PhD position: Computational methods for inferring tumour evolution (SW13453)

The goal of this project is to tackle statistical and algorithmic challenges in understanding the evolution of tumours based on genomic data. The Markowetz laboratory has a strong track record in developing methods to dissect tumour heterogeneity and infer life histories of tumours. The project is for a student with strong mathematical and statistical skills. Initially the focus will be on developing new methods to trace clonal evolution across multiple samples from the same patients by integration information on single nucleotide variants, copy number changes and structural variants. According to the student's interest, the project can develop into more methodological or more applied follow-up work.

Preferred skills - The successful applicant will have/or expect to gain a degree in a quantitative field like mathematics, computer science or statistics. They will have a detailed knowledge of statistics and algorithms and have experience in using R and Python. They should also have a strong desire to learn cancer biology. The research atmosphere in CRUK CI is highly collaborative and so excellent communication skills are key.

Funding - This studentship is funded by Cancer Research UK and includes full funding for University and College fees and in addition, a stipend of £19,000 per annum, initially for 3 years, with funding for a further year possible as required.

Eligibility - No nationality restrictions apply to this Cancer Research UK funded studentship. Applications are invited from recent graduates or final year undergraduates who hold or expect to gain a first/upper second class degree (or equivalent) in a relevant subject from any recognised university worldwide.

How to apply - Please follow the instructions on the following webpage: http://www.graduate.study.cam.ac.uk/courses/directory/cvcrpdmsc/apply (Reference: SW13453)

References

  1. Beerenwinkel et al (2014) Cancer evolution: mathematical models and computational inference, Systematic Biology.
  2. Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
  3. Schwarz et al (2015), Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction, PLoS Med, 12(2)
  4. Yuan et al (2015), BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies, Genome Biology, 16:36

 

PhD position: Clinical Research Fellowship in Computational Pathology (RA13541)

The Cancer Research UK Cambridge Center will fund up to 4 Clinical Research Training Fellowships for 2018 covering an exciting range of studies. Full details about the Clinical Research Fellowships are available at the CRUK Cambridge Center website. Applications for these Fellowships must be made via the University's Job Opportunities website. The deadline for applications is 30 November 2017.

In collaboration with Prof Rebecca Fitzgerald's lab, we are offering a project on computational pathology for early detection or oesophageal cancer. We will analyse data from a non-invasive, nonendoscopic diagnostic test for diagnosing Barrett’s oesophagus using a cell collection device called the Cytosponge™ (see here on Youtube) which is coupled with a TFF3 immunohistochemical assay to diagnose Barrett’s (PLOS Med 2015). Cytological atypia and p53 over expression are features predicting cancer risk which can be diagnosed on the same sample (Nature Genetics 2015, 2016, Lancet Gastro & Hepatol 2017).

Aims of the Project:

  1. Develop and test a computational algorithm for assessing the cytological and tissue features of Cytosponge samples in order to diagnose and risk stratify Barrett’s oesophagus;
  2. Evaluate the procedure on existing cohort and then apply to new cases;
  3. Automate the analysis in a standalone software.

This project is ideal if you want to learn computational skills, machine learning and image analysis techniques.

Full information about how to apply for a PhD place at the University can be found on the University’s Graduate Admissions website.

References

  1. Weaver JMJ et al. Ordering of mutations in preinvasive disease stages of esophageal carcinogenesis. Nat Genet. 2014 Aug;46(8):837-843.
  2. Ross-Innes CS et al.Whole-genome sequencing provides new insights into the clonal architecture of Barrett's esophagus and esophageal adenocarcinoma. Nat Genet. 2015 Sep;47(9):1038-1046.
  3. Secrier M et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat Genet. 2016 Oct;48(10):1131-41.
  4. Elliott DR et al. A non-endoscopic device to sample the oesophageal microbiota: a case-control study. Lancet Gastroenterol Hepatol. 2017 Jan;2(1):32-42.
  5. Yuan Y et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci Transl Med. 2012 Oct24;4(157):157ra143.
  6. Martins FC et al.Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss asa common driver event and prognostic classifier. Genome Biol. 2014 Dec 17;15(12):526.

 

2 PhD position: cancer evolution

Soon, PhD positions in tumor heterogeneity and cancer evolution will open funded by the H2020 ITN 'CONTRA'

Start of the PhD will be October 2018, just like for the position above. If interested in tumor evolution, please apply for SW13453.

I like a mix of quantitative, biological and medical background in the lab. Your undergrad training does not matter as long as you can motivate why you want to study tumour evolution with me.

 

Postdoc position: Pathways from single-cell CRISPR data

Several recent high-impact papers introduced experimental techniques for single-cell based genetic screens to understand gene function and cellular signalling pathways. These techniques combine single-cell RNA sequencing (scRNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)- based perturbations to massively scale up the resolution and scope of previous genetic screening technologies. The technology is flexible and will most likely soon be used very widely across molecular biology. As these technologies are brand-new, tailored computational analysis of these data is lagging behind experimental advances.

We will develop a machine learning approach to efficiently analyse scRNA-seq CRISPR screens and infer gene interaction networks and pathways of information flow in the cell. Our approach is based on an established machine learning method called Nested Effect Models (NEMs), which has been pioneered by the applicant. Over the last twelve years NEMs have been refined, extended, and applied by a world-wide community of independent groups, and now there exists a substantial body of methodological developments and experience in applications, which we propose to leverage for the analysis of scRNAseq CRISPR screens.

We will collaborate with Dr Sarah Teichmann's lab at the Sanger Institute and Dr Christoph Bock's lab at CEMM in Vienna. Working with these leading developers of scRNA-seq CRISPR screens, we will use our methodological advances to optimise the study design of future screens and showcase the power of our approach in collaborative case studies.

Funding - This project is funded by the BBSRC

Skills needed - Either you have very strong statistics and algorithmic skills, then this project and our collaborators will teach you biology. Or you have a biology background and enjoy exploring data with bioinformatics. Either way: please apply.

Application - The official job add is not yet out. Please contact Florian Markowetz directly.

 

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