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(12) Patent Application Publication (10) Pub. No.: US 2011/0230372 A1 Willman Et Al

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US 2011 0230372A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2011/0230372 A1 Willman et al. (43) Pub. Date: Sep. 22, 2011 (54) GENE EXPRESSION CLASSIFIERS FOR Related U.S. Application Data ESSESYSSSIM.A. (60) Provisional application No. 61/279,281, filed on Oct. CLASSIFICATION AND OUTCOME 16,s 2009,s provisional application No. 61/199,342s- as PREDCTION IN PEDIATRCB-PRECURSOR filed on Nov. 14, 2008. ACUTE LYMPHOBLASTICLEUKEMA Publication Classification (51) Int. Cl. (75) Inventors: Cheryl L. Willman, Albuquerque, C40B 40/06 (2006.01) NM (US); Richard Harvey, CI2O I/68 (2006.01) Placitas, NM (US); Huining Kang, GOIN 33/566 (2006.01) Albuquerque, NM (US); Edward CI2O I/44 (2006.01) Bedrick, Albuquerque, NM (US); CI2O I/527 (2006.01) Xuefei Wang, Creve Coeur, MO 5.9. 4.t 73 C (US); Susan R. Atlas, Albuquerque, ( .01) NM (US); I-Ming Chen (52) U.S. Cl. ........ 506/16:435/6.18; 435/6.17; 435/6.16; s s 435/6.1435/7.92.435/19435/4:435/15 Albuquerque, NM (US)(US s 435/6.13:435/7.1:436/501s s s s (73) Assignee: STC UNM (57) ABSTRACT The present invention relates to the identification of genetic (21) Appl. No.: 12/998,474 markers patients with leukemia, especially including acute lymphoblastic leukemia (ALL) at high risk for relapse, espe cially high risk B-precursor acute lymphoblastic leukemia (22) PCT Filed: Nov. 16, 2009 (B-ALL) and associated methods and their relationship to therapeutic outcome. The present invention also relates to (86). PCT No.: PCT/US2009/006117 diagnostic, prognostic and related methods using these genetic markers, as well as kits which provide microchips S371 (c)(1), and/or immunoreagents for performing analysis on leukemia (2), (4) Date: Jun. 6, 2011 patients. Patent Application Publication Sep. 22, 2011 Sheet 1 of 24 US 2011/0230372 A1 FIGURE 1 OO Low Risk (n=109) 25 HR3.31 P<0.0001 (logrank) Patent Application Publication Sep. 22, 2011 Sheet 2 of 24 US 2011/0230372 A1 FIGURE 2 A B 100 100 Low Risk (n=72) 75 75 ---. ---- High Risk (n=52) Flow MRD(-) Patients HR=2.8 HRe3.75 P-0.0001 (logrank) P=0.0004 (logrank) Low Risk (n=29) MRD+/Low Risk a "MROd. g 50 - s 50 High Risk l, - 25 High Risk 25 MRD4 Flow MRD(+) Patients (ra38) High Risk HRc282 - - - - P=0.0054 (logrank) YEARS YEARS Patent Application Publication Sep. 22, 2011 Sheet 3 of 24 US 2011/0230372 A1 FIGURE 3 MRD (n=96) HR3.04 HR2.8 PCO.0001 (logrank) O P=0.0001 (logrank) O O 2 4. 6 YEARS C 100 Low Risk (n=61) 75 --- ---- High Risk 2 (1535). 50 25 High'' Risk Flow MRD(-) Patients Flow MRD(+) Patients (n=34) HRs.2.54 HR-223 --- P=0.022 (ogrank) P=0.038 (logan) D 2 4 6 YEARS F 100 Low Risk (n=51 75 - 'i Intermediate Risk : ----(n59)- - - - - - - - - 50 25 High Risk (n=34) P<0.0001 (logrank) YEARS YEARS Patent Application Publication Sep. 22, 2011 Sheet 4 of 24 US 2011/0230372 A1 FIGURE 4 Patent Application Publication Sep. 22, 2011 Sheet 5 of 24 US 2011/0230372 A1 FIGURE 5 A B 100 100 LOW Risk (n=47 '1. Intermediate Risk (n522) High Risk (n=15). P=0.047 (ogrank) Patent Application Publication Sep. 22, 2011 Sheet 6 of 24 US 2011/0230372 A1 FIGURE 6 - - - inte?tmediate Intermediate Risk ---Risk. • High Risk 25 25 Patients with Kinase Signature -- Patients without Kinase Signature P=0.07 (togrank) P<0.0001 (logrank) - trtemediate - - - Risk High Risk Patients with AKmutations Patients without JAK mutations P=0.001 (logrank) PCO.0001 (logrank) O 2 4. s YEARS rterediate 25 High risk Patients with karos deletion Patients without karos deleton P=0.0065 (logrank) P<0.0001 (logrank) YEARS YEARS Patent Application Publication Sep. 22, 2011 Sheet 7 of 24 US 2011/0230372 A1 FIGURE 7 - Not studied (n = 65) --- Studied (n = 207) P - 0.9751 Patent Application Publication Sep. 22, 2011 Sheet 8 of 24 US 2011/0230372 A1 FIGURE 8 O C O O l?) O C O O S. cd d C. O) O as S. to -O E cd 5 Z O O CN O O C O w O O 50 100 150 200 Number of present calls Patent Application Publication Sep. 22, 2011 Sheet 9 of 24 US 2011/0230372 A1 FIGURE 9 v 00 0.5 10 15 2.0 2.5 Threshold Patent Application Publication Sep. 22, 2011 Sheet 10 of 24 US 2011/0230372 A1 FIGURE 10 1 5 9 15 21 30 45 60 90 40 300 700 2000 6000 Number of genes Patent Application Publication Sep. 22, 2011 Sheet 11 of 24 US 2011/0230372 A1 FIGURE 11 Randomly partition the sample into 5 parts, balanced to preserve the proportions of key sample characteristics Use each of the 20 models to predict on the test set. Fit Cox regression to the predicted score and RFS data of the test set and calculate the LRT score. Has procedure been repeated 20 times? Calculate the geometric mean of the 100LRTs corresponding to each of the 20's. Chonse the model with the that maximizes the mean TRT as the final model Patent Application Publication Sep. 22, 2011 Sheet 12 of 24 US 2011/0230372 A1 FIGURE 12 Leave one sample Out and use remaining -l samples as the training set Determine a best prediction model based on the training set using the 20x5-fold cross validation procedure described in Figure S5 Use the best model to make the prediction on the left-out sample Has every sample been left out and predicted on? Use the predicted score on each of samples to assess the significance of the model Compared to the clinical predictors Patent Application Publication Sep. 22, 2011 Sheet 13 of 24 US 2011/0230372 A1 FIGURE 13 Randomly partition the sample into 10 folds, balanced to preserve proportions of the maior known sample characteristics Combine 9 folds into a training set and leave one fold as the test set N Use the training dataset to calculate the modified t-test score for each gene. Rank Repeat until the genes on the absolute values of the scores. For P = 1,2,..., use the top P every fold genes to build a prediction model has been a test Set OC / Use each of the models to predict on the test set, and calculate the misclassification late. 100 repeats (repartitionings) cottoleted? Calculate the arithmetic mean of the 1000 misclassification rates corresponding to each of the P-gene models. Choose the model with the number of genes minimizing the mean misclassification rate as the final model. Patent Application Publication Sep. 22, 2011 Sheet 14 of 24 US 2011/0230372 A1 FIGURE 14 Omit one sample and use the remaining n-1 samples as the training set Repeat until Determine a best prediction model based on the training set using the 100x10-fold every cross validations described in Figure S7 sample has been left out alld predicted Use the best model to predict on the left-out sample 0. Has every sample been left out and predicted on Calculate the misclassification rate using the binary predictions on all the samples and the ROC curve accuracy using the continuous scores of all the samples. Patent Application Publication Sep. 22, 2011 Sheet 15 of 24 US 2011/0230372 A1 FIGURE 15 OO 0.5 10 15 2.0 2.5 Threshold Patent Application Publication Sep. 22, 2011 Sheet 16 of 24 US 2011/0230372A1 FIGURE 16 s s s Low Risk (n=88); S High Risk n=75) HR = 2.81 s s 5 P = 0.0001 (log rank) Years 8 s Low Risk (n=61 s High Risk (n=35) HR = 2.54 Flow MRD) Patients Flow MRD (--) Patients P = 0.022 (log rank) 8 P< 0.038 (log rank) Years Years 8 s Low Risk (n=61 MRD(-), HR = 277 loan Risk High MRC Poo08 (n=59) s - - - - - - - - - - HR = 2.26 MRD(+)| : P=0.0066 High MRC High Risk (n=34) P<0.0001 (log rank) . s P<0.0001 (log rank) Years Years Patent Application Publication Sep. 22, 2011 Sheet 17 of 24 US 2011/0230372 A1 FIGURE 17 Patent Application Publication Sep. 22, 2011 Sheet 18 of 24 US 2011/0230372 A1 FIGURE 18 See S. Patent Application Publication Sep. 22, 2011 Sheet 19 of 24 US 2011/0230372 A1 FIGURE 19 Patent Application Publication Sep. 22, 2011 Sheet 20 of 24 US 2011/0230372 A1 FIGURE 20 OOL Os 09 t Oe S9. OO. op Oa. S-se OO 09 Or Oe Sk Patent Application Publication Sep. 22, 2011 Sheet 21 of 24 US 2011/0230372 A1 FIGURE 21 - Not studied (n = 65) a Studied (n = 207) P. O.9751 Patent Application Publication Sep. 22, 2011 Sheet 22 of 24 US 2011/0230372 A1 FIGURE 22 Probeset 21215 at e --a of o sus 9 17 25 33 41 4957 65 38 8997 1613 2 1231371451516. 169171351932O Patent Application Publication Sep. 22, 2011 Sheet 23 of 24 US 2011/0230372 A1 FIGURE 23 Patent Application Publication Sep. 22, 2011 Sheet 24 of 24 US 2011/0230372A1 FIGURE 24 83 8 : S. 8 US 2011/0230372 A1 Sep. 22, 2011 GENE EXPRESSION CLASSIFIERS FOR myeloid leukemia in children from ages 1-15 years, the fre RELAPSE FREE SURVIVAL AND MINIMAL quency of ALL and AML in infants less than one year of age RESIDUAL DISEASE IMPROVERISK is approximately equivalent.
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  • Two Novel Disease-Causing Variants in BMPR1B Are Associated with Brachydactyly Type A1

    Two Novel Disease-Causing Variants in BMPR1B Are Associated with Brachydactyly Type A1

    European Journal of Human Genetics (2015) 23, 1640–1645 & 2015 Macmillan Publishers Limited All rights reserved 1018-4813/15 www.nature.com/ejhg ARTICLE Two novel disease-causing variants in BMPR1B are associated with brachydactyly type A1 Lemuel Racacho1,2, Ashley M Byrnes1,3, Heather MacDonald3, Helen J Dranse4, Sarah M Nikkel5,6, Judith Allanson6, Elisabeth Rosser7, T Michael Underhill4 and Dennis E Bulman*,1,2,5 Brachydactyly type A1 is an autosomal dominant disorder primarily characterized by hypoplasia/aplasia of the middle phalanges of digits 2–5. Human and mouse genetic perturbations in the BMP-SMAD signaling pathway have been associated with many brachymesophalangies, including BDA1, as causative mutations in IHH and GDF5 have been previously identified. GDF5 interacts directly as the preferred ligand for the BMP type-1 receptor BMPR1B and is important for both chondrogenesis and digit formation. We report pathogenic variants in BMPR1B that are associated with complex BDA1. A c.975A4C (p. (Lys325Asn)) was identified in the first patient displaying absent middle phalanges and shortened distal phalanges of the toes in addition to the significant shortening of middle phalanges in digits 2, 3 and 5 of the hands. The second patient displayed a combination of brachydactyly and arachnodactyly. The sequencing of BMPR1B in this individual revealed a novel c.447-1G4A at a canonical acceptor splice site of exon 8, which is predicted to create a novel acceptor site, thus leading to a translational reading frameshift. Both mutations are most likely to act in a dominant-negative manner, similar to the effects observed in BMPR1B mutations that cause BDA2.
  • Open Data for Differential Network Analysis in Glioma

    Open Data for Differential Network Analysis in Glioma

    International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes.