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Research overview

Dr Sunil Iyengar, Lymphoma Biological and Imaging Team

I have a keen interest in patient focused translational lymphoma research and have always aimed to develop my research questions from a clinical challenge or unmet need for patients. During my PhD I investigated the role of PI3 kinase isoforms in mantle cell lymphoma to explain the resistance to PI3 kinase delta inhibition seen in this disease.

The main observations from this research were that the PI3K p110 alpha isoform was overexpressed with relapsed disease in patient samples and this was the likely explanation for the short lived responses seen in clinical trials with PI3K delta inhibition in relapsed/refractory MCL. This upregulation of PI3K alpha was associated with persistent downstream Akt phosphorylation in MCL cell lines despite PI3K delta inhibition. These observations were published in Blood and I received a research medal from the Royal College of Pathologist's for this work. 

I plan to continue exploring mechanisms of resistance in mantle cell lymphoma to some of the novel therapies such as Ibrutinib and Venetoclax.

In addition, I have three other key areas of research interest in lymphoid malignancy:

  1. Rare lymphoid leukaemias such as T-PLL, LGL leukaemia and hairy cell leukaemia are diseases where there is relatively little understanding of reasons for failure of conventional treatment. This is an area currently being studied by my MD student Matthew Cross.
  2. Unclassifiable indolent B-cell lymphoid leukaemias/lymphomas pose a challenge to clinicians. Over the last 3 years we have systematically collected tumour and germline samples on over 70 patients with these cancers with matched clinical and immunophenotypic information. Next generation sequencing is being performed on these samples to understand these diseases better with an aim to improving their management.
  3. Lymphoma imaging and its interpretation has become critical in optimal management of patients and yet the quality of imaging reports is highly variable due to lack of time and education among radiologists. Along with my radiology colleagues and technology companies, I aim to develop artificial intelligence algorithms with an aim to assist radiologists and improve the quality and accuracy of lymphoma reporting.


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Research overview