Acute myeloid leukemia (AML) is a clonal hematological malignancy characterized by the accumulation of immature myeloid precursor cells in the bone marrow and is the most common type of adult leukemia. Although effective treatments have been used against AML for many years, remission and long-term survival rates are still low. AML contains a high degree of genetic heterogeneity. In addition to single nucleotide variations, these genetic changes include a variety of genomic abnormalities such as large insertions and deletions, fusion genes and other structural variations, altered gene expression profiles, and even epigenetic changes. It has been shown that more than 95% of patients have driver and passenger mutations independent of their cytogenetic abnormalities, and the AML genome has an average of 10-13 variations. With the advances in technology, many different subgroups of AML have been defined molecularly, and changes in the prognostic/therapeutic effects of each subgroup have begun to be determined. It has been shown that the detected heterogeneous variants affect not only classification but also long-term treatment response and survival. Currently, detecting these heterogeneous genomic variants for diagnosis, treatment, and elucidation of etiology requires complicated and expensive protocols often incomplete with current platforms. The genetic diagnosis of AML is still at the FISH/PCR level in our country. On the other hand, diagnostic next-generation sequencing (NGS) studies are limited to narrow panels and long report times, and patients cannot be treated according to current classifications.
Our aim in this project is to detect all genomic alterations in current diagnosis and treatment protocols in a short time by using long and short read next generation sequencing approaches in AML disease, which is extremely heterogeneous in terms of molecular structure and treatment response, To create a bioinformatics analysis flow that will reduce the diagnostic time with the transcriptomic approach and to identify new treatment targets by identifying isoforms that may cause treatment resistance. According to this;(i)Short-read RNA sequencing will be performed on diagnostic samples of 50 adult AML patients, and the molecular markers defined in the ELN-2022 classification will be evaluated in the bioinformatics analysis flow we developed, reducing the reporting period to 7 days.(ii)Unidentified genomic alterations will be determined in the genomic regions defined according to the ELN-2022 classification by long-read targeted DNA sequencing for the samples that are not classified and/or variants are not detected at the first stage.(iii)Differential alternative splicing analysis will be performed using short and long-read sequencing technologies at the single-cell level for at least three patients in the same risk group who differ in treatment response (resistant and responsive).
Within the scope of the project, an open-access bioinformatics analysis flow that can be used in the rapid diagnosis of AML and other leukemias, allowing both long-read and short-read RNA sequencing analysis, will be developed. With the Oxford Nanopore Technologies protocol, laboratory processes will be reduced to 1-3 days, and reporting time will be reduced to 7 days. In addition, novel and/or known splicing isoforms that may be related to treatment resistance by altering the transcriptomic profile will be identified at the single-cell level. This project is unique in developing a novel next-generation sequencing approach and a bioinformatics pipeline that will enable rapid and reliable detection of genomic alterations associated with AML. In addition, novel genomic alterations will be identified by long-read DNA sequencing, also the full-length isoforms that can both play a role in the pathogenesis of AML and cause treatment resistance will be determined at the single-cell level. This project will contribute to a better understanding of the pathogenesis of AML and pave the way for advanced diagnostic approaches, clinical evaluations, and new treatment strategies.