Development of bioinformatics techniques leads to biomarker discovery and realisation of commercial potential
Submitting InstitutionNottingham Trent University
Unit of AssessmentAllied Health Professions, Dentistry, Nursing and Pharmacy
Summary Impact TypeTechnological
Research Subject Area(s)
Mathematical Sciences: Statistics
Biological Sciences: Genetics
Information and Computing Sciences: Artificial Intelligence and Image Processing
Summary of the impact
The research led by Professor Graham Ball at Nottingham Trent University
has developed new bioinformatics techniques for mining complex post
genomic bio-profile data. The approach allows development of predictive
models to answer clinical questions using an optimum biomarker panel. The
impact of this work is through the filing of four patents associated with
algorithms, breast cancer and tuberculosis, subsequently licensed to a
spin-out company. To date three clinical trials have been supported with
others in the pipeline. Through the spin-out company the approach is being
applied to stratify patients in clinical collaborations and to optimise
biomarker panels for diagnostics companies.
The underpinning research was led by Professor Graham Ball (Professor of
Bioinformatics) in collaboration with Professor Robert Rees (Director of
The John van Geest Cancer Research Centre) over the last 12 years,
focusing on bioinformatics algorithms for biomarker discovery. Professor
Ball has been involved in a number of clinical collaborations leading to
publication of 72 papers since 2002, 50 of which have directly utilised
the algorithms developed.
The technique consists of algorithms based on artificial neural networks
(ANNs) that facilitate analysis of complex biological data, such as mass
spectrometry, gene expression arrays and miRNA arrays. One of the problems
with analysis of such data is its complexity and dimensionality. This
leads to over-fitting and false discovery. The algorithms developed by the
Ball group (which included L Lancashire, PhD student and then
post-doctoral fellow, 2001-2008) overcome these problems by utilising
extensive cross-validation coupled with biomarker selection based on a
stepwise additive approach. Numerous publications by the group demonstrate
that these limitations have been overcome, (e.g. Lancashire et al, 2009.
Briefings in Bioinformatics 10 (3): 315-329; Ref 1). The algorithms have
been applied to the analysis of clinical data to identify an optimised
subset of markers and incorporate them into a model that best predicts an
answer to a given clinical question. These markers and models provide an
insight into disease aetiology and can be used from a diagnostic
The group was one of the first to mine mass spectrometry data using an
ANN approach. The initial study identified biomarker ions that accurately
differentiated between astrocytoma and glioblastoma (Ref 2). This study
showed that it was possible to utilise the non-linear predictive
capabilities of ANNs to classify a clinical state using biomarker ions
from SELDI-MS data. These approaches were then developed further, and
applied to analysis of melanoma data for a larger cohort (Ref 3).
Subsequently the methods were refined, developed and validated to improve
performance, optimise the biomarker panels identified, overcome
limitations associated with high dimensionality and the complexity of the
data. These new methods facilitated improved predictive performance for
unseen cases from disease populations.
The approach has been applied:
- in a prostate cancer vaccine clinical trial in 2005 (Ref 4). This
study demonstrated that a cytokine profile derived using an ANN model
could stratify the response of patients on a clinical trial with high
sensitivity and specificity (Onyvax Ltd).
- to integrate immuno-histochemical data, pathological data, gene
expression and miRNA data (Habashy et al, 2008. European Journal of
Cancer, 44:11, 1541-1551. Lowery et al, 2009. Breast Cancer Research
- to the rapid typing of microbial pathogens from mass spectrometry data
(Ref 5), in collaboration with Public Health England (PHE) Colindale.
- in the characterisation of breast cancer - contributing to the
revision and remodelling of the Nottingham Prognostic Index (Ref 6).
References to the research
Citations and impact factors below refer information according to http://wok.mimas.ac.uk
on 18th October 2013.
1. LANCASHIRE L J, POWE D G, REIS-FILHO J S, RAKHA E, LEMETRE C, WEIGELT
B, ABDEL-FATAH TM, GREEN A. R., MUKTA R., BLAMEY R., PAISH E. C., REES R.
C, ELLIS I O, BALL G R 2010. A validated gene expression profile for
detecting clinical outcome in breast cancer using artificial neural
networks. Breast Cancer Research and Treatment Volume 120, Number 1,
83-93, DOI: 10.1007/s10549-009-0378-1. Impact Factor: 4.469, Citations 15.
2. BALL G, MIAN S, HOLDING F, ALLIBONE R O, LOWE J, ALI S, LI G, MCCARDLE
S, ELLIS I O, CREASER C and REES R C 2002. An integrated approach
utilizing artificial neural networks and SELDI mass spectrometry for the
classification of human tumours and rapid identification of potential
biomarkers. Bioinformatics vol 18 (3) , pp. 395-404. DOI:
10.1093/bioinformatics/18.3.395. Impact Factor: 5.523, Citations 153.
3. MIAN, S, UGUREL, S, PARKINSON, E, SCHLENZKA, I, DRYDEN, I, LANCASHIRE,
L, BALL, G, CREASER C, REES R and SCHADENDORF, D 2005. Serum proteomic
fingerprinting discriminates between clinical stages and predicts disease
progression in melanoma patients. Journal of Clinical Oncology vol 23 (22)
, pp. 5088-5093. DOI:10.1200/JCO.2005.03.164. Impact Factor: 18.038,
4. MICHAEL A, BALL G, QUATAN N, WUSHISHI F, RUSSELL N, WHELAN J,
CHAKRABORTY P, LEADER D, WHELAN M and PANDHA H. 2005. Delayed disease
progression after allogeneic cell vaccination in hormone-resistant
prostate cancer and correlation with immunologic variables. Clinical
Cancer Research vol 11 (12), pp. 4469-4478. DOI:
10.1158/1078-0432.CCR-04-2337 Impact Factor: 7.837, Citations 72.
5. LANCASHIRE L, SCHMID O, SHAH H and BALL G, 2005. Classification of
bacterial species from proteomic data using combinatorial approaches
incorporating artificial neural networks, cluster analysis and principal
components analysis. Bioinformatics vol 21 (10), pp. 2191-2199.
DOI:10.1093/bioinformatics/bti368. Impact Factor: 5.468, Citations 42.
6. BLAMEY, R W, ELLIS I O, PINDER, S E, LEE, A H S, MACMILLAN, R D,
MORGAN, D A L, ROBERTSON J F R, MITCHEL M J, BALL, G R, HAYBITTLE J L and
ELSTON C W, 2007. Survival of invasive breast cancer according to the
Nottingham Prognostic Index in cases diagnosed in 1990-1999. European
Journal of Cancer. vol 43 (10) , pp. 1548-1555. DOI:
10.1016/j.ejca.2007.01.016. Impact Factor: 5.061, Citations: 67.
Details of the impact
The area of biomarker discovery has seen significant developments over
the last 10 years and the team at NTU continues to offer unique and
leading approaches in the field. Novel non-linear approaches have been
applied to the identification of biomarkers, from complex genomic data,
addressing clinical questions such as prognosis and response to therapy.
These biomarkers have subsequently been validated using
immuno-histochemical techniques and applied in clinical practice and
Clinical collaborations with the Ian Ellis group, Nottingham University
Hospitals Trust, have identified biomarkers of proliferation and prognosis
in breast cancer (Refs 5 and 6). These have been used to re-define the
Nottingham Prognostic Index allowing more accurate prediction of prognosis
for the individual (Source to corroborate 1). This approach has been used
by multi-disciplinary teams in clinical decision making, and to evaluate
prognosis in medico-legal cases (Sources to corroborate 2, 3), and has
influenced the decisions around patient care and stratification.
Collaboration with the Steve Chan group at the Nottingham University
Hospital Trust has identified a set of core proliferation related markers
in breast cancer. The most influential marker predicts response to
Anthracycline and the set has been successfully evaluated as predictive
using immunohistochemistry. In addition the approaches developed have been
utilised in identifying markers associated with circulating miRNAs in
colorectal and breast cancer (Kerin Group, National University Ireland,
Galway), Tuberculosis (Public Health England, Porton Down and Colindale
(Source to corroborate 4), sepsis (Severnside Alliance for Translational
Research, Cardiff and Public Health England, Porton Down), ovarian cancer
(Chan Group, Nottingham University Hospital Trust), prostate cancer (Khan
Group, Leicester Royal Infirmary) and Alzheimer's Disease (Morgan Group,
University of Nottingham).
This biomarker discovery work has led to the filing of 4 patents
(application numbers cited):
- Data Analysis Method and System — PCT GB/2009/051412, EPO 09796034.8,
USA 13/125954 and China 200980143624.4
- Time to Event Data Analysis Method & System — Divisional
application US application No13/230956.
- TB Marker PCT GB2013/051635
- SPAG 5 Biomarker (Biomarker response to Anthracyclin)
In 2009 Lachesis funding and BioCity (Mobius) investment (initial funding
ca. £300,000) were secured to launch a spin out company utilising the
algorithms for biomarker discovery and patient stratification — CompanDX
Ltd (http://companDX.com)- which
exploits the IP within these patents (Source to corroborate 5). Patents 1
and 2 are currently exclusively licenced to CompanDX Ltd and the remaining
patents are currently under negotiation as a part of an exclusive IP
pipeline agreement between CompanDX and NTU. Further patents are currently
under development for pancreatic cancer, cardiovascular disease and
Alzheimer's disease. Professors Ball and Rees are founders and directors
of this company.
The company has secured significant contracts (to a value of around
£250,000) from large pharmaceutical and diagnostics companies to utilise
the bioinformatics technologies in international clinical trials in order
to identify biomarkers of response to cancer therapy (Astra Zeneca, Oxford
Biomedica, Diagenic). In these instances these approaches have impacted
upon trial design and have increased the efficiency of diagnostic
development, thus making trials and diagnostics more cost effective.
Further contracts on a fee for service model are currently in discussion.
The patents have recently resulted in an investment into CompanDX of
48million RMB (approx. £3.9M) in partnership with New Summit Biopharma Co
Ltd, a Chinese Clinical Research Organisation funded by the Shenyang
Regional Government (Sources to corroborate 5, 6). As a result of this
investment clinical trials are currently being undertaken in China to
evaluate the efficacy of diagnostics for time to event in breast cancer,
and diagnosis of tuberculosis. These diagnostic tests will be evaluated on
1000 cases. Sino Federal Drug Administration regulatory approval is
anticipated in 3 years' time. It is important as it will validate the
clinical trial in the context of regulatory approval.
Sources to corroborate the impact
- Nottingham University Hospitals Trust, Breast Cancer Pathologist. Will
corroborate the clinical impact of breast cancer biomarkers and the use
of such biomarkers within the development of the Nottingham Prognostic
- Potter Rees Ltd (serious injuries solicitors), letter of agreement
available to corroborate support given in medico legal cases where
models based on the NPI have been used to predict median survival.
- Pannone Law Group, letter of agreement available to corroborate
support given in medico legal cases where models based on the NPI have
been used to predict median survival.
- Public Health England, Principal Scientist. Will corroborate the
impact of biomarker identification in infectious diseases, including
those associated with tuberculosis.
- CompanDX, Chief Executive Officer. Will corroborate the commercial
impact of the algorithms and biomarkers, particularly projects running
— demonstrates the funding received from China for clinical work.