Cancer Cell

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Structure, Function, and Resistancein Chronic Myeloid Leukemia

Jerald Radich1,*1Fred Hutchinson Cancer Research Center, Seattle, WA 98104, USA*Correspondence: jradich@fhcrc.orghttp://dx.doi.org/10.1016/j.ccr.2014.08.010

Chronic myeloid leukemia (CML) is effectively treated by tyrosine kinase inhibitors (TKIs). Rarely, CML casesdevelop TKI resistance through acquisition of compound mutations. In this issue of Cancer Cell, Zabriskieand colleagues study how structural changes caused by compound mutations cause clinically relevantchanges in TKI sensitivity.

Chronic myeloid leukemia (CML) is a rare

clonal myeloproliferative disease, occur-

ring roughly in 2 of 100,000 persons,

but it has dramatically shaped the world

of molecular oncology and targeted ther-

apy. CML was the first disease in which

a unique chromosomal translocation,

t(9;22), the so called ‘‘Philadelphia chro-

mosome’’ (because it was discovered at

the Winstar Institute in Philadelphia), was

discovered. At the genetic level, it was es-

tablished that the translocation resulted in

the BCR-ABL chimeric gene; this unique

and universal event (because all CML

cases have the rearranged BCR-ABL)

then became a target for therapy and a

sensitive marker for disease monitoring.

The aberrant activity of the tyrosine ki-

nase ABL, when placed in the chimeric

BCR-ABL context, drives the pathogen-

esis of CML. The development of tyrosine

kinase inhibitors (TKIs) that inhibit ABL

has dramatically altered the natural his-

tory of CML. Before TKIs, the median sur-

vival of newly diagnosed CML patients

was roughly 5 years; with TKI therapy,

the 10 year survival rate is >80% (Druker

et al., 2006). Indeed, the therapy is so

effective that some patients become free

of any detectable disease, and, in some

patients, TKI therapy can be discontinued

without relapse. The evolution of treat-

ment strategies in CML in a little over a

decade is nothing short of remarkable,

and the success of TKI therapy is the

benchmark against all other targeted ther-

apy is judged.

There are several TKIs now in clinical

use for treating CML. Nonetheless, some

CML patients do become resistant to

TKI therapy (Apperley 2007). Often, TKI

resistance is caused by single amino

acid mutations in the ABL domain of the

BCR-ABL, which changes the protein

conformation to limit or exclude the TKI

binding. Some of these point mutations

have different sensitivities to different

TKIs, for example, insensitive to imatinib

but still sensitive to dasatinib. However,

the T315I mutation is resistant across a

broad range of TKIs and is only effectively

inhibited by the recently Food and Drug

Administration approved ponatinib, which

has activity against all known single amino

acid ABL mutations that are resistant to

clinical TKIs (Cortes et al., 2012; O’Hare

et al., 2009).

As the Borg will ruefully acknowledge,

resistance is not futile, and, in CML, the

strong selective pressures of ponatinib

have led to cases of resistance involving

compound mutations in ABL (Khorashad

et al., 2013; Soverini et al., 2013). These

are multiple point mutations occurring in

the same BCR-ABL allele, as opposed to

multiple clones with different mutations.

This phenomenon has been seen in other

diseases treated with targeted therapy

(Smith et al., 2013) and may become an

increasing common phenomenon as

more cancers are treated with targeted

therapy and tumors develop cleaver tac-

tics to evade selective pressure. In this

issue of Cancer Cell, Zabriskie et al.

(2014) study a series of CML cases with

compound mutations, and, in a series of

logical, thoughtful, and well-executed ex-

periments, build a solid understanding of

how the structure and function of these

mutations drive drug susceptibility and,

ultimately, patient response to therapy.

The authors first surveyed the land-

scape of compound mutations to under-

stand the patterns of mutations, because

one would expect that only certain combi-

nation ofmutations would cause sufficient

Cancer Cell 26, S

structural change in BCR-ABL to prevent

kinase inhibition by TKIs. While >100

different kinase point mutations have

been reported, only 12 positions have

been involved in the vast majority of

compound mutations. The authors then

looked at over 400 patients enrolled in a

phase 2 trial of ponatinib for patients

who were resistant or intolerant to the

‘‘second generation’’ TKIs dasatinib or ni-

lotinib or had the resistant T315I mutation.

Many of these patients had failed two

different TKIs before being in the study,

so their diseases had already passed

through a rigid test of natural selection,

thus enriching for complicated kinase

dependent or independent mechanisms

of resistance. Sixteen patients had com-

pound mutations evident at the end of

treatment (3 cases ended treatment

because of toxicity, 13 from lack of effi-

cacy of ponatinib), and, remarkably, the

compound mutations involved at least 2

of the key positions in all but one case.

To further understand the relationship

of compound mutations and sensitivity

to TKIs, the authors then performed prolif-

eration assays, comparing the growth of

Ba/F3 cells expressing ectopic wild-type

BCR-ABL to those expressing ectopic

BCR-ABL containing a spectrum of

single and compound mutations. While

ponatinib inhibited the growth of cells ex-

pressing any single mutations (including

the T315I), its effectiveness was greatly

decreased in T315I-containing com-

pound mutations. This was borne out in

the experience of patients in the clinical

trial, where patients whose CML had

baseline BCR-ABL compound mutations

involving T315I or a baseline BCR-ABL

T315I mutation and evolved a clone con-

taining an additional BCR-ABL mutation

eptember 8, 2014 ª2014 Elsevier Inc. 305

Cancer Cell

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showed clinical resistance to ponatinib.

However, 22 cases of compound muta-

tions without T315I patterns of variable

effectiveness was demonstrated across

the panel of TKIs, which suggests that

early detection of compound mutations

could be combined with sensitivity

studies to shape a therapeutic change in

TKIs and thus abort the emergence of

resistant clones.

Computer modeling suggested the

impact of compoundmutations on TKI ac-

tivity. The proliferation studies noted

above found that, for compound muta-

tions not containing T315I, ponatinib

anddasatinib had similar activities, except

with the Y253H/E55V compound muta-

tion, where dasatinib is considerably

more active. The binding sites of ponatinib

and dasatinib are known to be different;

can modeling explain the difference?

Indeed, it can; molecular dynamic simula-

tions showed that Y253H and E255V mu-

tations force a shift in the P loop of the

ABL kinase domain, obstructing the pona-

tinib binding site. Similar simulations sug-

306 Cancer Cell 26, September 8, 2014 ª201

gested poor ponatinib activity in clinically

relevant disease evolution, such as thedif-

ference in the binding domains of a single

T315I and the resistant T315I/E255V

mutation. Thus, the authors elegantly fol-

lowed the interplay of structure, in vitro

and in vivo function.

Why is this study important? First, it is a

demonstration of how clinical material,

wet bench work, and computer modeling

can be melded to develop a clear under-

standing of clinically important biology.

Second, it provides a clear roadmap of

how future studies can be performed to

understand disease resistance. As ‘‘tar-

geted therapy’’ becomes an increasing

reality in cancer care, it will become

increasingly important to understand

and anticipate how Darwinian selection

will select for resistance. This manuscript

helps prepare us for that future.

REFERENCES

Apperley, J.F. (2007). Lancet Oncol. 8, 1018–1029.

4 Elsevier Inc.

Cortes, J.E., Kantarjian, H., Shah, N.P., Bixby, D.,Mauro, M.J., Flinn, I., O’Hare, T., Hu, S., Narasim-han, N.I., Rivera, V.M., et al. (2012). N. Engl. J.Med.367, 2075–2088.

Druker, B.J., Guilhot, F., O’Brien, S.G., Gathmann,I., Kantarjian, H., Gattermann, N., Deininger, M.W.,Silver, R.T., Goldman, J.M., Stone, R.M., et al.; IRISInvestigators (2006). N. Engl. J. Med. 355, 2408–2417.

Khorashad, J.S., Kelley, T.W., Szankasi, P., Ma-son, C.C., Soverini, S., Adrian, L.T., Eide, C.A.,Zabriskie, M.S., Lange, T., Estrada, J.C., et al.(2013). Blood 121, 489–498.

O’Hare, T., Shakespeare, W.C., Zhu, X., Eide, C.A.,Rivera, V.M., Wang, F., Adrian, L.T., Zhou, T.,Huang, W.S., Xu, Q., et al. (2009). Cancer Cell 16,401–412.

Smith, C.C., Lasater, E.A., Zhu, X., Lin, K.C., Stew-art, W.K., Damon, L.E., Salerno, S., and Shah, N.P.(2013). Blood 121, 3165–3171.

Soverini, S., De Benedittis, C., Machova Pola-kova, K., Brouckova, A., Horner, D., Iacono, M.,Castagnetti, F., Gugliotta, G., Palandri, F., Pa-payannidis, C., et al. (2013). Blood 122, 1634–1648.

Zabriskie, M.S., Eide, C.A., Tantravahi, S.K., Vel-lore, N.A., Estrada, J., Nicolini, F.E., Khoury, H.J.,Larson, R.A., Konopleva, M., Cortes, J.E., et al.(2014). Cancer Cell.

DAISY: Picking Synthetic Lethalsfrom Cancer Genomes

Colm J. Ryan,1 Christopher J. Lord,2 and Alan Ashworth2,*1Systems Biology Ireland, Conway Institute, University College Dublin, Dublin 4, Ireland2The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London SW3 6JB, UK*Correspondence: alana@icr.ac.ukhttp://dx.doi.org/10.1016/j.ccr.2014.08.008

A better understanding of genetic interactions in cancer might help identify new therapeutic approaches thatexploit the concept of synthetic lethality. Ruppin and colleagues have developed a new computationalmethod, DAISY, that predicts such interactions and potentially facilitates the delineation and validation ofcomprehensive genetic interaction networks.

Most of the major recent advances in the

development of targeted therapies for

cancer have originated from the identifica-

tion and exploitation of genetic depen-

dencies that operate specifically in tumor

cells. Many of these dependencies are

considered as oncogene addiction ef-

fects, where tumor cells become reliant

upon the activity of key driver genes

such as BCR-ABL and EGFR; pharma-

cological inhibition of these drivers has

therapeutic benefit. Other dependencies

include those that arise as a consequence

of the ‘‘cancer state,’’ also called nonon-

cogene addictions (Luo et al., 2009). How-

ever, inmany cases these gene addictions

are difficult to target directly with drugs.

Moreover, there aremany recessive driver

mutations in tumor genomes, the so-

called tumor suppressors, in which absent

or reduced gene function contributes to

tumorigenesis. Synthetic lethality (SL)

provides a potential approach to targeting

these latter two classes of alterations. The

term describes the relationship between

two genes whereby individual defects in

either gene are compatible with cell

viability, but the synthesis or combination

of gene defects results in cell death (Ash-

worth et al., 2011). Recently the