studies suggest that these alterations converge
on inactivating NOTCH signaling in NOTCH
receptor wild-type HNSCC patients (fig. S17).
Thus, NOTCH is one of the most commonly
dysregulated pathways in HNSCC.
AsinthecaseofHNSCC,theidentification
of long tail genes that drive tumorigenesis in
other cancer types may show convergence on
specific pathways and reveal new regulators
of those pathways and could inform on cancer
biology and tumor evolution. This information
may even provide translational opportunities
and increase the number of patients who ben-
efit from a pathway-specific treatment. In ad-
dition, our work indicates that many long tail
genesfunctioninahaploinsufficient manner.
Because most algorithms used to delineate
driver from passenger mutations are based
on statistical enrichment of somatic point mu-
tations, amino acid conservation, or homozy-
gous deletions and amplification and never
take heterozygous deletions into account,
many haploinsufficient tumor suppressors
might have been overlooked. This could be
of special interest in the postgenomic era, in
whichmost,ifnotall,mutationsandcopy
number alterations have been identified, but
their functional annotation lags far behind.
In summary, this study shows the power of
integrating cancer genomics with mouse mod-
eling using in vivo CRISPR screens to uncover
tumor-suppressive pathways in the long tail of
cancer-associated mutations.
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ACKNOWLEDGMENTS
We thank all members of our laboratories for helpful comments;
Y. Q. Lu, D. Dervovic, and G. Mbamalu for assistance;
Z. Y. Lin for mass spectrometry assistance; C. Go and
J. D. R. Knight for access to the cell-map resource; The Centre
for Phenogenomics and Network Biology Collaborative Centre atLTRI;andD.Durocher,J.Wrana,L.Pelletier,R.Bremner,
J. McGlade, and J. Woodgett for critically reading the
manuscript.Funding:This work was supported by a project
grant to D.S. from the Canadian Institute of Health Research
(CIHR 365252) and the Krembil Foundation. D.S. is the
recipient of a career development award from HFSP
(CDA00080/2015). S.K.L. is the recipient of a Canadian
Cancer Society fellowship (BC-F-16#31919). A.C.G. was
supported by a Terry Fox Research Institute program grant.
Author contributions:S.K.L. performed all experiments. K.S.
performed Ajuba mouse experiments, immunohistochemistry,
and, together with K.T., helped in immunofluorescence
experiments. E.L. helped to prepare the viral library. R.T.
performed quantitative RT-PCR and CUT&RUN experiments.
R.H.O. helped with the random genes library. A.M. performed
all bioinformatic analysis. B.R. and A.-C.G. performed and
analyzed the mass spectrometry experiments. P.S.-T. helped
with the design of the mass spectrometry experiments. R.Q.
and T.J.P. performed bioinformatics analysis on human TCGA
data. D.S. coordinated the project and, together with S.K.L.,
designed the experiments and wrote the manuscript.
Competing interests:Theauthorsdeclarenocompeting
interests.Data and materials availability:All mass
spectrometry data have been deposited in the MassIVE
repository (MSV000083405 and PXD012600). Data for the
192 different BioID baits targeting the major subcellular
compartments of a human cell used to control for AJUBA
interactome specificity are available at https://humancellmap.
org. All code and the manifests used to analyze the human
HNSCC TCGA data are available under the R package
SchramekLOH v1.0.0 (https://github.com/pughlab/
SchramekLOH). All RNA-sequencing and CUT&RUN data are
available at the Gene Expression Omnibus (GEO) (GSE140495,
GSE140496, and GSE140497). All other data are available in
the main text or the supplementary materials.SUPPLEMENTARY MATERIALS
science.sciencemag.org/content/367/6483/1264/suppl/DC1
Materials and Methods
Figs. S1 to S19
Tables S1 to S7
References ( 31 – 51 )
View/request a protocol for this paper fromBio-protocol.20 February 2019; accepted 14 February 2020
10.1126/science.aax0902Loganathanet al.,Science 367 , 1264–1269 (2020) 13 March 2020 6of6
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