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                     we identified diverse genomic-dependent and genomic-independent AR splicing variants expressed in prostate cancer. Additionally, our early work focuses on prostate cancer epigenomic studies by analyzing the ChIP-seq data of AR and BRD4 in prostate cancer VCaP cell line. Our study revealed the crosstalk between AR and BRD4 signaling in prostate cancer progression.
Developing algorithms for detecting cancer biomarkers. We have been developing EgoNet algorithm by integrating gene expression micro- array data and protein interaction networks to identify network modules that can distinguishing different breast cancer subtypes.
1. Detecting novel indels from prostate cancer genome
We developed transIndel, a splice-aware algorithm that parses the chimeric alignments predicted by a short read aligner and reconstructs the mid- sized insertions and large deletions based on the linear alignments of split reads from DNA-seq or RNA-seq data. TransIndel exhibits competitive or superior performance over eight state-of-the-art
indel detection tools on benchmarks using both synthetic and real DNA-seq data. We applied transIndel to DNA-seq and RNA-seq datasets from 333 primary prostate cancer patients from The Cancer Genome Atlas (TCGA) and
59 metastatic prostate cancer patients from AACR-PCF Stand-Up- To-Cancer (SU2C) studies. TransIndel enhanced the taxonomy of DNA- and RNA-level alterations in prostate cancer by identifying recurrent FOXA1 indels (Figure 1).
2. Delineating lncRNA landscape in prostate cancer genome
Prostate cancer (PCa) is the most commonly diagnosed cancer in men in the United States, with significant health impact. Clinically, it is com- plicated with the lack of biomarkers and effective treatments for aggressive disease, particularly castrationresistant prostate cancer (CRPC). We have gained much insight into the biology of PCa through studying protein-coding genes, but they represent only a small fraction of our genome.
It is now well accepted that the vast majority of human genome (about 75%) is actively transcribed, but protein- coding genes only account for about 2% the genome. This means the majority of the
3. Detecting novel biomarkers for cancer immunotherapy
Immune checkpoint blockade therapy has proved to be effective on a number of cancer types
such as skin, lung and kidney cancer. However, only some of the patients have a response to immunotherapy drugs. We have developed a novel computational algorithm which can sensitively detect previously missed novel splicing events
in human transcriptome from RNA-seq data. We utilize whole exome sequencing and RNAseq from renal cell carcinoma, lung cancer and melanoma to correlation of the expression of our detected splicing event with immune checkpoint therapy response or resistance. This study aims to improve the computational methodology to detect and quantify novel alternative splicing events and to determine their involvement in immunotherapy associated phenotypes. Integrative analysis of DNA mutations and RNA splicing events in the responders and non-responder patients is able to identify a list of candidate genomic independent alternative splicing events that play a role under- lying the resistance of immunotherapy in the non-responders or the effects in the responders.
 Other professional activities:
Award:
DoD Idea Development Award 2018
Publication:
Wang TY, Wang L, Alam SK, Hoeppner LH, Yang R. ScanNeo: identifying indel- derived neoantigens using RNA-Seq data. Bioinformatics. 2019 Mar 18.
          Figure 1. TransIndel identified novel deletions in FOXA1 from ten prostate cancer specimens that were missed by original TCGA study (lower panel)
V82GfsX140
Detected by transIndel, 10 mutations FOXA1 NM_004496
F254_G257delinsC R262Sfs*57
human transcriptome is comprised of noncoding RNAs (ncRNAs).
Our current research is developing novel computational methods to achieve the first complete compen- dia of CRPC-associated lncRNAs and reveal the dynamic interplay between lncRNAs and tumorigenesis, pro- gression and metastasis, which will highlight the importance of lncRNAs in the etiology of PCa.
         2 E255_N256del
2 Q263Afs*60
A423Dfs*17
2
S472Aext*70 X473Mext*22
FRAMESHIFT
INFRAME
MISSENSE
UTR_3
                     50 100 150
 200
     250
      00 450
300   350 4
                         G279PfsX34
                     Forkhead_N
FH FORKHEAD
DNA binding
HNF_C HNF3 C-terminal domain
  Forkhead N-terminal region
DNA binding site [nucleotide binding]
                                                                      D226N
D249V M253K F254V
THE HORMEL INSTITUTE // UNIVERSITY OF MINNESOTA PG 41
Y460X
L379_Q385delinsW
F266QfsX5
K264_G283delinsR
N252_C258del D249_M253delinsV














































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