The Progress of Next Generation Sequencing in the Assessment of Myeloid Malignancies

Acute Myeloid Leukemia and Next-Generation Sequencing Panels for Diagnosis: A Comprehensive Review

Acute myeloid leukemia (AML) is a genetically heterogeneous clonal disorder characterized by the accumulation of acquired somatic genetic alterations in hematopoietic progenitor cells, which alter the normal mechanisms of self-renewal, proliferation, and differentiation. Due to significant technological advancements in sequencing technologies in the last 2 decades, classification and prognostic scoring of AML has been refined, and multiple guidelines are now available for the same. The authors have tried to summarize, latest guidelines for AML diagnosis, important markers associated, epigenetics markers, various AML fusions and their importance, etc. Review of literature suggests lack of study or comprehensive information about current NGS panels for AML diagnosis, genes and fusions covered, their technical know-how, etc. To solve this issue, the authors have tried to present detailed review about currently in use next-generation sequencing myeloid panels and their offerings.

Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA)

BACKGROUND

Despite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance.

METHODS

We developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm  with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4-11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors.

RESULTS

Biologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4-11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4-11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks.

CONCLUSIONS

We developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples.

Next Generation Sequencing in MPNs. Lessons from the Past and Prospects for Use as Predictors of Prognosis and Treatment Responses

The myeloproliferative neoplasms (MPNs) are acquired hematological stem cell neoplasms characterized by driver mutations in JAK2, CALR, or MPL. Additive mutations may appear in predominantly epigenetic regulator, RNA splicing and signaling pathway genes. These molecular mutations are a hallmark of diagnostic, prognostic, and therapeutic assessment in patients with MPNs. Over the past decade, next generation sequencing (NGS) has identified multiple somatic mutations in MPNs and has contributed substantially to our understanding of the disease pathogenesis highlighting the role of clonal evolution in disease progression. In addition, disease prognostication has expanded from encompassing only clinical decision making to include genomics in prognostic scoring systems. Taking into account the decreasing costs and increasing speed and availability of high throughput technologies, the integration of NGS into a diagnostic, prognostic and therapeutic pipeline is within reach. In this review, these aspects will be discussed highlighting their role regarding disease outcome and treatment modalities in patients with MPNs.