Kjong Lehmann, PhD. Computational Biology

Post Doc

E-Mail
kjong.lehmann@get-your-addresses-elsewhere.inf.ethz.ch
Phone
+41 43 254 0225
Address
Biomedical Informatics Group
Schmelzbergstrasse 26
SHM 26 B 5
8006 Zürich
Room
SHM 26 B 5

I received my PhD in Computational Biology from the University of Southern California, after which I joined the Rätsch group at Memorial Sloan Kettering Cancer Center in New York. My main interests are trying to understand genetic architecture underlying complex phenotypes using and developing state of the art approaches, including variant effect predictions and linear mixed models. I am also deeply involved in large-scale cancer genomics efforts such as TCGA PanCanAtlas and ICGC Pan-Cancer Analysis Group.

Abstract Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. While the role of promoters as driver elements in cancer has been recognized, the contribution of alternative promoters to regulation of the cancer transcriptome remains largely unexplored. Here we show that active promoters can be identified using RNA-Seq data, enabling the analysis of promoter activity in more than 1,000 cancer samples with matched whole genome sequencing data. We find that alternative promoters are a major contributor to tissue-specific regulation of isoform expression and that alternative promoters are frequently deregulated in cancer, affecting known cancer-genes and novel candidates. Noncoding passenger mutations are enriched at promoters of genes with lower regulatory complexity, whereas noncoding driver mutations occur at genes with multiple promoters, often affecting the promoter that shows the highest level of activity. Together our study demonstrates that the landscape of active promoters shapes the cancer transcriptome, opening many opportunities to further explore the interplay of regulatory mechanism and noncoding somatic mutations with transcriptional aberrations in cancer.

Authors Deniz Demircioğlu, Martin Kindermans, Tannistha Nandi, Engin Cukuroglu, Claudia Calabrese, Nuno A. Fonseca, Andre Kahles, Kjong Lehmann, Oliver Stegle, PCAWG-3, PCAWG-Network, Alvis Brazma, Angela Brooks, Gunnar Rätsch, Patrick Tan, Jonathan Göke

Submitted bioRxiv

Link DOI

Abstract We present a genome-wide analysis of splicing patterns of 282 kidney renal clear cell carcinoma patients in which we integrate data from whole-exome sequencing of tumor and normal samples, RNA-seq and copy number variation. We proposed a scoring mechanism to compare splicing patterns in tumor samples to normal samples in order to rank and detect tumor-specific isoforms that have a potential for new biomarkers. We identified a subset of genes that show introns only observable in tumor but not in normal samples, ENCODE and GEUVADIS samples. In order to improve our understanding of the underlying genetic mechanisms of splicing variation we performed a large-scale association analysis to find links between somatic or germline variants with alternative splicing events. We identified 915 cis- and trans-splicing quantitative trait loci (sQTL) associated with changes in splicing patterns. Some of these sQTL have previously been associated with being susceptibility loci for cancer and other diseases. Our analysis also allowed us to identify the function of several COSMIC variants showing significant association with changes in alternative splicing. This demonstrates the potential significance of variants affecting alternative splicing events and yields insights into the mechanisms related to an array of disease phenotypes.

Authors Kjong Van Lehmann, Andre Kahles, Cyriac Kandoth, William Lee, Nikolaus Schultz, Oliver Stegle, Gunnar Rätsch

Submitted Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Link Pubmed