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SUMMARY:Functional studies of genetic variation using precision genome edi
 ting - Dr Francisco J Sanchez-Rivera\, Koch Institute at MIT
DTSTART:20240205T123000Z
DTEND:20240205T133000Z
UID:TALK211414@talks.cam.ac.uk
CONTACT:Kate Davenport
DESCRIPTION:Functional studies of genetic variation using precision genome
  editing \nMany human diseases have a strong association with diverse type
 s of genetic alterations. These diseases \ninclude cancer\, in which tumor
  genomes often harbor a complex spectrum of single-nucleotide alterations 
 \nand chromosomal rearrangements that can perturb gene function in ways th
 at remain poorly understood. \nSome cancer-associated genes exhibit a trem
 endous degree of mutational heterogeneity\, which may \nimpact disease ini
 tiation\, progression\, and therapy responses. For example\, TP53\, the mo
 st frequently \nmutated gene in cancer\, shows extensive allelic variation
  that leads to the generation of altered proteins that \ncan produce funct
 ionally distinct phenotypes. Whether distinct variants of TP53 and other g
 enes encode \nproteins with loss-of-function\, gain-of-function\, or other
 wise neomorphic phenotypes remains both \ncontroversial and technically ch
 allenging to assess\, particularly at the endogenous level. \nPrecision ge
 nome editing technologies like base editing and prime editing are uniquely
  suited to tackle this \nproblem. Nevertheless\, deploying these methods f
 or systematic variant-function studies and disease \nmodeling in vivo has 
 not been straightforward due to lack of robust and scalable platforms capa
 ble of \nassessing editing efficiency and precision\, particularly at endo
 genous loci. With this goal in mind\, we \npreviously developed and applie
 d high-throughput base editing ‘sensor’ approaches that link endogenou
 s \ngenome editing outcomes with synthetic DNA-based readouts and cellular
  fitness measurements \n(PMID: 35165384). Using these approaches\, we foun
 d that several previously uncharacterized mutant p53 \nalleles are bona fi
 de drivers of cancer cell proliferation and in vivo tumor development. \nB
 uilding upon this work\, we recently developed new prime editing guide RNA
  design tools and sensorbased approaches that similarly couple quantitati
 ve editing outcomes to cellular fitness\, allowing us to \nsignificantly e
 xpand the breadth and complexity of cancer-associated mutations that can b
 e interrogated \nusing these technologies. We used this strategy to screen
  the largest collection of endogenous cancerassociated TP53 variants asse
 mbled to date\, identifying both known and novel alleles that impact p53 \
 nfunction in mechanistically diverse ways. Intriguingly\, we find that cer
 tain types of endogenous TP53\nvariants\, particularly those in the p53 ol
 igomerization domain\, display opposite phenotypes in exogenous \ngene ove
 rexpression systems. These include disease-relevant variants found in huma
 ns with cancer \npredisposition syndromes that encode altered proteins wit
 h unique molecular properties. \nOur results emphasize the physiological i
 mportance of gene dosage in shaping native protein stoichiometry \nand pro
 tein-protein interactions\, highlight the limitations of using exogenous o
 verexpression systems to \ninterpret pathogenic alleles\, and establish a 
 computational and experimental framework for studying \ndiverse types of g
 enetic variants in their endogenous context\, providing insight into varia
 nt-function \nrelationships that could be leveraged to develop more precis
 e therapies.
LOCATION:CRUK CI Lecture Theatre
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