RNAmut: robust identification of somatic mutations in acute myeloid leukemia using RNA-sequencing

De novo TLK1 and MDM1 mutations in a patient with a neurodevelopmental disorder and immunodeficiency

The Tousled-like kinases 1 and 2 (TLK1/TLK2) regulate DNA replication, repair and chromatin maintenance. TLK2 variants underlie the neurodevelopmental disorder (NDD) 'Intellectual Disability, Autosomal Dominant 57' (MRD57), characterized by intellectual disability and microcephaly. Several TLK1 variants have been reported in NDDs but their functional significance is unknown. A male patient presenting with ID, seizures, global developmental delay, hypothyroidism, and primary immunodeficiency was determined to have a heterozygous TLK1 variant (c.1435C>G, p.Q479E), as well as a mutation in MDM1 (c.1197dupT, p.K400∗). Cells expressing TLK1 p.Q479E exhibited reduced cytokine responses and elevated DNA damage, but not increased radiation sensitivity or DNA repair defects. The TLK1 p.Q479E variant impaired kinase activity but not proximal protein interactions. Our study provides the first functional characterization of NDD-associated TLK1 variants and suggests that, such as TLK2, TLK1 variants may impact development in multiple tissues and should be considered in the diagnosis of rare NDDs.

Stellae-123 gene expression signature improved risk stratification in taiwanese acute myeloid leukemia patients

The European Leukemia Net recommendations provide valuable guidance in treatment decisions of patients with acute myeloid leukemia (AML). However, the genetic complexity and heterogeneity of AML are not fully covered, notwithstanding that gene expression analysis is crucial in the risk stratification of AML. The Stellae-123 score, an AI-based model that captures gene expression patterns, has demonstrated robust survival predictions in AML patients across four western-population cohorts. This study aims to evaluate the applicability of Stellae-123 in a Taiwanese cohort. The Stellae-123 model was applied to 304 de novo AML patients diagnosed and treated at the National Taiwan University Hospital. We find that the pretrained (BeatAML-based) model achieved c-indexes of 0.631 and 0.632 for the prediction of overall survival (OS) and relapse-free survival (RFS), respectively. Model retraining within our cohort further improve the cross-validated c-indexes to 0.667 and 0.667 for OS and RFS prediction, respectively. Multivariable analysis identify both pretrained and retrained models as independent prognostic biomarkers. We further show that incorporating age, Stellae-123, and ELN classification remarkably improves risk stratification, revealing c-indices of 0.73 and 0.728 for OS and RFS, respectively. In summary, the Stellae-123 gene expression signature is a valuable prognostic tool for AML patients and model retraining can improve the accuracy and applicability of the model in different populations.

Genotoxic aldehyde stress prematurely ages hematopoietic stem cells in a p53-driven manner

Aged hematopoietic stem cells (HSCs) display diminished self-renewal and a myeloid differentiation bias. However, the drivers and mechanisms that underpin this fundamental switch are not understood. HSCs produce genotoxic formaldehyde that requires protection by the detoxification enzymes ALDH2 and ADH5 and the Fanconi anemia (FA) DNA repair pathway. We find that the HSCs in young Aldh2-/-Fancd2-/- mice harbor a transcriptomic signature equivalent to aged wild-type HSCs, along with increased epigenetic age, telomere attrition, and myeloid-biased differentiation quantified by single HSC transplantation. In addition, the p53 response is vigorously activated in Aldh2-/-Fancd2-/- HSCs, while p53 deletion rescued this aged HSC phenotype. To further define the origins of the myeloid differentiation bias, we use a GFP genetic reporter to find a striking enrichment of Vwf+ myeloid and megakaryocyte-lineage-biased HSCs. These results indicate that metabolism-derived formaldehyde-DNA damage stimulates the p53 response in HSCs to drive accelerated aging.

Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. Robust identification of clinically and biologically relevant molecular subtypes from nongenomic high-throughput sequencing data remains challenging. We established the largest multicenter AML cohort (n = 655) in China, with all patients subjected to RNA sequencing (RNA-Seq) and 619 (94.5%) to targeted or whole-exome sequencing (TES/WES). Based on an enhanced consensus clustering, eight stable gene expression subgroups (G1-G8) with unique clinical and biological significance were identified, including two unreported (G5 and G8) and three redefined ones (G4, G6, and G7). Apart from four well-known low-risk subgroups including PML::RARA (G1), CBFB::MYH11 (G2), RUNX1::RUNX1T1 (G3), biallelic CEBPA mutations or -like (G4), four meta-subgroups with poor outcomes were recognized. The G5 (myelodysplasia-related/-like) subgroup enriched clinical, cytogenetic and genetic features mimicking secondary AML, and hotspot mutations of IKZF1 (p.N159S) (n = 7). In contrast, most NPM1 mutations and KMT2A and NUP98 fusions clustered into G6-G8, showing high expression of HOXA/B genes and diverse differentiation stages, from hematopoietic stem/progenitor cell down to monocyte, namely HOX-primitive (G7), HOX-mixed (G8), and HOX-committed (G6). Through constructing prediction models, the eight gene expression subgroups could be reproduced in the Cancer Genome Atlas (TCGA) and Beat AML cohorts. Each subgroup was associated with distinct prognosis and drug sensitivities, supporting the clinical applicability of this transcriptome-based classification of AML. These molecular subgroups illuminate the complex molecular network of AML, which may promote systematic studies of disease pathogenesis and foster the screening of targeted agents based on omics.

Oncogenic lesions and molecular subtypes in adults with B-cell acute lymphoblastic leukemia

B-cell acute lymphoblastic leukemia (B-ALL), a genetically heterogeneous disease, is classified into different molecular subtypes that are defined by recurrent gene rearrangements, gross chromosomal abnormalities, or specific gene mutations. Cells with these genetic alterations acquire a leukemia-initiating ability and show unique expression profiles. The distribution of B-ALL molecular subtypes is greatly dependent on age, which also affects treatment responsiveness and long-term survival, partly accounting for the inferior outcome in adolescents and young adults (AYA) and (older) adults with B-ALL. Recent advances in sequencing technology, especially RNA sequencing and the application of these technologies in large B-ALL cohorts have uncovered B-ALL molecular subtypes prevalent in AYA and adults. These new insights supply more precise estimations of prognoses and targeted therapies informed by sequencing results, as well as a deeper understanding of the genetic basis of AYA/adult B-ALL. This article provides an account of these technological advances and an overview of the recent major findings of B-ALL molecular subtypes in adults.

FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines

The quality control of variants from whole-genome sequencing data is vital in clinical diagnosis and human genetics research. However, current filtering methods (Frequency, Hard-Filter, VQSR, GARFIELD, and VEF) were developed to be utilized on particular variant callers and have certain limitations. Especially, the number of eliminated true variants far exceeds the number of removed false variants using these methods. Here, we present an adaptive method for quality control on genetic variants from different analysis pipelines, and validate it on the variants generated from four popular variant callers (GATK HaplotypeCaller, Mutect2, Varscan2, and DeepVariant). FVC consistently exhibited the best performance. It removed far more false variants than the current state-of-the-art filtering methods and recalled ~51-99% true variants filtered out by the other methods. Once trained, FVC can be conveniently integrated into a user-specific variant calling pipeline.

FVC is a method for calling specific gene variants from whole genome data, for potential use in clinical diagnosis and human genetics research.

Papillary Renal Neoplasm With Reverse Polarity Is Often Cystic: Report of 7 Cases and Review of 93 Cases in the Literature

Papillary renal neoplasm with reverse polarity (PRNRP) is a newly proposed entity with distinct histology and frequent KRAS mutations. To date, 93 cases of PRNRPs have been reported. In this study, we present 7 new cases of PRNRP and review the literature. Most of the pathologic features in our 7 cases are similar to those previously reported cases. However, all 7 of our cases showed at least partial cystic changes, which was not stressed in prior studies. Single-nucleotide polymorphism-microarray based chromosomal analysis demonstrated no trisomy or other alteration of chromosomes 7 or 17; and no loss or other alteration of chromosome Y was detected in all 7 cases. Next-generation sequencing detected KRAS missense mutations in 4 of 7 cases. No fusion genes were detected. In summary, PRNRP is a small, well-circumscribed often encapsulated and cystic neoplasm with loose papillary formations. Cuboidal tumor cells always have eosinophilic cytoplasm and nuclei located at the pole opposite the basement membrane with a low World Health Organization (WHO)/International Society of Urologic Pathologists (ISUP) nuclear grade. The fibrovascular cores can be hyalinized or edematous. Macrophage aggregates and intracellular hemosiderin are uncommon, and no psammoma bodies or necrosis should be seen. Immunophenotypically, this tumor is always positive for CK7 and GATA3, and negative for CD117 and vimentin. CD10 and AMACR can be positive, but often weakly and focally. PRNRP often has KRAS mutations, however, only 32% of cases have chromosomal abnormalities in chromosomes 7, 17, and Y. No recurrences, metastases, or tumor-related deaths have been reported following complete resection.

Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation.

The Application of Targeted RNA Sequencing for KMT2A-Partial Tandem Duplication Identification and Integrated Analysis of Molecular Characterization in Acute Myeloid Leukemia

The partial tandem duplication of histone-lysine N-methyltransferase 2A (KMT2A-PTD) is an important genetic alteration in acute myeloid leukemia (AML) and is associated with poor clinical outcome. Accurate and rapid detection of KMT2A-PTD is important for outcome prediction and clinical management, but next-generation sequencing-based quantitative research is still lacking. In this study, we developed a targeted RNA-based next-generation sequencing panel, together with single primer enrichment and unique molecular identifiers, to identify KMT2A-PTD as well as AML-related gene fusions and other driver mutations. Our panel showed high sensitivity, accuracy, and reproducibility in detecting the fusion ratio of KMT2A-PTD. We characterized the mutation profile of KMT2A-PTD-positive patients with AML and found different distribution patterns of driver mutations according to KMT2A-PTD fusion ratio level. Survival analyses revealed that the fusion ratio of KMT2A-PTD did not affect clinical outcome, but a novel molecular combination, namely, KMT2A-PTD/DNMT3A/FMS-like tyrosine kinase 3-internal tandem duplication, was associated with poor prognosis. Finally, we proved that the dynamic changes in the KMT2A-PTD fusion ratio were consistent with the overall process of disease progression. In summary, we applied the unique molecular identifier-based RNA panel to quantitatively detect KMT2A-PTD and elucidate its clinical relevance, which complemented the integrative network of various genetic alterations in AML.

CLEC12A and CD33 coexpression as a preferential target for pediatric AML combinatorial immunotherapy

Emerging immunotherapies such as chimeric antigen receptor T cells have advanced the treatment of acute lymphoblastic leukemia. In contrast, long-term control of acute myeloid leukemia (AML) cannot be achieved by single lineage-specific targeting while sparing benign hematopoiesis. In addition, heterogeneity of AML warrants combinatorial targeting, and several suitable immunotargets (HAVCR2/CD33 and HAVCR2/CLEC12A) have been identified in adult AML. However, clinical and biologic characteristics of AML differ between children and the elderly. Here, we analyzed 36 bone marrow (BM) samples of pediatric AML patients and 13 age-matched healthy donors using whole RNA sequencing of sorted CD45dim and CD34+CD38-CD45dim BM populations and flow cytometry for surface expression of putative target antigens. Pediatric AML clusters apart from healthy myeloid BM precursors in principal-component analysis. Known immunotargets of adult AML, such as IL3RA, were not overexpressed in pediatric AML compared with healthy precursors by RNA sequencing. CD33 and CLEC12A were the most upregulated immunotargets on the RNA level and showed the highest surface expression on AML detected by flow cytometry. KMT2A-mutated infant AML clusters separately by RNA sequencing and overexpresses FLT3, and hence, CD33/FLT3 cotargeting is an additional specific option for this subgroup. CLEC12A and CD33/CLEC12Adouble-positive expression was absent in CD34+CD38-CD45RA-CD90+ hematopoietic stem cells (HSCs) and nonhematopoietic tissue, while CD33 and FLT3 are expressed on HSCs. In summary, we show that expression of immunotargets in pediatric AML differs from known expression profiles in adult AML. We identify CLEC12A and CD33 as preferential generic combinatorial immunotargets in pediatric AML and CD33 and FLT3 as immunotargets specific for KMT2A-mutated infant AML.

Atypical acute myeloid leukemia-specific transcripts generate shared and immunogenic MHC class-I-associated epitopes

Acute myeloid leukemia (AML) has not benefited from innovative immunotherapies, mainly because of the lack of actionable immune targets. Using an original proteogenomic approach, we analyzed the major histocompatibility complex class I (MHC class I)-associated immunopeptidome of 19 primary AML samples and identified 58 tumor-specific antigens (TSAs). These TSAs bore no mutations and derived mainly (86%) from supposedly non-coding genomic regions. Two AML-specific aberrations were instrumental in the biogenesis of TSAs, intron retention, and epigenetic changes. Indeed, 48% of TSAs resulted from intron retention and translation, and their RNA expression correlated with mutations of epigenetic modifiers (e.g., DNMT3A). AML TSA-coding transcripts were highly shared among patients and were expressed in both blasts and leukemic stem cells. In AML patients, the predicted number of TSAs correlated with spontaneous expansion of cognate T cell receptor clonotypes, accumulation of activated cytotoxic T cells, immunoediting, and improved survival. These TSAs represent attractive targets for AML immunotherapy.

Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is characterized by genetic alterations with high heterogeneity. Precise subtypes with distinct genomic and/or gene expression patterns have been recently revealed using high-throughput sequencing technology. Most of these profiles are associated with recurrent non-overlapping rearrangements or hotspot point mutations that are analogous to the established subtypes, such as DUX4 rearrangements, MEF2D rearrangements, ZNF384/ZNF362 rearrangements, NUTM1 rearrangements, BCL2/MYC and/or BCL6 rearrangements, ETV6-RUNX1-like gene expression, PAX5alt (diverse PAX5 alterations, including rearrangements, intragenic amplifications, or mutations), and hotspot mutations PAX5 (p.Pro80Arg) with biallelic PAX5 alterations, IKZF1 (p.Asn159Tyr), and ZEB2 (p.His1038Arg). These molecular subtypes could be classified by gene expression patterns with RNA-seq technology. Refined molecular classification greatly improved the treatment strategy. Multiagent therapy regimens, including target inhibitors (e.g., imatinib), immunomodulators, monoclonal antibodies, and chimeric antigen receptor T-cell (CAR-T) therapy, are transforming the clinical practice from chemotherapy drugs to personalized medicine in the field of risk-directed disease management. We provide an update on our knowledge of emerging molecular subtypes and therapeutic targets in BCP-ALL.