The WT1 gene encoding a zinc finger polypeptide is a tumor suppressor gene that plays a key role in the carcinogenesis of Wilms' tumor. Reverse transcriptase-polymerase chain reaction (RT-PCR) was used to examine relative levels of WT1 gene expression (defined in K562 cells as 1.00) in 45 patients with acute myelogenous leukemia (AML), 22 with acute lymphocytic leukemia (ALL), 6 with acute mixed lineage leukemia (AMLL), 23 with chronic myelogenous leukemia (CML), and 24 with non- Hodgkin's lymphoma. Significant levels of WT1 gene were expressed in all leukemia patients and for CML the levels increased as the clinical phase progressed. In striking contrast with acute leukemia, the levels of WT1 gene expression for NHL were significantly lower or even undetectable. Clear correlation was observed between the relative levels of WT1 gene expression (< 0.6 v > or = 0.6) and the prognosis for acute leukemia (AML, ALL, and AMLL). Patients with less than 0.6 levels had significantly higher rates of complete remission (CR), disease-free survival, and overall survival than those with > or = 0.6 levels, whereas CR could not be induced in any of the 7 patients with acute leukemia having greater than 1.0 levels of WT1 gene expression. The quantitation of the WT1 gene expression made it possible to detect minimal residual disease (MRD) in acute leukemia regardless of the presence or absence of tumor-specific DNA markers. Continuous monitoring of the WT1 mRNA was performed for 9 patients with acute leukemia. In 4 patients, MRD was detected 2 to 8 months before clinical relapse became apparent. In 2 other patients, the WT1 mRNA gradually increased after discontinuation of chemotherapy. No MRD was detected in the remaining 3 patients with AML who received intensive induction and consolidation therapy. Simultaneous monitoring of MRD by RT-PCR using primers for specific DNA markers in 3 patients (2 AML-M3 with PML/RAR alpha, and 1 AML-M2 with AML1/ETO) among these 9 patients detected MRD comparable with that obtained from quantitation of WT1 gene expression. In a patient with acute promyelocytic leukemia, the limits of leukemic cell detection by RT-PCR using either WT1 or promyelocytic leukemia/retinoic acid receptor-alpha gene primers were 10(-3) to 10(- 4) and 10(-4) for bone marrow, and 10(-5) and 10(-4) for peripheral blood, respectively. Therefore, we conclude that WT1 is a new prognostic factor and a new marker for the detection of MRD in acute leukemia.

20of the 3D genome in cell nuclei. Here, we describe a 4D simulation method, PHi-C (Polymer 21 dynamics deciphered from Hi-C data), that depicts dynamic 3D genome features through 22 polymer modelling. This method allows for demonstrations of dynamic characteristics of 23 genomic loci and chromosomes, as observed in live-cell imaging experiments, and provides 24 physical insights into Hi-C data. 25 Genomes consist of one-dimensional DNA sequences and are spatio-temporally organized 26 within the cell nucleus. Contact frequencies in the form of matrix data, measured using genome-27 wide chromosome conformation capture (Hi-C) technologies, have uncovered three-dimensional 28 (3D) features of average genome organization in a cell population 1, 2 . Moreover, live-cell imaging 29 experiments can reveal dynamic chromatin organization in response to biological perturbations 30 within single cells 3, 4 . Bridging the gap between these different sets of data derived from population 31 and single cells is a challenge for modelling dynamic genome organization 5, 6 . 32Several modelling methods have been developed to reconstruct 3D genome structures and 33 predict Hi-C data 7, 8 . In addition, there has been development of bioinformatic normalization 34 techniques in Hi-C matrix data processing to reduce experimental biases 9-11 . However, the mean-35 ing of a contact matrix as quantitative probability data has not been discussed; moreover, a four-36 dimensional (4D) simulation method to explore dynamic 3D genome organization remains lacking. 37Here, we introduce PHi-C, a method that can overcome these challenges by polymer mod-38 elling from a mathematical perspective and at low computational cost. PHi-C is a method that 39 2 deciphers Hi-C data into polymer dynamics simulations ( Fig. 1a, https://github.com/ 40 soyashinkai/PHi-C). PHi-C uses Hi-C contact matrix data generated from a hic file through 41 JUICER 12 as input ( Supplementary Fig. 1a). PHi-C assumes that a genomic region of interest at 42 an appropriate resolution can be modelled using a polymer network model, in which one monomer 43 corresponds to the genomic bin size of the contact matrix data with attractive and repulsive interac-44 tion parameters between all pairs of monomers described as matrix data (Methods, Supplementary 45 Note). Instead of finding optimized 3D conformations, we can utilize the optimization procedure 46 ( Supplementary Fig. 1b,c) to obtain optimal interaction parameters of the polymer network model 47 by using an analytical relationship between the parameters and the contact matrix. We can then 48 reconstruct an optimized contact matrix validated by input Hi-C matrix data using Pearson's cor-49 relation r. Finally, we can perform polymer dynamics simulations of the polymer network model 50 equipped with the optimal interaction parameters. 51First, we evaluated PHi-C's theoretical assumption about chromosome contact. Here, we 52 started with a simple polymer model called the bead-spring model, in which the characteristic 53 length...

Genomes are spatiotemporally organized within the cell nucleus. Genome-wide chromosome conformation capture (Hi-C) technologies have uncovered the 3D genome organization. Furthermore, live-cell imaging experiments have revealed that genomes are functional in 4D. Although computational modeling methods can convert 2D Hi-C data into population-averaged static 3D genome models, exploring 4D genome nature based on 2D Hi-C data remains lacking. Here, we describe a 4D simulation method, PHi-C (polymer dynamics deciphered from Hi-C data), that depicts 4D genome features from 2D Hi-C data by polymer modeling. PHi-C allows users to interpret 2D Hi-C data as physical interaction parameters within single chromosomes. The physical interaction parameters can then be used in the simulations and analyses to demonstrate dynamic characteristics of genomic loci and chromosomes as observed in live-cell imaging experiments. PHi-C is available at https://github.com/soyashinkai/PHi-C.

We determine the integral cohomology ring of the homogeneous space E 8 /T 1 ·E 7 by the Borel presentation and a method due to Toda. Then using the Gysin exact sequence associated with the circle bundle S 1 → E 8 /E 7 → E 8 /T 1 ·E 7 , we also determine the integral cohomology of E 8 /E 7 .