exemplify the success of molecularly based therapy. Most patients now have long-term survival; however, TKI resistance is a persistent clinical problem. TKIs are effective in the BCR::ABL1-driven chronic phase of CML but are relatively ineffective for clinically defined advanced phases. Genomic investigation of drug resistance using next-generation sequencing for CML has lagged behind other hematological malignancies. However, emerging data show that genomic abnormalities are likely associated with suboptimal response and drug resistance. This has already been supported by the presence of BCR::ABL1 kinase domain mutations in drug resistance, which led to the development of more potent TKIs. Next-generation sequencing studies are revealing additional mutations associated with resistance. In this review, we discuss the initiating chromosomal translocation that may not always be a straightforward reciprocal event between chromosomes 9 and 22 but can sometimes be accompanied by sequence deletion, inversion, and rearrangement. These events may biologically reflect a more genomically unstable disease prone to acquire mutations. We also discuss the future role of cancer-related gene mutation analysis for risk stratification in CML.

BCORL1 S878G, GNB1 G116S, SH2B3 A536T, and KMT2D S3708R tetramutation co-contribute to a pediatric acute myeloid leukemia: Case report and literature review

Acute myeloid leukemia (AML) is a clinically, morphologically, and genetically heterogeneous group of malignancies characterized by a wide range of genomic alterations responsible for defective regulation of the differentiation and self-renewal programs of hematopoietic stem cells. Here, we report a 4-month-old boy who had acute onset with leukocytosis and abdominal mass. The morphological analysis of bone marrow (BM) smear revealed extremely marrow hyperplasia, large quantities of immature cells, and primary and immature monocytic hyperplasia accounting for 57.5% of nucleated cells. The chromosome karyotype of the case was complex, representing 48, XY, +13, +19[12]/48, idem, del (p12)[8]. After RNAs sequencing, a mutation (c.346G > A, p.G116S) of the GNB1 gene was detected and localized to the mutational hotspot in Exon 7. Meanwhile, the other three mutations were identified by next-generation sequencing (NGS) and whole-exome sequencing (WES) of DNA from the BM aspirate and oral swab, including BCORL1 mutation [c.2632A > G, p.S878G, mutation allele frequency (VAF): 99.95%], SH2B3 mutation (c.1606G > A, p.A536T, VAF: 51.17%), and KMT2D mutation (c.11124C > G, p.S3708R, VAF: 48.95%). BCORL1 mutations have been associated with the pathogenesis of AML, whereas other mutations have rarely been previously reported in pediatric AML. The patient did not undergo the combination chemotherapy and eventually died of respiratory failure. In conclusion, the concurrence of BCORL1, GNB1, SH2B3, and KMT2D mutations may be a mutationally detrimental combination and contribute to disease progression.

Hierarchical distribution of somatic variants in newly diagnosed chronic myeloid leukaemia at diagnosis and early follow-up

There is an emerging body of evidence that patients with chronic myeloid leukaemia (CML) may carry not only breakpoint cluster region-Abelson murine leukaemia viral oncogene homologue 1 (BCR-ABL1) kinase domain mutations (BCR-ABL1 KD mutations), but also mutations in other genes. Their occurrence is highest during progression or at failure, but their impact at diagnosis is unclear. In the present study, we prospectively screened for mutations in 18 myeloid neoplasm-associated genes and BCR-ABL1 KD in the following populations: bulk leucocytes, CD34⁺ CD38⁺ progenitors and CD34⁺ CD38^- stem cells, at diagnosis and early follow-up. In our cohort of chronic phase CML patients, nine of 49 patients harboured somatic mutations in the following genes: six ASXL1 mutations, one SETBP1, one TP53, one JAK2, but no BCR-ABL1 KD mutations. In seven of the nine patients, mutations were detected in multiple hierarchical populations including bulk leucocytes at diagnosis. The mutation dynamics reflected the BCR-ABL1 transcript decline induced by treatment in eight of the nine cases, suggesting that mutations were acquired in the Philadelphia chromosome (Ph)-positive clone. In one patient, the JAK2 V617F mutation correlated with a concomitant Ph-negative myeloproliferative neoplasm and persisted despite a 5-log reduction of the BCR-ABL1 transcript. Only two of the nine patients with mutations failed first-line therapy. No correlation was found between the mutation status and survival or response outcomes.

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

The BCR-ABL1 fusion gene, which causes aberrant kinase activity and uncontrolled cell proliferation, is the hallmark of chronic myeloid leukemia (CML). The development of tyrosine kinase inhibitors (TKI) that target the BCR-ABL oncoprotein has led to dramatic improvement in CML management. However, some challenges remain to be addressed in the TKI era, including patient stratification and the selection of frontline TKIs and CML progression. Additionally, with the emerging goal of treatment-free remission (TFR) in CML management, biomarkers that predict the outcomes of stopping TKI remain to be identified. Notably, recent reports have revealed the power of genome screening in understanding the role of genome aberrations other than BCR-ABL1 in CML pathogenesis. These studies have discovered the presence of disease-phase specific mutations and linked certain mutations to inferior responses to TKI treatment and CML progression. A personalized approach that incorporates genetic data in tailoring treatment strategies has been successfully implemented in acute leukemia, and it represents a promising approach for the management of high-risk CML patients. In this article, we will review current knowledge about the mutational profile in different phases of CML as well as patterns of mutational dynamics in patients having different outcomes. We highlight the effects of somatic mutations involving certain genes (e.g. epigenetic modifiers) on the outcomes of TKI treatment. We also discuss the potential value of incorporating genetic data in treatment decisions and the routine care of CML patients as a future direction for optimizing CML management.