JP-2022532108-A5 -

Dates

Publication Date

20230516

Application Date

20200506

Description

This invention relates to disease processes. The regulatory and causal aspects of cancer disease processes are complex and cannot be easily elucidated using available DNA and protein classification methods. Diffuse large B-cell lymphoma (DLBCL) is a cancer of B cells, a type of white blood cell involved in antibody production. It is the most common type of non-Hodgkin lymphoma among adults, with an incidence of 7-8 cases per 100,000 people per year in the United States and the United Kingdom. However, the outcome of the disease process is not well understood. Prostate cancer is caused by abnormal, uncontrolled growth of cells in the prostate gland. While prostate cancer survival rates improve every decade, the disease remains largely incurable. According to the American Cancer Society, the one-year relative survival rate for all stages of prostate cancer combined is 20%, and the five-year relative survival rate is 7%. The inventors have identified subgroups of patients defined by the chromosomal conformational signatures of prostate cancer, diffuse large B-cell lymphoma (DLBCL), and lymphoma. According to the present invention, a process is provided for detecting chromosomal states representing subgroups within a population, the process comprising the step of determining whether chromosomal interactions associated with the chromosomal state exist within a defined region of the genome; and - the chromosomal interactions are optionally identified by a method for determining which chromosomal interactions are associated with a chromosomal state corresponding to a subgroup of the population, the method comprising the steps of contacting a first set of nucleic acids from subgroups having different chromosomal states with a second set of index nucleic acids, and enabling hybridization of complementary sequences, wherein the nucleic acids in the first and second sets of nucleic acids represent a ligated product containing sequences from both chromosomal regions joined by the chromosomal interaction, and the pattern of hybridization between the first and second sets of nucleic acids makes it possible to determine which chromosomal interactions are specific to the subgroup ; and - the subgroups are associated with the prognosis of prostate cancer, and the chromosomal interactions are (i) located in any of the regions or genes listed in Table 6, and/or (ii) corresponding to any of the chromosomal interactions represented by any of the probes shown in Table 6, and/or (iii) located in a 4,000 base region containing or adjacent to (i) or (ii); or - Subgroups are associated with the prognosis of DLBCL, and chromosomal interactions are, a) located in any of the regions or genes listed in Table 5, and/or b) corresponding to any of the chromosomal interactions represented by any of the probes shown in Table 5, and/or c) located in a 4,000-base region containing or adjacent to (a) or (b); or - Subgroups are associated with the prognosis of lymphoma, and chromosomal interactions are, (iv) Located in any of the regions or genes listed in Table 8, and/or (v) corresponding to any of the chromosomal interactions shown in Table 8, and/or (vi) located in a 4,000-base region containing or adjacent to (iv) or (v). The principal component analysis (PCA) of a prostate cancer study is shown.This shows a comparison of two PCA prognostic classifiers, VENN.The PCA analysis of DLBCL is shown.This shows the PCA for the seven BTK markers (OBD RD051) of DLBCL.This provides an example of how the classification of chromosome interactions can be performed.The markers from canine lymphoma studies that can be used in the method of the present invention are shown. Figure 6 shows marker reduction. 70% of the 38 samples were used as a training set (28) and used for marker selection. The remaining 10 were used as a test set. Multiple training and test sets were used. Univariate analysis, Fisher's exact test (results in columns D and E), and multivariate analysis with penaltyd logistic modeling (GLMNET, results in columns B and C). Markers 2–18 are lymphoma markers, and 19–23 are controls. The top 11, which are all loops present in lymphoma, were selected for classification.This table shows canine markers for human genes. The table displays the top 11 canine markers mapped to the human genome (Hg38) with the closest mapping genomic region. Adjacent networks are constructed using 11 markers (dark), light-colored nodes, and linker proteins from the NCI database.This shows canine markers for human genes. Similar to before, but with pathway enrichment of the network. Only 11 canine mapping loci were used for enrichment; ligated modes were omitted from the enrichment. Lightly colored nodes belong to the KEGG CML pathway.The XGBooster 11 mark models for training set 1 and test set 1 are shown.The XGBooster 11 mark models for training set 2 and test set 2 are shown.The XGBooster 11 mark models for training set 3 and test set 3 are shown.The logistic PCA for tr