Analysis of circulating tumor DNA to enhance precision oncology
Ellen Heitzer, Institute of Human Genetics, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz
Prof. Dr. Ellen Heitzer|
|Availability:||This position is available.|
Medical University of Graz
|Application deadline:||Applications are accepted between August 14, 2020 00:00 and October 10, 2020 23:59 (Europe/Zurich)|
Precision oncology hypothesizes that anticancer therapy should be matched to each patient in accordance with the molecular profile of a tumor(1). In contrast to molecular profiling of primary tumors, the analysis of circulating tumor DNA (ctDNA) from plasma representsanon-invasive,cost-effective alternative and is increasingimplemented in oncologyasthese biopsies are associated with significant risk, when tumor tissue is insufficient or inaccessible, and/or serial assessment of tumor molecular abnormalities is needed to optimize treatment(2-4).Numerous studies indicate the association of ctDNA with tumor burdenandthe utiltyof ctDNA analysis forMRDdetection. Moreover,ctDNA analysis enablesa betterunderstanding of mechanisms of resistance to treatmentand adynamicguidingof therapy. Longitudinal ctDNA analysis will improveour knowledge of the tumor evolution, will accelerate drug development, and will contribute to the implementation of precision medicine to improve clinical outcomes(2, 3).ctDNA is thought to reflect tumor-derived fragmentsfrom all sites of disease and originates mostlyfrom apoptotic tumor cells, which is why ctDNA is highly fragmented and representsnucleosome–protected DNA(5).Our group has extensive expertise in the analysis of plasma DNA which is reflected in numerous publicationsin high-raking journaland we have established a variety of ctDNA analysis approaches ranging from high-resolutionsingle single targetassaysto comprehensive wohle genome analyses(6-12). Moreover, we recently demonstratedthat plasma DNA analyses has more to offer than the detection of mutationsand copy numberalterationsand developed new machine learning approaches, which relateto nucleosome positionsand enablethe assessment of theactivationstatus of a gene or theactivity of transcriptionfactors from DNA sequencing data(9, 13).Therefore,we think acombination of geneticprofilingand nucleosome positioning patterns mightcontribute to an improved understanding tumor biology and tretamentresponse.
Hypothesis and Objectives:
The main aim of this project is to leverage non-invasive means to select appropriate treatment strategies and to better understand treatment outcome of advanced cancer patients. In particular, we aim to correlate genetic as well as epigenetic data to functional analyses based on nucleosome occupancy to identify aberrant cancer pathways, which can be targeted by selective drugs. Moreover, since most available liquid profiling approaches focus on mutations,we intend to identify and characterize cancer driver genes in high-level focal amplifications, since the stability/consistency of gene expression patterns in focal amplifications is yet unknown and has not been followed over time.
- The student employs and further develops a broad range of next generation sequencing aproaches.
- These analyses include high resolution ssays, gene panels, whole genome sequencing or bisulfite sequencing on clinical samplesin order to investigate the activity of cancer driver genes and pathways over time and possibly identify actionable targets or mechanisms of resistance against targeted therapies
- Moreover, the candidate will make use of bioinformatics approaches such as variant/copy number calling and interpretation, read depth analysis, pathwayanalysis, or machinelearning technologies. Therefore, knowledge of Linux and programming skills are beneficial, but not mandatory
1.Schwartzberg L, Kim, E.S., Liu, D., and Schrag, D. . Precision Oncology: Who, How, What, When, and When Not? Am Soc Clin Oncol Educ Book. 2017(37):160-9.
2.Heitzer E, Haque IS, Roberts CES, SpeicherMR. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet. 2019;20(2):71-88.
3.Heitzer E, Ulz P, Geigl JB. Circulating tumor DNA as a liquid biopsy for cancer. Clinical chemistry. 2015;61(1):112-23.
4.Heitzer E. Circulating Tumor DNA for Modern Cancer Management. Clin Chem. 2019.
5.Heitzer E, Auinger L, Speicher MR. Cell-Free DNA and Apoptosis: How Dead Cells Inform About the Living. Trends Mol Med. 2020;26(5):519-28.
6.Moser T, Ulz P, Zhou Q, Perakis S, Geigl JB, Speicher MR, et al. Single-Stranded DNA Library Preparation Does Not Preferentially Enrich Circulating Tumor DNA. Clin Chem. 2017;63(10):1656-9.
7.Smith CG, Moser T, Mouliere F, Field-Rayner J, Eldridge M, Riediger AL, et al. Comprehensive characterization of cell-free tumor DNA in plasma and urine of patients with renal tumors. Genome Med. 2020;12(1):23.
8.Suppan C, Brcic I, Tiran V, Mueller HD, Posch F, Auer M, et al. Untargeted Assessment of Tumor Fractions in Plasma for Monitoring and Prognostication from Metastatic Breast Cancer Patients Undergoing Systemic Treatment. Cancers (Basel). 2019;11(8).
9.Ulz P, Perakis S, Zhou Q, Moser T, Belic J, Lazzeri I, et al. Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection. Nat Commun. 2019;10(1):4666.
10.Weber S, Spiegl B, Perakis SO, Ulz CM, Abuja PM, Kashofer K, et al. Technical Evaluation of Commercial Mutation Analysis Platforms and Reference Materials for Liquid Biopsy Profiling. Cancers (Basel). 2020;12(6).
11.Zhou Q, Perakis SO, Ulz P, Mohan S, Riedl JM, Talakic E, et al. Cell-free DNA analysis reveals POLR1D-mediated resistance to bevacizumab in colorectal cancer. Genome Med. 2020;12(1):20.
12.Ulz P, Belic J, Graf R, Auer M, Lafer I, Fischereder K, et al. Whole-genome plasma sequencing reveals focal amplifications as a driving force in metastatic prostate cancer. Nat Commun. 2016;7:12008.
13.Ulz P, Thallinger GG, Auer M, Graf R, Kashofer K, Jahn SW, et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat Genet. 2016;48(10):1273-8.