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“The Landscape of the Heritable Cancer Genome
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strong>
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Giovanni Stracqu
adanio is an UKRI EPSRC fellow\, Senior Lecturer (Associate Professor) in
Synthetic Biology and co-director of the Edinburgh Genome Foundry (EGF). H
e group is interested in understanding the molecular mechanisms underpinni
ng complex phenotypes and diseases using two of the most disruptive techno
logies available: synthetic biology and machine learning. His long-term go
al is to reverse-engineer biological systems to develop generative algorit
hms to design\, build and test biological agents for addressing healthcare
problems\, such as rare metabolic diseases and cancer\, and industrial bi
otechnology challenges\, like de-novo enzyme engineering.
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Dr Strac
quadanio obtained a PhD in Informatics from the University of Catania (Ita
ly) in 2010\, working on global optimisation methods for protein structure
prediction and metabolic engineering. He then received postdoctoral train
ing in synthetic biology in Joel Bader and Jef Boeke labs at the Johns Hop
kins University working on the synthetic yeast genome.
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Dr Stracqua
danio was a main contributor to the Synthetic Yeast (Sc2.0) genome project
\, pioneering algorithms and developing software at the foundation of the
first synthetic eukaryotic genome. He has also developed tools used in lar
ge-scale synthesis projects\, streamlining chromosomes engineering and the
assembly of biological pathways.
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In 2014\, he moved to the Ludwig
Institute for Cancer Research at the University of Oxford UK to work on c
ancer genetics\, in Gareth Bond’s lab\; here\, he focused on studying how
high-frequency inherited p53 mutations affect the risk of cancer and respo
nse to treatment using statistical genetics methods and cell line models.<
/p>\n
In 2016\, prior to joining the University of Edinburgh\, Dr Stracq
uadanio was an assistant professor at the School of Computer Science and E
lectronic Engineering of the University of Essex\, where he received the W
ellcome Trust Seed Award in Science to study metabolic switching in renal
cell carcinoma.
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In 2021\, he was awarded the UKRI EPSRC fellowship
to design and manufacture enzyme replacement therapies for Fabry’s diseas
e using generative deep learning and yeast. He also collaborates with indu
strial stakeholders in the biotechnology field\, such as Fujifilm Diosynth
\, on protein design\, codon optimisation and lab automation.
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Dr Stracquadanio has authored more than 40 research articles publish
ed in international peer-reviewed journals\, including Science\, Nature Re
v. Cancer\, Cancer Research and PNAS. He also serves as Associate Editor f
or BMC Genomics and as reviewer and panel member for EPSRC\, BBSRC\, MRC a
nd FLF. Since 2021\, he is also a member of the EPSRC Peer Review Associat
e College.
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To join the live event please reque
st the link by emailing: icm@jhu.edu<
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Recording
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\n Abstract
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“The Lan
dscape of the Heritable Cancer Genome”
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p>\n
Genome-wide association studies (GWAS) have found hundreds of singl
e nucleotide polymorphisms (SNPs) associated with increased risk of cancer
. However\, the amount of heritable risk explained by SNPs is limited\, le
aving most of cancer heritability unexplained. Tumor sequencing projects h
ave shown that causal mutations are enriched in genic regions. We hypothes
ized that SNPs located in protein coding genes and nearby regulatory regio
ns could explain a significant proportion of the heritable risk of cancer.
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To perform gene-level heritability analysis\, we developed a new
method\, called Bayesian Gene HERitability Analysis (BAGHERA)\, to estimat
e the heritability explained by all genotyped SNPs and by those located in
genic regions using GWAS summary statistics [1]. BAGHERA-based analysis o
f 38 cancers reported in the UK Biobank showed that SNPs explain at least
10% of the heritable risk for 14 of them\, including late onset malignanci
es. We then identified 1\,146 genes\, called cancer heritability genes (CH
Gs)\, explaining a significant proportion of cancer heritability. CHGs wer
e involved in hallmark processes controlling the transformation from norma
l to cancerous cells. Importantly\, 60 of them also harbored somatic drive
r mutations\, and 27 are tumor suppressors. Evidence for a causal role of
CHGs was shown in testicular cancer\, where a cluster of SNPs in the KITLG
CHG is responsible for p53-mediated responses to genotoxic therapies [2].
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Our results suggest that germline and somatic mutation informatio
n could be exploited to identify subgroups of individuals at higher risk o
f cancer in the broader population and could prove useful to establish str
ategies for early detection and cancer surveillance.
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Recordin
g
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