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THE OFFICIAL JOURNAL OF MEDITERRANEAN
MULTIDISCIPLINARY ONCOLOGY FORUM
APRIL 2012
Issue 1 n Volume 2
Molecular Oncology:
Basic - Translational - Clinical/Hypothesis
Somatic KRAS mutations and resistance to EGFR-targeted therapies:
expanding potential?
Murray S, Linardou H, Bafaloukos D, Kosmidis P, Papadimitriou CA, Siannis F
Pages 11-16
Murray S, et al. MOJ 2012:1;11-16.
Expanding potential of KRAS 01/02
Somatic KRAS mutations and resistance to EGFR-targeted
therapies: expanding potential?
Murray S1, Linardou H2, Bafaloukos D2, Kosmidis P3, Papadimitriou CA4, Siannis F5
Department of Molecular Oncology, GeneKor SA, Athens, Greece, 21st Department of Medical Oncology, Metropolitan
Hospital, Athens, Greece, 32nd Department of Medical Oncology, Hygeia Hospital, Athens, Greece, 4Department
of Clinical Therapeutics, University of Athens School of Medicine, Alexandra Hospital, Athens, Greece, 5Department
of Mathematics, University of Athens, Greece; and MRC Biostatistics Unit, Cambridge, UK
1
Received 29 February 2012; accepted 31 March 2012
Abstract
The tantalising possibility of personalised medicine has recently opened its door to several cancer types, and will
not look back. Rapid developments were foreseen with respect to obtaining improved responses and outcomes to
anti-epidermal growth factor receptor (EGFR) targeted strategies following the identification of the first biomarker
capable of predicting the likelihood of a lack of response to these agents; somatic KRAS mutations. Extrapolation
of the intrinsic nature of this constitutive molecular aberration in predicting an absence of response to anti-EGFR
targeted monoclonal antibodies (cetuximab and panitumumab) in colorectal cancer and to the anti-EGFR tyrosine
kinase inhibitors (TKIs: gefitinib and erlotinib) in non-small-cell lung cancer (NSCLC) has recently been demonstrated.
In doing so we questioned the rationale behind, on the one hand, the adoption of a negative predictor of response
in colorectal cancer capable of yielding improved survival times following patient stratification, and on the other,
the apparent indifference to what could be considered a similar situation in NSCLC. Herein, we update the relevant
sensitivity and specificity of somatic KRAS mutational status in predicting the absence of response to TKIs in NSCLC
discussing the pros and cons of its foreseen clinical implementation in NSCLC. Given the virtual complete nonresponsiveness of anti-EGFR agents in the presence of KRAS mutations it appears unlikely that additional data will
alter their specificity regarding response. However, this does not appear to be the situation with regard to their effect
on solid outcomes of survival. Although several studies have included biomarker analysis including KRAS and EGFR
(mutual exclusivity), there is insufficiently detailed data available from several studies to sufficiently address the effect
of KRAS stratification on outcomes. Bearing in mind that there is a trend towards a poorer outcome in KRAS positive
cases, extractability of EGFR and KRAS mutation positive cases is clouding the true effect of KRAS. Therein, it appears
timely that additional analyses to determine the effect of stratification according to KRAS status on survival outcomes
are conducted. MOJ 2012, 1:11-16
Key words: Resistance, predictive, prognostic, personalized medicine, biomarker, EGFR, KRAS.
Hypothesis
Therapies targeting the epidermal growth factor receptor
(EGFR), both in the form of monoclonal antibodies, such
as cetuximab (Erbitux®; Imclone LLC, New York, NY) and
panitumumab (Vectibix®; Immunex Corporation, Thousand
Oaks, CA); and small molecule inhibitors of the receptor,
such as erlotinib (Tarceva®; OSI Pharmaceuticals, New York,
NY) and gefitinib (Iressa®; AstraZeneca, Macclesfield, UK),
are increasingly being implemented in the treatment of solid
tumours, including lung and colorectal cancer [1]. Given
the universally low response rates and moderate survival
benefits observed when EGFR-targeted therapies are used
in unselected patients, there is great interest regarding the
identification of molecular markers for the selection of
patients that will obtain benefit from their use.
With the current understanding that there are numerous
*Corresponding author: Samuel Murray PhD, Lead Oncology Biomarkers
and R&D, GeneKor SA, 52 Spaton Ave, Gerakas, 15433, Athens, Greece.
Tel.: +30 210 6032138, Fax: +30 210 6032148, E-mail: [email protected],
[email protected]
MOJ 2012, 1:11-16
molecular aberrations present within the EGFR signalling
pathway there has been an intense effort to correlate specific
aberrations according to response and patient outcome in a
variety of tumour types [1]. Through such efforts, and using
our growing understanding of the mechanisms of EGFR
signalling, it is anticipated that molecular predictors of both
response and resistance will be more rapidly identified and
implemented into routine clinical practice. Recently there
has been rapid progress along these lines. Our ability to
identify metastatic colorectal cancer populations with an
enhanced probability of response to anti-EGFR monoclonal
antibodies has become possible following the demonstration
that KRAS mutations correlated with unequivocal nonresponsiveness (lack of response) and that this was linked
to improvements in survival outcomes [2, 3, 4].
Using a similar approach, we sought to investigate KRAS
mutations as predictors of resistance to small molecule EGFR
kinase inhibitors in non-small-cell lung cancer (NSCLC),
a malignancy accounting for more than 160,000 deaths
annually in the USA alone [5]. Insights regarding predictors
of response to TKIs have generated much enthusiasm;
11
Expanding potential of KRAS
however, little emphasis has been placed on the role of
negative predictors of response such as KRAS even following
the routine clinical implementation of KRAS based patient
stratification in colorectal cancer [6, 7].
Although the incidence of KRAS mutations in NSCLC
is only half that of colorectal cancer, the updated analysis
presented herein (Tables I, II and III) indicate the consistency
of the specificity of KRAS mutations for predicting a lack of
response (non-responsiveness) to TKIs in NSCLC (LR+: 40.65;
LH-: 0.82; Sensitivity: 0.19 (95% CI: 0.15 - 0.23); Specificity:
0.99 (95% CI: 0.95 - 1.00)) that occur in approximately
20% of NSCLC cases [8]. The likelihood ratio for a patient
with a KRAS mutation not obtaining a response (SD+PD)
is approximately 40 times more than the same patient
presenting with a response (CR+PR) to an anti-EGFR TKI.
The diagnostic or predictive odds ratio (POR) for this is also
high at approximately 50. More importantly, these analyses
show that the specificity of the biomarker in NSCLC is
essentially superior to that in colorectal cancer, supporting
the biological mechanisms underlying the signalling cascade
and the potential extrapolation of hypothesis-generating
data across cancer types. After all, isn’t what is good for the
goose good for the gander?
Our predictive biomarker analysis is based on response
data alone, which does not always correlate with survival
results. However, in an analysis (including 191 trials)
conducted by Johnson KR, et al., the authors indicated
that in NSCLC a predictive difference… ‘in response of
18% for 750 patients, 21% for 500 patients, and 30% for
250 patients’… would correlate with survival benefits.
Similarly they calculated that for time to progression… ‘the
incremental gain needed to predict a survival improvement
was a median of 1.8 months for trials with 750 patients, 2.2
months for 500 patients, and 3.3 months for 250 patients’…;
with both of these outcomes matching what is observed
for at least EGFR mutation positive cases in NSCLC [9].
Additional meta-analytical approaches addressing the
correlation between response and outcome have generated
clear evidence that response rate differences observed for
both somatic KRAS and EGFR mutation positive patients
should relate to significant differences in their respective
survival outcomes [10, 11, 12]. In fact, in examining the
high response rates to TKIs in EGFR mutant cases we
demonstrated a clearly significant likelihood of response
to TKIs compared to wild-type (not taking into account
KRAS status), and have also shown that this high response
rate translates into improvements in PFS/TTP and OS, as
predicted by Johnson KR, et al. [13, 14]. With respect to
KRAS however, the almost absolute lack of responses to
EGFR-targeted therapies in KRAS mutated lung cases has
not sufficiently been explored. Considering data indicating
a survival advantage in KRAS wild type patients compared
to KRAS mutation carriers in metastatic colorectal cancer
[3, 4], could it also be anticipated that a similar observation
will become evident in NSCLC?
The intrinsic caveat in analysing NSCLC is the mutually
exclusive nature of somatic EGFR mutations, and the
recent licensing approval of anti-EGFR TKIs for patients
solely with EGFR mutations [6, 15]. Fortuitously, EGFR
and KRAS mutations are mutually exclusive events [6, 15],
while in colorectal cancer there is no apparent contradictory
biomarker (for in NSCLC EGFR correlates with response
and KRAS with a lack of response) [7]. To date no direct
survival benefit has been supported as yet by individual phase
III data for TKIs versus chemotherapy in EGFR mutant
NSCLC patients, however, as mentioned above several levels
of evidence indicate that patients with EGFR mutations have
a significantly improved response rates compared to wild
type patients that translate into significant improvements
in PFS/TTP and overall survival [13, 14, 16]. Trial design is
partially responsible for a lack of direct comparisons. However,
it has become apparent that the principle underlying issue
that has not been properly addressed is that NSCLC needs
to be stratified into three populations, those with EGFR
mutations (responders to TKIs), those with KRAS mutations
(non-responders to TKIs) and the remaining population
(essentially uncharacterised responses).
We believe that a more concerted effort should be made
to understand not only the nature of the lack of response of
KRAS mutation positive individuals but to also understand
their response and outcome(s) to conventional therapies
as speculated by recent data from the TAILOR study [17].
The authors therein indicate in a subgroup analysis that
KRAS mutation positive patients had an inferior overall
survival (HR=1.42, 95% CI: 1.055-1.94, p=0.02) compared
to wild type patients (with EGFR mutation positive cases
previously excluded) when treated with chemotherapy. One
additional reason for conducting three way stratification
Table I - Likelihood of non-response
Total Patient No.
(Study No.)
1957
(n=26)
KRASRR (%)RR (%)
+LR
-LR
(%)Pre-screeningPost-screening
335
388/1957
384/1622
40.65 0.82
(17.1)
(19.8)
(23.7)
Sensitivity
(95% CI)
0.19
(0.15 - 0.23)
Specificity
(95% CI)
0.99
(0.95 - 1.00)
Response rates and likelihood ratios for stratification according to KRAS mutational status. Cumulative (and updated sensitivity and specificity analysis)
data for: Total No., eligible population according to meta-analysis [3]; KRAS, number of patients with KRAS mutations; RR (%), Pre-screening, response
rate (complete response + partial response) prior to patient stratification according to KRAS mutational status; RR (%), Post-screening, response rate in
KRAS wild type population. +LR, positive likelihood ratio; -LR, negative likelihood ratio; Sensitivity and specificity [3].
12
MOJ 2012, 1:11-16
Molecular Oncology: Basic - Translational - Clinical/Hypothesis 01/02
relates to data from BR-21, a study that demonstrated and
led to the licensing approval of Tarceva® for second line
NSCLC patients irrespective of their molecular genotype
[18]. In this study, EGFR mutations did not correlate with
outcomes, however, more than half of them were uncommon
mutations for which no clear evidence exists even in relation
to response. Furthermore, if the remaining wild type group
(after extraction of KRAS patients) do obtain clinical benefit
this is important information that needs to be addressed
as previously indicated. Assuming that first line NSCLC
patients are offered a TKI on the basis of their EGFR status
there is no current good practice to reflex test (i.e. re-stratify)
the remaining population by a secondary biomarker.
Furthermore, if there were a further method of stratifying
patients following extraction of cases with EGFR mutations
based on their KRAS mutational status and possible reflex
application of novel predictors such as Veridex® [19, 20] for
anti-EGFR TKIs, a larger pool of NSCLC patients could gain
benefit from anti-EGFR TKIs yet in a more defined format.
Of course there has been long discussion and there is clear
evidence regarding the positive predictive ability of increased
EGFR GCN (gene copy number) and outcomes to TKIs,
so evidently there are still some remaining issues for this
approach and how best to implement it into clinical practice
[16, 21, 22]. The question is ‘‘what do we need?’’, and ‘‘what
is an appropriate ethical approach?’’ Surely non-responsive
patients should be offered an alternative treatment? If this
is the case then KRAS mutation positive individuals could
easily be identified and steered away from receiving antiEGFR TKIs in second and subsequent lines of treatment. The
decision on how best to stratify the remaining patients to
a TKI in these settings (and possibly in the first line setting
also) remains an open question.
Bearing this in mind, we stress the argument for reflex
testing for KRAS in NSCLC for several compelling reasons
that are not limited to: continuation or implementation
of a treatment strategy that offers no obvious clinical
improvement to the individual based upon our current
approach of monitoring treatment effectiveness according to
RECIST; lack of response is a more easily definable measure
than response (this could be obscured by SD and such an
analysis would greatly assist in our appraisal of KRAS); and
that KRAS is more than likely a poor prognostic marker in
NSCLC [23]. The data that we present herein (Table III)
Table II – Effect of patient stratification based on somatic KRAS mutational analysis in NSCLC.
StudyPatient No.
Amann JM, et al. [23]
40
Cappuzzo F, et al. [24]
37
Douillard JY, et al. [25]
114
Endoh H, et al. [26]
52
Felip E, et al. [27]
39
Fujimoto N, et al. [28]
42
Han SW, et al. [29]
69
Hirsch FR, et al. [30]
93
Hirsch FR, et al. [31]
138
Hirsch FR, et al. [32]
69
Hurbin A, et al. [33]
34
Ichihara S, et al. [34]
98
Jackman DM, et al. [35]
175
Lara-Guera H, et al. [36]
33
Ludovini V, et al. [37]
162
Massarelli E, et al. [38]
70
Pao W, et al. [39]
24
Sartoti G, et al. [40]
154
Sasaki H, et al. [41]
27
Schneider RP, et al. [42]
89
Varella-Garcia M, et al. [43]
30
Wang S, et al. [44]
120
Wu CC, et al. [45]
53
Zander T, et al. [46]
28
Zhu CQ, et al. [47]
118
Zucali PA, et al. [21]
49
Cumulative
1957
Number KRAS
(%)
9 (22.5)
1 (2.7)
20 (17.5)
6 (11.5)
7 (18.0)
7 (16.7)
9 (13.0)
6 (6.5)
36 (26.1)
10 (14.5)
6 (17.6)
8 (8.2)
41 (23.4)
6 (18.2)
11 (6.8)
16 (22.9)
5 (20.8)
56 (36.4)
1 (3.7)
11 (12.4)
4 (13.3)
19 (15.8)
1 (1.9)
4 (14.3)
20 (17.0)
15 (30.6)
335/1957 (17.1%)
OR all patients OR wild type patients Percentage improvement
(%)
(%)
OR (%)
1/40 (2.5)
1/31 (3.2)
28.0
18/37 (48.7)
18/36 (50.0)
2.7
9/114 (7.9)
9/94 (9.6)
21.5
24/52 (46.2)
24/46 (52.2)
13.0
3/39 (7.8)
3/32 (9.4)
20.5
4/42 (9.5)
4/35 (11.4)
20.0
16/69 (23.2)
16/60 (26.7)
15.1
7/93 (7.5)
7/87 (8.1)
8.0
16/138 (11.6)
14/102 (13.7)
18.1
11/69 (15.9)
11/59 (18.6)
17.0
12/34 (35.3)
12/28 (42.9)
21.5
25/98 (25.5)
25/90 (27.7)
8.6
36/175 (20.6)
36/134 (27.0)
31.1
4/33 (12.1)
4/27 (14.8)
22.3
54/162 (33.3)
54/151 (35.8)
7.5
5/70 (7.1)
5/54 (9.3)
31.0
12/24 (50.0)
12/19 (63.2)
26.4
29/154 (18.8)
29/98 (29.6)
57.4
9/27 (33.3)
9/26 (34.6)
3.9
7/89 (7.1)
7/78 (9.0)
26.8
14/30 (46.7)
14/26 (53.9)
15.4
31/120 (25.8)
30/101 (29.7)
15.1
24/53 (45.3)
24/52 (46.2)
2.0
3/28 (10.7)
3/24 (12.5)
16.8
11/118 (9.3)
10/98 (10.2)
9.7
3/49 (6.1)
3/34 (8.8)
44.3
388/1957 (19.8%)
384/1622 (23.7%)
19.7%
Response rates pre- and post- stratification of populations based on KRAS mutational status. OR = complete + partial response rate. Cumulative data is
presented.
MOJ 2012, 1:11-16
13
Expanding potential of KRAS
indicates that with respect to response, only 4/335 (1.2%)
KRAS cases responded, and this compared to 384/1522
(23.7%) for the remaining to single agent anti-EGFR TKI.
Note: in a subgroup analysis of KRAS, EGFR and remaining
patients the response rates were 0.9%, 68.8% and 8.9%
respectively (data not shown). Such data clearly indicates
that apart from EGFR mutation positive patients responding,
only a minority of the remaining population demonstrates
responses in the order of 10%; and most importantly these
are not the KRAS mutation positive group.
In assessing the effect of KRAS mutational status on
survival outcomes the data sets become more complex, Table
IV. There are in the first instance only a limited number of
studies, some of which are biased by cross-over. If we limit
any analysis to mutation versus no-mutation (independent
of interaction) the current data sets suggest a high degree
of heterogeneity with respect to survival outcomes, in fact
the data from Zucali PA, et al. suggest superior survival
for KRAS mutant cases [24]. Although there appears to be
a trend towards a negative impact on PFS in the presence
of KRAS mutations, no study was specifically designed to
address the question of the prognostic (and/or predictive)
nature of KRAS mutations, and furthermore, understanding
that EGFR mutation positive cases are in the non-KRASmutation group adds another level of complexity. According
Table III - Definition of true positive for likelihood
of non-response to TKIs in KRAS mutant NSCLC.
Gold Standard
No ResponseResponse
(SD+PD)
(CR+PR)
+
New test
KRAS Mut (+)
TP
FP
KRAS WT (-)
FN
TN
True Positive (TP) represents the Gold Standard, in this case lack of
response (CR+PR) for a patient harboring a KRAS mutation, and True
Negative (TN) represents a KRAS wild type patient obtaining a response.
Therein the False Positive (FP) is a patient with a KRAS mutation
responding, and a patient with no response (the Truth) without a KRAS
mutation (wild type) is the False Negative (FN).
to the analysis of by Johnson KR, et al. [9], it appears that all
current studies were underpowered to address the effect of
KRAS on survival rendering this a topic of continued debate.
Be that as it may, the meta-analytical data regarding
response have major implications on therapeutic decision
making, health-economic/cost effectiveness issues, and
provide further insight into a biomarker capable of sparing
otherwise ineffective and arguably expensive drugs. The
negative predictive nature of KRAS mutations, if implemented
appropriately into clinical practice, could potentially stratify
approximately 20% of patients affected by lung cancer,
Table IV – Survival outcomes according to KRAS and EGFR mutational status.
Amann JM, et al. [23]
Douillard JY, et al. [25]
Endoh H, et al. [26]
Felip E, et al. [27]
Han SW, et al. [29]
Jackman DM, et al. [35]
Massarelli E, et al. [38]
Schneider RP, et al. [42]
Varella-Garcia M, et al. [43]
Wang S, et al. [44]
Zhu CQ, et al. [47]
Zucali PA, et al. [21]
GenotypePatient No.PFS
OS
WT
32
2.2 m
7.3 m
P=0.91
p=0.95
KRAS
9
1.8 m
5.6 m
WT
104
2.6 m
7,5 m
KRAS
22
1.4 m
7.8 m
WT
71
HR : 2.542 [1.408-6.168] KRAS
7p=0.039 (Favors WT)
WT
32
HR: 1.058 (Favors WT)
HR: 1.238 (Favors WT)
KRAS
7
WT
60
13.8 m
KRAS
7
2.3 m
#
WT
130
14.0 m
#
KRAS
41
11.0 m
WT
54
2.9 m
9.4 m
P=0.62
P=0.0025
KRAS
16
1.7 m
5.0 m
WT
78
HR: 1.56 [0.92-2.65]
HR: 1.64 [0.97–2.80]
P= 0.064
KRAS
11
p=0.094 (Favors WT)
#
WT
37
146 days
NR
P=0.0248
P=0.4156
#400 days
KRAS
4
87 days
WT
90
8.1 m
20.6 m
P=0.006
P=0.151
KRAS
30
2.7 m
13.3 m
WT
107 7.5 m [5.4-10.7]
KRAS
11 3.7 m [1.9-7.9]
WT
34
2.8 m
5.6 m
P=0.164
P=0.95
KRAS
15
4.9 m
7.3 m
Manuscripts not included were either: not extractable; included only one event for a particular biomarker; had a total patient number <20; or represented
data sets included in larger patient series or longer follow-up. #, Manually extracted data; NR, not reached; m, months; for PFS and OS, the 95%
confidence intervals are indicated in the brackets [].
14
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Molecular Oncology: Basic - Translational - Clinical/Hypothesis 01/02
towards alternative treatments. Coupling this with an
additional 15% EGFR mutation positive and another 3-5%
ALK positive [25], suggests that a large proportion of NSCLC
cases can be directed to some form of personalised therapy.
In conclusion, somatic KRAS mutational analysis for
the purposes of patient stratification is clearly justified in
light of its high specificity for predicting lack of response
to TKIs. Although the incidence of mutations is relatively
low, its specificity as a resistance biomarker remains close
to 100%, indicating that the potential cost savings from
basing treatment decisions on mutational analysis, i.e.
limiting TKI use to EGFR positive and KRAS wild type
patients, would still outweigh the cost of performing the
analysis in the combined approach of reflex testing for
both biomarkers. More rigorous investigation aimed at
unravelling the prognostic and predictive nature of KRAS
mutations in general appears timely.
Research Methodology
The information for this review was obtained by searching the
PubMed and MEDLINE databases for articles published until
31st August 2011 (last search 17/09/2011). Electronic early-release
publications were also included. We searched journals known to
publish information relevant to our topic and cross-referenced
the reference lists of recovered articles. We did not impose
language restrictions. Search terms included: “non-small-cell
lung cancer”; “lung cancer”, “epidermal growth factor receptor”,
“EGFR”, “erlotinib”, “tarceva”, “gefitinib”, “iressa”, “mutation”,
“amplification”, “KRAS”, “RAS”. Cell line and other in-vitro data
have been used for mechanistic descriptions; however, precedence
has been given to clinical evidence. Data was processed as per the
somatic mutations-EGFR database format [51]. Additional field
analysis and data extraction was performed in a similar format
for the new fields included in this review. Due to the possibility
of republication of data sets, several additional fields were also
extracted (year of sample acquisition, hospital and source of
samples, ethnicity and full author lists) to select only the largest
or most comprehensive (full data field availability) data sets.
Observations conveyed to the authors by personal communication
and unpublished observations were also included. We also
contacted experts in the field to broaden our yield of potentially
eligible articles. Studies published exclusively in abstract form
were not considered to be included (they were considered open
to subsequent modification).
Conflict of Interest
Consultant or Advisory role: Dr S. Murray, Merck
KGaA, Darmstadt, Germany. Merck distribute the MoAb
Cetuximab (ERBITUX™).
No other author has declared a conflict of interest.
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