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Selam, a rising senior at the University of California, Santa Cruz, studying bioinformatics, looked at significantly recurrent genomic rearrangements in patient tumors and cancer cell lines.

Cancer drivers are DNA mutations that give cells survival advantage and are present in many tumors across different types of cancers. This experience was truly transformative for me, not just in terms of being surrounded by so much scientific excellence. I also learned a lot about myself and have changed a lot. I would like to leave a quote I wrote down at one of our faculty meetings “Balance comes from within. External balance can't be achieved without internal balance” - Dr. Jose Florez. Make sure you give yourself lots of grace to fail. I learned this summer that I learn the most from failing or when things don’t go exactly my way. I’ve also learned that taking care of myself is a radical act that will enable me to be happier and do better science. So take care of yourself, push yourself past your comfort zone, make deep connections with peers, and lean on each other during hard times. I am truly grateful for this experience to do research and meet all the wonderful people I’ve met this past summer.
One of the types of mutations that can drive cancer are genomic rearrangements which are large mutations that connect two or more distant loci. While there are tools to detect driver single-nucleotide variants (SNVs), tools to detect recurrent rearrangements have been limited. Our lab developed a tool called SVSig-2D that estimates expected rearrangement rates between any pair of loci to identify significantly recurrent rearrangements. The method was previously applied to over 2,500 tumors and 80 significantly recurrent rearrangements including known and novel events were found. Here, we applied SVSig-2D to cancer cell lines and identified 30 recurrent rearrangements. With these results we first identified recurrent rearrangements in common between the patient tumors and the cell lines. We next compared gene expression patterns in samples with vs. without recurrent rearrangements in the cell line and tumor datasets. These findings help show the utility of SVSig-2D as a tool to identify biologically meaningful driver rearrangements.

Project: Recurrent genomic rearrangements and their transcriptomic effects on patient tumors and cancer cell lines

Mentors: Simona Dalin and Shu Zhang, Cancer Program