This project is a computer-based project requiring no access to the laboratory.

You will explore datasets from real world, in-house screens of our Phytotitre library comprising 800 extracts of traditional medicines and herbs. The aim is to identify natural products that are capable of inhibiting growth of the following tumour cell-lines: A2780 (ovarian cancer), HepG2 (liver cancer), MCF-7 (breast cancer) and PC3 (prostate cancer).

The data are given in the form of raw absorbance values from each plate measurement in Microsoft Excel format. You will first learn how to perform background corrections and normalisation of data to positive and negative controls. You will then calculate Z' factor scores to explore assay reproducibility for each plate. After mapping the resulting data to respective extract IDs, you will aim to identify the top hits for inhibition of growth of each tumour cell-type. Correlations can then be sought between the observed bioactivities and the properties of each of the extracts. You will then explore whether the bioactivity of the top hits can be validated by performing four parameter dose curve fitting to calculate the IC50 of hit extracts.

Four Excel files are available to download - one per cell type - each with over 8,000 data points for analysis.

Through this project, you will gain experience of many skills which are in much demand currently from employers. For example, you will practise general Microsoft Excel data handling techniques, calculation of assay Z' factor, alignment of phenotypic data with plate maps, preparation of appropriate charts and relevant statistical analyses.

A very basic version of this project can be completed in several weeks. However, this timeframe can be extended substantially by exploring more complex correlation analyses, or comparing your findings with the existing literature in more depth, on the advice of your supervisor.

To get started, simply read the project outline given below, then follow each of the method sheets in the order shown in the table to explore this particular research question.

Please click here to download the Project Outline for the Cancer Drug Discovery Data Analysis Project.

Method Sheets for Cancer Biology Data Analysis Project 1 - Discovery of new anti-cancer agents

Method	Technique
50	Introduction to the Phytotitre and Puretitre libraries
51	Basic data handling using Microsoft Excel
52	Background correction of absorbance values
53	Calculation of bioassay ‘Z-prime’ score
54	Calculation of mean and SD of repeat experiments
55	Mapping experimental data to compound or extract map
56	Drawing appropriate charts of experimental data
57	Adding error bars to charts
58	Identifying ‘hits’ from a compound screen
59	Basic correlation analysis
60	Basic Student’s T-test using Microsoft Excel
62	Calculation of IC50 by 4 paramater curve fitting using Microsoft Excel