The power and potential of integrated diagnostics in acute myeloid leukaemia

Haferlach, Torsten; Schmidts, Ines

doi:10.1111/bjh.16360

Cited by 52 publications

(46 citation statements)

References 61 publications

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“…The state-of-the-art in AML diagnosis has dramatically changed over the last few decades [33]. In this study, prognostic risk groups were based on cytogenetics as defined by the Eastern Cooperative Oncology Group/Southwest Oncology group classification scheme rather than the European LeukemiaNet (ELN) risk stratification system, therefore known prognostic mutations such as NPM1, FLT3 -ITD, CEBPA, RUNX1, ASXL1, and TP53 were not included when defining the three ordinal classes.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Variable Selection For High-Dimensional Data With An Ordinal Response: Application Predicting Prognostic Risk Group In Acute Myeloid Leukemia

Zhang

Archer

2021

Preprint

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Background: Acute myeloid leukemia (AML) is a heterogeneous cancer of the blood, though specific recurring cytogenetic abnormalities in AML strongly are associated with attaining complete response after induction chemotherapy, remission duration, and survival. Therefore recurring cytogenetic abnormalities have been used to segregate patients into favorable, intermediate, and adverse prognostic risk groups. However, it is unclear how expression of genes is associated with these prognostic risk groups. We postulate that expression of genes monotonically associated with these prognostic risk groups may yield important insights into leukemogenesis. Therefore, in this paper we propose penalized Bayesian ordinal response models to predict prognostic risk group using gene expression data. We consider a double exponential prior, a spike-and-slab normal prior, a spike-and-slab double exponential prior, and a regression-based approach with variable inclusion indicators for modeling our high-dimensional ordinal response, prognostic risk group, and identify genes through hypothesis tests using Bayes Factor. Results: Gene expression was ascertained using Affymetrix HG-U133Plus2.0 GeneChips for 97 favorable, 259 intermediate, and 97 adverse risk AML patients. When applying our penalized Bayesian ordinal response models, genes identified for model inclusion were consistent among the four different models. Additionally, the genes included in the models were biologically plausible, as most have been previously associated with either AML or other types of cancer. Conclusion: These findings demonstrate that our proposed penalized Bayesian ordinal response models are useful for performing variable selection for high-dimensional genomic data and have the potential to identify genes relevantly associated with an ordinal phenotype.

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Section: Discussionmentioning

confidence: 99%

Bayesian Variable Selection For High-Dimensional Data With An Ordinal Response: Application Predicting Prognostic Risk Group In Acute Myeloid Leukemia

Zhang

Archer

2021

Preprint

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“…Gene expression profiling has revealed various AML subtypes related to diagnosis, therapy response and prognosis [55,56]. Although gene expression profiling has not yet been integrated into clinical practice, this is expected to happen in near future.…”

Section: Discussionmentioning

confidence: 99%

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with Doxorubicin as well as etoposide

Turk

Akbar

et al. 2020

PLoS ONE

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Despite the availability of various treatment protocols, response to therapy in patients with Acute Myeloid Leukemia (AML) remains largely unpredictable. Transcriptomic profiling studies have thus far revealed the presence of molecular subtypes of AML that are not accounted for by standard clinical parameters or by routinely used biomarkers. Such molecular subtypes of AML are predicted to vary in response to chemotherapy or targeted therapy. The Renin-Angiotensin System (RAS) is an important group of proteins that play a critical role in regulating blood pressure, vascular resistance and fluid/electrolyte balance. RAS pathway genes are also known to be present locally in tissues such as the bone marrow, where they play an important role in leukemic hematopoiesis. In this study, we asked if the RAS genes could be utilized to predict drug responses in patients with AML. We show that the combined in silico analysis of up to five RAS genes can reliably predict sensitivity to Doxorubicin as well as Etoposide in AML. The same genes could also predict sensitivity to Doxorubicin when tested in vitro. Additionally, gene set enrichment analysis revealed enrichment of TNF-alpha and type-I IFN response genes among sensitive, and TGF-beta and fibronectin related genes in resistant cancer cells. However, this does not seem to reflect an epithelial to mesenchymal transition per se. We also identified that RAS genes can stratify patients with AML into subtypes with distinct prognosis. Together, our results demonstrate that genes present in RAS are biomarkers for drug sensitivity and the prognostication of AML.

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“…Gene expression profiling (GEP) has proven a valuable strategy to advance our understanding of the molecular landscape and drug resistance mechanisms of several cancer types, including breast cancer (BC) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy

et al. 2021

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The use of gene expression profiling (GEP) in cancer management is rising, as GEP can be used for disease classification and diagnosis, tailoring treatment to underlying genetic determinants of pharmacological response, monitoring of therapy response, and prognosis. However, the reliability of GEP heavily depends on the input of RNA in sufficient quantity and quality. This highlights the need for standard procedures to ensure best practices for RNA extraction from often small tumor biopsies with variable tissue handling. We optimized an RNA extraction protocol from fresh-frozen (FF) core needle biopsies (CNB) from breast cancer patients and from formalin-fixed paraffin-embedded (FFPE) tissue when FF CNB did not yield sufficient RNA. Methods to avoid ribonucleases andto homogenize or to deparaffinize tissues and the impact of tissue composition on RNA extraction were studied. Additionally, RNA’s compatibility with the nanoString nCounter® technology was studied. This technology platform enables GEP using small RNA fragments. After optimization of the protocol, RNA of high quality and sufficient quantity was obtained from FF CNB in 92% of samples. For the remaining 8% of cases, FFPE material prepared by the pathology department was used for RNA extraction. Both resulting RNA end products are compatible with the nanoString nCounter® technology.

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The power and potential of integrated diagnostics in acute myeloid leukaemia

Cited by 52 publications

References 61 publications

Bayesian Variable Selection For High-Dimensional Data With An Ordinal Response: Application Predicting Prognostic Risk Group In Acute Myeloid Leukemia

Bayesian Variable Selection For High-Dimensional Data With An Ordinal Response: Application Predicting Prognostic Risk Group In Acute Myeloid Leukemia

Renin angiotensin system genes are biomarkers for personalized treatment of acute myeloid leukemia with Doxorubicin as well as etoposide

Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy

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