In this issue of Haematologica, Vantyghem et al.[1] demonstrate, in a real-life setting, how the application of a customized next-generation sequencing (NGS) panel can support routine diagnostics, deliver prognostic information, and may even directly influence the choice of therapy towards a more personalized treatment.The diagnostic workup for patients with unexplained cytopenia or proliferative blood counts currently follows specific clinical procedures: After a deep dive into the patient’s history and an examination of his or her complete blood count, a peripheral blood smear is evaluated microscopically. At the same time, several medical imaging techniques, such as ultrasound or computed tomography scans, are used to determine the plausible cause of the observed signs and symptoms. If a malignant hematologic disorder is suspected, a bone marrow aspirate and biopsy are often the next steps. The material obtained is then analyzed morphologically, cytogenetically, and histologically. [2] However, even combining the results of the various investigations (cytomorphology, cytochemistry, cytogenetics, fluorescence in situ hybridization, histology, immunohistology, and multiparameter flow cytometry) does not guarantee a precise diagnosis in all cases, e.g., a malignant hematologic disorder or reactive/normal/benign conditions. For patients without a firm diagnosis, the treatment options dwindle down to ‘watch-and-wait’ often accompanied by the application of more diagnostic techniques (e.g., magnetic resonance imaging, positron emission tomography), and “reevaluation in 3 months if blood counts remain abnormal”.However, with the increased importance and acceptance of molecular genetics in the last decade, substantial progress has been achieved. The work of various research groups has made it possible to define the landscape of molecular findings for the diagnosis of myelodysplastic syndrome, myeloproliferative neoplasm, myelodysplastic syndrome/myeloproliferative neoplasm and severe aplastic anemia.[3-7] This not only fosters diagnostic clarity but also has an increasing impact on prognosis and therapeutic options, including targeted treatment.Vantyghem et al. divided their cohort of 177 patients with suspected chronic myeloid malignancies into two overlapping groups to exemplify the clinical impact of targeted sequencing to confirm or discard a suspected diagnosis (group A) and to assess the therapeutic consequences of somatic mutations (group B). A panel of 34 genes was used to search for clonal hematopoiesis in group A patients for whom the gold standard routine workup had not yielded a conclusive diagnosis. Cytogenetic chromosomal banding analysis, carried out in 86% of the cases of group A, revealed a normal karyotype in 72% of the tested patients. Only in 8% of the cases was a cytogenetic aberration found that might have been useful for diagnosis and prognosis according to international risk scoring systems. However, the identification of clonal hematopoiesis in 33% of the patients confirmed the diagnosis in 31 patients, whereas the absence of clonal hematopoiesis ruled out a chronic myeloid malignancy in 47 patients. Moreover, in group B prognostic mutations were identified in 33% of the patients, with this prognostic information affecting treatment choices in 18 cases.The results clearly demonstrate the advantages of molecular approaches for, but not limited to, patients without a firm diagnosis from classical investigations. The authors concluded that for those patients cell pellets or DNA should be stored at first investigation to provide the possibility of any kind of molecular investigations at a later time point.The approach described also has a socio-economic impact: any repeated testing procedures submit patients, who are already stressed by the uncertainty of their disease state, to additional discomfort and put a strain on the healthcare system by causing extra costs and consuming valuable resources, such as the time of doctors and laboratories.On the other hand, any kind of targeted treatment, guided by NGS results, will not only serve patient’s needs best but will also avoid treatment costs for suboptimal outcomes. Depending on molecular genetic findings, treatment modalities might be initiated, altered, postponed or in some cases even stopped. The limit of the theranostic impact of NGS is far from being reached. With the establishment of multicenter, large-scale sequencing projects, more information on frequency and diversity of germline and especially somatic variants across different disease entities will be gained, improving variant interpretation and increasing the number of potentially actionable targets.[8] In the present study, Vantyghem et al. identified multiple variants in genes associated with myeloid malignancies but due to the limited amount of information for these variants, they had to be classified as variants of unknown significance, excluding them (so far) from the therapeutic decision-making process.The increase in knowledge will also lead to optimized bioinformatics workflows to improve the sensitivity and specificity of variant detection and to decrease the influence of technical noise, which results in technical artifacts that currently have to be removed through painstaking manual efforts. Various collaborations are underway to create consensus somatic pathogenicity datasets for standardized variant interpretation.[9] For high-throughput laboratories, parallelization of analysis pipelines will help to keep turn-around times within a reasonable time frame, allowing a personalized molecular analysis for every patient before treatment starts. Turn-around times might be further reduced by using machine-learning techniques for speedy and accurate evaluation of test results.[10]We must serve our patients to the best of our capacity by applying state-of-the-art methodologies to extend and supplement standard diagnostic criteria. Thus, any new data, any new scientific finding and any new assay needs to be evaluated, tested, and validated based on the best of the current available guidelines.[11,12] In addition, analysis pipelines must be benchmarked to ensure flawless behavior with acceptable error margins.[13] Following rigorous testing and scientific validation, we have the mandate to integrate new techniques with diagnostic, prognostic or therapeutic benefit into clinical routine practice as early as possible. Vantyghem et al. showed that in 83% of patients, a mutational profile was useful for making an integrated final diagnosis. In 19% the additional information gained by NGS data had prognostic impact and led to treatment modifications.If we want to reduce the level of uncertainty in the diagnosis of hematologic malignancies, NGS might provide us with the necessary additional information already today.[14,15] Even if NGS is still comparably expensive, the confidence gained regarding a correct, final diagnosis, which reduces a patient’s fear and prevents wrong treatment, justifies its application already now.Overview of the composition of the cohort and the analytical workflow in the study by Vantyghem The composition of the cohort of patients, with regards to their type of disorder, is depicted as a pie chart in the upper right corner with different shades of blue representing the various entities. The aims and results of the analyses are shown in the different boxes for the respective groups. AA: aplastic anemia; CH: clonal hematopoiesis; hMDS: hypoplastic myelodysplastic syndrome; ICUS: idiopathic cytopenia of undetermined significance; MDS: myelodysplastic syndrome; MPN: myeloproliferative neoplasm.
Authors: Lawrence J Jennings; Maria E Arcila; Christopher Corless; Suzanne Kamel-Reid; Ira M Lubin; John Pfeifer; Robyn L Temple-Smolkin; Karl V Voelkerding; Marina N Nikiforova Journal: J Mol Diagn Date: 2017-03-21 Impact factor: 5.568
Authors: Peter Valent; Wolfgang Kern; Gregor Hoermann; Jelena D Milosevic Feenstra; Karl Sotlar; Michael Pfeilstöcker; Ulrich Germing; Wolfgang R Sperr; Andreas Reiter; Dominik Wolf; Michel Arock; Torsten Haferlach; Hans-Peter Horny Journal: Int J Mol Sci Date: 2019-02-12 Impact factor: 5.923
Authors: T Haferlach; Y Nagata; V Grossmann; Y Okuno; U Bacher; G Nagae; S Schnittger; M Sanada; A Kon; T Alpermann; K Yoshida; A Roller; N Nadarajah; Y Shiraishi; Y Shiozawa; K Chiba; H Tanaka; H P Koeffler; H-U Klein; M Dugas; H Aburatani; A Kohlmann; S Miyano; C Haferlach; W Kern; S Ogawa Journal: Leukemia Date: 2013-11-13 Impact factor: 11.528
Authors: Sophie Vantyghem; Pierre Peterlin; Sylvain Thépot; Audrey Ménard; Viviane Dubruille; Camille Debord; Thierry Guillaume; Alice Garnier; Amandine Le Bourgeois; Soraya Wuilleme; Catherine Godon; Olivier Theisen; Marion Eveillard; Jacques Delaunay; Hervé Maisonneuve; Nadine Morineau; Bruno Villemagne; Stéphane Vigouroux; François Subiger; Elsa Lestang; Marion Loirat; Anne Parcelier; Pascal Godmer; Mélanie Mercier; Adrien Trebouet; Damien Luque Paz; Ronan Le Calloch; Lenaig Le Clech; Céline Bossard; Anne Moreau; Valérie Ugo; Mathilde Hunault; Philippe Moreau; Steven Le Gouill; Patrice Chevallier; Marie C Béné; Yannick Le Bris Journal: Haematologica Date: 2021-03-01 Impact factor: 9.941