Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

BACKGROUND

In spite of the recent discovery of genetic mutations in most myelodysplasic (MDS) patients, the pathophysiology of these disorders still remains poorly understood, and only few in vivo models are available to help unravel the disease.

METHODS

We performed global specific gene expression profiling and functional pathway analysis in purified Sca1+ cells of two MDS transgenic mouse models that mimic human high-risk MDS (HR-MDS) and acute myeloid leukemia (AML) post MDS, with NRASD12 and BCL2 transgenes under the control of different promoters MRP8NRASD12/tethBCL-2 or MRP8[NRASD12/hBCL-2], respectively.

RESULTS

Analysis of dysregulated genes that were unique to the diseased HR-MDS and AML post MDS mice and not their founder mice pointed first to pathways that had previously been reported in MDS patients, including DNA replication/damage/repair, cell cycle, apoptosis, immune responses, and canonical Wnt pathways, further validating these models at the gene expression level. Interestingly, pathways not previously reported in MDS were discovered. These included dysregulated genes of noncanonical Wnt pathways and energy and lipid metabolisms. These dysregulated genes were not only confirmed in a different independent set of BM and spleen Sca1+ cells from the MDS mice but also in MDS CD34+ BM patient samples.

CONCLUSIONS

These two MDS models may thus provide useful preclinical models to target pathways previously identified in MDS patients and to unravel novel pathways highlighted by this study.

The rapidly growing collection of fruit fly embryo images makes automated Image Segmentation and classification an indispensable requirement for a large-scale analysis of in situ hybridization (ISH) - gene expression patterns (GEP). We present here such an automated process flow for Segmenting, Classification, and Clustering large-scale sets of Drosophila melanogaster GEP that is capable of dealing with most of the complications implicated in the images.

With the rapid expansion of GEPs(genetically engineered plants), people are more and more concerned about the ecological risks brought by their release. Assessing the effect of GEPs on soil microbial ecology is indispensable to study their ecological risks. In our study, the phospholipids fatty acid ( PLFA) method was used to analyze the microbial community of soil samples collected from fields with two types of GEPs-Bt transgenic corn and PVY ( potato virus Y) cell protein gene transgenic potato. The principal components analysis (PCA) showed all controls were on the right of related GEPs samples along the PC1 (the first principal component) axis, which means a decrease of fungi in soils with genetically engineered crop since most of PLFAs that are strongly positively correlated with PC1 represent fungi. For samples collected from Bt transgenic cornfield, the ratios of gram-positive to gram-negative bacteria were less than those of controls. For samples of transgenic potato field, these ratios were lower than those of controls when soils were collected from deep layer (20-40 cm), but were higher when soils collected from surface layer(0-20 cm). For soils collected from 0-20 cm, the ratios of fungi to bacteria for all GEPs samples were at the same level. So were such rations for all controls. Changes of soil microbial community in two types of GEPs fields were detected in our study, but the causes and more information still needs further study.

Researchers have exerted sustained efforts to improve the viability of somatic cell nuclear transfer (SCNT) embryos, testing their experimental designs and probing the resultant embryos. However, the lack of a reliable method to estimate the efficacy of these experimental attempts is a chief hindrance to tackling the low-viability problem in SCNT. Here, we introduce a procedure that assesses the degree of difference in gene expression profiles (GEPs) of blastocysts from each other as a representative control of good quality. We first adapted a multiplex reverse transcription-polymerase chain reaction strategy to obtain GEPs for 15 reprogramming-related genes from single mouse blastocysts. GEPs of individual blastocysts displayed a broad range of variations, the extent of which was calculated using a weighted root mean square deviation (wRMSD). wRMSD-based quantitation of GEP difference (qGEP) found that GEP difference between in vivo-derived blastocysts (in vivo) and SCNT blastocysts was greater than the difference between in vivo blastocysts and in vitro-produced (IVP) blastocysts, demonstrating that the SCNT group was more distantly related to the in vivo group than the IVP group. Our qGEP approach for grading individual blastocysts would be useful for selecting a better protocol to derive embryos of better quality prior to field applications.

Importance: Current guidelines for cutaneous malignant melanoma (CMM) provide general recommendations regarding surveillance while indicating that management should be tailored to patients' individual probability of recurrence. A 31-gene expression profile (31-GEP) test to predict metastatic risk has been previously validated, and classifies patients as either Class 1 (low risk) or Class 2 (high risk).

Objective: To determine the impact of the 31-GEP test's result on clinical decision-making.

Design, Setting, and Participants: Dermatology residents who attended a national educational conference were presented with clinical validity evidence for the 31-GEP. Respondents were given six CMM patient vignettes with descriptions of clinical features and answered questions about their willingness to recommend sentinel lymph node biopsy (SLNBx) or imaging based on each scenario. Additionally, respondents were asked to provide the Breslow thickness (BT), ranging from 0.7-1.5mm in 0.1mm increments, at which they would recommend SLNBx, imaging, or oncology referral.

Main Outcomes and Measures: The number of respondents who would recommend each management modality based upon three outcomes (no result, Class 1, or Class 2) was quantified. Differences between response groups were assessed using Fisher's exact test.

Results: The majority of respondents (62%, 57%, and 55%, respectively) indicated a 1.0mm BT as the guiding modality, reflecting adherence to current guidelines. After inclusion of a Class 2 result, the BT used to guide SLNBx, oncology referral, and imaging was changed in 47%, 50% and 47% of the responses, respectively, with 95%, 84% and 97% of the cases, respectively, changed in a risk-appropriate direction (decreased BT). Based on a 31-GEP Class 1 or Class 2 result, risk appropriate recommendations were more likely to be made for each management modality tested in five of the six patient vignettes (P less than 0.05).

Conclusions and Relevance: The 31-GEP test had a significant and appropriate impact on management while remaining within the context of established guidelines.

J Drugs Dermatol. 2017;16(5):428-431.

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We evaluated the prognostic significance of CD43 (SPN), a membrane glycoprotein, in 140 patients with diffuse large B-cell lymphoma (DLBCL) by tissue microarray (TMA) immunostaining, and gene expression profiling (GEP) in 43 patients. CD43 protein was expressed in 19% of the cases and was strongly related to the non-germinal centre B-cell (non-GCB) subgroup by both TMA and GEP. Patients with CD43(+) DLBCL had an inferior 3-year overall survival (OS) compared to those with CD43(-) DLBCL (50% vs. 76%, P = 0·01). Within the non-GCB subgroup, patients with CD43(+) DLBCL had a particularly poor 3-year OS (32% vs. 71%, P < 0·001). Gene set enrichment analysis within the activated B-cell subgroup revealed significant enrichment in the stromal-1 signature in CD43(-) cases. We conclude that CD43 is an adverse prognostic marker in DLBCL, and is preferentially expressed in the non-GCB subgroup. The dismal outcome of CD43(+) cases in the non-GCB subgroup may be explained, at least in part, by a less favourable microenvironment.

Grazing profoundly influences vegetation and the subsequent carbon fluxes in various ecosystems. However, little effort has been made to explore the underlying mechanisms for phenological changes and their consequences on carbon fluxes at ecosystem level, especially under the coupled influences of human disturbances and climate change. Here, a manipulative experiment (2012-2013) was conducted to examine both the independent and interactive effects of grazing and watering on carbon fluxes across phenological phases in a desert steppe. Grazing advanced or delayed phenological timing, leading to a shortened green-up phase (GrP: 23.60 days) in 2013 and browning phase (BrP: 12.48 days) in 2012 from high grazing, and insignificant effects on the reproductive phase (ReP) in either year. High grazing significantly enhance carbon uptake, while light grazing reduce carbon uptake in ReP. Watering only delayed the browning time by 5.01 days in 2013, producing no significant effects on any phenophase. Watering promoted the net ecosystem exchange (NEE), ecosystem respiration (ER), and gross ecosystem productivity (GEP) only in the GrP. When calculating the yearly differences in phenophases and the corresponding carbon fluxes, we found that an extended GrP greatly enhanced NEE, but a prolonged ReP distinctly reduced it. The extended GrP also significantly promote GEP. Increases in growing season length appeared promoting ER, regardless of any phenophase. Additionally, the shifts in NEE appeared dependent of the variations in leaf area index (LAI).

The statistician George E. P. Box stated, 'Essentially all models are wrong, but some are useful.' (Box GEP, Draper NR: Empirical Model-Building and Response Surfaces. Wiley; 1987). Modeling biological processes is challenging for many of the reasons classically trained developmental biologists often resist the idea that black and white equations can explain the grayscale subtleties of living things. Although a simplified mathematical model of development will undoubtedly fall short of precision, a good model is exceedingly useful if it raises at least as many testable questions as it answers. Self-organizing Turing models that simulate the pattern of digits in the hand replicate events that have not yet been explained by classical approaches. The union of theory and experimentation has recently identified and validated the minimal components of a Turing network for digit pattern and triggered a cascade of questions that will undoubtedly be well-served by the continued merging of disciplines.

A collaborative exercise was carried out by the Spanish and Portuguese ISFG Working Group (GEP-ISFG) in order to evaluate the performance of two Y-chromosome STR PCR tetraplexes, which include the loci DYS461, GATA C4, DYS437 and DYS438 (GEPY I), and DYS460, GATA A10, GATA H4 and DYS439 (GEPY II). The participating laboratories were asked to type three samples for the eight markers, using a specific amplification protocol. In addition, two control samples, with known haplotypes, were provided. The results obtained by the 13 different participating laboratories were identical, except for two laboratories that failed to type correctly the same two samples for GATA C4. By sequence analyses, two different GATA C4 allele structures were found. One control sample (allele 21) and two questioned samples (allele 22, correctly typed by all the laboratories, and allele 25) presented the following repeat structure: (TCTA)4(TGTA)2(TCTA)2(TGTA)2(TCTA)n, but different from the one found for allele 26 in one sample included in this exercise, as well as in the second control sample (allele 23), namely (TCTA)4(TGTA)2(TCTA)2(TGTA)2(TCTA)2(TGTA)2(TCTA)n. The collaborative exercise results proved that both Y-tetraplexes produce good amplification results, with the advantage of being efficiently typed using different separation and detection methodologies. However, since GATA C4 repeat presents a complex structure, with alleles differing in sequence structure, efficient denaturing conditions should be followed in order to avoid typing errors due to sizing problems.

Follicular lymphoma (FL) is characterized by an indolent and relapsing course. Recently, the clinical outcome of FL has been distinguished by immune microenvironment-associated gene signatures. In our study, gene expression profiling (GEP) was performed in 31 non-selected patients with follicular lymphoma (FL), 12 of whom were in relapse and the remaining 19 newly diagnosed. A custom oligonucleotide microarray (Agilent 8 × 15K) was used which contained probes for about 3500 genes, including those that had been previously published as demonstrating significant prognostic value. An unsupervised approach was not able to recognize clinically different FLs. As the previously published prognostically relevant gene signatures could not be properly verified, probably due to microarray platform differences, template matching was therefore used in order to define two gene sets with differential gene expression among our samples. These gene sets shared an overrepresentation of genes with similar biological functions and were termed 'T-CELL' and 'PROLIFERATION' profiles. The 'poor profile' was then defined by a high PROLIFERATION score (upper tertile) and/or low T-CELL score (lower tertile). The 'poor profile' cohort contained a significantly higher proportion of relapsed cases (p < 0.05, Fisher's exact test). Additionally, a comparison of samples from initial diagnosis and from relapse showed significant differences mainly in the T-CELL profile (p = 0.036; χ(2)). This supports the hypothesis that the number of T-cells and their expression pattern play a major role in FL development.

Diffuse large B cell lymphoma (DLBCL) is a heterogeneous group of aggressive lymphomas that can be classified into three molecular subtypes by gene expression profiling (GEP): GCB, ABC and unclassified. Immunohistochemistry-based cell of origin (COO) classification, as a surrogate for GEP, using three available immunohistochemical algorithms was evaluated in TMA-arranged tissue samples from 297 patients with de novo DLBCL treated by chemoimmunotherapy (R-CHOP and R-CHOP-like regimens). Additionally, the prognostic impacts of MYC, BCL2, IRF4 and BCL6 abnormalities detected by FISH, the relationship between the immunohistochemical COO classification and the immunohistochemical expression of MYC, BCL2 and pSTAT3 proteins and clinical data were evaluated. In our series, non-GCB DLBCL patients had significantly worse progression-free survival (PFS) and overall survival (OS), as calculated using the Choi, Visco-Young and Hans algorithms, indicating that any of these algorithms would be appropriate for identifying patients who require alternative therapies to R-CHOP. Whilst MYC abnormalities had no impact on clinical outcome in the non-GCB subtype, those patients with isolated MYC rearrangements and a GCB-DLBCL phenotype had worse PFS and therefore might benefit from novel treatment approaches.

The impacts of ultrafine airborne particles generated by diesel or gasoline engines on human lung cells have been investigated using a new in vitro cellular exposure technique. This technique enables direct deposition of the gasoline engine exhaust particles (GEP) and diesel engine exhaust particles (DEP) on human lung cells located at the air-cell interface on a transwell membrane in an exposure apparatus. The cellular responses to particle exposure were measured by the levels of IL-8 chemokines produced as a function of exposure time. The findings suggest that GEP and high-sulfur DEP induced the production of similar levels of IL-8 by unprimed A549 cells. The level of IL-8 produced by unprimed A549 cells in response to low-sulfur DEP was found lower than that produced in response to high-sulfur DEP and GEP. When cells were primed, simulating predisposed conditions, significant levels of IL-8 were produced. GEP triggered a much higher level of IL-8 production than DEP did. Furthermore, the time profile of IL-8 production induced by GEP was markedly different from that induced by DEP. The findings indicate that GEP could induce the production of higher levels of chemokines (i.e., IL-8) than DEP did, implying that exposure to GEP could be a greater health risk than exposure to DEP.

The Spanish and Portuguese ISFG Working Group (GEP-ISFG) carried out a collaborative exercise in order to asses the performance of two Y chromosome STR tetraplexes, which include the loci DYS461, GATA C4, DYS437 and DYS438 (GEPY I), and DYS460, GATA A10, GATA H4 and DYS439 (GEPY II). The groups that reported correct results in all the systems were also asked to analyse a population sample in order to evaluate the informative content of these STRs in different populations. A total of 1020 males out of 13 population samples from Argentina, Brazil, Costa Rica, Macao, Mozambique, Portugal and Spain were analysed for all the loci included in the present study. Haplotype and allele frequencies of these eight Y-STRs were estimated in all samples. The lowest haplotype diversity was found in the Lara (Argentina) population (95.44%) and the highest (99.90%) in Macao (China). Pairwise haplotype analysis showed the relative homogeneity of the Iberian origin samples, in accordance with what was previously found in the European populations for other Y-STR haplotypes (http://www.ystr.org). As expected, the four non-Caucasian samples, Macao (Chinese), Mozambique (Africans), Costa Rica (Africans) and Argentina (Lara, Amerindians), show highly significant Phist values in the pairwise comparisons with all the Caucasian samples.

Many previous attempts to acquire gustatory evoked potentials (GEPs) in both men and animals could not satisfy strict requirements on purely unimodal stimulation free of contaminating artifacts. Therefore we developed an advanced new gustometer, based on our previous experience on olfactory evoked potentials, especially on magnetic valves as also used in our "Erlangen olfactometer". Several newly constructed magnetic valves were tested; the best one (MV3) enables us to fulfill the following indispensable conditions for proper averaging by computer: precise onset and duration of stimulus, and shortest possible stimulus rise and fall times. Tactile artifacts were avoided by constant rinsing of the tongue with neutral water, in which the taste stimulus was embedded without turbulences. By recording and averaging the galvanic skin response (GSR) at the same time, its influence on GEPs was compensated. To evaluate successfully objective correlates of taste sensation in man is believed to be of great basic and clinical importance.

BACKGROUND

The surgeon's role in the follow-up of pathologic stage I and II melanoma patients has traditionally been minimal. Melanoma genetic expression profile (GEP) testing provides binary risk assessment (Class 1-low risk, Class 2-high risk), which can assist in predicting metastasis and formulating appropriate follow up. We sought to determine the impact of GEP results on the management of clinically node negative cutaneous melanoma patients staged with sentinel lymph node biopsy (SLNB).

METHODS

A retrospective review of prospectively gathered data consisting of patients seen from September 2015 - August 2016 was performed to determine whether GEP class influenced follow-up recommendations. Patients were stratified into four groups based on recommended follow-up plan: Dermatology alone, Surgical Oncology, Surgical Oncology with recommendation for adjuvant clinical trial, or Medical and Surgical Oncology.

RESULTS

Of ninety-one patients, 38 were pathologically stage I, 42 stage II, 10 stage III, and 1 stage IV. Combining all stages, GEP Class 1 patients were more likely to be followed by Dermatology alone and less like to be followed by Surgical Oncology with recommendation for adjuvant trial compared to Class 2 patients (P less than 0.001). Among stage 1 patients, Class 1 were more likely to follow up with Dermatology alone compared to Class 2 patients (82 vs. 0%; P less than 0.001). Among stage II patients, GEP Class 1 were more likely to follow up with Dermatology alone (21 vs 0%) and more Class 2 patients followed up with surgery and recommendations for adjuvant trial (36 vs 64%; P less than 0.05). There was no difference in follow up for stage III patients based on the GEP results (P=0.76).

CONCLUSIONS

GEP results were significantly associated with the management of stage I-II melanoma patients after staging with SLNB. For node negative patients, Class 2 results led to more aggressive follow up and management. J Drugs Dermatol. 2018;17(2):196-199.

The clinical significance of cytogenetic abnormalities (CA) present in randomly sampled (RS) or focal lesion (FL) bone marrow sites was examined in 419 untreated myeloma patients. Among 290 patients with gene expression profiling (GEP) data generated from RS sites, GEP-defined high-risk was present in 52% of the RS+/FL+ group but in only 9% of the remainder (P < 0.001). The RS+/FL+ constellation (18%) was an independent predictor of poor survival, also after adjusting for GEP-derived risk and TP53 status (Hazard ratio = 2.42, P = 0.004). The prevalence of high-risk myeloma in the RS+/FL+ group may reflect a dissemination-prone condition not shared by the other three groups.

Gene expression profiling (GEP) testing using 12-gene recurrence score (RS) assay (EndoPredict®), 58-gene RS assay (Prosigna®), and 21-gene RS assay (Oncotype DX®) is available to aid in chemotherapy decision-making when traditional clinicopathological predictors are insufficient to accurately determine recurrence risk in women with axillary lymph node-negative, hormone receptor-positive, and human epidermal growth factor-receptor 2-negative early-stage breast cancer. We examined the cost-effectiveness of incorporating these assays into standard practice. A decision model was built to project lifetime clinical and economic consequences of different adjuvant treatment-guiding strategies. The model was parameterized using follow-up data from a secondary analysis of the Anastrozole or Tamoxifen Alone or Combined randomized trial, cost data (2017 Canadian dollars) from the London Regional Cancer Program (Canada) and secondary Canadian sources. The 12-gene, 58-gene, and 21-gene RS assays were associated with cost-effectiveness ratios of $36,274, $48,525, and $74,911/quality-adjusted life year (QALY) gained and resulted in total gains of 379, 284.3, and 189.5 QALYs/year and total budgets of $12.9, $14.2, and $16.6 million/year, respectively. The total expected-value of perfect information about GEP assays' utility was $10.4 million/year. GEP testing using any of these assays is likely clinically and economically attractive. The 12-gene and 58-gene RS assays may improve the cost-effectiveness of GEP testing and offer higher value for money, although prospective evidence is still needed. Comparative field evaluations of GEP assays in real-world practice are associated with a large societal benefit and warranted to determine the optimal and most cost-effective assay for routine use.

OBJECTIVE

To evaluate the necessity of morphological images (MI) in reading somatostatin receptor scintigraphy (SRS) in patients with suspected neuroendocrine gastroenteropancreatic (GEP) tumors.

METHODS

A Japanese multicenter clinical trial of SRS was conducted in 40 patients with suspected GEP tumors. Three experienced radiologists interpreted the images in three separate sessions in a blinded manner (1: SRS images alone, 2: MI alone, 3: SRS and MI analyzed simultaneously), and the reading results of each session were compared. In addition, the diagnostic abilities of SRS, MI and SRS alone and simultaneous SRS and MI readings were compared for patients where final diagnosis was obtained.

RESULTS

SRS detected more suspected lesions (positive or inconclusive uptake) than morphological images did (51 vs 27 lesions), but included many physiological uptakes detected as positive or inconclusive uptakes. Combined reading of SRS and morphological images helped to correctly recognize these physiological uptakes, and also helped in determining the anatomical localization of the abnormal uptakes. Combined reading of SRS and morphological images gave the highest diagnostic impact.

CONCLUSIONS

The sensitivity of SRS with regard to GEP is high. However the specificity is very low. Morphologic imaging is necessary for the exclusion of physiological uptake and correct anatomic location of an abnormal tracer uptake. The combined reading of SRS and morphologic imaging studies gives the highest diagnostic impact.

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are a heterogeneous group of rare tumors whose site-specific tumor incidence and clinical behavior vary widely. Genetic alterations associated with familial inherited syndromes have been well defined; however, the genetic profile of sporadic tumors is less clear as their tumorigenesis does not appear to be controlled by classic oncogenes such as P53, RB, or KRAS. Even within GEP-NETs, there are no common oncogenic drivers; for example, DAXX/ATRX mutations are strongly implicated in the tumorigenesis of pancreatic but not small bowel NETs. Accordingly, the dysregulation of epigenetic mechanisms has been hypothesized as a potential regulator of GEP-NET tumorigenesis and has become a major focus of recent studies. Despite the heterogeneity of tumor cohorts evaluated in these studies, it is obvious that there are methylation patterns, chromatin remodeling alterations, and microRNA and long non-coding RNA (lncRNA) differential expression profiles that are distinctive of GEP-NETs, some of which are correlated with significant differences in clinical outcomes. Several translational studies have provided convincing data identifying potential prognostic biomarkers, and some of these have demonstrated preliminary success as serum biomarkers that can be used clinically. Nevertheless, there are many opportunities to further define the mechanisms by which these epigenetic modifications influence tumorigenesis, and this will provide better insight into their prognostic and therapeutic utility. Furthermore, these findings form the foundation for future studies evaluating the clinical efficacy of epigenetic modifications as prognostic biomarkers, as well as potential therapeutic targets.

A risk score based on three biological features (CD38, ZAP-70, and IGHV mutational status) was previously developed to predict progression-free survival (PFS) in untreated Binet A CLL patients. Here we perform a score validation analysis in a prospective and independent cohort of patients. Biological markers (CD38, ZAP-70, and IGHV mutational status) and gene expression profiles (GEP) of leukemic cells from CLL patients included in a prospective multicenter observational study (O-CLL1-GISL protocol, clinicaltrial.gov ID:NCT00917549) were used to assess the value and reproducibility of this score. To date, 468 Binet A patients were classified as low- (0 positive marker), intermediate- (1 positive marker), or high-risk (2 or 3 positive markers) using the progression risk score. The 3-year PFS probability was 91.7%, 82.9%, and 57.4% for low-, intermediate-, and high-risk (P < 0.0001) cases, respectively. These values were similar to those found in the original cohort. At Cox multivariate analysis, Rai stage, absolute lymphocyte count, progression risk score, and β-2 microglobulin maintained an independent prognostic impact on PFS. This score remained a predictor of progression when analysis was limited to 371 Rai 0 cases (P < 0.0001). Finally, the cells from the different CLL risk groups showed differences in their gene expression patterns. These results confirm the ability of this progression risk score to predict PFS among Binet A patients. The utility of the score was also extended by demonstrating that it retains prognostic value when applied exclusively to Rai 0 patients. Specific transcriptional patterns were significantly associated with risk groups.

A decade ago, gene expression profiling (GEP) was successfully introduced in haematological research. Considering the heterogeneity of haematological malignancies, the growing arsenal of compounds, allowing targeted therapy, e.g. in myelodysplastic syndromes (MDS) or chronic myeloid leukaemia (CML), and the more differentiated indication to allogeneic stem cell transplantation, routine diagnostic procedures would highly benefit from an introduction of this novel methodology: by now, the majority of genetically defined leukaemia subtypes has been accurately reproduced on the basis of distinct gene expression patterns by various independent research groups. Moreover, classification of histomorphologically overlapping lymphoma subentities (e.g. Burkitt lymphoma and diffuse large B-cell lymphoma, DLBCL), was considerably improved by GEP. Beyond that, differential gene expression has provided the basis for assays being able to predict prognosis of individual patients as well as the response to specific treatment approaches, e.g. to lenalidomide in MDS. In a high proportion of Philadelphia positive acute lymphoblastic leukaemia (ALL) patients, prognostically adverse deletions of the IKZF1 gene coding for a specific transcription factor were identified with GEP analysis, which revealed new insights in the clinical variability of this disorder. Given these advantages of GEP, the introduction of this methodology in current diagnostic algorithms of haematological malignancies should further be validated in clinical studies.