WO2016154690A1

WO2016154690A1 - Biomarkers for classifying acute leukemias

Info

Publication number: WO2016154690A1
Application number: PCT/BR2015/000049
Authority: WO
Inventors: Carolina PEREIRA DE SOUZA; Juliana GODOY ASSUMPÇÃO; Catharina BRANT CAMPOS
Original assignee: Biocod Biotecnologia Ltda
Priority date: 2015-04-01
Filing date: 2015-04-02
Publication date: 2016-10-06
Also published as: BR102015007391B1; BR102015007391A2

Abstract

This invention patent concerns the area of health, more specifically the area of onco-hematology. It involves the detection of biomarkers (genes) and the use of the expression pattern of these genes to classify subtypes of acute leukemia. The biomarkers described in the present patent can be used to define the risk group for patients with acute leukemia since the genetic alterations detected by them have a prognostic impact.

Description

"BIOMA KERS FOR CLASSIFYING ACUTE LEUKEMIAS"

101] This invention patent concerns the area of health, more specifically the area of onco-hematology. It involves the detection of biomarkers (genes) and the use of the expression pattern of these genes to classify subtypes of acute leukemia.

[02] The term leukemia covers a group of heterogeneous diseases, since the neoplastic process that gives rise to the leukemic clone can come about at various stages of different bone marrow cell lineage development (Lusis; 2000). According to the cell of origin, they are classified as myeloids or lymphoids, and according to their progress, as acute or chronic. Acute leukemias are divided into two major groups: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Each group is further divided into various categories that differ with respect to the genetic changes that they display, which greatly influences treatment and prognosis.

{03] In 1997, a group of hematologists sponsored by the World Health Organization (WHO) met to update the classification of hematological diseases, which until then were divided up according to criteria based on morphology and cytochemistry alone (FAB classification). The new classification incorporated the analyses of immunophenotyping and cytogenetics, as well as clinical criteria prior to diagnosis, such as myelodysplasia and leukemia arising out of treatment (WHO classification) (Vardiman et al., 2002). In 2008, WHO also incorporated provisional categories based on the presence of genetic mutations detected by molecular biology, such as NPM1 and CEBPA in AML (Swerdlow et al., 2009; Vardiman et al., 2009).

[04] As we come to know the biology of leukemia better, new markers are incorporated into the classification system. The molecular tests currently .adopted for classification are becoming obsolete seeing as they evaluate each genetic alteration individually, that is, one test is necessary for each gene. The tendency is that in the near future genomic technologies will be used, with a single test providing all the information necessary to classify the disease and stratify patients into relapse risk groups. [05] In this invention, we used gene expression microarray technology. This .test allows for the simultaneous analysis of virtually all genes from the human genome. icroarrays were carried out on genetic material in the form of blood and bone marrow obtained from 254 patients with acute leukemia. By means of bioinformatics analysis, we selected genes differentially expressed among the leukemia subtypes in order to define a "gene signature" for each category. As such, a set of genes was selected for each subtype whose expression is characteristic of that group.

[06] Description of the state of the art

[07] The use of a patient's gene expression profile (GEP) to classify the leukemia group was first described by Golub et al. (1999). This group ' of researchers used gene expression microarrays to select genes important in the differentiation of AML and ALL, and then developed an algorithm based on the expression of these genes to predict the class of new patients with leukemia.

[08] In ALL, the first work to publish the detection of gene signatures by microarrays characteristic of subtypes carrying certain genetic alterations was performed by Yeoh et al. (2002). Expression patterns were identified in ALL subtypes including T-ALL, ALL, E2A-PBX1 (Tcf3- PBX1), BCR-ABL, TEL- AML1 , MLL rearranged and hyperdiploidy with more than 50 chromosomes. In AML, the first work to identify subtypes with recurrent gene rearrangements [t(15;17) PML-RARa, t(8;21) RUNX1-RUNX1T1 and inv(16) CBFB-MYH11] through microarrays was by Schoch et al (2002).

[09] In subsequent years, a series of works carried out by European and American research groups showed that the analysis of gene expression on a large scale in patients with Acute Leukemia is viable and enables the identification of the disease's main subtypes (Kohlmann et al., 2003; Ross et al., 2004; Rozovskaia et al., 2003; Marasca et al., 2006; Andersson et al., 2007; Kohlmann et al., 2008; Haferlach et al., 2010; !acobucci et al., 2012). Groups with characteristic genetic alterations, such as for example, the t (15; 17), t (8; 21 ), inv (16), or rearrangements in the MLL gene, were confirmed not only by using different oligo designs (Schoch et al, 2002;. Valk et al, 2004) but also by the application of different microarray platforms (Bullinger et al., 2004; Radmacher et al., 2006).

[010] The first group to prospectively validate their data was the European MILE (Microarray Innovations in Leukemia) study funded by Roche (Roche Molecular Systems Inc., Pleasanton, CA; Haferlach et al., 2010). It consisted in the largest microarray study ever conducted on samples of patients with onco-hematological diseases and included 3,334 patients. The precision of the classifier algorithm generated by this analysis reached 91.5% for acute leukemia. i [011] Besides the scientific papers published in international journals, patent applications involving the use of gene expression microarrays to classify acute leukemia were also filed, as described below.

[012] US2004018513 - Methods of assigning a subject affected by leukemia to a leukemia risk group, predicting whether a subject affected by leukemia has an increased risk of relapse, increased risk of developing secondary ^•acute myeloid leukemia; kits - only deals with ALL (does not include AML), the list of genes selected as biomarkers differs from the present invention.

[013] CN203021568 - Combined parallel detection and diagnosis kit of leukemia-fused gene - includes ALL and AML, but in the AML it does not include biomarkers for FLT3 and NPM1. The list of genes selected as biomarkers differs from this invention.

[014] CN202558869 - Gene chip for leukemia diagnosis - includes ALL and AML, specifically relates to a chip for leukemia classification, and not to biomarkers. The probes in the chip are for fusion genes, do not detect FLT3 and NPM1 mutations, and do not coincide with the genes selected in the present invention.

[015] CN102676648 - Gene chip for leukemia diagnosis and treatment - includes ALL and AML, also includes the detection of various gene mutations, including FLT3 and NPM1 , however, the biomarker list differs from the present invention.

[016] US20100055686 - Methods for diagnosis of pediatric common acute lymphoblastic leukemia by determining the level of gene expression - solely deals with pediatric B-ALL, does not include AML, does not include T-ALL, does not include adults. The biomarker list differs from the present invention.

[017] WO2013090419 - Signatures d'expression genique destinees a la detection d'6v6nements analogues au chromosome philadelphie (ph-like) sous-jacents et ciblage therapeutique de la leucemie - solely deals with ALL, more specifically in the distinction between patients who respond or not to treatment with tyrosine kinase inhibitors. The biomarker list differs from the present invention.

[018] CN 102758006 - Kit for detecting relative expression of leukemia BCR/ABL (b3a2, b2a2) fusion gene - only deals with leukemias carrying the BCR-ABL fusion gene. The biomarkers for BCR-ABL are exclusive and differ from the present invention.

[019] US2006057630 - MLL translocations specify a distinct gene expression profile, distinguishing a unique leukemia - solely deals with leukemias carrying rearrangements in the MLL gene. The biomarkers for MLL rearrangements are exclusive and differ from the present invention.

[020] WO2006048264 - Gene expression profiling in acute lymphoblastic leukemia (ALL), biphenotypic acute leukemia (BAL), and acute myeloid leukemia (AML) mO - includes ALL, biophenotypic acute leukemia, and AML subtype M0, does not include the other AML subtypes. The list of biomarkers differs from the present invention.

[021] WO2006048263 - Gene expression profiling in acute promyelocytic leukemia - only deals with APL (=AML subtype M3). Does not include ALL and other AML subtypes. The biomarker list differs from the present invention.

[022] There are three main differences between the present invention and others described in scientific publications or previously filed patents, included in the state of the art.

[023] The first difference relates to the population of individuals studied. All of the previously conducted studies involved populations from developed ^"countries. There has been no work that based its gene signatures for acute leukemia classification on the Brazilian population. The distribution of leukemia subtypes in the Latin American population differs from that of developed countries, as observed by Douer et al., (1996). There is no published set of biomarkers in leukemia that has been identified in the Brazilian population.

[024] The second difference is in the set of biomarkers found. Because of the genetics of the evaluated population, and also the microarray platform chosen, some biomarkers were detected that had not been related to acute leukemias. The association between leukemia subtypes and a few genes of this gene signature had already been described, but most of the selected genes are unprecedented.

[025] The third difference is the identification of a special category of non- coding genes as important markers of leukemia subtypes. Because of the platforms and chips used in other works, gene signatures containing genes that encode proteins or microRNAs (miRNAs) genes were previously identified. There is no specific reference in the gene signatures to long non- coding RNAs, known as IncRNAs ("/one; non-coding RNAs").

[026] The results of the present patent show that approximately 8% of the biomarkers detected in the gene signatures are IncRNAs. Most of them consist of intergenic RNAs ("lincRNAs, long intergenic RNAs"). The others are RNAs transcribed from the antisense strand of some gene that encodes protein ("aRNA, antisense RNA" or "NAT, natural antisense transcript "), in addition to a few transcripts of genes classified as pseudogenes.

[027] The IncRNAs are a little studied category of RNAs with regulatory function. Their importance was only recognized through the Human Transcriptome project, and therefore almost all the relevant literature is quite recent (reviewed by Pointing et al., 2009).

[028] The interaction mechanisms of the IncRNAs in the control of specific gene expression are now being discovered. The altered expression of the IncRNAs has been observed in a series of diseases and can provide data on its etiology. Particularly in leukemias, some studies show the involvement of incRNA deregulation with the disease (Yu et al., 2008; lacobucci et al., 2011. -Benetatos et al., 2010; Khoury et al., 2010; Garding et al., 2013; Hajjari et a!., 2013). However, these studies do not indicate the use of IncRNAs to classify leukemias, nor do they describe the same IncRNAs used in the present invention's gene signatures. [029] The works described above show that the present invention's set; of markers is innovative and could be used to obtain a precise classification of the leukemia subtype.

[030] Methodology - Gene Expression Microarrays

[031] The methodology described below is part of the state of the art and has no inventive activity.

[032] RNA extraction was carried out between 6 and 24 hours after collection by using PAXgene Blood RNA and Bone Marrow PAXgene RNA (QIAGEN) kits, following manufacturer's instructions. The quality of the RNA samples was determined using the NanoDrop 2000 spectrophotometer (Thermo Scientific) for the quantification and identification of 260/230 and 260/280 ratios, and the Agilent 2100 Bioanalyzer to determine RNA integrity. For microarray analysis, samples with RNA concentration above 6.6 ng/μΙ, with 260/230 and 260/280 ratios above 1.5 and 1.8 respectively, and with RIN (RNA Integrity Νυηη θή above 6.0, were considered appropriate.

[033] To identify a possible difference in gene expression between the acute leukemia subtypes, we used the Agilent Technologies platform with slides in 8 x 60k format (G4851A-Agilent Technologies) prepared by the SurePrint process. To capture images, we used the SureScan Microarray Scanner System (Agilent Technologies) with the parameters adjusted to the Cy3 cyanine channel. Data were extracted by the Agilent Feature Extraction program (version 11.0.1.1 , Agilent one-color GE1_1100_Jul11) and normalized using the Gene Spring program (version 12.5, Agilent Technologies).

[034] 66 slides were hybridized (with samples each). 467 (88%) samples of peripheral blood and bone marrow passed all the quality criteria and had the expression data extracted. In total, 254 patients with acute leukemia were analyzed. In the construction of the initial model we initially used bone marrow samples (BM), from 110 patients with AML and 97 with ALL.

[035] Bioinformatics Analysis of Gene Expression Data

[036] Supervised and unsupervised analyses of the data were performed. In the first, the leukemia subtypes of each patient were reported to the program (which were determined from the classic immunophenotyping, cytogenetics and molecular biology tests), and the program then used the gene expression patterns of each subtype to define the most important gene markers for the classification algorithm. In the other analysis type, the leukemia subtype was not reported to the program and the grouping (clustering) of patients was done based on the similarity of gene expression patterns. In the unsupervised analysis, a hierarchical clustering was performed using the 60 best probes of each group tested in the supervised analysis.

[037] Gene expression data analyses were performed using algorithms present in the GenePattern Platform (Broad Institute, USA). The gene expression profiles of the patients were assessed by classification programs such as the Support Vector Machine, Weighted Voting and K-nearest Neighbors.

[038] The main objective of the generated classifier was to determine a minimum panel of informative genes (corresponding to the probes tested by microarray) capable of classifying samples of unknown origin according to previous training with bone marrow samples.

[039] Preferably, programs were used in the cross-validation leave-one-out type mode. In this mode, the whole input database (dataset ri) is used to compose the training of the machine with the exception of one sample. This single sample composes the test dataset, and is then reclassified (since the a priori classification is already known) based on the model created from the training. In the absence of the cross-validation mode, the dataset was divided between training and test randomly, with 75% of the total dataset for training and the remaining 25% for test.

[040] The parameters used in the K-nearest Neighbor were: features - "60", feature selection statistic - "T-Test", neighbors = "3", weighting type - "distance" and distance measure - "Euclidean distance". The parameters; in the Weighted Voting were: features = 60 and feature selection statistic = "T- Test". The Support Vector Machine program does not require choice of parameters to function.

[041] Measurements for sensitivity, specificity, precision and accuracy were calculated from the classification information obtained through the classical tests, according to the formulas below: [042] Sensitivity = True positives / (True positives + False negatives)

[043] Specificity - True negatives / (True negatives + False positives)

[044] Precision = True positives / (True positives + False positives)

[045] Accuracy = (True positives + TN) / (True positives + True negatives +

False positives + False negatives)

[046] Biomarker validation by RT-RQ-PCR

[047] To ensure that the expression of the biomarkers (genes) selected by the bioinformatics analysis is in fact associated with the leukemia subtypes, it is necessary to confirm this differential expression by another technique. We used the quantitative RT-PCR (RT-RQ-PCR) with TaqMan probes (Taqman Gene Expression Assays, Applied Biosystems). For cDNA synthesis, Improm II™ (Promega) reverse transcriptase was used and the protocol was performed according to the manufacturer's instructions. 7 genes were selected for which specific probes and primers were designed.

[048] Two normalizations were performed using the 2- ΔΔΟΤ method. Firstly, we used control genes (ABL1 and GAPDH) to normalize expression variation in each individual, in accordance with the following formulas.

[049] ACt patient with leukemia: Ct Target - Ct Control Gene

[050] ACt Individual without disease: Ct Target - Ct Control Gene

[051] Secondly, gene expression in patients was compared with the expression in individuals without hematological disease using the following formula:

[052] AACt Patients with Leukemia: ACt Patient - ACt Mean of individuals without disease.

[053] Next, the median value of a patient group from the same subtype was compared with the median value of a group from another subtype.

[054] Results - Samples included

[055] Marrow and blood samples were again collected from individuals with acute leukemia, in addition to samples from 17 patients with related hematological diseases and 14 individuals without a hematological disease. The data from bone marrow samples submitted to the microarray quality criteria (n = 231) had its expression data used to construct the classifier. Bone marrow samples were divided into categories according to the presence of translocation or genetic alteration recommended by the WHO classification system, as described in Table 1 below, which represents: diagnosis j of patients whose gene expression patterns from bone marrow samples underwent bioinformatics analysis.

[056] Selected Biomarkers (Genes)

[057] The inventive step and non-obviousness of this invention is related to the selected biomarkers group and its use in acute leukemia classification. Through supervised analysis, the 60 best probes for the differentiation of the two subgroups were selected.

[058] To distinguish between ALL groups and other patients (including AML, chronic leukemia and non-hematological diseases) the 60 best probes found detect the genes described in Seq Id 1 to 60.

[059] To distinguish between AML groups and other patients (including ALL, chronic leukemia and non-hematological diseases) the 60 best probes found •detect the genes described in Seq Id 1 ,6,7,10, 11 , 14,15,19, 23, 26, 30, 32, 33, 34, 35, 36, 37, 38, 40, 41 , 42, 45, 44, 47, 49, 51, 52, 54, 55, 57 and 61 to 88.

[060] To distinguish between B-lineage ALL and T-lineage ALL groups, the 60 best probes found detect the genes described in Seq Id 11 , 40, 44, 61 , 69 and 89 to 140.

[061] To distinguish between ALL groups carrying the BCR-ABL fusion gene and ALL groups not carrying BCR-ABL, the 60 best probes found detect the genes described in Seq Id 110 and 141 to 195.

[062] To distinguish between ALL groups carrying the ETV6-RUNX1 (TEL- AML1) fusion gene and ALL groups not carrying ETV6-RUNX1 (TEL-AML1), the 60 best probes found detect the genes described in Seq Id 185 and 196 to 254.

[063] To distinguish between ALL groups carrying the TCF3-PBX1 fusion gene ALL groups not carrying TCF3-PBX1, the 60 best probes found detect the genes described in Seq Id 145, 154, 190 and 255 to 309.

[064] To distinguish between AML groups carrying the PML-RARA fusion gene, also called Acute Promyelocyte Leukemia, and AML groups not carrying PML-RARA, the 60 best probes found detect the genes described in Seq Id 5, 59, 232 and 310 to 361.

[065] To distinguish between AML groups carrying the RUNX1 -RUNX1 T1 fusion gene and AML groups not carrying RUNX1-RUNX1T1, the 60 best probes found detect the genes described in Seq Id 16, 24, 113, 166, 231 , 352 and 362 to 412.

[066] To distinguish between AML groups carrying inv16 CBFB-MYH11 and AML groups not carrying CBFB-MYH11 , the 60 best probes found detect the genes described in Seq Id 200, 367, 388 and 413 to 469.

[067] To distinguish between AML groups carrying MLLT3-MLL and AML groups not carrying MLLT3-MLL, the 60 best probes found detect the genes described in Seq Id 269, 302, 311 , 332, 464 and 470 to 523. [068] To distinguish between AML groups carrying internal duplication in tandem in the FLT3 (FLT3-DIT) gene without another translocation and AML groups not carrying FLT3-DIT, the 60 best probes found detect the genes described in Seq Id 122, 321 , 333, 360, 361 , 393, 448, 483 and 524 to 573.

[069] To distinguish between AML groups carrying internal duplication in tandem in the FLT3 (FLT3-DIT) gene with or without another translocation and AML groups not carrying FLT3-DIT, the 60 best probes found detect the genes described in Seq Id 138, 448, 460, 527, 529, 530, 538, 540, 542, 555, 557 and 574 to 621.

[070] To distinguish between AML groups carrying NPM1 gene exon 12 insertion and AML groups not carrying NPM1 gene exon 12 insertion, the 60 best probes found detect the genes described in Seq Id 64, 328, 350, 374, 461 , 486, 500 and 622 to 674.

[071] To distinguish between AML groups carrying PML-RARA fusion gene, long and variable isoform (bcr 1 and bcr2) and AML groups carrying PML- RARA fusion gene, short isoform (bcr3), the 60 best probes found detect the genes described in Seq Id 154, 258, 626 and 675 to 729.

[072] To distinguish between AML groups carrying PML-RARA fusion gene and internal duplication in tandem in the FLT3 (FLT3-DIT) gene from AML ^"groups carrying PML-RARA fusion gene without internal duplication in tandem in the FLT3 gene, the 60 best probes found detect the genes described in Seq Id 132, 209, 249, 258, 386, 440, 448, 454, 529, 677, 681 , 689, 697, 701 and 730 to 774.

[073] To distinguish between groups: ALL and AML, the 60 best probes found detect the genes described in Seq Id 1 , 6, 7, 8, 9, 10, 11 , 12, 14, 19, 23, 33, 34, 36, 37, 40, 41 , 42, 44, 45, 47, 48, 49, 51 , 52, 54, 55, 60, 64, 66, 70, 71 , 75, 78, 80, 84, 85, 88, and 775 to 793.

[074] An important part of the invention relates to the fact that a considerable part of the biomarkers detected are non-coding RNAs described in Seq Id 1 , 12, 16, 37, 89, 92, 105, 107, 110, 114, 128, 129, 135, 143, 150, 176, 216, 253, 258, 275, 328, 329, 331 , 339, 359, 380, 395, 442, 488, 503, 506, 525, 529, 532, 548, 559, 568, 585, 647, 653, 654, 675, 680, 691 , 695, 696, 709, 728, 750, 765, 771 , 787.

[075] The present patent is described in detail according to the attached figures, where:

[076] In figure 1 , the Heatmap illustrates the separation between the leukemia sub groups. The 60 best probes are shown on the vertical and the patients on the horizontal. The red squares refer to genes that are moreexpressed than the mean and the blue squares indicate the less expressed genes. Separation between B-ALL and T-ALL; two main clusters of patients are identified: T-ALL (in brown) and B-ALL (in grey).

[077] In figure 2, the Heatmap illustrates the separation between the leukemia sub-groups. The 60 best probes are shown on the vertical and the patients on the horizontal. The red squares refer to genes that are more expressed than the mean and the blue squares indicate the less expressed genes. Separation between B-ALL with t(1 ;19) and B-ALL without t(1 ;19); two main clusters of patients are identified: B-ALL with t(1 ;19) (in brown) and B- ALL without this translocation (in grey).

[078] In figure 3, the Heatmap illustrates the separation between the leukemia sub groups. The 60 best probes are shown on the vertical and the patients on the horizontal. The red squares refer to genes that are more expressed than the mean and the blue squares indicate the less expressed genes. Separation between AML with t(15;17) and AML without t(15;17); two main clusters of patients are identified: AML with t(15;17) (in brown) and AML without this translocation (in grey).

[079] In figure 4, the Heatmap illustrates the separation between the leukemia sub groups. The 60 best probes are shown on the vertical and the patients on the horizontal. The red squares refer to genes that are more expressed than the mean and the blue squares indicate the less expressed genes. Separation between ALL and AML; two main clusters of patients are identified: AML (in brown) and ALL (in grey).

[080] Figure 5 illustrates Part of the Hierarchical Cluster resulting from the unsupervised analysis of patients with AML. Two main clusters are shown. The first (I), marked in green, groups together patients carrying the PML- RARa fusion gene, whether with isoform S (GX and GL codes) or with isoform L (FX code). Patients carrying the P L-RARa fusion gene and the FLT3-DIT mutation are found in a single cluster (GL code) within this group. The second large cluster (II) is divided into two sub clusters, one being composed ; of patients carrying the RUNX1-RUNX1T1 [t(8;21)] rearrangement, marked ! in red.

[081] In figure 6, the RT-RQ-PCR illustrates the differential expression of genes selected as biomarkers. Validation of biomarker LOC728743 (pseudo •gene) more expressed in ALL than in AML.

[082] In figure 7, the RT-RQ-PCR 7 illustrates the differential expression of genes selected as biomarkers. Validation of biomarker XLOC_009378 (lincRNA) more expressed in patients with AML carrying t(8;21) than non- carriers.

[083] In figure 8, the RT-RQ-PCR illustrates the differential expression of genes selected as biomarkers. Validation of biomarker UBL7-AS1 (aRNA) more expressed in T-ALL than in B-ALL.

[084] In figure 9, RT-RQ-PCR illustrates the differential expression of genes selected as biomarkers. Validation of uncharacterized biomarker whose probe is located in chromosomal position chrl 1 :017366661-017366720 more expressed in APL [acute promyelocytic leukemia, the same as AML M3, characterized by the PML-RARa/t(15;17) fusion gene] more expressed than in .AML non PML-RARa carriers.

[085] In figure 10, RT-RQ-PCR illustrates the differential expression of genes selected as biomarkers. Validation of MARCH3 biomarker more expressed in patients with APL [acute promyelocytic leukemia, the same as AML M3, characterized by the PML-RARa/t(15;17) fusion gene] without interna! duplication type mutation in tandem in the FLT3 (FLT3-DIT) gene more expressed than in patients with APL with FLT3-DIT.

[086] Figures 1 to 4 show the separation between leukemia groups based on 60 probes.

[087] B Cross-validation [088] Next, utilizing a list with 60 probes for each model, the reclassification of samples randomly removed from the group total was performed. The results obtained in the supervised analysis for the classification of leukemia subtypes, comparing subtypes two by two, are described in table 2.

[089] Table 2: Results for Sensitivity, Specificity, Precision, and Accuracy obtained with the constructed classifier.

ALL, Acute Lymphoblastic Leukemia; AML, Acute Myeloid Leukemia; APL, Acute promyelocyte leukemia, the same as AML subtype M3.

[090] Reclassification based on gene expression data was considered successful for groups in which measurements above 95% were found. When testing pairs to the large groups of cell lineage that give rise to leukemia (e.g.: AML x ALL and B-ALL x T-ALL) very high precision and accuracy of more than 96% was achieved in reclassification. Similarly, excellent precision and accuracy was obtained for some subgroups characterized by the presence of chromosomal translocations when comparing the presence versus absence of this alteration within its lineage (myeloid or lymphoid) [(ex: t(15;17) or t(8;21) in AML and t(1 ;19) in ALL].

[091] Among the chromosomal translocations, t(9;22) had the worst performance using the developed classifier. Six carriers of this translocation were classified as non-carriers and 4 non-carriers were classified as carriers. There are already records in literature of some patients negative for t(9;22) who showed a gene expression pattern that mimics that of positive patients. This mimetic pattern could be associated to a subtype called "BCR-ABL like" (Den Boer et al., 2009; Mulligan et al., 2009).

[092] The issue of t(9;22) carriers incorrectly classified is more difficult toresolve and it will be necessary to use other bioinformatics methodology (in assessment phase) to deal with this question. In a previous work, Haferlach et al. (2005) obtained the same accuracy for this group and they hypothesized that mutations in other genes can activate the same path activated by the BCR-ABL protein. The solution will be to incorporate probes to the classification kits that directly detect the BCR-ABL fusion transcript.

[093] In relation to the FLT3-ITD and NPM1-A mutations associated with AML, there were cases of false positives and false negatives in our classification system. Therefore, in the international literature, there is evidence of low precision in these groups. The expression profile of patients with gene mutations in FLT3, NPM1 and CEBPa was described in a recent work (Balgobind et al., 2011) and it was noted that the classifiers obtained for these mutations are less precise than in the cases of translocations, as is the case in our work.

[094] PB Cross validation

[095] One of the objectives is to verify if the test that is being developed can be applied to peripheral blood samples (PB), in order to avoid the aspiration of bone marrow, which is painful and traumatic.

[096] In all, 236 patients with acute leukemia had their PB samples analyzed by gene expression microarrays. The PB samples were submitted to the classifier developed from the bone marrow data. The result of the reclassification of PB samples is described in table 3. [097] Table 3: Results for Sensitivity, Specificity, Precision and Accuracy obtained with the classifier constructed with BM and tested with PB.

[098] In defining cell lineage (ALL versus rest, AML versus rest, B-ALL versus T-ALL) and for chromosomal translocations, the parameters were below those tested on bone marrow samples. This demonstrates that in order to classify fusion gene carriers resulting from recurrent translocations in acute leukemias, bone marrow is the ideal material for the test based on gene expression. For the FLT3-DIT and NPM1 mutations, the parameters obtained in the reclassification with PB were better than those of BM.

[099] It is important to note that the precision of some categories was positive, suggesting that in the absence of BM material, as occurs when the ^•doctor cannot perform the marrow aspiration ("dry tap"), expression tests using peripheral blood can help in the classification and stratification of the patient.

[0100] Unsupervised analysis

[0101] To verify how the studied samples are grouped based on the gene expression, regardless of the clinical exams performed, a Hierarchical Cluster was made. The 60 best probes from each group tested in the unsupervised analysis were used. The unsupervised analysis showed that the main subgroups of ALL and AML were correctly grouped, with few exceptions. As with the supervised analysis, grouping errors occurred in genetically heterogeneous groups (BCR-ABL, FLT3-ITD and NPM1). Figure 5 show an example of separation into groups ("clustering") resulting from the unsupervised analysis.

[0102] RT-RQ-PCR Validation

[0103] Qualitative PCR tests were performed on 7 genes selected in the gene signatures described above.

[0104] The first test conducted was for IncRNA (UBL7-AS1) identified as differentially expressed between B-ALL and T-ALL. Data show that considering the 20 patients tested this gene is in fact more expressed in the T- ALL (median 2^Λ-ΔΔα = 2.2945) than in the B-ALL (median 2^Λ-ΔΔΟΙ = 0.0853) (table 4).

[0105] Table 4: 2^ΛΔΔΟί of gene UBL7-AS1 calculated for patients with T-ALL and B-ALL.

Sample CT U BL7-AS1 CT ABLl 2^Λ-ΔΔα

255 T 25.32898331 24.6479435 24.47325998

255 T 25.36930084 24.7137413

136 B 30.59900284 24.8140316 0.779145708

136 B 30.37941933 24.88145638

140 T 27.19399643 25.33075523 10.67567918

140 T 27.26460266 25.39748955

143 B 33.22258377 25.37119865 0.191169793

143 B 32.90429688 25.41866302

231 T 27.30504417 24.75714302 6.715965566

231 T 27.28697586 24.76719856

235 T 29.83384323 24.62584496 1.120319255

235 T 29.93565941 24.9086113

256 T 29.05452919 24.97676849 2.419143044

256 T 28.82070541 24.88459396

137 T 29.15194702 25.55691147 3.454938547

137 T 29.23792076 25.84741211

146 T 29.9486351 25.37805557 1.667486074

146 T 29.97013474 25.45319939

275 T 29.19913673 24.95389557 2.19974276

275 T 29.03263474 24.98968124

184 B 31.37033653 25.47336578 0.648624694

184 B 31.39322662 25.47824478

194 B 33.8439064 26.9816494 0.331430513

194 B 33.83239365 26.94534111

244 B 33.27459335 25.11280441 0.163097245 244 B 33.27072906 25.63725853

246 B 35.39299011 25.87295341 0.050080913

246 B 35.69634628 26.014328

252 B 33.84157181 26.76087379 0.249103263

252 B 33.88293076 26.39039421

163 B 32.90409851 24.95896339 0.146838063

163 B 33.13555527 24.98242378

174 T 28.5255146 25.56119728 4.81425648

174 T 28.63019943 25.56627846

204 B 33.26116943 24.18104172 0.0643842

204 B 33.61371231 24.21666908

217 B 30.08679962 22.36889839 0.204843366

217 B 30.02798271 22.6082058

303 B 31.64113426 24.07883263 0.208595098

303 B 31.63346672 24.11045647

[0106] In 6 of the 7 genes tested, the Q-PCR results confirm the differential expression of the genes selected by gene expression microarrays. Figures 6 to 10 show the differential expression between the genes validated by leukemia subtype.

[0107] Other applications

[0108] The biomarkers described in the present patent can be used for the diagnosis of patients with suspicion of acute leukemia.

[0109] The biomarkers described in the present patent can be used to confirm the presence of gene mutations or translocations when other methodologies currently used fail or produce dubious results.

[0110] The biomarkers described in the present patent can be used to define the risk group for patients with acute leukemia since the genetic alterations detected by them have a prognostic impact.

[0111] The present invention can be applied in the preparation of a kit based on gene expression for the classification of acute leukemias.

Claims

1. "SET OF BIOMARKERS FOR CLASSIFICATION OF ACUTE LEUKEMIAS", characterized by containing the combination of least ten of the genes described in the sequences Seq ID 1 to 787.

2. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for the biomarkers from claim 1.

3. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", according to claim 2, characterized by classifying the following acute leukemia subtypes: ALL, B-ALL, T-ALL, B-ALL BCR-ABL, B-ALL ETV6-RUNX1 (TEL- AML1), B-ALL TCF3-PBX1 , AML, AML PML-RARA (APL), AML PML-RARA with FLT3-DIT, AML PML-RARA without FLT3-DIT, AML RUNX1- RUNX1T1 , AML CBFB-MYH11 , AML FLT3-DIT, AML NPMImut.

4. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", according to a claim 2, characterized by assessing the gene expression hybridization of the patient's RNA with immobilized probes.

5. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", according to claim 2, characterized by assessing gene expression through the hybridization of the patient's cDNA with immobilized probes.

6. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", according to claim 2, characterized by assessing gene expression through the amplification of the patient's cDNA using real-time PGR.

7. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", according to claim 2, characterized by assessing gene expression from RNA extracted from bone marrow cells.

8 "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", according to claim 2, characterized by assessing gene expression from RNA extracted from peripheral blood cells.

9. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized b using a determination of gene expression for at least three of the genes from Seq ID 1 to 60 to distinguish Acute Lymphoid Leukemia from other hematological diseases and individuals without disease.

10. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 1 ,6,7,10, 11, 14,15,19, 23, 26, 30, 32, 33, 34, 35, 36, 37, 38, 40, 41 , 42, 45, 44, 47, 49, 51 , 52, 54, 55, 57 and 61 to 88 to distinguish Acute Myeloid Leukemia from other hematological diseases and individuals without disease.

11. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID Id 11 , 40, 44, 61 , 69 and 89 to 140 to distinguish T-lineage Acute Lymphoid Leukemia from B-lineage Acute Lymphoid Leukemia.

^."CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 110 and 141 to 195 to distinguish patients with B-lineage Acute Lymphoid Leukemia carrying the BCR-ABL fusion gene from other B-ltneage Acute Lymphoid Leukemia patients.

13. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 185 and 196 to 254 to distinguish patients with B-lineage Acute Lymphoid Leukemia carrying the ETV6-RUNX1 fusion gene from other B-lineage Acute Lymphoid Leukemia patients.

1 . "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 145, 154, 190 and 255 to 309 to distinguish patients with B-lineage Acute Lymphoid Leukemia carrying the TCF3-PBX1 fusion gene from other B-lineage Acute Lymphoid Leukemia patients.

15. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 5, 59, 232 and 310 to 361 to distinguish patients with Acute Myeloid Leukemia carrying the PML-RARA fusion gene from other patients with Acute Myeloid Leukemia.

16. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 16, 24, 113, 166, 231 , 352 and 362 to 412 to distinguish patients with Acute Myeloid Leukemia carrying the RUNX1- RUNX1T1 fusion gene from the other patients with Acute Myeloid Leukemia

^."CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at feast three of the genes from Seq ID 200, 367, 388 and 413 to 469 to distinguish patients with Acute Myeloid Leukemia carrying the CBFB-MYHII fusion gene from other Acute Myeloid Leukemia patients.

^."CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 269, 302, 311 , 332, 464 and 470 to 523 to distinguish patients with Acute Myeloid Leukemia carrying the MLL-AF9 fusion gene from the other patients with Acute Myeloid Leukemia .

19. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq !D 122, 321 , 333, 360, 361 , 393, 448, 483 and 524 to 573 to distinguish patients with Acute Myeloid Leukemia carrying the FLT3-ITD mutation without associated fusion genes from other Acute Myeloid Leukemia patients.

20. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 138, 448, 460, 527, 529, 530, 538, 540, 542, 555, 557, 574 to 621 to distinguish patients with Acute Myeloid Leukemia carrying the FLT3-ITD mutation from the other Acute Myeloid Leukemia patients.

21. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 64, 328, 350, 374, 461 , 486, 500 and 622 to 674 to distinguish patients with Acute Myeloid Leukemia carrying NPM1 gene exon 12 mutation from other Acute Myeloid Leukemia patients.

22 "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 154, 258, 626 and 675 to 729 to distinguish patients with Acute Myeloid Leukemia carrying the S isoform of the PML- RARA fusion gene from patients with Acute Myeloid Leukemia carrying the L or V isoform of the PML-RARA fusion gene.

23. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 132, 209, 249, 258, 386, 440, 448, 454, 529, 677, 681 , 689, 697, 701 and 730 to 774 to distinguish patients with Acute Myeloid Leukemia carrying the PML-RARA fusion gene and FLT3- ITD mutation from the other Acute Myeloid Leukemia patients carrying the PML-RARA fusion gene.

24. CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the genes from Seq ID 1 , 6, 7, 8, 9, 10, 11 , 12, 14, 19, 23, 33, 34, 36, 37, 40, 41 , 42, 44, 45, 47, 48, 49, 51 , 52, 54, 55, 60, 64, 66, 70, 71 , 75, 78, 80, 84, 85, 88, and 775 to 793 to distinguish patients with Acute Myeloid Leukemia from patients with Acute Lymphoid Leukemia.

25. "CLASSIFICATION PROCESS FOR ACUTE LEUKEMIAS", characterized by using a determination of gene expression for at least three of the non-coding RNAs described in Seq ID 1 , 12, 16, 37, 89, 92, , 107, 110, 114, 128, 129, 135, 143, 150, 176, 216, 253, 258, 275, 328,, 331 , 339, 359, 380, 395, 442, 488, 503, 506, 525, 529, 532, 548, 559,, 585, 647, 653, 654, 675, 680, 691 , 695, 696, 709, 728, 750, 765, 771 , for the classification of acute leukemias.