Abstract:
This present disclosure provides a kit and method for detecting at least one KANSARL fusion transcript from a biological sample from a subject. The kit comprises at least one of the following components: (a) at least one probe, wherein each of the at least one probe comprises a sequence that hybridizes specifically to a junction of the at least one KANSARL fusion transcript; (b) at least one pair of probes, wherein each of the at least one pair of probes comprises: a first probe comprising a sequence that hybridizes specifically to KANSL1; and a second probe comprising a sequence that hybridizes specifically to ARL17A; or (c) at least one pair of amplification primers, wherein each of the at least one pair of amplification primers are configured to specifically amplify the at least one KANSARL fusion transcript.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    The present application is a continuation-in-part of U.S. patent application Ser. No. 14/792,613, filed Jul. 7, 2015, the contents of which are hereby incorporated by reference in its entirety. 
     
    
     REFERENCE TO SEQUENCE LISTING SUBMITTED ELECTRONICALLY 
       [0002]    The content of the electronically submitted sequence listing, file name Human_Cancer_Fusion_Transcripts_20160621 ST25.txt, size 199,892 Kbytes and date of creation Jun. 21, 2016, filed herewith, is incorporated herein by reference in its entirety. 
       BACKGROUND 
       [0003]    Genetic predisposition to cancer has been well known for centuries initially via observation of unusual familial clustering of cancer, and later through identification of studying cancer-prone families that demonstrate Mendelian inheritance of cancer predisposition (Rahman 2014). 114 cancer predisposition genes (CPG) have been identified so far, including BRCA1 and BRCA2, the DNA-mismatch-repair genes (relevant for colon cancer), TP53 in Li-Fraumeni syndrome, and APC in familial adenomatous polyposis (Rahman 2014). All of these 114 CPG have derived from known genes, but none of them are fusion genes (Rahman 2014). Despite extensive research, known genetic factors can explain only a small percentage of familial cancer risk, implying that so-called low-hanging fruit of novel candidate genes remain to be discovered (Stadler, Schrader et al. 2014). 
         [0004]    Recent rapid advances in RNA-seq make it possible to systematically discover fusion transcripts, and to use this technique for direct cancer diagnosis and prognosis (Mertens, Johansson et al. 2015). In the last several years, RNA-seq data have growing exponentially, and around 30,000 novel fusion transcripts and genes have been identified and accumulated by scientific and medical communities so far (Yoshihara, Wang et al. 2014, Mertens, Johansson et al. 2015). 
         [0005]    The key challenge of this technique is how to fast and accurately map RNA-seq reads to the genomes. Although enormous progresses have been made, and more than 20 different software systems have been developed for the identification of fusion transcripts, none of these algorithms and software systems can achieve both fast speeds and high accuracies (Liu, Tsai et al. 2015). 
       SUMMARY OF THE INVENTION 
       [0006]    Previously, the applicant had disclosed a method of identifying fusion transcripts, whose content has been provided in U.S. Patent Application (Publication No. US20160078168 A1). In one aspect of the present disclosure, the applicant has used the method as disclosed above to analyze RNA-seq data from human cancer and other diseases, and has identified 886,543 novel fusion transcripts. A set of isolated, cloned recombinant or synthetic polynucleotides are herein provided. Each polynucleotide encodes a fusion transcript, the fusion transcript comprising a 5′ portion from a first gene and a 3′ portion from a second gene. The 5′ portion from the first gene and the 3′ portion from the second gene is connected at a junction; and the junction has a flanking sequence, comprising a sequence selected from the group of nucleotide sequences as set forth in SEQ ID NOs: 1-886,543 or from a complementary sequence thereof. 
         [0007]    In another aspect, the present application provides a kit and method for detecting at least one KANSARL fusion transcript from a biological sample from a subject. 
         [0008]    The kit comprises at least one of the following components: 
         [0009]    (a) at least one probe, wherein each of the at least one probe comprises a sequence that hybridizes specifically to a junction of the at least one KANSARL fusion transcript; 
         [0010]    (b) at least one pair of probes, wherein each of the at least one pair of probes comprises: a first probe comprising a sequence that hybridizes specifically to KANSL1; and a second probe comprising a sequence that hybridizes specifically to ARL17A; or 
         [0011]    (c) at least one pair of amplification primers, wherein each of the at least one pair of amplification primers are configured to specifically amplify the at least one KANSARL fusion transcript. 
         [0012]    In some embodiments, the kit can further include compositions configured to extract RNA sample in the biological sample, and compositions configured to generate cDNA molecules from RNA sample in the biological sample. 
         [0013]    The biological sample can be a cell line, buccal cells, adipose tissue, adrenal gland, ovary, appendix, bladder, bone marrow, cerebral cortex, colon, duodenum, endometrium, esophagus, fallopian tube, gall bladder, heart, kidney, liver, lung, lymph node, pancreas, placenta, prostate, rectum, salivary gland, skeletal muscle, skin, blood, small intestine, smooth muscle, spleen, stomach, testis, thyroid, and tonsil. The biological sample can be prepared in any methods. For example, the biological samples can be buccal cells prepared by buccal swabs, or can be a tissue sample prepared by biopsy, or can be a blood sample prepared by liquid biopsy. There are no limitations herein. 
         [0014]    In embodiments of the kit comprising components as set forth in (a), the junction of the at least one KANSARL fusion transcript comprises a nucleotide sequence as set forth in SEQ ID NOs: 886,550-886,555. Optionally, the components as set forth in (a) comprise a plurality of probes and a substrate, wherein the plurality of probes are immobilized on the substrate to thereby form a microarray. As such, the kit as set forth in (a) can be used to detect at least one KANSARL fusion transcript by microarray analysis, but the kit can also be used for analysis using other hybridization-based method. 
         [0015]    In embodiments of the kit comprising components as set forth in (b), each of the at least one pair of probes comprises a pair of nucleotide sequences selected from one of SEQ ID NO: 886556 and SEQ ID NO: 886,567, SEQ ID NO: 886566 and SEQ ID NO: 886567, SEQ ID NO: 886568 and SEQ ID NO: 886569, SEQ ID NO: 886560 and SEQ ID NO: 886561, SEQ ID NO: 886558 and SEQ ID NO: 886559, SEQ ID NO: 886564 and SEQ ID NO: 886565, and SEQ ID NO: 886562 and SEQ ID NO: 886563. These pairs of probes are configured to detect the presence or absence of any of the KANSARL fusion transcript isoforms 1-6, among which, the probe pair SEQ ID NO: 886556 and SEQ ID NO: 886,567 is used for detection of isoform 1; the probe pair SEQ ID NO: 886566 and SEQ ID NO: 886567, and the probe pair SEQ ID NO: 886568 and SEQ ID NO: 886569, are used for isoform 2; the probe pair SEQ ID NO: 886560 and SEQ ID NO: 886561 for isoform 3; the probe pair SEQ ID NO: 886558 and SEQ ID NO: 886559 for isoform 4; the probe pair SEQ ID NO: 886564 and SEQ ID NO: 886565 for isoform 5; and the probe pair SEQ ID NO: 886562 and SEQ ID NO: 886563 for isoform 6, respectively. In these embodiments, these probe pairs are respectively used to detect the presence of any of the KANSARL fusion transcript isoforms by co-hybridization of the first probe and the second probe in a hybridization reaction, including in situ hybridization and Northern blot. 
         [0016]    In some of the embodiments as described above, the first probe and the second probe respectively comprises a first moiety and a second moiety, configured to indicate co-hybridization of the first probe and the second probe in a hybridization reaction to thereby detect a presence of the at least one KANSARL fusion transcript. The first moiety and the second moiety can be fluorescence dyes, radioactive labels, or some other moiety capable of being conveniently recognized. The co-hybridization of the first probe and the second probe in a hybridization reaction refers to simultaneous detecting of the hybridization of the first probe and the second probe in one hybridization reaction. Examples include co-localization of the first probe and the second probe in an in situ hybridization assay, such as fluorescence in situ hybridization (FISH), and also include co-localization of the first probe and the second probe in a Northern blot analysis. There are no limitation herein. 
         [0017]    In embodiments of the kit comprising components as set forth in (c), each of the at least one pair of amplification primers comprises a pair of nucleotide sequences selected from one of SEQ ID NO: 886556 and SEQ ID NO: 886,567, SEQ ID NO: 886566 and SEQ ID NO: 886567, SEQ ID NO: 886568 and SEQ ID NO: 886569, SEQ ID NO: 886560 and SEQ ID NO: 886561, SEQ ID NO: 886558 and SEQ ID NO: 886559, SEQ ID NO: 886564 and SEQ ID NO: 886565, and SEQ ID NO: 886562 and SEQ ID NO: 886563. Each of these pairs of amplification primers is configured to amplify one isoform of the KANSARL fusion transcript by PCR. 
         [0018]    Among these, the primer pair SEQ ID NO: 886556 and SEQ ID NO: 886,567 is used for PCR amplification of isoform 1 (with an expected size of 379 by for the PCR product); the primer pair SEQ ID NO: 886566 and SEQ ID NO: 886567, and the primer pair SEQ ID NO: 886568 and SEQ ID NO: 886569, are used for amplification of isoform 2 (with an expected size of 431 by and 236 bp, respectively, for the PCR product); the primer pair SEQ ID NO: 886560 and SEQ ID NO: 886561 for amplification of isoform 3 (with an expected size of 149 by for the PCR product); the primer pair SEQ ID NO: 886558 and SEQ ID NO: 886559 for amplification of isoform 4 (with an expected size of 385 by for the PCR product); the primer pair SEQ ID NO: 886564 and SEQ ID NO: 886565 for amplification of isoform 5 (with an expected size of 304 by for the PCR product); and the primer pair SEQ ID NO: 886562 and SEQ ID NO: 886563 for amplification of isoform 6 (with an expected size of 160 by for the PCR product), respectively. 
         [0019]    In some of the embodiments as disclosed above, the components of the kit as set forth in (c) can further comprise a DNA polymerase, configured to amplify the at least one KANSARL fusion transcript using the at least one pair of amplification primers. Optionally, the components of the kit as set forth in (c) can further include an instruction of how to perform the PCR reaction for amplification of the isoforms. 
         [0020]    In a third aspect, the present disclosure provides a method for detecting presence or absence of at least one KANSARL fusion transcript in a biological sample from a subject utilizing the kit as described above. The method includes the steps of: (i) treating the biological sample to obtain a treated sample; (ii) contacting the treated sample with at least one components as set forth in (a), (b), or (c) of the kit for a reaction; and (iii) determining that the at least one KANSARL fusion transcript is present in the biological sample if the reaction generates a positive result, or that the at least one KANSARL fusion transcript is absent in the biological sample if otherwise. 
         [0021]    In some embodiments of the method, the reaction in step (ii) can be a hybridization reaction. In some of the embodiments where the components as set forth in (b) are utilized, the positive result in step (iii) is co-localization of the first probe and the second probe in the hybridization reaction, and the hybridization reaction in step (ii) can be in situ hybridization (ISH) or Northern blot. In some of the embodiments where the components as set forth in (a) are utilized, the positive result in step (iii) is hybridization of the at least one probe with at least one polynucleotide in the treated sample. The hybridization reaction in step (ii) can be Southern blot, dot blot, or microarray, and the treated sample in step (i) can be a cDNA sample, and step (i) comprises the sub-steps of: isolating a RNA sample from the biological sample; and obtaining the cDNA sample from the RNA sample. 
         [0022]    In some embodiments of the method, the reaction in step (ii) can be amplification reaction. Under such a case, the components as set forth in (c) are utilized, and the positive result in step (iii) is obtaining of at least one amplified polynucleotide of expected size. In preferred embodiments, step (iii) can further comprise verification of the at least one amplified polynucleotide by sequencing. 
         [0023]    Specifically as examples, each of the at least one pair of amplification primers in the components as set forth in (c) can comprise a pair of nucleotide sequences selected from one of SEQ ID NO: 886556 and SEQ ID NO: 886,567; SEQ ID NO: 886566 and SEQ ID NO: 886567; SEQ ID NO: 886568 and SEQ ID NO: 886569; SEQ ID NO: 886560 and SEQ ID NO: 886561; SEQ ID NO: 886558 and SEQ ID NO: 886559; SEQ ID NO: 886564 and SEQ ID NO: 886565; and SEQ ID NO: 886562 and SEQ ID NO: 886563; and the expected size of the at least one amplified polynucleotide is 379 bp, 431 bp, 236 bp, 149 bp, 385 bp, 304 bp, or 160 bp. 
         [0024]    Among these, the primer pair SEQ ID NO: 886556 and SEQ ID NO: 886,567 can be used for PCR amplification of isoform 1 (with an expected size of 379 by for the PCR product); the primer pair SEQ ID NO: 886566 and SEQ ID NO: 886567, and the primer pair SEQ ID NO: 886568 and SEQ ID NO: 886569, can be used for amplification of isoform 2 (with an expected size of 431 by and 236 bp, respectively, for the PCR product); the primer pair SEQ ID NO: 886560 and SEQ ID NO: 886561 for amplification of isoform 3 (with an expected size of 149 by for the PCR product); the primer pair SEQ ID NO: 886558 and SEQ ID NO: 886559 for amplification of isoform 4 (with an expected size of 385 by for the PCR product); the primer pair SEQ ID NO: 886564 and SEQ ID NO: 886565 for amplification of isoform 5 (with an expected size of 304 by for the PCR product); and the primer pair SEQ ID NO: 886562 and SEQ ID NO: 886563 for amplification of isoform 6 (with an expected size of 160 by for the PCR product), respectively. 
         [0025]    In a fourth aspect, the present disclosure provides a method for detecting the presence of KANSARL fusion gene from a genomic DNA sample of a subject. The method comprises: (i) contacting the treated sample with at least one primer pair for PCR amplification; and (ii) determining that the KANSARL fusion gene is present in the genomic DNA sample if the PCR amplification generates a positive result, or that the KANSARL fusion gene is absent in the genomic DNA sample if otherwise. Herein the positive result refers to the generation of a PCR product of expected size after PCR amplification. In some preferred embodiments, the PCR product can further undergo sequencing for verification. 
         [0026]    In one specific embodiment, a primer pair as set forth in SEQ ID NO: 886,574 and SEQ ID NO: 886,575 can be used, and the positive result is the generation of a PCR product of 360 bp. The genomic DNA sample can be prepared from a tissue sample, obtained from any method. For example, it can be prepared from buccal cells via buccal swabs. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0027]      FIG. 1  shows schematic diagrams of identification and characterization of  KANS L1- ARL 17A (KANSARL) fusion transcripts. a). Schematic diagrams of putative mechanisms via an inversion or a duplication to form a genomic structure of KANSL1→ARL17A from a genomic structure of ARL17A→KANSL1. Solid black, gray and white arrows represent KANSL1, ARL17A and other genes indicated by letters and their orientations, respectively. Solid and grey squares represent KANSL1 and ARL17A exons, Vertical triangles are introns. Dashed lines show omitted exons and introns. Dashed lined horizontal arrow indicates unknown genomic sequences; b). Schematic diagrams show the six KANSARL isoforms identified and their junction sequences. The black and grey letters represent KANSL1 and ARL17A cDNA sequences; c) A graphic diagram shows the distribution of the raw counts of the six KANSARL fusion transcripts identified in the ECD39, where the numbers indicate the KANSARL isoforms; d). A graphic diagram shows that 11 cancer lines in the ECD39 have been identified to have KANSARL fusion transcripts. The black bars indicate raw counts of the total KANSARL fusion transcripts; e) A diagram shows distributions of normalized counts of KANSARL fusion transcripts observed in the 11 cancer lines. Y-axis represents the number of splice junctions per million reads (NSJPMR). 
           [0028]      FIG. 2  shows the KANSARL isoform RNA and protein sequences. a) KANSARL isoform 1; b) KANSARL isoform 2; c) KANSARL isoform 3; d) KANSARL isoform 4; e) KANSARL isoform 5; and f) KANSARL isoform 6; The black and underlined letters indicate peptide sequences from KANSL1 and ARL17A genes, respectively. 
           [0029]      FIG. 3  shows schematic procedure of validation of KANSARL isoform 1 and 2 in A549, Hela-3 and K562. a). RT-PCR amplification of KANSARL isoform 1 in A549, Hela-3, K562, 786-O and OS-RC-2; b). RT-PCR amplification of KANSARL isoform 2 in A549, Hela-3, K562, 786-O and OS-RC-2; c). RT-PCR amplification of KANSL1 in A549, Hela-3, K562, 786-O and OS-RC-2; d). RT-PCR amplification of ARL17A in A549, Hela-3, K562, 786-O and OS-RC-2; e). RT-PCR amplification of GAPDH in A549, Hela-3, K562, 786-O and OS-RC-2; f). Sequencing validation of KANSARL isoform 1 splice junctions; g). Sequencing validation of KANSARL isoform 2 splice junctions; h). A graphic diagram shows the relative expression levels of KANSARL isoform 1 and 2 in A549, Hela-3 and K562; and i). A graphic diagram shows the differences between KANSARL isoform 1 and 2 in A549, Hela-3 and K562. Y-axis indicates folds. 
           [0030]      FIG. 4  shows analyses of RNA-seq datasets from diverse types of cancer to illuminate that those KANSARL fusion transcripts are rarely found in cancer patients from Asia and Africa and are detected predominantly in cancer patients from North America. a). Analysis of KANSARL fusion transcripts in the CGD glioblastoma RNA-seq datasets. CE, NE and Normal represent contrast-enhancing regions (CE) of diffuse glioblastomas (GBM), nonenhancing regions (NE) of GBM and brain tissues of non-neoplastic persons as a normal control, respectively. b). Comparative analysis of KANSARL fusion transcripts between the BGD and CGD datasets. BGD and CGD represent 272 glioblastoma patients from Beijing Neurosurgical Institute and 27 glioblastoma patients of Columbia University Medical Center, respectively. c) Comparative analysis of KANSARL fusion transcripts between the VPD and BPD datasets. VPD and BPD are 25 prostate patient samples from Vancouver Prostate Centre and 14 prostate tumor samples from Beijing Genome Institute (BGI), respectively. d). Comparative analysis of KANSARL fusion transcripts between the MULCD and SLCD datasets. MULCD and SLCD represent 20 lung cancer patients from University of Michigan and 168 lung cancer samples from South Korean Genomic Medicine Institute. e). Comparative analysis of KANSARL fusion transcripts between the HIBCD and SKBPD datasets. HIBCD and SKBPD represent 163 breast cancer samples from Hudson Alpha Institute for Biotechnology and 78 breast cancer patients from South Korean, respectively. f). Comparative analysis of KANSARL fusion transcripts among the NLD, BCLD, YLD and ULD datasets. NLD, BCLD, YLD and ULD represent 41 sporadic forms Burkitt Lymphoma from National Cancer Institute, 13 cutaneous T cell lymphoma from Yale University, 23 diffuse large B-cell lymphoma from BC Cancer Agency, and 20 lymphoma samples from Uganda. Black and gray bars indicate total numbers of samples and numbers of samples having KANSARL fusion transcripts, respectively. Dark gray cylinders are percentages of KANSARL-positive samples in the datasets. 
           [0031]      FIG. 5  shows Venn diagrams of overlaps between KANSARL and TMPRSS2-ERG fusion transcripts. a). KANSARL+ tumors; b). KANSARL+ adjacent tissues; and c). KANSARL− tumors. Gray, white and black circles represent the KANSARL-positive, TMPRSS2-ERG and KANSARL-negative, respectively. 
           [0032]      FIG. 6  shows family inherence and population genetics of KANSARL fusion transcripts. a). Diagrams of KANSARL inheritance in the CEPH/Utah Pedigree 1463, which includes four grandparents, two parents and eleven children. Black and white squares represent KANSARL-positive and KANSARL-negative males while black and white squares indicate KANSARL-positive and KANSARL-negative females. The black lines are relationships among the family members. b). frequncies of KANSARL fusion transcripts in some populations of European and African ancestries. GBR is British from England and Scotland); FIN represents Finnish in Finland; TSI is Toscani in Italia; and YRI represents Yoruba in Ibadan, Nigeria. 
           [0033]      FIG. 7  shows RNA-typing of KANSARL fusion transcripts in cancer cell lines. a). RT-PCR amplifications of breast cancer cell lines including MCF7, BT-20, H5578T, HCC1937, AU656, HCC1550, SKBR-3, T47D, MDA-436, SUM-159 and b). RT-PCR amplification of lymphomas cell lines including. DHL-5, DHL-8, Ly-10, Val, DLH-4, DHL-10, Ly-01, and Ferage c) KANSL1 gene; d) ARL17A; and e) GAPDH. All markers are 100 bp DNA markers; 
           [0034]      FIG. 8  shows RT-PCR amplification of KANSARL isoforms. a). KANSARL isoform 1; b). KANSARL isoform 2; c). KANSARL isoform 3; d). KANSARL isoform 4; e). KANSARL isoform 5; f). KANSARL isoform 6; g). GAPDH was used as a control; DNA markers are 100 by markers. Cell lines used for RT-PCR amplification include A549, Hela-3, 293T, K562, HT29, Ly-10 DHL-5, DHL-8, and Val. 
       
    
    
     BRIEF DESCRIPTION OF THE SEQUENCE LISTING 
       [0035]    The instant disclosure includes a plurality of nucleotide sequences. Throughout the disclosure and the accompanying sequence listing, the WIPO Standard ST.25 (1998; hereinafter the “ST.25 Standard”) is employed to identify nucleotides. The sequences of sequence ID 1 to sequence 886,543 are novel fusion transcripts. The sequences of sequence ID 886,544 to 886,549 are putative fusion polypeptides of KANSARL isoform 1, 2, 3, 4, 5 and 6. The sequences of sequence ID 886,550 to 886,555 are junction sequences of the putative fusion mRNA sequences of KANSARL isoform 1, 2, 3, 4, 5 and 6. The sequences from sequence ID 886,556 to sequence ID 886, 581 are primers used for RT-PCR and DNA amplifications. 
       DETAILED DESCRIPTION 
       [0036]    Kinsella et al. have developed a method of ambiguously mapped RNA-seq reads to identify KANSL1-ARL17A fusion transcripts (Kinsella, Harismendy et al. 2011), which have been shown to have identical fusion junction with a cDNA clone of BC006271 (Strausberg et al. 2002). However, they are not verified experimentally. There is little information how this fusion transcript is related to cancer, which mutations cause fusion, which person has it, how it is inherited, and where it expressed. 
         [0037]    KANSL1 and ARL17A genes are located at the chromosome 17q21.31. KANSL1 encodes an evolutionarily conserved nuclear protein, and is a subunit of both the MLL1 and NSL1 complexes, which are involved in histone acetylation and in catalyzing p53 Lys120 acetylation (Li, Wu et al. 2009). KANSL1 protein also ensures faithful segregation of the genome during mitosis (Meunier, Shvedunova et al. 2015). It has been found that there are two haplotypes, H1 and inverted H2 forms of which contain independently derived, partial duplications of the KANSL1 gene. These duplications have both recently risen to high allele frequencies (26% and 19%) in the populations of Europeans ancestry origin (Boettger, Handsaker et al. 2012). Some mutations have similar functions to the duplications, and both result in the Koolen-de Vries syndrome (KdVS) (OMIM #610443) characterised by developmental delay, intellectual disability, hypotonia, epilepsy, characteristic facial features, and congenital malformations in multiple organ systems (Koolen, Pfundt et al. 2015). ARL17A gene encodes a member of the ARF family of the Ras superfamily of small GTPases that are involved in multiple regulatory pathways altered in human carcinogenesis (Yendamuri, Trapasso et al. 2008) 
         [0038]    Previously, we have observed that recently-gained human spliceosomal introns have a signature of identical 5′ and 3′ splice sites (Zhuo, Madden et al. 2007). Based on this finding, we have found that both 5′ exonic sequences (E5) immediately upstream of introns and 3′ intronic sequences (I3) were dynamically conserved, and appears rather reminiscent of self-splicing group II ribozymes and of constraints imposed by base pairing between intronic-binding sites (IBSs) and exonic-binding sites (EBSs). Therefore, we have proposed that both E5 and I3 sequences constitute splicing codes, which are deciphered by splicer proteins/RNAs via specific base-pairing (Zhuo D 2012). This splicing code model suggested that a yet-to-be characterized splicer proteins/RNA would decode identical sequences in all pre-mRNAs in conjugation with U snRNAs and spliceosomes, regardless whether the E5 and I3 sequences are in the one molecule or two different molecules. Using this splicing code model, we have developed a computation system to analyze RNA-seq datasets to study gene expression, to discover novel isoforms, and to identify fusion transcripts. 
         [0039]    Based on our splicing code model, we have implemented a simple computation system to identify perfectly-identical fusion transcripts of two different traditional transcriptional units. In the previous application of U.S. patent application Ser. No. 14/792,613, filed Jul. 7, 2015, we had used this splicingcode system to analyze RNA-seq datasets from cancer cell lines and cancer patients in ENCODE project and NCBI database and had identified 252,664 novel fusion transcripts. Since then, we have continued to analyze RNA-seq datasets from cancer, other disease and normal samples in the NCBI. After we removed the fusion transcripts identified previously, we have identified total 886,543 novel fusion transcripts of unique fusion junctions. The sequences of these fusion transcripts have been set forth in Seq ID Nos.: 1-886,543. 
         [0040]    To demonstrate the feasibilities and reliabilities of our approaches, we have selected  KANS L1- ARL 17A (KANSARL) fusion transcripts for systematical investigation. Existence and abundances of multiple KANSARL isoforms in a cell line rule out the possibilities that KANSARL fusion transcripts are trans-spliced products and therefore KANSL1 and ARL17A are adjacent.  FIG. 1 a    shows that a putative inversion or duplication of a normal genomic structure of ARL17A and KANSL1 genes at 17.q21.32 results in a inverted genomic structure of KANSL1 and ARL17A gene order ( FIG. 1 a    Right).  FIG. 1 b    has shown that six KANSARL fusion transcripts of unique splicing junctions have been identified in the ECD39 datasets, which are described in the previous patent application. From these six KANSARL isoforms, the KANSL1 gene has used three splice junctions of exons 2, 3 and 6, suggesting that 5′ breakpoint occurs at least downstream of the exon 2 and it may be downstream of the exon 6 in some cell lines. ARL17A has returned exons 3, 4, 7 and 8, indicating that the 3′ breakpoint occurs upstream of the ARL17A exon 3 ( FIG. 1 b   ). Sequence analysis has shown that the KANSARL isoform 2 has an identical fusion junction with a cDNA clone BC006271 (Strausberg, Feingold et al. 2002) and KANSL1-ARL17A fusion transcripts reported previously (Kinsella, Harismendy et al. 2011). 
         [0041]      FIG. 2  shows that the six KANSARL fusion transcripts encode 437, 483, 496, 505, 450 and 637 aa proteins, respectively, majorities of which are from KANSL1 sequences. Consequently, KANSARL fusion transcripts will retain only coiled coil domain and results in loss of WDR5 binding region, Zn finger, domain for KAT8 activity and PEHE, suggesting KANSARL fusion transcripts are similar to some KANSL1 mutations (Koolen, Pfundt et al. 2015). 
         [0042]    To estimate gene expression levels of these six KANSARL isoforms in cancer, we have analyzed distribution of the copy numbers of the six fusion transcripts.  FIG. 1 c    and Table 1 have shown distribution of raw counts of the six KANSARL fusion transcripts. The KANSARL isoform 2 is expressed at the highest levels among the six fusion transcripts and is 50 folds and 1216 folds higher than KANSARL isoform 1 and isoform 3. 
         [0000]    
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Distribution of KANSARL isoforms in the ECD39 dataset. 
               
             
          
           
               
                 KANSARL Isoforms 
                 Counts 
                 % of Total Expression 
                 Folds 
               
               
                   
               
             
          
           
               
                 1 
                 48 
                 1.93 
                 50.69 
               
               
                 2 
                 2433 
                 97.87 
                 1 
               
               
                 3 
                 2 
                 0.08 
                 1216.5 
               
               
                 4 
                 1 
                 0.04 
                 2433 
               
               
                 5 
                 1 
                 0.04 
                 2433 
               
               
                 6 
                 1 
                 0.04 
                 2433 
               
               
                   
               
             
          
         
       
     
         [0043]    To study the KANSARL fusion transcript expression patterns in cancer cell lines, we have analyzed distribution of the total KANSARL fusion transcripts among the individual cell lines.  FIG. 1 d    and Table 2 have shown that the KANSARL fusion transcripts have been detected in 11 out of 39 cancer cell lines, which included A375, A549, G401, H4, Hela-3, HT29, K562, Karpas422, M059J, OCI-Ly7 and SK-N-DZ (Cautions should be taken for OCI-Ly7 since ENCSR001HHK dataset of Encode project is shown to be KANSARL-negative while ENCSR740DKM dataset is KANSARL-positive). As Table 2 shows, the KANSARL positive cells are from varieties of tissues and cell types as well as diversities of cancer types. Out of 11 cell lines of the positive KANSARL fusion transcripts, the genetic lineages are 6 Caucasian, one black (Hela-3) and 4 unknown genetic backgrounds. To rule out the effects of RNA-seq dataset sizes, we have normalized expression of the KANSARL fusion transcripts.  FIG. 1 e    has shown that the highest expressed fusion transcripts have been found in Karapas-422 cancer cell line. A549, H4, HT29, A375, SK-N-SH, and K562 are among highly-expressed cancer cell lines. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Basic information of KANSARL-positive 
               
               
                 cancer cell lines in the ECD39 dataset. 
               
             
          
           
               
                 Cell Lines 
                 NSJPM 
                 Tissues 
                 Tumors 
                 Sexes 
                 Ages 
                 Ethnic 
               
               
                   
               
             
          
           
               
                 A375 
                 0.13 
                 Skin 
                 malignant 
                 Female 
                 54 
                 Cauca- 
               
               
                   
                   
                   
                 melanoma 
                   
                   
                 sian 
               
               
                 A549 
                 0.11 
                 Lung 
                 Carcinoma 
                 Male 
                 58 
                 Cauca- 
               
               
                   
                   
                   
                   
                   
                   
                 sian 
               
               
                 G401 
                 0.03 
                 Kidney 
                 rhabdoid 
                 Male 
                 0.25 
                 Cauca- 
               
               
                   
                   
                   
                 tumor 
                   
                   
                 sian 
               
               
                 H4 
                 0.14 
                 Brain 
                 neuroglioma 
                 Male 
                 37 
                 Cauca- 
               
               
                   
                   
                   
                   
                   
                   
                 sian 
               
               
                 Hela-3 
                 0.06 
                 cervix 
                 adeno- 
                 Female 
                 31 
                 Black 
               
               
                   
                   
                   
                 carcinoma 
               
               
                 HT29 
                 0.42 
                 colon 
                 colorectal 
                 Female 
                 44 
                 Cauca- 
               
               
                   
                   
                   
                 adeno- 
                   
                   
                 sian 
               
               
                   
                   
                   
                 carcinoma 
               
               
                 K562 
                 0.22 
                 Bone 
                 Leukemia 
                 Female 
                 53 
                 Un- 
               
               
                   
                   
                 Marrow 
                   
                   
                   
                 known 
               
               
                 Karpas422 
                 1.01 
                 B cells 
                 non-Hodgkin&#39;s 
                 Female 
                 73 
                 Un- 
               
               
                   
                   
                   
                 lymphoma 
                   
                   
                 known 
               
               
                 M059J 
                 0.09 
                 Brain 
                 malignant 
                 Male 
                 33 
                 Un- 
               
               
                   
                   
                   
                 glioblastoma 
                   
                   
                 known 
               
               
                 OCI-Ly7 
                 0.02 
                 B cells 
                 non-Hodgkin&#39;s 
                 Male 
                 48 
                 Un- 
               
               
                   
                   
                   
                 lymphoma 
                   
                   
                 known 
               
               
                 SK-N-DZ 
                 0.61 
                 Brain 
                 neuroblastoma 
                 Female 
                 2 
                 Cauca- 
               
               
                   
                   
                   
                   
                   
                   
                 sian 
               
               
                   
               
             
          
         
       
     
         [0044]    Since  FIG. 1 d    shows that A549, HeLa-3 and K562 express KANSARL fusion transcripts, we have then first sought to verify them at sequence levels. To this end, we have designated primers specific to all six KANSARL fusion transcripts to perform RT-PCR on total RNAs isolated from A549, HeLa-3 and K562 (Table 2) while cell lines 786-O and OS-RC-2 are used as negative controls.  FIG. 3 a    shows that amplification of A549, HeLa3 and K562 cDNAs by KANSARLIsoF1 (Seq ID NO.: 886,556) and KANSARLIsoR1 (Seq ID NO.: 888,557) generate expected 379 by PCR fragments. Sequencing of the PCR fragments has confirmed that cDNAs have the splice junction generated by RNA-seq analysis ( FIG. 2 f   ).  FIG. 2 b    shows that KANSARLF1 (Seq ID NO.: 886,566) and KANSARLR1 (Seq ID NO.: 886,567) are used to amplify A549, HeLa3 and K562 cDNAs to produce expected 431 by fragments, which are confirmed by DNA sequencing to have the expected splice junction ( FIG. 3 g   ). To check whether these KANSARL-positive cancer cell lines have intact KANSARL parental genes, KANSL1 and ARL17A, we have designated primers across breakpoints of both KANSL1 and ARL17 genes to perform RT-PCR amplification on these five cancer cell lines.  FIGS. 2 c    &amp;  2   d  have shown that A549, HeLa-3 and K562, similar to 786-O and OS-RC-2, have RT-PCR products detected, indicating that these cell lines have at least one copy of KANSL1 and one copy of ARL17A while PCR products generated by GAPDHF1 (Seq ID NO.: 886,570) and GAPDHR1 (Seq ID NO.: 886,571) are used as a control ( FIG. 3 e   ). 
         [0000]    
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 The primers used to perform RT-PCR and real- 
               
               
                 time PCR 
               
             
          
           
               
                   
                   
                   
                 KAN- 
               
               
                   
                   
                   
                 SARL 
               
               
                   
                   
                 SEQ ID 
                 Iso- 
               
               
                 Primer IDs 
                 Primer Sequences 
                 NOs 
                 forms 
               
               
                   
               
               
                 KANARLIsolF1 
                 CAAGCCAAGCAGGTTGAGA 
                 886556 
                 1 
               
               
                 KANARLIso1R1 
                 TCTCCACACAGAAACAGGGGTA 
                 886557 
                   
               
               
                   
               
               
                 KANARLIso4F1 
                 TTGTGCAAGCCAAGCAGGTT 
                 886558 
                 4 
               
               
                 KANARLIso4R1 
                 TGGGAAGCTGATAGCTAGGGGT 
                 886559 
                   
               
               
                   
               
               
                 KANARLIso3F1 
                 TCAGAATGGAAATGGGCTGCA 
                 886560 
                 3 
               
               
                 KANARLIso3R1 
                 TTCCTGGGCTTCTGGCACCTT 
                 886561 
                   
               
               
                   
               
               
                 KANARLIso6F1 
                 AGACGCAGGTCAGAATGGAAAT 
                 886562 
                 6 
               
               
                 KANARLIso6R1 
                 AAACTGGGAAGCTGATAGCTCT 
                 886563 
                   
               
               
                   
               
               
                 KANARLIso5F1 
                 TGTCTTGGCAGACCACATTC 
                 886564 
                 5 
               
               
                 KANARLIso5R1 
                 GGAAAAAGGCTCACCATTTCA 
                 886565 
                   
               
               
                   
               
               
                 KANSARLF1 
                 GCCTTGAGAA AAGCTGCCAG 
                 886566 
                 2 
               
               
                 KANSARLR1 
                 aacatcccagacagcgaagg 
                 886567 
                   
               
               
                   
               
               
                 KANSARLF2 
                 GAGACGCAGGTCAGAATGGA 
                 886568 
                 2 
               
               
                 KANSARLR2 
                 Aaatgc tgc cac agaggtct 
                 886569 
                   
               
               
                   
               
               
                 GAPDHF1 
                 CAAGGTCATCCATGACAACTTTG 
                 886570 
                   
               
               
                 GAPDHR1 
                 GTCCACCACCCTGTTGCTGTAG 
                 886571 
                   
               
               
                   
               
               
                 GAPDHqF1 
                 GCGACACCCACTCCTCCACCTTT 
                 886572 
                   
               
               
                 GAPDHqR1 
                 TGCTGTAGCCAAATTCGTTGTCATA 
                 886573 
                   
               
               
                   
               
               
                 KANSARLgF1 
                 TGTGCAGCCTAAGCATGATCCT 
                 886574 
                   
               
               
                 KANSARLgR1 
                 GACACAGTGGCTCATGCCTGTAAT 
                 886575 
               
               
                   
               
             
          
         
       
     
         [0045]      FIGS. 3 a    &amp;  3   b  demonstrate that A549, Hela-3 and K562 express both KANSARL isoform 1 and 2 and Table 1 shows that the counts of KANSARL isoform 2 reads is 50-fold higher than those of KANSARL isoform 1, suggesting that KANSARL isoform 2 is expressed at much higher level than the KANSARL isoform 1. To establish a relationship between KANSARL expression levels and counts of RNA-seq reads crossing splice junctions, we have performed real-time PCR of A549, Hela-3 and K562 on KANARL isoform 1 by KANSARLIsoF1 (Seq ID NO.: 886,556) and KANSARLIsoR1 (Seq ID NO.: 886,557) and on KANARL isoform 2 by KANSARLF2 (Seq ID NO.: 886,568) and KANSARLR2 (Seq ID NO.: 886,569) while products amplified by GAPDHqF1 (Seq ID NO.: 886,572) and GAPDHqR1 (Seq ID NO.: 886,573) is used as reference control.  FIG. 3 h    shows relative expression levels of KANSARL isoform 1 (grey bars) and 2 (black bars) in A549, Hela-3 and K562. 
         [0046]    Table 4 shows that KANSARL isoform 2 are expressed at 0.35%, 0.28% and 1.28% of the GAPHD expression in A549, Hela-3 and K562, respectively while KANSARL isoform 1 are expressed only at 0.0056%, 0.0037% and 0.015% of the GAPHD expression in A549, Hela-3 and K562, respectively.  FIG. 3 i    and Table 4 show that KANSARL isoform 2 (black bars) are expressed at average 73 fold higher than KANSARL isoform 1 (gray bars), which ranged from 63.7 folds in A549 to 82.9 folds in K562. These qPCR differences between two KANSARL isoforms are generally consistent with that obtained from RNA-seq data analysis ( FIGS. 1 c   &amp; d ). K562 expresses KANSARL isoform 2 at 1.2% of GAPDH gene expression levels; while A549 and Hela-3 express this isoform at 0.3% of GAPDH ones ( FIG. 3 h   ). The former level is about 4 fold of the latter, which are also consistent with data obtained from RNA-seq data analysis ( FIG. 1 e   ). Further study is required to confirm whether four folds of the qPCR differences between A549 and Hela-3/K562 are genotype differences between KANSARL + /KANSARL +  and KANSARL + /KANSARL −  or gene expression differences among different cancer types. 
         [0000]    
       
         
               
             
               
               
             
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Real-time PCR Quantifications of KANSARL isoform 1 and 2 in A549, Hela-3 and K562. 
               
             
          
           
               
                   
                 Ratios of KANSARL soforms and GAPHD Expression Levels 
               
             
          
           
               
                 Cell Lines 
                 Gene/Isoforms 
                 Rep 1 
                 Rep 2 
                 Rep 3 
                 Average 
                 SD 
                 Folds 
               
               
                   
               
             
          
           
               
                 A549 
                 GAPHD 
                 1 
                 1 
                 1 
                 1 
                 0 
                   
               
               
                   
                 KANSARL Iso2 
                 0.0035 
                 0.0037 
                 0.0036 
                 0.0036 
                 0.000087 
                 63.7 
               
               
                   
                 KANSARL Iso1 
                 5.69E−05 
                 5.5E−05 
                 5.7E−05 
                 5.6E−05 
                 1.12E−06 
               
               
                 Hela-3 
                 GAPHD 
                 1 
                 1 
                 1 
                 1 
                 0 
               
               
                   
                 KANSARL Iso2 
                 0.00288 
                 0.00288 
                 0.00278 
                 0.0028 
                 5.66E−05 
                 76.3 
               
               
                   
                 KANSARL Iso1 
                 3.58E−05 
                 3.8E−05 
                 3.8E−05 
                 3.7E−05 
                 1.36E−06 
               
               
                 K562 
                 GAPHD 
                 1 
                 1 
                 1 
                 1 
                 0 
               
               
                   
                 KANSARL Iso2 
                 0.0129 
                 0.0128 
                 0.0128 
                 0.0128 
                 5.132E−05  
                 82.9 
               
               
                   
                 KANSARL Iso1 
                 0.70002 
                 0.0002 
                 0.0002 
                 0.00015 
                 1.07E−06 
               
               
                   
               
             
          
         
       
     
         [0047]    As Table 2 shows that KANSARL fusion transcripts are expressed in diverse cancer types, this has prompted us to analyze RNA-seq data from varieties of cancer types to identify and characterize KANSARL gene expression among diverse cancer RNA-seq datasets. To investigate whether KANSARL fusion transcripts are expressed in brain cancer and tissues, we have downloaded and analyzed the glioblastoma RNA-seq dataset of Columbia University Medical Center (designated as CGD), which has total of 94 samples included 39 contrast-enhancing regions (CE) of diffuse glioblastomas (GBM), 36 nonenhancing regions of GBM (NE) and 19 non-neoplastic brain tissues (Normal) from 17 samples (Gill, Pisapia et al. 2014). The CGD has total 27 patients and both CE and NE datasets have 24 patients, respectively, 21 of which are overlapped. 
         [0048]      FIG. 3 a    and Table 5 show that KANSARL fusion transcripts have been found in 13 CE patients and 11 NE patients. Together, 14 (51.9%) of the 27 GBM patients have been found to have fusion transcripts. In contrast, KANSARL fusion transcripts have been detected only in 2 (or 11.7%) of 17 non-neoplastic brain tissues. The KANSARL-positive glioblastomas patients are 30% higher than the non-neoplastic persons ( FIG. 4 a   ). The difference is shown to be statistically significant (Z=2.03, p&lt;0.04), demonstrating that KANSARL fusion transcripts are associated with diffuse glioblastomas. In contrast, the difference in numbers of KANSARL-positive NC and NE samples is statistically insignificant (Z=0.577, p&gt;0.8), suggesting that KANSARL genotypes of the NE samples are similar to those of the NC samples. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 Statistical analysis of number differences of KANSARL+ 
               
               
                 samples between glioblastomas CE and NE samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 CE 
                 24 
                 13 
                 54.17 
                 0.577 
                 0.5637 
               
               
                 NE 
                 24 
                 11 
                 45.83 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 Comparison of number differences of KANSARL+ 
               
               
                 samples between glioblastomas and non-neoplastic samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 Total 
                 27 
                 14 
                 51.85 
                 2.029 
                 0.042 
               
               
                 Normal 
                 17 
                 2 
                 11.76 
               
               
                   
               
             
          
         
       
     
         [0049]    Since we have shown that KANSARL fusion transcripts are associated with diffuse glioblastomas, to characterize that KANSARL fusion transcripts in other glioblastoma datasets, we have performed comparative analysis of the glioblastoma dataset deposited by Beijing Neurosurgical Institute (designated as BGD), which have 272 gliomas of different clinic prognosis stages (Bao, Chen et al. 2014). Surprisingly, only two KANSARL-positive samples have been detected out of 272 BGD glioblastoma ( FIG. 4 b   ). Only less than 1% of BGD glioblastoma is KANSARL-positive and is 52 times lower than that in the CGD dataset. Table 7 shows that the difference between BGD and CGD is statistically significant (Z=11.26, p&lt;0.0005), suggesting that the BGD&#39;s KANSARL genotypes are divergent from those of CGD. Larger numbers of high-quality RNA-seq reads per sample in the BGD&#39;s dataset rule out the possibilities that the RNA-seq datasets are responsible for the difference between the two datasets (Gill, Pisapia et al. 2014). 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 7 
               
             
             
               
                   
               
               
                 Comparison of number differences of KANSARL+ 
               
               
                 samples between BGD and CGD samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 Proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 BGD 
                 272 
                 2 
                 0.74 
                 11.26 
                 &lt;0.00001 
               
               
                 CGD 
                 27 
                 14 
                 51.85 
               
               
                   
               
             
          
         
       
     
         [0050]    The dramatic differences of KANSARL fusion transcripts between the CGD and BGD have raised the possibility that KANSAR fusion transcripts are associated with the cancer patients of European ancestry origins, but absent in cancer patients of Asian ancestry. To study this possibility, we have systematically performed comparative analyses of RNA-seq datasets of prostate cancer, breast cancer, lung cancer and lymphomas around the world. Prostate cancer is the most common nonskin cancer and the second leading cause of cancer-related death in men in the United States. We have downloaded and performed analysis of the prostate cancer dataset from Vancouver Prostate Centre (designated as VPD), which contains 25 high-risk primary prostate tumors and five matched adjacent benign prostate tissues (Wyatt, Mo. et al. 2014), and BGI prostate cancer dataset (BPD), which contain 14 pairs of prostate cancer and normal samples (Ren, Peng et al. 2012). We have detected KANSARL fusion transcripts in 13 (52%) out of the 25 VPD prostate samples ( FIG. 4 c   ) and 4 out of 5 adjacent benign prostate tissues. KANSARL isoform 1, 2, and 3 have been detected in the VPD samples and have very similar patterns to those observed in ECD39 ( FIG. 1 c   ). In contrast, we have found no single copy of KANSARL fusion transcript in the BPD prostate tumors and their matched normal samples ( FIG. 4 c   ). Table 8 shows that the difference between VPD and BPD is statistically significant (z=3.118; p&lt;0.05). It is well known that TMPRSS2-ERG is one of the most frequent fusion genes in prostate tumors (Wyatt, Mo. et al. 2014). To investigate relationship between KANSARL and TMPRSS2-ERG fusion transcripts, we have performed analysis of TMPRSS2-ERG fusion transcripts and have detected 15 out 25 prostate tumors to have TMPRSS2-ERG fusion transcripts. Even more surprisingly, 13 out of 15 TMPRSS2-ERG-positive prostate tumors are KANSARL-positive or all of 13 KANSARL-positive prostate tumors are shown to have TMPRSS2-ERG fusion transcripts ( FIG. 5 a   ). In contrast, only two TMPRSS2-ERG-positive prostate tumors are detected in 12 KANSARL-negative prostate tumors ( FIG. 5 b   ). Table 8 shows that the differences of TMPRSS2-ERG fusion transcripts between KANARL-positive and KANSARL-negative tumors is significant (z=4.25, p&lt;0.0005), suggesting that KANSARL fusion transcripts are closely associated with TMPRSS2-ERG and may play roles in generating TMPRSS2-ERG in prostate tumors. On the other hand, two samples out of 5 adjacent benign prostate tissues have been shown to have both TMPRSS2-ERG and KANSARL fusion transcripts ( FIG. 5 c   ), suggesting that prostate tumor cells are present the adjacent benign tissues. In contrast, only one BPD&#39;s patient has been found to have TMPRSS2-ERG fusion transcripts. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 8 
               
             
             
               
                   
               
               
                 Comparison of number differences of KANSARL+ 
               
               
                 samples between VPD and BPD samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 VPD 
                 25 
                 13 
                 52 
                 3.118 
                 0.002 
               
               
                 BPD 
                 14 
                 0 
                 0 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 9 
               
             
             
               
                   
               
               
                 Overlaps between KANSAL and TMPRSS2-ERG 
               
               
                 fusion transcripts in the VPD samples 
               
             
          
           
               
                   
                   
                 # of 
                 % of 
                   
                   
               
               
                 Sample 
                 # of 
                 TMPRSS2- 
                 TMPRSS2- 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 ERG+ 
                 ERG+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 KANSARL+ 
                 13 
                 13 
                 100 
                 4.25 
                 2.10E−05 
               
               
                 KANSARL− 
                 12 
                 2 
                 16.67 
               
               
                   
               
             
          
         
       
     
         [0051]    To investigate whether KANSARL fusion transcripts are associated with other fusion transcripts, we have investigated differentially expressed fusion transcripts in both VPD prostate and CGD glioblastomas. To count fusion transcripts as a differentially-expressed fusion transcripts in cancer, fusion transcripts must have ≧75% of ≧5 samples in one group. Supplementary Table 9 shows that KANSARL-positive prostate cancer patients 26 differentially-expressed fusion transcripts, 81% of them are read-through (epigenetic) fusion transcripts while KANSARL-negative patients have 16 differentially-expressed fusion transcripts, 69% of which are read-through fusion transcripts. On the other hand, KANSARL-positive glioblastomas patients have 20 differentially-expressed fusion transcripts, 95% of which are read through while KANSARL-negative glioblastomas patients have only 6 differentially-expressed fusion transcripts, all of which are breakthroughs (Table 10). Data analysis shows that there are no overlapped fusion transcripts between prostate cancer and glioblastomas patients, suggesting these fusion transcripts are tissue-specific and cancer-specific. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
               
             
               
               
               
               
               
               
             
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 10 
               
             
             
               
                   
               
               
                 Comparison of differentially-expressed fusion transcripts 
               
               
                 in KANSARL-positive and KANSARL-negative patients. 
               
             
          
           
               
                   
                 KANSARL-positive 
                   
                 KANSARL-negative 
                   
               
             
          
           
               
                   
                 Counts 
                 % 
                 Counts 
                 % 
               
               
                   
                   
               
             
          
           
               
                 a Prostate Cancer 
               
             
          
           
               
                   
                 Genetic 
                 5 
                 19.23 
                 5 
                 31.25 
               
               
                   
                 Epigenetic 
                 21 
                 80.77 
                 11 
                 68.75 
               
               
                   
                 Total 
                 26 
                   
                 16 
               
             
          
           
               
                 b Glioblastomas 
               
             
          
           
               
                   
                 Genetic 
                 1 
                 5 
                 0 
                 0 
               
               
                   
                 Epigenetic 
                 19 
                 95 
                 6 
                 100 
               
               
                   
                 Total 
                 20 
                   
                 6 
               
               
                   
                   
               
             
          
         
       
     
         [0052]    Lung cancer is the leading cause of cancer deaths in the World, especially in Asia. To investigate the expression of KANSARL fusion transcripts, we have analyzed the Korean Lung Cancer RNA-seq dataset (designated as SKLCD), which has 168 lung cancer samples (Ju, Lee et al. 2012) and Michigan of University Lung Cancer Dataset (designated as MULCD), which contains 20 lung tissue samples (Balbin, Malik et al. 2015). We have found that eight (40%) out of 20 MULCD samples have KANSARL fusion transcripts ( FIG. 4 d   ). Even though SKLCD data are more than five folds larger than the MULCD ones, no single copy of KANSARL fusion transcripts have been detected in 168 SKLCD samples ( FIG. 4 d   ). Table 11 shows that the differences of KANSARL fusion transcripts between MULCD and SKLCD is significant (z=8.38, p&lt;0.0005), suggesting that KANSARL fusion transcripts are associated with MULCD lung cancer patients. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 11 
               
             
             
               
                   
               
               
                 Comparison of number differences of KANSARL+ 
               
               
                 samples between MULCD and SKLCD samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 MULCD 
                 20 
                 8 
                 40 
                 8.3777483 
                 &lt;0.00001 
               
               
                 SKLCD 
                 168 
                 0 
                 0 
               
               
                   
               
             
          
         
       
     
         [0053]    Breast Cancer is the most common incident form of cancer in women around the world and about 1 in 8 (12%) women in the US will develop invasive breast cancer during their lifetime. To investigate whether KANSARL fusion transcripts are expressed in breast cancer, we have performed analyses on the breast cancer dataset from USA Hudson Alpha Institute for Biotechnology (designated as HIBCD), which consists of 28 breast cancer cell lines, 42 ER+ breast cancer primary tumors, 30 uninvolved breast tissues adjacent to ER+ primary tumors, 42 triple negative breast cancer (TNBC) primary tumors, 21 uninvolved breast tissues adjacent to TNBC primary tumors and 5 normal breast tissues (Varley, Gertz et al. 2014), and breast cancer samples from South Korean (designated as SKBCP), which have samples from 22 HRM (high-risk for distant metastasis) and 56 LRM (low-risk for distant metastasis) breast cancer patients (PRJEB9083 2015).  FIG. 4 e    shows that 50 (or about 30%) HIBCD breast samples have been found to have KANSARL fusion transcripts while no SKBCP patients have been observed to have KANSARL fusion transcripts. Table 12 shows that the difference between HIBCD and SKBCP has been shown by χ2-test to be statistically significant (p≦0.001), suggesting that breast cancer patients from South Korea have no KANSARL fusion transcripts. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 12 
               
             
             
               
                   
               
               
                 Comparison of number differences of KANSARL+ 
               
               
                 samples between HIBCD and SKBCP samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 HIBCD 
                 163 
                 49 
                 30.06 
                 5.43 
                 &lt;0.00001 
               
               
                 SKBCP 
                 78 
                 0 
                 0 
               
               
                   
               
             
          
         
       
     
         [0054]    Since HIBCD have multiple breast cancer types, we have performed further data analysis of the HIBCD breast samples.  FIG. 4 g    and Table 13 shows that normal tissues, breast cancer cell lines, TNBC primary tumors and uninvolved breast tissues adjacent to TNBC primary tumors have 23.8% to 28.5% of KANSARL-positive samples while ER+ breast cancer primary tumors and uninvolved breast tissues adjacent to ER+ primary tumor are 35.7% and 40%. KANSARL-positive percentages of The TNBC samples are much closer to the normal one, which are shown to have no statistical differences. On the other hand, the KANSARL-positive ratios in the ER+ samples are 15% higher than the normal one, suggesting that KANSARL fusion transcripts have much bigger impacts on ER+ breast cancer than TNBC breast cancer. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 13 
               
             
             
               
                   
               
               
                 Comparison of number differences of KANSARL+ samples 
               
               
                 among different subtypes of breast cancers in HIBCD samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 ER+ 
                 42 
                 15 
                 35.71 
                 −0.386 
                 0.699 
               
               
                 ER+BTA 
                 30 
                 12 
                 40 
                 0.882 
                 0.378 
               
               
                 Normal 
                 5 
                 1 
                 20 
                 −0.188 
                 0.852 
               
               
                 TNBC 
                 42 
                 10 
                 23.81 
                 −0.416 
                 0.498 
               
               
                 TNBCBTA 
                 21 
                 6 
                 28.57 
                 0.280 
                 0.779 
               
               
                 BCCL 
                 28 
                 7 
                 25 
               
               
                   
               
             
          
         
       
     
         [0055]    To investigate whether the KANSARL fusion transcripts are expressed in cancer samples from the African population, we have analyzed the Uganda lymphomas dataset (designated as ULD), which contains 20 lymphoma samples (Abate, Ambrosio et al. 2015). We have performed analyses of multiple lymphoma RNA-seq datasets including NCI lymphoma dataset (designated as NLD), which has 28 sporadic form Burkitt Lymphoma (BL) patient biopsy samples and 13 BL cell lines (Schmitz, Young et al. 2012), Yale University T-cell lymphoma dataset (designated as YLD), which has 13 cutaneous T cell lymphoma and BC Cancer Agency lymphoma data (designated as BLD), in which 23 RNA-seq data of diffuse large B-cell lymphoma have been identified (Morin, Mungall et al. 2013). Even though lymphoma subtypes and the sample sizes are different, we have found that have 34% to 38% of NLD, YLD and BLD samples have KANSARL fusion transcripts ( FIG. 4 f   ). On the other hand, no single copy of KANSARL fusion transcripts have been detected in 20 ULD lymphoma samples ( FIG. 4 f   ). Table 14 shows that the differences of KANSARL-positive samples between Uganda and North America are statistically significant (Z≧3.0; p≦0.0026) and suggested that Uganda lymphomas are not associated with KANSARL fusion transcripts. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 14 
               
             
             
               
                   
               
               
                 Comparison of number differences of KANSARL+ 
               
               
                 samples among the NLD, BCLD, YLD and ULD samples 
               
             
          
           
               
                   
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 Types 
                 Samples 
                 KANSARL+ 
                 KANSARL+ 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 NLD 
                 41 
                 15 
                 36.59 
                 3.11 
                 0.002 
               
               
                 BCLD 
                 23 
                 8 
                 34.78 
                 3.23 
                 0.001 
               
               
                 YLD 
                 13 
                 5 
                 38.46 
                 3.01 
                 0.003 
               
               
                 ULD 
                 20 
                 0 
                 0 
               
               
                   
               
             
          
         
       
     
         [0056]    As shown in  FIG. 4 , samples of diverse types of cancer from North America (USA and Canada) have been found to have highly recurrent KANSARL fusion transcripts, which ranged from 30% in breast cancer to 52% in prostate tumors. In contrast, KANSARL fusion transcripts have been detected in two glioblastoma samples from China and Hela-3 cancer cell line, ethnicity of which is black. No KANSARL fusion transcripts have been found in the rest of the cancer samples from South Korea, China and Uganda. Based on localities of health services, we can conclude that KANSARL fusion transcripts have been rarely found in the cancer samples from Asian and African ancestry origins and are specifically associated with cancer samples of European ancestry origins. 
         [0057]    Presence of KANSARL fusion transcripts in normal and adjacent tissues raised the possibility that KANSARL fusion transcripts are an inherited germline fusion gene. To test this possibility, we have performed RNA-seq data analysis of the lymphoblastoid cell lines of families from the CEU population (CEPH/Utah Pedigree 1463, Utah residents with ancestry from northern and western Europe), which has a 17-individual, three-generation family (Li, Battle et al. 2014). Table 15 shows that KANSARL fusion transcripts have been detected in 15 of 17 family members as indicated by black squares and circles ( FIG. 6 a   ). Only the father (NA12877) and daughter (NA12885) are not KANSARL carriers. Based on these data, if we can assume that the father and mother is KANSARL − /KANARL − , and KANSARL + /KANARL + , their sons and daughters would have KANSARL + /KANARL −  except for one daughter. The daughter (NA12885) is an outlier, which may have the mutated gene or may be promiscuous. However, based on the RNA-seq data, the more reasonable explanation is that the father (NA12877) may be mixed up with one of his sons during experiments and have a genotype of KANSAR + /KANARL − . Consequently, their sons and daughters would have one quarter of KANSARL + /KANARL +  and are well fit with what is predicted by Mendel&#39;s law. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 15 
               
             
             
               
                   
               
               
                 Distribution of KANSARL fusion transcripts 
               
               
                 in the CEPH/Utah Pedigree 1463 
               
             
          
           
               
                   
                 Individual ID 
                 Run ID 
                 MB 
                 KANSARL+ 
               
               
                   
                   
               
             
          
           
               
                   
                 NA12877 
                 SRR1258217 
                 4670 
                 0 
               
               
                   
                 NA12878 
                 SRR1258218 
                 3709 
                 10 
               
               
                   
                 NA12879 
                 SRR1258219 
                 4759 
                 11 
               
               
                   
                 NA12880 
                 SRR1258220 
                 4523 
                 3 
               
               
                   
                 NA12881 
                 SRR1258221 
                 3548 
                 7 
               
               
                   
                 NA12887 
                 SRR1258222 
                 3900 
                 5 
               
               
                   
                 NA12888 
                 SRR1258223 
                 3141 
                 2 
               
               
                   
                 NA12892 
                 SRR1258224 
                 3509 
                 7 
               
               
                   
                 NA12893 
                 SRR1258225 
                 3529 
                 8 
               
               
                   
                 NA12882 
                 SRR1258226 
                 3801 
                 10 
               
               
                   
                 NA12883 
                 SRR1258227 
                 2644 
                 3 
               
               
                   
                 NA12884 
                 SRR1258228 
                 3086 
                 4 
               
               
                   
                 NA12885 
                 SRR1258229 
                 4242 
                 0 
               
               
                   
                 NA12886 
                 SRR1258230 
                 3485 
                 11 
               
               
                   
                 NA12889 
                 SRR1258231 
                 3313 
                 15 
               
               
                   
                 NA12890 
                 SRR1258232 
                 3145 
                 1 
               
               
                   
                 NA12891 
                 SRR1258233 
                 3189 
                 5 
               
               
                   
                   
               
             
          
         
       
     
         [0058]      FIG. 4  shows that KANSARL fusion transcripts are rarely detected in cancer samples from Asia and Africa, but are observed in 30-52% of tumor samples from North America and  FIG. 6 a    shows that KANSARL fusion transcripts are an inherited germline fusion gene. To estimate the percentages of general populations, we have downloaded and analyzed RNA-seq data analysis of the lymphoblastoid cell lines of the 1000 Genome Project (Genomes Project, Auton et al. 2015). Table 16 has shown that no single copy of KANSARL fusion transcripts has been detected in the Nigeria YRI (Yoruba in Ibadan) populations and that KANSARL fusion transcripts have been found in 33.7% GBR (British from England and Scotland), 26.3% FIN (Finnish in Finland) and 26.9% TSI (Toscani in Italia) populations, respectively ( FIG. 6 b   ). Table 16 shows that the differences of KANSARL frequencies among the GBR, FIN and TSI populations are not statistically significant (Z≦1.11, p&gt;0.27), suggesting these differences may be caused by sampling errors. On the other hand, their difference with the YRI KANSARL frequencies is statistically significant (Z≧5.2; p&lt;0.00001), confirming the previous observation that KANSARL fusion transcripts rarely exist in the tumor samples from African ancestry. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 16 
               
             
             
               
                   
               
               
                 Comparison of KANSARL frequency differences 
               
               
                 of GBR, FIN, TSI and YRI populations 
               
             
          
           
               
                 Sample 
                 # of 
                 # of 
                 % of 
                 Z 
                 proba- 
               
               
                 IDs 
                 Samples 
                 KANSARL 
                 KANSARL 
                 Scores 
                 bilities 
               
               
                   
               
             
          
           
               
                 GRB 
                 95 
                 32 
                 33.68 
                 6.024 
                 &lt;0.00001 
               
               
                 FIN 
                 95 
                 25 
                 26.32 
                 5.206 
                 &lt;0.00001 
               
               
                 TSI 
                 93 
                 25 
                 26.88 
                 5.266 
                 &lt;0.00001 
               
               
                 YRI 
                 89 
                 0 
                 0.00 
               
               
                   
               
             
          
         
       
     
         [0059]    As shown above, KANSARL fusion transcripts seem to be expressed in many human tissues and organs. To systematically understand the patterns of KANSARL gene expression in human bodies, we have downloaded and analyzed RNA-seq datasets from Science for Life Laboratory, Sweden (designated as SSTD), which originated from tissue samples of 127 human individuals representing 32 different tissues (Uhlen, Fagerberg et al. 2015). Table 17 shows that KANSARL fusion transcripts have been detected in 28 of 32 tissues analyzed. Only bone marrow, kidney, stomach and smooth muscle have not been found to have KANSARL fusion transcripts. Since G401 and K562 originated from Kidney and bone marrow, respectively, our data suggest that KANSARL transcripts are expressed in the most human tissues if they are not ubiquitously expressed in the human tissues and organs and may be similar to the KANSL1 gene expression patterns. 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 17 
               
             
             
               
                   
               
               
                 Distribution of KARSARL fusion transcripts 
               
               
                 in human tissues and organs 
               
             
          
           
               
                   
                 Tissues 
                 KANSARL 
               
               
                   
                   
               
               
                   
                 adipose tissue 
                 + 
               
               
                   
                 adrenal gland 
                 + 
               
               
                   
                 ovary 
                 + 
               
               
                   
                 appendix 
                 + 
               
               
                   
                 bladder 
                 + 
               
               
                   
                 bone marrow 
                 − 
               
               
                   
                 cerebral cortex 
                 + 
               
               
                   
                 colon 
                 + 
               
               
                   
                 duodenum 
                 + 
               
               
                   
                 endometrium 
                 + 
               
               
                   
                 esophagus 
                 + 
               
               
                   
                 fallopian tube 
                 + 
               
               
                   
                 gall bladder 
                 + 
               
               
                   
                 heart 
                 + 
               
               
                   
                 kidney 
                 − 
               
               
                   
                 liver 
                 + 
               
               
                   
                 lung 
                 + 
               
               
                   
                 lymph node 
                 + 
               
               
                   
                 pancreas 
                 + 
               
               
                   
                 placenta 
                 + 
               
               
                   
                 prostate 
                 + 
               
               
                   
                 rectum 
                 + 
               
               
                   
                 salivary gland 
                 + 
               
               
                   
                 skeletal muscle 
                 + 
               
               
                   
                 skin 
                 + 
               
               
                   
                 small intestine 
                 + 
               
               
                   
                 smooth muscle 
                 − 
               
               
                   
                 spleen 
                 + 
               
               
                   
                 stomach 
                 − 
               
               
                   
                 testis 
                 + 
               
               
                   
                 thyroid 
                 + 
               
               
                   
                 tonsil 
                 + 
               
               
                   
                   
               
             
          
         
       
     
         [0060]    In order to verify KANSARL fusion transcripts could be detected at such highly frequencies, we have performed RT-PCR amplification of uncharacterized samples of breast cancer cell lines and lymphomas available.  FIG. 7 a    showed that we have performed RT-PCR on 10 breast cancer cell lines and 4 of them have been found to have KANSARL isoform 2. These four KANSARL positive breast cancer cell lines are HCC-1937, T47D, MAD-436 and SUM-157, all of which have Caucasian ethnic backgrounds. Furthermore, we have performed RT-PCR amplification on 8 lymphomas cell lines. KANSARL isoform 2 has been detected in DHL-5, DHL-8, OCI-Ly10 and Val ( FIG. 7 b   ) as does KANSARL isoform 1 (data not shown).  FIGS. 7 c    &amp;  7   d  showed that all eight lymphomas have at least one copy of KANSL1 and one copy of ARL17A gene while  FIG. 7 e    showed RT-PCR amplification of GAPHD mRNA as controls. Even though the numbers of breast cancer and lymphomas are relatively small, the percentages of KANSARL-positive cell lines are within those obtained from RNA-seq data analysis, suggesting that KANSARL fusion transcripts are highly recurrent in the cancer samples of European ancestry origin. 
         [0061]      FIG. 3  and  FIG. 7  show that many cancer cell lines have been shown to have dominant KANSARL isoform 2. To investigate the KANSARL isoform expression, we have performed RNA amplifications of all KANSARL isoforms on some of the KANSARL positive cell lines.  FIG. 8  shows that all KANSARL isoforms except for the KANSARL isoform 6 have been detected in nine KANSARL-positive cancer cell lines, including A549, Hela-3, 293T, K562, HT29, LY10, DHL-5, DHL-8 and VAL. This suggests that RT-PCR amplification can be used to detect KANSARL fusion transcripts expressed at &lt;0.05% of the GAPHD gene expression levels. 
         [0062]    We have demonstrated that KANSARL fusion transcripts are familial-inherited, and that KANSARL are expressed in the majorities of tissues. Supplementary Table 8 has shown that KANSARL fusion transcripts have been found in an average of 28.9% of the population of European ancestry, which ranges from 26.3% FIN to 33.7% GBR ( FIG. 6 b   ). No previous evidence has suggested that KANSARL fusion transcripts are associated with cancer or are derived from cancer predisposition gene. We have provided four lines of evidence supporting that the KANSARL fusion transcripts are associated with multiple types of cancer. First, the frequency of KANSARL fusion transcripts in the CGD glioblastomas patients is significantly higher than the non-neoplastic (normal) control. Second, all KANSARL-positive prostate tumor patients also have prostate cancer biomarker TMPRSS2-ERG fusion transcripts. Third, we have shown that 4 out of 10 breast cancer cell lines and 4 out of 8 lymphoma cell lines have been detected to have KANSARL fusion transcripts. Fourth, the high frequencies of KANSARL fusion transcripts in glioblastomas, prostate, breast cancer, lung cancer and lymphomas patients from North America suggest that KANSARL fusion transcripts are associated with multiple types of cancer. Therefore, we can conclude that KANSARL fusion transcripts are derived from the cancer predisposition fusion gene. 
         [0063]      FIG. 2  has shown that six KANSARL isoforms identified encode proteins with 437, 483, 496, 505, 450 and 637 aa, majorities of which come from the KANSL1 sequences and bear similarities to some KANSL1 mutations (Koolen, Pfundt et al. 2015). KANSARL putative proteins would lack the WDR5 binding region and the Zn finger domains responsible for KAT8 activity, and PEHE domain. Loss of these domains results in KAT8 HAT inactivation to catalyze H4K16 acetylation (Huang, Wan et al. 2012), which is recently recognized as a common hallmark of human tumors (Fraga, Ballestar et al. 2005). In addition inactivation of KAT8 to catalyze p53 Lys120 acetylation inhibits the abilities of p53 to activate downstream p53 target genes, which regulate p53-mediated apoptosis and can promote cancer (Mellert, Stanek et al. 2011). Association between KANSARL and TMPRSS2-ERG fusion transcripts have been observed in prostate tumors, but not in glioblastomas or any other types of cancer analyzed so far, suggest that genomic alternations are tissue-specific and cancer-specific. Understanding these specific genetic abbreviations not only help us to develop better detection of much early stages of tumors, but also enable us to identify drug targets to block these processes. Supplementary Table 9 shows that KANSARL fusion transcripts are specifically associated with many read-through fusion transcripts, which are thought to be epigenetic. Understanding how KANSARL affect how epigenetic alternations will result in tumor genesis. One approach is to use KANSARL-specific antibodies or siRNAs to degrade KANSARL mRNA or proteins and to check whether such degradation will restore epigenetic changes. It has of great interests to investigate whether blood transfusions from KANSARL carriers cause cancer because blood is more likely to have cancer progenitor cells and KANSARL may activate epigenetic pathways in weak patients. If cancer patients express KANSARL fusion transcripts and will reduce histone acetylation, these patients may be sensitive to histone deacetylase inhibitors (HDAC inhibitors). Therefore, typing of KANSARL fusion transcripts will improve outcomes of HDAC inhibitors. 
         [0064]    This research has used RNA-seq datasets from diverse laboratories around the World to identify and analyze KANSARL fusion transcripts. The qualities, lengths and numbers of RNA-seq read are greatly variable from sample to sample. The main issues to analyze RNA-seq data—“Big Data” are fast and accurate. To solve both problems, we have used splicing code table and removed majorities of highly-repetitive splicing sequences from the current version of the implementation. Because our model requires that both 5′ and 3′ genes are present in the splicingcode table, we have greatly improved the accuracy of detecting the fusion transcripts and dramatically increased computation speeds. In addition, we have identified only fusion transcripts, whose sequences have to be identical to reference sequences. Because of these quality improvements, the maximum random error to generate a fusion transcript is 1.2×10 −24  and the medium error is 1×10 −59 . Since the number of RNA-seq reads would dramatically affect detecting KANSARL fusion transcripts, especially if the samples are KANSARL negative, we have selected potential KANSARL-negative datasets with higher qualities and at least 20 million of effective RNA-seq reads. These quality controls have greatly increased data reproducibility and reduced data errors. For example, the CGD dataset has 27 glioblastoma patents, which have 39 CE samples and 36 NE samples that are effectively constituted as multiple duplication experiments. All KANSARL-positive samples have been detected in the corresponding CE and NE samples and the duplication samples and all KANSARL-negative samples are also reproducible. That is, 100% of both KANSARL-positive and KANSARL-negative samples can be reproducible. If cancer samples might contain different ethnic backgrounds, especially samples from North American may have higher possibilities of having patients from African and Asian ancestry origins, it would have some negative impacts on our data analysis. However, these minor imperfections would not affect our conclusion that KANSARL fusion transcripts are associated with cancer samples of European ancestry origin. 
         [0065]    As shown in  FIG. 4 , KANSARL fusion transcripts are specific to European ancestry origin and likely result from inversion of ARL17-KANSL1 genes or local duplication. The genes KANSL1, ARL17A and MAPT located in 1 Mb inversion of chromosomal band 17q21.31 have been shown to have polymorphism. This inversion has resulted in the H1 and H2 haplotypes of 17q21.31, which have been shown to reach high allele frequency (26% and 19%, respectively) in West Eurasian populations, but absent in both African and Asian populations (Boettger, Handsaker et al. 2012). Analysis of genomic structures has shown that the population of European ancestry origin have short (155 kbp) and long (205 kbp) duplications corresponding to the promoter and first exon of KANSL1 associated with the H2 and H1 haplotypes, respectively (Steinberg, Antonacci et al. 2012). Both duplications have resulted in novel KANSL1 transcripts. The cDNA clone BC006271 identified in ovary adenocarcinoma (Strausberg, Feingold et al. 2002) has later been detected in one lymphoblastoid cell line of H113 population of the European ancestry origin (Boettger, Handsaker et al. 2012), and has been shown to have identical fusion junction to KANSARL isoform 2. 
         [0066]    Isolation of Total RNAs from the Cell Lines. 
         [0067]    Cell growth media were removed from the petri dishes. 1 ml of Trizol reagent (Invitrogen, CA) was added directly into the cells in the culture dishes per 10 cm 2  of the culture dishes. The cells were lysed directly by vortex for 15 second vigorously and the mixes were incubated at room temperature for 2-3 min. The samples were centrifuged at 4000 g for 15 minutes to separate the mixtures into a lower red, phenol-chloroform phase and a colourless upper aqueous phase. The aqueous phase was transferred to a fresh tube. The organic phase is saved if isolation of DNA or protein is desired. The RNA was precipitated by mixing with 0.5 volumes of isopropyl alcohol. After incubating samples at room temperature for 10 minutes, the RNA precipitate was pelleted by centrifuging at 12,000 g for 10 minutes at room temperature. The RNA pellet was washed twice with 1 ml of 75% ethanol and was centrifuged at 7500 g for 5 min at 4° C. The RNA pellet was air-dried at room temperature for 20 min and was dissolved in 40-80 μL RAase-free water. 
         [0068]    Isolation of Genomic DNAs from Cell Lines. 
         [0069]    The gemomic DNAs were isolated from A549, HeLa3 and K562 by QiagenBlood &amp; Cell Culture DNA Mini Kit as suggested by the manufactures. In brief, 5×10 6  cells were centrifuged at 1500×g for 10 min. After the supernatants were discarded, the cell pellets were washed twice in PBS and resuspended in PBS to a final concentrations of 10 7  cells/ml. 0.5 ml of suspension cells were added to 1 ml of ice-cold Buffer C1 and 1.5 ml of ice-cold distilled water and mixed by inversion several time. After the mixes were incubated on ice for 10 min, the lysed cells were centrifuged at 1,300×g for 15 min. After the supernatants were discarded, the pelleted nuclei were resuspended in 0.25 ml of ice-cold Buffer C1 and 0.75 ml of ice-cold distilled water and mixed by vortexing. The nuclei were centrifuged again at 4° C. for 15 min and the supernatants were discarded. The pellets were resuspended in 1 ml of Buffer G2 by vortexing for 30 sec at the maximum speed. After adding 25 ul of proteinase K, the mixes were incubated at 50° C. for 60 min. After A Qiagen Genomic-tip G20 was equilibrated with 1 ml of Buffer QBT and emptied by gravity flow, the sample were applied to the equilibrated Genomic-tip G20 and allowed to enter resin by gravity flow. After the Genomic-tip G20 was wash by 1 ml of Buffer QC three times, the genomic DNA was eluted by 1 ml of Buffer QF twice. The eluted DNA was precipitated by adding 1.4 ml of isopropanol by mixing several times and immediately centrifuged at 5,000×g for 15 min at 4° C. After removing the supernatants, the DNA pellet was washed by 70% of ethanol three times. After air drying for 10 min, the DNA pellet was resuspended in 0.2 ml of TE buffer to the final concentration of 0.5 ug/ul. 
         [0070]    cDNA Synthesis 
         [0071]    The first-strand cDNA synthesis is carried out using oligo(T)15 and/or random hexamers by TaqMan Reverse Transcription Reagents (Applied Biosystems Inc., Foster City, Calif., USA) as suggested by the manufacturer. In brief, to prepare the 2×RT master mix, we pool 10 μl of reaction mixes containing final concentrations of 1×RT Buffer, 1.75 mM MgCl 2 , 2 mM dNTP mix (0.5 mM each), 5 mM DTT, 1× random primers, 1.0 U/μl RNase inhibitor and 5.0 U/μl MultiScribe RT. The master mixes are prepared, spanned down and placed on ice. 10 μl of 2×RNA mixes containing 2 ug of total RNA are added into 10 μl 2× master mixes and mixed well. The reaction mixes are then placed in a thermal cycler of 25° C., 10 min, 37° C. 120 min, 95° C., 5 min and 4° C., ∞. The resulted cDNAs are diluted by 80 μl of H 2 O. 
         [0072]    RT-PCR Amplification 
         [0073]    To identify novel human fusion transcripts, fusion transcript specific primers have been designed to cover the 5′ and 3′ fusion transcripts. The primers are designed using the primer-designing software (SDG 2015). 5 μl of the cDNAs generated above are used to amplify fusion transcripts by PCR. PCR reactions have been carried out by HiFi Taq polymerase (Invitrogen, Carlsbad, Calif., USA) using cycles of 94° C., 15″, 60-68° C., 15″ and 68° C., 2-5 min. The PCR products are separated on 2% agarose gels. The expected products are excised from gels and cloned Fusion transcripts are then verified by blast and manual inspection. 
         [0074]    Quantitative Real-Time PCR. 
         [0075]    To quantify expression levels of different KANSARL isoforoms, The primers are designed using the primer-designing software (SDG 2015). 5 μl of the cDNAs generated above are used to amplify fusion transcripts by PCR. PCR reactions have been carried out using SYBR Green PCR Master Mix (Roche) on a LightCycler 48011 system (Roche) as manufacturer suggested. For each reaction, 5 ul of 480 SYBR Green I Master Mix (2×), 2 ul of primers (10×) and 3 ul of H 2 O were pooled into a tube and mixed carefully by pipetting up and down. 15 ul of PCR mix were pepetted into each well of the LightCycler® 480 Multiwell Plate, 5 ul of cDNA were added into the wells. The Multiwell Plate was sealed with LightCycler® 480 Multiwell sealing foil. The Plate was centrifuged at 1500×g for 2 min and transferred into the plate holder of the LightCycler 480 Instrument. The PCR was performed for 45 amplification cycles. 
         [0076]    PCR amplification of genomic DNAs 0.25 ug of human A549, HeLa3 and K562 genomic DNAs were used for PCR amplification. Genomic KANSARL fusion gene was amplified by primers KANSARLgF1 (Seq ID NO.: 886,574) and KANSARLgR1 (Seq ID NO.: 886,575). PCR reactions have been carried out by HiFi Taq polymerase (Invitrogen, Carlsbad, Calif., USA) using cycles of 94° C., 15″, 60° C., 15″ and 68° C., 2-5 min. The PCR products are separated on 1.5% agarose gels and generate a 360 by PCR fragments. 
         [0077]    Statistical Analysis. 
         [0078]    To compare two different populations, we have used the two-tailed Z score analyses to whether two populations differ significantly on the genetic characteristics. We set the null hypothesis to be that there is no difference between the two population proportions. Z scores are calculated based on the following the formula: 
         [0000]    
       
         
           
             Z 
             = 
             
               
                 
                   ( 
                   
                     
                       
                         p 
                         _ 
                       
                       1 
                     
                     - 
                     
                       
                         p 
                         _ 
                       
                       2 
                     
                   
                   ) 
                 
                 - 
                 0 
               
               
                 
                   
                     
                       p 
                       _ 
                     
                      
                     
                       ( 
                       
                         1 
                         - 
                         
                           p 
                           _ 
                         
                       
                       ) 
                     
                   
                    
                   
                     ( 
                     
                       
                         1 
                         
                           n 
                           1 
                         
                       
                       + 
                       
                         1 
                         
                           n 
                           2 
                         
                       
                     
                     ) 
                   
                 
               
             
           
         
       
     
       REFERENCES 
       [0000]    
       
         Abate, F., M. R. Ambrosio, L. Mundo, M. A. Laginestra, F. Fuligni, M. Rossi, S. Zairis, S. Gazaneo, G De Falco, S. Lazzi, C. Bellan, B. J. Rocca, T. Amato, E. Marasco, M. Etebari, M. Ogwang, V. Calbi, I. Ndede, K. Patel, D. Chumba, P. P. Piccaluga, S. Pileri, L. Leoncini and R. Rabadan (2015). “Distinct Viral and Mutational Spectrum of Endemic Burkitt Lymphoma.”  PLoS Pathog  11(10): e1005158. 
         Balbin, O. A., R. Malik, S. M. Dhanasekaran, J. R. Prensner, X. Cao, Y M. Wu, D. Robinson, R. Wang, G Chen, D. G Beer, A. I. Nesvizhskii and A. M. Chinnaiyan (2015). “The landscape of antisense gene expression in human cancers.”  Genome Res  25(7): 1068-1079. 
         Bao, Z. S., H. M. Chen, M. Y Yang, C. B. Zhang, K. Yu, W. L. Ye, B. Q. Hu, W. Yan, W. Zhang, J. Akers, V. Ramakrishnan, J. Li, B. Carter, Y W. Liu, H. M. Hu, Z. Wang, M. Y. Li, K. Yao, X. G Qiu, C. S. Kang, Y. P. You, X. L. Fan, W. S. Song, R. Q. Li, X. D. Su, C. C. Chen and T. Jiang (2014). “RNA-seq of 272 gliomas revealed a novel, recurrent PTPRZ1-MET fusion transcript in secondary glioblastomas.”  Genome Res  24(11): 1765-1773. 
         Boettger, L. M., R. E. Handsaker, M. C. Zody and S. A. McCarroll (2012). “Structural haplotypes and recent evolution of the human 17q21.31 region.”  Nat Genet  44(8): 881-885. 
         Fraga, M. F., E. Ballestar, A. Villar-Garea, M. Boix-Chornet, J. Espada, G Schotta, T. Bonaldi, C. Haydon, S. Ropero, K. Petrie, N. G Iyer, A. Perez-Rosado, E. Calvo, J. A. Lopez, A. Cano, M. J. Calasanz, D. Colomer, M. A. Piris, N. Ahn, A. Imhof, C. Caldas, T. Jenuwein and M. Esteller (2005). “Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common hallmark of human cancer.”  Nat Genet  37(4): 391-400. 
         Genomes Project, C., A. Auton, L. D. Brooks, R. M. Durbin, E. P. Garrison, H. M. Kang, J. O. Korbel, J. L. Marchini, S. McCarthy, G A. McVean and G R. Abecasis (2015). “A global reference for human genetic variation.”  Nature  526(7571): 68-74. Gill, B. J., D. J. Pisapia, H. R. Malone, H. Goldstein, L. Lei, A. Sonabend, J. Yun, J. Samanamud, J. S. Sims, M. Banu, A. Dovas, A. F. Teich, S. A. Sheth, G M. McKhann, M. B. Sisti, J. N. Bruce, P. A. Sims and P. Canoll (2014). “MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma.”  Proc Natl Acad Sci USA  111(34): 12550-12555. 
         Huang, J., B. Wan, L. Wu, Y. Yang, Y. Dou and M. Lei (2012). “Structural insight into the regulation of MOF in the male-specific lethal complex and the non-specific lethal complex.”  Cell Res  22(6): 1078-1081. 
         Ju, Y. S., W. C. Lee, J. Y Shin, S. Lee, T. Bleazard, J. K. Won, Y T. Kim, J. I. Kim, J. H. Kang and J. S. Seo (2012). “A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing.”  Genome Res  22(3): 436-445. 
         Kinsella, M., O. Harismendy, M. Nakano, K. A. Frazer and V. Bafna (2011). “Sensitive gene fusion detection using ambiguously mapping RNA-Seq read pairs.”  Bioinformatics  27(8): 1068-1075. 
         Koolen, D. A., R. Pfundt, K. Linda, G Beunders, H. E. Veenstra-Knol, J. H. Conta, A. M. Fortuna, G Gillessen-Kaesbach, S. Dugan, S. Halbach, O. A. Abdul-Rahman, H. M. Winesett, W. K. Chung, M. Dalton, P. S. Dimova, T. Mattina, K. Prescott, H. Z. Zhang, H. M. Saal, J. Y. Hehir-Kwa, M. H. Willemsen, C. W. Ockeloen, M. C. Jongmans, N. Van der Aa, P. Failla, C. Barone, E. Avola, A. S. Brooks, S. G Kant, E. H. Gerkes, H. V Firth, K. Ounap, L. M. Bird, D. Masser-Frye, J. R. Friedman, M. A. Sokunbi, A. Dixit, M. Splitt, D. D. D. Study, M. K. Kukolich, J. McGaughran, B. P. Coe, J. Florez, N. Nadif Kasri, H. G Brunner, E. M. Thompson, J. Gecz, C. Romano, E. E. Eichler and B. B. de Vries (2015). “The Koolen-de Vries syndrome: a phenotypic comparison of patients with a 17q21.31 microdeletion versus a KANSL1 sequence variant.”  Eur J Hum Genet.    
         Li, X., A. Battle, K. J. Karczewski, Z. Zappala, D. A. Knowles, K. S. Smith, K. R. Kukurba, E. Wu, N. Simon and S. B. Montgomery (2014). “Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants.”  Am J Hum Genet  95(3): 245-256. 
       
     
         [0090]    Li, X., L. Wu, C. A. Corsa, S. Kunkel and Y Dou (2009). “Two mammalian MOF complexes regulate transcription activation by distinct mechanisms.” Mol Cell 36(2): 290-301.
   Liu, S., W. H. Tsai, Y Ding, R. Chen, Z. Fang, Z. Huo, S. Kim, T. Ma, T. Y Chang, N. M. Priedigkeit, A. V. Lee, J. Luo, H. W. Wang, I. F. Chung and G C. Tseng (2015). “Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data.”  Nucleic Acids Res . Mellert, H. S., T. J. Stanek, S. M. Sykes, F. J. Rauscher, 3rd, D. C. Schultz and S. B. McMahon (2011). “Deacetylation of the DNA-binding domain regulates p53-mediated apoptosis.”  J Biol Chem  286(6): 4264-4270.   Mertens, F., B. Johansson, T. Fioretos and F. Mitelman (2015). “The emerging complexity of gene fusions in cancer.”  Nat Rev Cancer  15(6): 371-381.   Meunier, S., M. Shvedunova, N. Van Nguyen, L. Avila, I. Vernos and A. Akhtar (2015). “An epigenetic regulator emerges as microtubule minus-end binding and stabilizing factor in mitosis.”  Nat Commun  6: 7889.   Morin, R. D., K. Mungall, E. Pleasance, A. J. Mungall, R. Goya, R. D. Huff, D. W. Scott, J. Ding, A. Roth, R. Chiu, R. D. Corbett, F. C. Chan, M. Mendez-Lago, D. L. Trinh, M. Bolger-Munro, G Taylor, A. Hadj Khodabakhshi, S. Ben-Neriah, J. Pon, B. Meissner, B. Woolcock, N. Farnoud, S. Rogic, E. L. Lim, N. A. Johnson, S. Shah, S. Jones, C. Steidl, R. Holt, I. Birol, R. Moore, J. M. Connors, R. D. Gascoyne and M. A. Marra (2013). “Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing.”  Blood  122(7): 1256-1265.   Rahman, N. (2014). “Realizing the promise of cancer predisposition genes.”  Nature  505(7483): 302-308.   Ren, S., Z. Peng, J. H. Mao, Y. Yu, C. Yin, X. Gao, Z. Cui, J. Zhang, K. Yi, W. Xu, C. Chen, F. Wang, X. Guo, J. Lu, J. Yang, M. Wei, Z. Tian, Y. Guan, L. Tang, C. Xu, L. Wang, X. Gao, W. Tian, J. Wang, H. Yang, J. Wang and Y. Sun (2012). “RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings.”  Cell Res  22(5): 806-821.   Schmitz, R., R. M. Young, M. Ceribelli, S. Jhavar, W. Xiao, M. Zhang, G Wright, A. L. Shaffer, D. J. Hodson, E. Buras, X. Liu, J. Powell, Y Yang, W. Xu, H. Zhao, H. Kohlhammer, A. Rosenwald, P. Kluin, H. K. Muller-Hermelink, G Ott, R. D. Gascoyne, J. M. Connors, L. M. Rimsza, E. Campo, E. S. Jaffe, J. Delabie, E. B. Smeland, M. D. Ogwang, S. J. Reynolds, R. I. Fisher, R. M. Braziel, R. R. Tubbs, J. R. Cook, D. D. Weisenburger, W. C. Chan, S. Pittaluga, W. Wilson, T. A. Waldmann, M. Rowe, S. M. Mbulaiteye, A. B. Rickinson and L. M. Staudt (2012). “Burkitt lymphoma pathogenesis and therapeutic targets from structural and functional genomics.”  Nature  490(7418): 116-120.   SDG (2015). “http://www.yeastgenome.org”.   Stadler, Z. K., K. A. Schrader, J. Vijai, M. E. Robson and K. Offit (2014). “Cancer genomics and inherited risk.”  J Clin Oncol  32(7): 687-698.   Steinberg, K. M., F. Antonacci, P. H. Sudmant, J. M. Kidd, C. D. Campbell, L. Vives, M. Malig, L. Scheinfeldt, W. Beggs, M. Ibrahim, G Lema, T. B. Nyambo, S. A. Omar, J. M. Bodo, A. Froment, M. P. Donnelly, K. K. Kidd, S. A. Tishkoff and E. E. Eichler (2012). “Structural diversity and African origin of the 17q21.31 inversion polymorphism.”  Nat Genet  44(8): 872-880.   Strausberg, R. L., E. A. Feingold, L. H. Grouse, J. G Derge, R. D. Klausner, F. S. Collins, L. Wagner, C. M. Shenmen, G D. Schuler, S. F. Altschul, B. Zeeberg, K. H. Buetow, C. F. Schaefer, N. K. Bhat, R. F. Hopkins, H. Jordan, T. Moore, S. I. Max, J. Wang, F. Hsieh, L. Diatchenko, K. Marusina, A. A. Farmer, G M. Rubin, L. Hong, M. Stapleton, M. B. Soares, M. F. Bonaldo, T. L. Casavant, T. E. Scheetz, M. J. Brownstein, T. B. Usdin, S. Toshiyuki, P. Carninci, C. Prange, S. S. Raha, N. A. Loquellano, G J. Peters, R. D. Abramson, S. J. Mullahy, S. A. Bosak, P. J. McEwan, K. J. McKernan, J. A. Malek, P. H. Gunaratne, S. Richards, K. C. Worley, S. Hale, A. M. Garcia, L. J. Gay, S. W. Hulyk, D. K. Villalon, D. M. Muzny, E. J. Sodergren, X. Lu, R. A. Gibbs, J. Fahey, E. Helton, M. Ketteman, A. Madan, S. Rodrigues, A. Sanchez, M. Whiting, A. Madan, A. C. Young, Y. Shevchenko, G G Bouffard, R. W. Blakesley, J. W. Touchman, E. D. Green, M. C. Dickson, A. C. Rodriguez, J. Grimwood, J. Schmutz, R. M. Myers, Y. S. Butterfield, M. I. Krzywinski, U. Skalska, D. E. Smailus, A. Schnerch, J. E. Schein, S. J. Jones, M. A. Marra and T. Mammalian Gene Collection Program (2002). “Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences.”  Proc Natl Acad Sci USA  99(26): 16899-16903.   Uhlen, M., L. Fagerberg, B. M. Hallstrom, C. Lindskog, P. Oksvold, A. Mardinoglu, A. Sivertsson, C. Kampf, E. Sjostedt, A. Asplund, I. Olsson, K. Edlund, E. Lundberg, S. Navani, C. A. Szigyarto, J. Odeberg, D. Djureinovic, J. O. Takanen, S. Hober, T. Alm, P. H. Edqvist, H. Berling, H. Tegel, J. Mulder, J. Rockberg, P. Nilsson, J. M. Schwenk, M. Hamsten, K. von Feilitzen, M. Forsberg, L. Persson, F. Johansson, M. Zwahlen, G von Heijne, J. Nielsen and F. Ponten (2015). “Proteomics. Tissue-based map of the human proteome.”  Science  347(6220): 1260419.   Varley, K. E., J. Gertz, B. S. Roberts, N. S. Davis, K. M. Bowling, M. K. Kirby, A. S. Nesmith, P. G Oliver, W. E. Grizzle, A. Forero, D. J. Buchsbaum, A. F. LoBuglio and R. M. Myers (2014). “Recurrent read-through fusion transcripts in breast cancer.”  Breast Cancer Res Treat  146(2): 287-297.   Wyatt, A. W., F. Mo, K. Wang, B. McConeghy, S. Brahmbhatt, L. Jong, D. M. Mitchell, R. L. Johnston, A. Haegert, E. Li, J. Liew, J. Yeung, R. Shrestha, A. V. Lapuk, A. McPherson, R. Shukin, R. H. Bell, S. Anderson, J. Bishop, A. Hurtado-Coll, H. Xiao, A. M. Chinnaiyan, R. Mehra, D. Lin, Y. Wang, L. Fazli, M. E. Gleave, S. V. Volik and C. C. Collins (2014). “Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer.”  Genome Biol  15(8): 426.   Yendamuri, S., F. Trapasso and G A. Calin (2008). “ARLTS1—a novel tumor suppressor gene.”  Cancer Lett  264(1): 11-20.   Yoshihara, K., Q. Wang, W. Torres-Garcia, S. Zheng, R. Vegesna, H. Kim and R. G Verhaak (2014). “The landscape and therapeutic relevance of cancer-associated transcript fusions.”  Oncogene.      Zhuo D, C. W., Zhu S, Dong C and Glass ADM (2012).  Decipering splicing codes of spliceosomal introns  BIOCOMP 2012, Las Vagas, Nev., USA, CSREA Press.   Zhuo, D., R. Madden, S. A. Elela and B. Chabot (2007). “Modern origin of numerous alternatively spliced human introns from tandem arrays.”  Proc Natl Acad Sci USA  104(3): 882-886.