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Biology subjects

Teh, C. E.

Publications and source records attributed to Teh, C. E..

2 recordsLinked to original sources

Early immune pressure imposed by tissue resident memory T cells sculpts tumour evolution in non-small cell lung cancer

Tissue-resident memory T cells (TRM) provide immune defence against local infection and can inhibit cancer progression. However, it is unclear to what extent chronic inflammation impacts TRM activation and how the immune pressure exerted by TRM affects developing tumours in humans. We performed deep profiling of lung cancers arising in never-smokers (NS) and ever-smokers (ES), finding evidence of enhanced TRM immunosurveillance in ES lung. Only tumours arising in ES patients underwent clonal immune escape, even when evaluating cancers with similar tumour mutational burden to NS patients, suggesting that the timing of immune pressure exerted by TRM is a critical factor in the evolution of tumour immune evasion. Tumours grown in T cell quiescent NS lungs displayed little evidence of immune evasion and had fewer neoantigens with low diversity, paradoxically making them amenable to treatment with agonist of the costimulatory molecule, ICOS. These data demonstrate local environmental insults enhance TRM immunosurveillance of human tissue, shape the evolution of tumour immunogenicity and that this interplay informs effective immunotherapeutic modalities.

cancer biology

CytofRUV: Removing unwanted variation to integrate multiple CyTOF datasets

Mass cytometry (CyTOF) is a technology that has revolutionised single cell biology. One illuminating application of CyTOF has been in understanding the mechanisms of blood cancer resistance to therapy. Longitudinal studies of clinical cohorts during drug treatment provide a deeper understanding of the molecular changes that underlie sensitivity or resistance to treatment in each patient. However, understanding the biological impact of a cancer drug in such studies necessitates the integration of multiple CyTOF batches. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches which includes an R-Shiny application with diagnostics plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.

bioinformatics