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SUMMARY:A mathematical framework for detecting dynamic changes in the immu
 ne repertoire of longitudinal and spatial cancer samples - Tahel Ronel (In
 stitute of Cancer Research (ICR))
DTSTART:20250918T094500Z
DTEND:20250918T095000Z
UID:TALK236089@talks.cam.ac.uk
DESCRIPTION:A patient&rsquo\;s tumour microenvironment\, and especially th
 e interaction between a patient&rsquo\;s cancer and their immune system\, 
 plays a crucial role in disease progression\, response to treatment and pr
 ognosis. This interaction should therefore be considered to meaningfully c
 apture a person&rsquo\;s disease through the creation of digital twins.\n&
 nbsp\;\nT cell receptor (TCR) sequencing data\, while providing an invalua
 ble window into cancer-immune system co-evolution from the perspective of 
 the immune system\, are notoriously difficult to model and analyse. This i
 s primarily due to the vast diversity in sample sizes between different TC
 R samples\, the massive down-sampling step from population to sample\, and
  the unique fingerprint a person&rsquo\;s TCR repertoire possesses. Howeve
 r\, addressing these issues is key to being able to create a &lsquo\;digit
 al twin&rsquo\; of a person&rsquo\;s immune response to cancer\, and centr
 al to understanding their disease.\n&nbsp\;\nTo this end\, we propose a ma
 thematical/statistical framework that is able to identify whether related 
 TCR samples\, such as those from different regions of a patient&rsquo\;s t
 umour\, or different timepoints\, are likely to have come from the same re
 pertoire (population)\, or whether we can detect significant changes betwe
 en them (such as expanding T cell clones)\, for example\, in response to t
 reatment or metastases.\n&nbsp\;\nWe have designed a mixed effects model\,
  where TCR clone size frequencies are modelled as a random effect with dif
 ferent dispersion parameters for each sample. The model tests whether ther
 e is evidence for differences in clone frequencies between samples\, beyon
 d those due to sample size.\nWe show that our model can identify inherentl
 y similar / different repertoires generated from simulated data and from a
 n experimental dataset of technical repeats\, then demonstrate its use on 
 longitudinal and spatial metastatic colorectal cancer patient data. Our mo
 del mitigates the need for steep subsampling and the associated data loss\
 , which is currently the most commonly used method for immune repertoire s
 ample comparison.\n&nbsp\;\nAs next steps\, we will build on the model to 
 further interrogate the cell dynamics that could lead to the observed clon
 e size distributions. These could include\, for example\, the growth rate 
 of clones and measurement of the influx of new clonotypes. This will ultim
 ately result in a mechanistic model of changes in the immune repertoire th
 rough these processes.
LOCATION:Seminar Room 1\, Newton Institute
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