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Page 278 Cancer Drug Resist 2018;1:266-302 I http://dx.doi.org/10.20517/cdr.2018.18

Conclusion: Our data demonstrate that BAP1 controls BRCA1 expression through regulating its protein
stability. Bap1 status is a major determinant of BRCA1 expression and in efficacy microtubule poisons such
as vinorelbine, a hypothesis that we will test in our randomised Medusa and VIM trial.

24. Investigating resistance mechanisms to CB-839 in RCC using a genome-wide CRISPR/
Cas9 approach

Aleksandra Raczka

Medical and Biological Sciences Building, University of St Andrews, North Haugh, St Andrews, Fife KY16
9TF, UK

Renal cell carcinoma (RCC) affects 1 in 82 men and 1 in 57 women, and accounts for about 3% of all can-
cer deaths worldwide. It is characterised by significant tumour heterogeneity and inherent (in 25%-30%
cases) or acquired resistance to available chemotherapeutics. Most patients who initially respond to treat-
ment develop resistance within 6-15 months. Glutamine addiction is a potential new therapeutic target for
kidney cancers. These cancer cells use glutamine rather than glucose to meet their enhanced bioenerget-
ics demands to sustain their rapid growth. They are very often deficient in functional von Hippel Lindau
(VHL) protein, which leads to abnormal accumulation of several proteins involved in angiogenesis and cell
division. VHL-/- tumours have been recently shown not only to rely on glutamine for energy generation
and maintenance of redox homeostasis, but also for de novo pyrimidine synthesis. CB-839 is a small, orally
administered reversible inhibitor of human kidney-type glutaminase (GLS). GLS converts glutamate to
glutamine and is upregulated in glutamine-addicted cancers, such as RCC and triple negative breast cancer
(TNBC). CB-839 is currently in clinical trials as a monotherapy and also as part of a combination therapy,
and shows promising results. Given the significant incidence of resistance to previously approved therapies,
we are applying a genome-wide CRISPR/Cas9 approach to identify candidate genes, which when knocked
down will confer resistance to CB-839. We are screening the genome-scale GeCKOv2 sgRNA library, pre-
viously used to identify genes involved in resistance to vemurafenib in melanoma cells. Since most RCCs
are VHL-/-, we are using 786-O cells as a model of RCC where CB-839 inhibits their growth. This screen
should identify candidate genes mediating resistance to CB-839 that could be useful as biomarkers or as
part of rational drug combinations.

25. Multivariate variable selection for integrative analysis of cancer drug data

Jian Zhang, Elaheh Oftadeh

School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury CT2 7FS, UK

Cancer drugs exert their function through binding to one or more protein targets (Wang et al., 2014). Early
“one gene, one drug, one cancer” paradigm considers the role of individual genes and their changes in
drug-perturbed states, which largely ignore a target’s cellular and physiological context. Meanwhile, can-
cer gene-centric methods largely ignore the multi-factor-driven attribute of cancer diseases at the cellular
level. With the generation of rich data resources for genome-wide gene expressions and drug- and cancer-
induced perturbations, we propose an integrative analysis of these data to provide systematic insights
into mechanisms of drugs and cancers in a “multiple genes, multiple drugs, multiple types of cancers”
paradigm. The proposed method, called principal variable analysis, aims at selecting predictor variables
with relatively higher coefficient variations in a multivariate regression model. The basic premise behind
the proposal is to scan through a predictor variable space with a series of forward filters named null-