pancreatic model. Combination therapy en-
hanced the antitumor effects compared with
either agent alone. (Fig. 3D).
Collectively, these data reveal SIM as a
common mechanism facilitating the evolution
of human cancer and are broadly in agree-
ment with recent observations in colorectal
cancer ( 28 ). As with error-prone and faithful
DNA polymerases, the advantage conferred
by SIM in a metazoan context is not clear
and may simply be an evolutionary remnant.
MTOR-mediated SIM slows replication and
fosters genomic instability by impairing accu-
rate DNA repair, thereby enhancing genetic
diversity and facilitating resistance to ther-
apy ( 29 ) (Fig. 4A). Both suppression of MTOR
signaling and DNA repair also directly confer
an intrinsic fitness penalty by inhibition of
growth signaling and by increasing the ac-
cumulation of deleterious mutations, respec-
tively. Rather than being a simple process, our
data suggest that drug resistance may be the
compound result of two independent but re-
lated mechanisms. Initially, net fitness balances
the intrinsic fitness penalty of MTOR-mediated
SIM with the generation of resistant genotypes
undergoing extreme selection. Subsequently,
normalization of SIM and fixation of stably
resistant genomic configurations establish a
new adaptive equilibrium (Fig. 4B). From a clin-
ical perspective, our findings may explain the
observation that agents targeting MTOR or
the upstream phosphoinositide 3-kinase path-
way have greater effects on objective responses
or progression-free survival than on overall sur-
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ACKNOWLEDGMENTS
We thank K. Simpson, P. Madhamshettiwar, and J. Luu for
providing the shRNA library and for QC analysis; G. Mir Arnau
for assistance with RNA and DNA sequencing; C.L. Chan for
assistance with the preparation of DNA libraries; W. Hughes
for assistance with confocal microscopy; O. Martin and
H. Reza Bigdeli for assistance with quantification of DNA damage;
L. Lara-Gonzalez, N. Thio, and M. Zethoven for assistance with
bioinformatics; and L. Caldon, G. Rancati, and D. Bowtell for their
critical reading of the manuscript.Funding:This research was
supported by the Australian National Health and Medical Research
Council (NHMRC project grant no. 1088353 to D.M.T., A.C., and
D.L.G.) and by the Girgensohn Foundation (A.C.). In vivo studies
were supported by NHMRC 1162860, NHMRC 1162556, and Cancer
Australia 1143699 project grants (M.P.). J.B. was supported by
the Stafford Fox Centenary Fellowship in Bioinformatics and
Computational Biology of Rare Cancers.Author contributions:A.C.
and D.M.T. conceived and supervised the study, wrote the
manuscript, and acquired research funding; A.C., M.J.M., A.T.P.,
S.R.J., U.N., and A.G.R. performed the experiments and analyzed the
data; D.L.G., S.K., and J.B. performed bioinformatics analyses.
D.G.G, N.M.C., G.V.L., M.P, and J.-Y.B. provided the clinical samples;
A.C.V. and M.R.Q. analyzed the clinical samples; P.L. developed the
JCountPro software.Competing interests:D.M.T. is a paid
consultant and received research support from Amgen, Eisai,
Pfizer, Roche, Astra Zeneca, Novartis, and Bayer. G.V.L. is a paid
consultant for Aduro Biotech Inc., Pierre-Fabre Medicament,
Bristol-Myers Squibb, Amgen Inc., Merck Sharp & Dohme,
Novartis, Array Biopharma Pty Ltd., and Sandos. The other
authors declare no competing interests associated with this work.
Data and materials availability:The plasmids pLV-mCherry,
psPAX2, pCAG-VSVG, and pPB-DEST-53BP1trunc, are available from
AddGene under a material transfer agreement. The pCMV-hyPBase
plasmid is available upon request to the Sanger Institute Archives
(http://www.sanger.ac.uk/technology/clonerequests/). DNA-
sequencing data have been deposited in the NCBI’s BioProject
database (ID: PRJNA623123). RNA-sequencing data have been
deposited in NCBI’s Gene Expression Omnibus (accession nos.
GSE148342 and GSE148344). Computer codes are available at
GitHub (https://github.com/dlgoode/Cipponi_Science_2020).SUPPLEMENTARY MATERIALS
science.sciencemag.org/content/368/6495/1127/suppl/DC1
Materials and Methods
Figs. S1 to S27
Tables S1 to S9
References ( 31 – 72 )
View/request a protocol for this paper fromBio-protocol.11 September 2018; resubmitted 9 November 2019
Accepted 10 April 2020
10.1126/science.aau8768Cipponiet al.,Science 368 , 1127–1131 (2020) 5 June 2020 5of5
Fig. 4. Conserved mechanisms underpinning SIM in human cancer.(A) Selection by cytostatic
anticancer therapies leads to increased levels of DNA damage and impaired mTOR signaling, providing
genetic diversity and accelerating adaptation to anticancer treatments. (B) During the initial phase of
adaptation, the fitness landscape is a dynamic balance between the deleterious effect of intrinsic selection
and the beneficial effect conferred by resistant genomic configurations. Fit genotypes are progressively
stabilized during the second equilibrium phase.
RESEARCH | REPORT