Integrating complex and diverse data to better characterise risk and disease phenotypes in asymptomatic patients with Aortic Stenosis

Supervisors:

• Dr Anvesha Singh  as707@le.ac.uk
• Dr Emer Brady
• Dr Colleen Maxwell

Project Description:

Aortic stenosis (AS) is the commonest valve disorder requiring intervention in the developed world, with an increasing prevalence as the population ages. There is no medical therapy of proven benefit. Surgical or transcatheter aortic valve replacement (AVR), the only available treatment, is recommended once symptoms or left ventricular (LV) dysfunction develop. The pressure overload exerted by AS leads to adverse LV remodelling and myocardial fibrosis, driven by inflammation and apoptosis, which are key determinants of heart failure and poor outcome in AS1, 2. Studies using cardiac magnetic resonance (CMR) imaging have shown that focal fibrosis is present in up to 50% of those with asymptomatic AS3, progresses rapidly4, remains irreversible at 1 year after AVR5, and is an independent predictor of mortality in the long run even after AVR6. However, both disease progression and the remodelling response to AS are highly variable.

There is therefore a need to: (i) risk stratify those with asymptomatic severe AS, to identify those with imminent symptoms or adverse cardiac remodelling, who would benefit from earlier intervention, and (ii) identify novel therapeutic targets to ameliorate adverse remodelling early in AS. Improving risk stratification in asymptomatic AS is an important area of unmet clinical need. Identification of novel circulating biomarkers has the potential to provide mechanistic insights into adverse remodelling/disease progression/symptom onset, improve risk stratification and identify potential therapeutic targets for future research. A ‘multi-marker, multi-modal approach’ may be the best approach in this heterogenous disease with complex pathophysiology7. With the advent of novel plasma analysis techniques and bioinformatics tools, there is an opportunity to integrate multi-modal data and identify novel biomarker signatures associated with remodelling progression and adverse outcomes in early AS, identify distinct disease endotypes to target early treatment to those at highest risk, as well as identify novel therapeutic targets for future research. This PhD proposal aims to do just that.

The aim of this project is to identify phenotypic clusters within our cohort of asymptomatic patients with AS using cutting-edge bioinformatic tools including graph technology and relationship analytics to combine proteomics, imaging, demographic and clinical data. This is an exciting opportunity to develop unique research skills in ‘big data’ and ‘deep phenotyping’ by combining precision ‘omics’ with state-of-the-art imaging. The student will benefit from a strong multi-disciplinary team of supervisors with a strong track record in nurturing the careers of outstanding scientists who aspire to cure heart disease.

We have recently been awarded a BHF Project Grant (PG_23_11577) to clinically validate a proteomic panel based on an initial pilot agnostic discovery proteomics study of asymptomatic patients with moderate to severe AS, using mass spectrometry. The discovery work was performed on a subset of stored plasma samples from the ‘PRIMID-AS’ study, which was a prospective, multi-centre, observational study with extensively phenotyped asymptomatic patients with moderate to severe AS8, and has identified an initial panel of 22 biomarkers. 

In this project, the student will aim to utilise advanced bioinformatics tools to extract data, then integrate and model our multidimensional dataset to unmask distinct endotypes within the AS cohorts and obtain new knowledge through meaningful relationships and/or clusters across the patient group. Under the supervision of a multi-disciplinary group of supervisors, the student will work alongside the post-doctoral research associate on the above-mentioned Project Grant to gain an in-depth understanding of the proteomics pipeline and perform an automated, high-throughput agnostic discovery proteomics experiment using shotgun mass spectrometry in stored plasma. Having performed an agnostic proteomics discovery study, the student will develop skills in data integration and advanced bioinformatic techniques. In addition to the proteomics work, the student will work with the cardiac imaging group to gain insight and understanding of clinical and imaging data. The multi-modal models will be validated in external cohorts, along with integration of the final validated biomarkers from the 22-marker panel mentioned above.

References: 

1. Hein, S.;  Arnon, E.;  Kostin, S.;  Schonburg, M.;  Elsasser, A.;  Polyakova, V.;  Bauer, E. P.;  Klovekorn, W. P.; Schaper, J., Progression from compensated hypertrophy to failure in the pressure-overloaded human heart: structural deterioration and compensatory mechanisms. Circulation 2003, 107 (7), 984-91.
2. Dweck, M. R.;  Joshi, S.;  Murigu, T.;  Alpendurada, F.;  Jabbour, A.;  Melina, G.;  Banya, W.;  Gulati, A.;  Roussin, I.;  Raza, S.;  Prasad, N. A.;  Wage, R.;  Quarto, C.;  Angeloni, E.;  Refice, S.;  Sheppard, M.;  Cook, S. A.;  Kilner, P. J.;  Pennell, D. J.;  Newby, D. E.;  Mohiaddin, R. H.;  Pepper, J.; Prasad, S. K., Midwall fibrosis is an independent predictor of mortality in patients with aortic stenosis. Journal of the American College of Cardiology 2011, 58 (12), 1271-9.
3. Balciunaite, G.;  Skorniakov, V.;  Rimkus, A.;  Zaremba, T.;  Palionis, D.;  Valeviciene, N.;  Aidietis, A.;  Serpytis, P.;  Rucinskas, K.;  Sogaard, P.; Glaveckaite, S., Prevalence and prognostic value of late gadolinium enhancement on CMR in aortic stenosis: meta-analysis. Eur Radiol 2020, 30 (1), 640-651.
4. Singh, A.;  Chan, D. C. S.;  Kanagala, P.;  Hogrefe, K.;  Kelly, D. J.;  Khoo, J. P.;  Sprigings, D.;  Greenwood, J. P.;  Abdelaty, A.;  Jerosch-Herold, M.;  Ng, L. L.; McCann, G. P., Short-term adverse remodeling progression in asymptomatic aortic stenosis. Eur Radiol 2020.
5. Treibel, T. A.;  Kozor, R.;  Schofield, R.;  Benedetti, G.;  Fontana, M.;  Bhuva, A. N.;  Sheikh, A.;  Lopez, B.;  Gonzalez, A.;  Manisty, C.;  Lloyd, G.;  Kellman, P.;  Diez, J.; Moon, J. C., Reverse Myocardial Remodeling Following Valve Replacement in Patients With Aortic Stenosis. Journal of the American College of Cardiology 2018, 71 (8), 860-871.
6. Musa, T. A.;  Treibel, T. A.;  Vassiliou, V. S.;  Captur, G.;  Singh, A.;  Chin, C.;  Dobson, L. E.;  Pica, S.;  Loudon, M.;  Malley, T.;  Rigolli, M.;  Foley, J. R. J.;  Bijsterveld, P.;  Law, G. R.;  Dweck, M. R.;  Myerson, S. G.;  McCann, G. P.;  Prasad, S. K.;  Moon, J. C.; Greenwood, J. P., Myocardial Scar and Mortality in Severe Aortic Stenosis. Circulation 2018, 138 (18), 1935-1947.
7. Redfors, B.;  Furer, A.;  Lindman, B. R.;  Burkhoff, D.;  Marquis-Gravel, G.;  Francese, D. P.;  Ben-Yehuda, O.;  Pibarot, P.;  Gillam, L. D.;  Leon, M. B.; Genereux, P., Biomarkers in Aortic Stenosis: A Systematic Review. Structural Heart 2017, 1 (1-2), 18-30.
8. Singh, A.;  Greenwood, J. P.;  Berry, C.;  Dawson, D. K.;  Hogrefe, K.;  Kelly, D. J.;  Dhakshinamurthy, V.;  Lang, C. C.;  Khoo, J. P.;  Sprigings, D.;  Steeds, R. P.;  Jerosch-Herold, M.;  Neubauer, S.;  Prendergast, B.;  Williams, B.;  Zhang, R.;  Hudson, I.;  Squire, I. B.;  Ford, I.;  Samani, N. J.; McCann, G. P., Comparison of exercise testing and CMR measured myocardial perfusion reserve for predicting outcome in asymptomatic aortic stenosis: the PRognostic Importance of MIcrovascular Dysfunction in Aortic Stenosis (PRIMID AS) Study. European heart journal 2017, 38 (16), 1222-1229.