Abstract: In one embodiment, a gene expression signature for predicting risk of developing therapy-related myelodysplasia or acute myeloid leukemia (t-MDS/AML) after autologous hematopoietic cell transplantation (aHCT) is provided. In another embodiment, a method for predicting a risk for development of t-MDS/AML after aHCT is provided. Such a method may include providing a biological sample that contains CD34 cells from a subject; detecting a test expression level of a set of two or more genes of a gene expression signature; comparing the test expression level of a set of corresponding training expression levels that include a training case expression level and a training control expression level; and predicting a high risk of developing t-MDS/AML when the test expression level is at or about the training case expression level or predicting a low risk of developing t-MDS/AML when the test expression level is at or about the training control expression level.

Abstract: In one embodiment, a gene expression signature for predicting risk of developing therapy-related myelodysplasia or acute myeloid leukemia (t-MDS/AML) after autologous hematopoietic cell transplantation (aHCT) is provided. In another embodiment, a method for predicting a risk for development of t-MDS/AML after aHCT is provided. Such a method may include providing a biological sample that contains CD34 cells from a subject; detecting a test expression level of a set of two or more genes of a gene expression signature; comparing the test expression level of a set of corresponding training expression levels that include a training case expression level and a training control expression level; and predicting a high risk of developing t-MDS/AML when the test expression level is at or about the training case expression level or predicting a low risk of developing t-MDS/AML when the test expression level is at or about the training control expression level.

Abstract: A method for analyzing large data arrays is provided. In one aspect, the invention provides a method for analyzing data from two or more data arrays. Each array includes a plurality of members, each member provides a signal, and the data is indexed by one or more parameters. In one embodiment, the method includes fitting a model to the data; determining the goodness of the fit by evaluating the statistical significance of the fit; and determining the statistical significance of the signal. In another embodiment, the method further includes correcting the data for heterogeneity among members prior to fitting the model to the data.