Molecular profiling of advanced breast cancer tumors is beneficial in assisting clinical treatment plans

Abstract

We used data obtained by Caris Life Sciences, to evaluate the benefits of tailoring treatments for a breast carcinoma cohort by using tumor molecular profiles to inform decisions. Data for 92 breast cancer patients from the commercial Caris Molecular Intelligence database was retrospectively divided into two groups, so that the first always followed treatment recommendations, whereas in the second group all patients received at least one drug after profiling that was predicted to lack benefit. The biomarker and drug associations were based on tests including fluorescent in situ hybridization and DNA sequencing, although immunohistochemistry was the main test used.

Patients whose drugs matched those recommended according to their tumor profile had an average overall survival of 667 days, compared to 510 days for patients that did not (P=0.0316). In the matched treatment group, 26% of patients were deceased by the last time of monitoring, whereas this was 41% in the unmatched group (P=0.1257). We therefore confirm the ability of tumor molecular profiling to improve survival of breast cancer patients. Immunohistochemistry biomarkers for the androgen, estrogen and progesterone receptors were found to be prognostic for survival.

Full text

Oncotarget1
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Molecular profiling of advanced breast cancer tumors is beneficial
in assisting clinical treatment plans
Philip Carter1, Costi Alifrangis2, Biancastella Cereser1, Pramodh Chandrasinghe1,3,
Lisa Del Bel Belluz1, Nina Moderau1, Fotini Poyia1, Lee S. Schwartzberg4, Neha
Tabassum1, Jinrui Wen1, Jonathan Krell1 and Justin Stebbing1
1Department of Surgery and Cancer, Imperial College, London, UK
2Department of Oncology, University College Hospital, London, UK
3Department of Surgery, University of Kelaniya, Kelaniya, Sri Lanka
4WEST Cancer Center, The University of Tennessee, Memphis, USA
Correspondence to: Philip Carter, email: phil.carter@imperial.ac.uk
Keywords: tumor profiling; breast cancer; cancer treatment
Received: July 29, 2017 Accepted: October 28, 2017 Published: February 24, 2018
Copyright: Carter et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0
(CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source
are credited.
ABSTRACT
We used data obtained by Caris Life Sciences, to evaluate the benefits of tailoring
treatments for a breast carcinoma cohort by using tumor molecular profiles to inform
decisions. Data for 92 breast cancer patients from the commercial Caris Molecular
Intelligence database was retrospectively divided into two groups, so that the first
always followed treatment recommendations, whereas in the second group all
patients received at least one drug after profiling that was predicted to lack benefit.
The biomarker and drug associations were based on tests including fluorescent in
situ hybridization and DNA sequencing, although immunohistochemistry was the main
test used.
Patients whose drugs matched those recommended according to their tumor
profile had an average overall survival of 667 days, compared to 510 days for patients
that did not (P=0.0316). In the matched treatment group, 26% of patients were
deceased by the last time of monitoring, whereas this was 41% in the unmatched
group (P=0.1257). We therefore confirm the ability of tumor molecular profiling to
improve survival of breast cancer patients. Immunohistochemistry biomarkers for
the androgen, estrogen and progesterone receptors were found to be prognostic for
survival.
INTRODUCTION
Breast cancer is the most prevalent form of cancer
in women, causing approximately one in four of all cases
worldwide. In 2012, there were 1.68 million diagnoses and
522,000 deaths according to the World Health Organization,
and around 80% of cases occur within patients over the age
of 50. Risk factors include obesity, lack of exercise, alcohol
consumption, age, family history and age at menarche. The
long-term outcome for patients depends on the stage of the
tumor and its characteristics at diagnosis.
Established evidence-based treatments for advanced
disease includes radiation, chemotherapy, hormonal
therapy, and targeted therapies. Due to the application
of these treatments, in the developed world survival is
relatively high, with between 80% and 90% of those in
England and the USA surviving for at least five years.
Hereditary genetic factors are thought to play
a minor role in sporadic breast carcinoma, but in
approximately 5% of cases it is significant. Germline
mutations in the genes BRCA1, BRCA2, p53, PTEN,
STK11, CHEK2, ATM, BRIP1 and PALB2 are all
considered important in breast cancer tumorigenesis.
The genetics of sporadic breast cancer is now better
understood, due to genomic sequencing of many such
tumors [1]. Somatic driver variants and the mutational
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processes underlying them have now been identified [2],
and the sequencing of 560 breast cancer genomes [3] has
furthered progression towards a complete description of
the molecular events that cause these tumors. In total, 93
protein-coding cancer genes were found to have probable
driver mutations. This and other data including exon
sequencing [4], whole genome [5], transcriptional [6], and
methylation-based studies [7] have been used to develop a
molecular taxonomy of breast cancer.
Molecularly defined characteristics have been
used as predictive and prognostic biomarkers in breast
cancer to define therapeutic approaches. The earliest such
example is the identification of the estrogen receptor
(ER) overexpression in a subset of breast carcinomas in
the 1970s, and its subsequent targeting with ER directed
therapies [8]. Similarly HER2 (human epidermal growth
factor receptor 2) overexpression and its targeting with
Herceptin [9] have further defined a subset of this disease
that behaves and responds uniquely to HER2-directed
therapies. Gene chip technologies that use gene expression
profiling of the primary tumor such as OncotypeDX, have
been FDA approved as a decision aid in early breast cancer
to help define prognostic features [10]. Several preclinical
studies have identified drug-genome interactions [11].
These have been borne through with great successes in
specific situations, such as the EML4-ALK translocation in
non-small cell carcinoma of the lung. These specific tissue
and gene scenarios have been validated in prospective
clinical studies, and have transformed clinical practice [12].
An approach that has gained traction in recent years
is the application of molecular characterization beyond
established immunohistochemistry (IHC) biomarkers.
This has been used to guide therapeutic decision making
across many tissue types, after failure of standard
therapies. Genomic sequencing of the cancer [13] enables
identification of somatic driver variants, which have
Table 1: A summary of patient information comparing the matched and unmatched groups against all patients
overall
Patient & Tumor Information
Group Age Ethnicity Histology Grade Stage Survival (days) Mortality
All patients
(92) 57
White: 71
Black/African
American: 13
Asian: 4
Hawaiian/Pacific
Islander: 2
American Indian/
Alaskan Native: 1
Other/Unknown: 1
Infiltrating duct carcinoma, NOS: 50
Infiltrating ductular carcinoma: 11
Carcinoma, NOS: 10
Lobular carcinoma, NOS: 5
Adenocarcinoma, NOS: 4
Infiltrating lobular carcinoma, NOS: 3
Metaplastic carcinoma, NOS: 2
Ductal carcinoma, NOS: 2
Intraductal papillary adenocarcinoma with
invasion: 1
Infiltrating duct and lobular carcinoma: 1
Infiltrating duct mixed with other types of
carcinoma, in situ: 1
Intraductal papillary-mucinous carcinoma,
invasive: 1
Infiltrating lobular mixed with other types of
carcinoma: 1
Grade 3/
Poorly
differentiated:
41 (45%)
Grade 2 /
Moderately
differentiated:
44 (48%)
Grade 1 / Well
differentiated:
2 (2%)
Unknown / Not
determined: 4
(4%)
None / Not
applicable: 1
(1%)
IV: 18 (19%)
III no IIIC: 21 (23%)
IIIC: 9 (10%)
II: 31 (34%)
I: 10 (11%)
Unknown: 3 (3%)
583 34%
Matched
only (43) 55.8
White: 34
Asian: 3
Black/African
American: 3
American Indian/
Alaskan Native: 1
Other/Unknown: 1
Hawaiian/Pacific
Islander: 1
Infiltrating duct carcinoma, NOS: 21
Carcinoma, NOS: 8
Infiltrating ductular carcinoma: 5
Adenocarcinoma, NOS: 2
Infiltrating lobular carcinoma, NOS: 2
Lobular carcinoma, NOS: 2
Ductal carcinoma, NOS: 1
Metaplastic carcinoma, NOS: 1
Infiltrating duct and lobular carcinoma: 1
Grade 3/
Poorly
differentiated:
15 (35%)
Grade 2 /
Moderately
differentiated:
28 (65%)
II: 13 (30%)
III no IIIC: 10 (23%)
IV: 10 (23%)
IIIC: 4 (10%)
I: 3 (7%)
Unknown: 3 (7%)
667 26%
Unmatched
(49) 58.1
White: 37
Black/African American:
10
Asian: 1
Hawaiian/Pacific
Islander: 1
Infiltrating duct carcinoma, NOS: 29
Infiltrating ductular carcinoma: 6
Lobular carcinoma, NOS: 3
Adenocarcinoma, NOS: 2
Carcinoma, NOS: 2
Ductal carcinoma, NOS: 1
Infiltrating lobular carcinoma, NOS: 1
Infiltrating duct mixed with other types of
carcinoma, in situ: 1
Intraductal papillary adenocarcinoma with
invasion: 1
Metaplastic carcinoma, NOS: 1
Infiltrating lobular mixed with other types of
carcinoma: 1
Intraductal papillary-mucinous carcinoma,
invasive: 1
Grade 3/
Poorly
differentiated:
26 (53%)
Grade 2 /
Moderately
differentiated:
16 (33%)
Grade 1 / Well
differentiated:
2 (4%)
Unknown / Not
determined: 4
(8%)
None / Not
applicable: 1
(2%)
IV: 8 (16%)
III no IIIC: 11 (23%)
IIIC: 5 (10%)
II: 18 (37%)
I: 7 (14%)
510 41%

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been associated with therapeutic outcomes in preclinical
or clinical studies [14]. The efficacy of this approach is
currently unclear across tumor types, as some mutations
are only known to be prognostic for response in particular
situations; some early attempts at matching therapies
failed for this reason, e.g. the use of BRAF inhibitors in
BRAF mutant colorectal cancer [15].
It has been shown that tumor profiling of non-
responsive breast cancer resulted in better clinical
treatments [14], while other studies have demonstrated
the benefit of profiling in other tumor types [16]. To
investigate the effectiveness of one such profiling method,
we evaluated data provided by Caris Life Sciences
from their CODE database (version 1.0). This resource
describes molecular profiling data that has been used to
recommend clinical treatments, and drug regimens used
before and after clinicians received this information along
with their outcomes. The impact of profiling on drug usage
and survival was evaluated here.
RESULTS
Patient characteristics
Data from the Caris CODE database of 92 advanced
stage breast cancer patients who underwent treatment was
analyzed. These patients were retrospectively divided
into two groups, based on their matching of treatments
to recommendations that had been generated according to
their profiles. In the matched treatment group, 43 patients
received at least one recommended drug after collection of
tumor sample for profiling and none that were predicted
to lack benefit, whereas in the unmatched treatment
group 49 patients were given one or more drugs that were
classified as having a lack of benefit at any time following
profiling. Information about the patients in both groups is
summarized in Table 1 (age, ethnicity, histology, tumor
grade and stage, and survival information).
Treatment analysis
Do patients whose treatments consistently follow
profile-based recommendations fare better than patients
whose treatments that do not? To compare the overall
survival of the two groups that will be referred to as
matched and unmatched, waterfall plots for both are shown
in Figure 1, where each bar represents a treatment schedule
for a breast cancer patient. The 92 bars shown denote 43
matched and 49 unmatched patients (on the left and right
respectively). Each set is ordered by survival time following
profiling, so that from left to right in the plots patients are
displayed as their post-profiling survival time increases.
Green lines indicate administration of drugs predicted to
be of benefit (and therefore more prevalent in the matched
group), red lines are drugs that have a lack of benefit, and
yellow corresponds to times when both of these types of
drug were received by the patient. The recommendations
from Caris are mostly based on the literature.
Table 2 shows the drugs most frequently given to all
patients compared to the matched and unmatched groups.
Figure 1: On the left (darker grey background) - treatment regimens followed by 43 matched patients, in ascending
post-profiling survival time; on the right (lighter grey background) - 49 unmatched patients ordered by post-profiling
survival time. Each column represents one patient. The y-axis is time (days) where zero is the time of profiling. Dark grey within a
column shows the total time monitored from diagnosis to either death or last follow-up; a black line at the top of a column indicates death;
green bars represents time on a drug of benefit; red is a lack of benefit drug; yellow is time on a combination therapy associated with both
benefit and lack of benefit. Blue bars represent time on a neutral therapy associated with neither benefit nor lack of benefit.

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The number of patients treated with a drug is shown in
the first column, and the number of continuous treatment
periods is shown in all other columns i.e. treatments of
the same patient with intervening periods are counted
separately. The drugs given to the most number of patients
were cyclophosphamide (70 patients), doxorubicin
hydrochloride (58) and docetaxel (56). Overall the most
commonly administered drugs were cyclophosphamide
(given for 76 time periods), doxorubicin hydrochloride
(61), and docetaxel (58). In the matched group docetaxel
was given more often than doxorubicin hydrochloride,
although cyclophosphamide was still given most
often. However, in the matched group after profiling,
cyclophosphamide, doxorubicin hydrochloride, paclitaxel
and trastuzumab were given less frequently.
On average patients received 5.8 drug treatments.
Of these, 40% (2.3 drugs) were predicted to be of
benefit, 19% (1.1 drugs) lacked benefit, and 41% (2.4)
being neither. Matched patients on average had 5.6 drug
treatments – 49% (2.7 drugs) of these were profiled to be
of benefit, 6% (0.4) lacked benefit, and 45% (2.5) being
neither. Unmatched patients received an average of 5.9
drug treatments; 33% (1.9 drugs) of these were of benefit,
30% (1.8) lacked benefit, and 38% (2.2) neither.
In the unmatched set, 76% of patients received at
least one drug treatment predicted to be of benefit, and
49% received two or more drug treatments of this type.
The most commonly given drugs of benefit
were letrozole (28), doxorubicin hydrochloride (22),
trastuzumab (22), and docetaxel (21). The most
commonly given lack of benefit drugs were doxorubicin
hydrochloride (32), trastuzumab (11) and docetaxel
(11). Some of the administered drugs did not have a
recommendation for or against, and appear in the “neither”
category. This neither class makes up 45% of drugs
administered in the matched cohort versus 38% in the
unmatched cohort. The most common agent by far in the
neither category was cyclophosphamide (given for 73 time
periods, i.e. 13% of all drug treatments for this cohort).
As might be expected, some of the drugs that were
most commonly used were administered at similar rates
whether or not they were predicted to be of benefit to
Table 2: Most frequently given drug treatments in the matched and unmatched groups, compared with all patients,
and the most popular drugs overall that were predicted to be of benefit, lacking benefit, or neither of these
Number of
Patients Treated Most Frequently Administered Drugs (Total Treatment Periods)
All Patients
Treated
All Patients –
Treatment Periods
Matched Only
Patients, All
Treatments
Matched,
After Profiling
Treatments Only
Unmatched
Patients, All
Treatments
Unmatched,
After Profiling
Treatments Only
Drugs Predicted of
Benefit
Drugs Predicted to
Lack Benefit
Drugs with No
Prediction (Neither
of Benefit or Lack
of Benefit)
cyclophosphamide
– 70 patients
cyclophosphamide
(76)
cyclophosphamide
(32)
letrozole; docetaxel
(11)
cyclophosphamide
(44) docetaxel (13) letrozole (28) doxorubicin
hydrochloride (32)
cyclophosphamide
(73)
doxorubicin
hydrochloride – 58
patients
doxorubicin
hydrochloride (61) docetaxel (29) - doxorubicin
hydrochloride (36) letrozole (11)
doxorubicin
hydrochloride;
trastuzumab (22)
trastuzumab;
docetaxel (11) docetaxel (24)
docetaxel – 56
patients docetaxel (58) doxorubicin
hydrochloride (25)
carboplatin;
capecitabine (7) docetaxel (29) gemcitabine
hydrochloride (10) - - paclitaxel (18)
carboplatin – 32
patients carboplatin (36) carboplatin (20) - trastuzumab (22) capecitabine (9) docetaxel (21) carboplatin (10) capecitabine (13)
letrozole – 31
patients trastuzumab (35) paclitaxel (17)
exemestane;
gemcitabine
hydrochloride (6)
letrozole (17) anastrozole (8) tamoxifen citrate
(18) capecitabine (6) carboplatin (12)
paclitaxel – 29
patients
letrozole; paclitaxel
(32) letrozole (15) -
capecitabine;
carboplatin;
gemcitabine
hydrochloride (16)
cyclophosphamide
(6) anastrozole (17) gemcitabine
hydrochloride (5)
gemcitabine
hydrochloride (9)
capecitabine – 27
patients - trastuzumab (13) nab-paclitaxel (5) -
methotrexate;
doxorubicin
hydrochloride (5)
carboplatin (13) methotrexate (5) fulvestrant (8)
gemcitabine
hydrochloride;
trastuzumab – 22
patients
capecitabine;
gemcitabine
hydrochloride (27)
gemcitabine
hydrochloride;
capecitabine; nab-
paclitaxel (11)
cyclophosphamide;
tamoxifen citrate;
anastrozole (4)
- - gemcitabine
hydrochloride (12) nab-paclitaxel (4) nab-paclitaxel (8)
- - - - paclitaxel (15)
carboplatin;
fluorouracil;
vinorelbine tartrate
(4)
exemestane (9)
anastrozole;
pegylated liposomal
doxorubicin
hydrochloride;
paclitaxel (3)
bevacizumab;
vinorelbine tartrate
(7)
anastrozole;
nab-paclitaxel;
tamoxifen citrate –
19 patients
anastrozole; nab-
paclitaxel (21) - - anastrozole (11) - fluorouracil (8) - -
Most commonly given drugs are listed in descending order going down, with the total number of treatments shown in parentheses.

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Figure 2: Differences between matched and unmatched groups in biomarker statuses, survival, demographics and
tumour grade. Left: Comparison of biomarkers between matched and unmatched groups; positive ratio represents the percentage of the
cases that have “positive” biomarker results. Specifically, for IHC, positive is defined as protein expression being above a predetermined
threshold. For sequencing biomarkers, positive is defined as a gene mutation (usually pathogenic). The size of the circle indicates the
number of cases. Top-right: A Kaplan-Meier curve showing the increase in overall survival from time of profiling for those patients treated
only with therapies predicted to be of benefit by their molecular profile, compared to those patients who received at least one therapy
predicted to lack benefit. Middle-right and lower-right: Comparison of age of patients, survival time, treatment numbers, grade of samples,
between matched and unmatched. Blue denotes matched patients and red is unmatched patients in all plots.
Figure 3: Volcano plot of biomarkers’ prognostic value for a Caris breast cancer dataset. Biomarkers of significance that
can be used to indicate differences in survival are found in a cluster on the top right – these are the immunohistochemistry androgen receptor
(AR), estrogen receptor (ER) and progesterone receptor (PR) markers. Color code: green = the hazard rate of a positive biomarker result
is significantly lower than that of a negative biomarker result; grey = the difference between a positive biomarker result and a negative
biomarker result is not significant.

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the patient. However, 31% of the times that trastuzumab
was given it was expected to lack benefit. Doxorubicin
hydrochloride was only given 38% of the time when it
was thought to be beneficial, while 55% of the time it
was predicted to lack benefit. Letrozole was used when
profiled to be of help 88% of the time. We note that
palbociclib was not used in combination with letrozole in
any of the cases in the breast cohort studied here, but most
of these cases were prior to the FDA approval of this drug.
Tamoxifen citrate was prescribed 95% of the time when
it was expected to be favorable, and anastrozole was also
well matched, being given 85% of the time that it was
predicted to be of value.
Survival analysis
Patients in the matched group on average survived
for 667 days after the day of profiling, compared to 510
days for patients whose treatments did not match their
molecular profile (P=0.0316); this is an increase of 31%.
In the matched group 26% of patients were deceased
by the end of the time of monitoring compared to 41% of
the unmatched group of patients (P=0.1257).
Patients who received more than one drug in the
lack-of-benefit category trended towards worse overall
survival (OS) than patients who received only a single
drug in this category: 550 days versus 461 days.
A Kaplan-Meier curve (Figure 2, top-right) shows
the improvement in OS from time of profiling for patients
treated only with therapies predicted to be of benefit
by their molecular profile, and separately, patients who
received at least one drug predicted to lack benefit. Figure
2 (left) also gives a comparison of biomarkers between
matched and unmatched groups, and (middle-right and
lower-right plots) matched versus unmatched: age of
patients, survival time, treatment numbers and grade of
samples.
DISCUSSION
Predictive biomarkers – matched treatments
better than unmatched
This report looked at data from a breast carcinoma
cohort made available from Caris Life Sciences via their
CODE database. This was a retrospective review of a
cohort of patients that were profiled using established
IHC biomarkers, along with fragment analysis, in situ
hybridization and sequencing. Their treatments were either
matched or unmatched based on whether the treatment
chosen by their physician was predicted to be beneficial
by Caris Life Sciences using the molecular profile of the
tumor. Patients whose treatments subsequently agreed
with these recommendations were compared to those
who received at least one drug that was predicted to lack
benefit, i.e. their regimen did not agree with their tumor
profile-based treatment predictions. Comparing these two
groups showed that the matched treatment group had an
increase of 31% in survival compared to the average for
the unmatched group, an increase of 157 days from 510 to
667 days (P=0.0316).
When comparing the matched and unmatched
groups, in terms of HER2 and ER directed therapies,
there was a similar level of use – in the matched group
87% of treatments were of either of these types, and in
the unmatched group 83% of the treatments were one of
these two types.
The unmatched group received 0.32 more lines of
therapy on average than the matched group, survived for
less time, and had a higher mortality rate. This could have
been influenced by the tendency for the unmatched group
to have tumors that were generally more advanced than in
the matched group, as shown in Table 1. The unmatched
group may have tended to receive more treatments and
be less adherent to the recommended treatments, due to
clinicians trying all possible options as a last resort as the
disease advanced, although this is speculative.
Interestingly, across this cohort of patients, the
expression of ER and PR was found to be prognostic
for overall survival (see Figure 3). The expression of the
androgen receptor (AR) was also found to be prognostic;
this agrees with previously published data that shows
improved long-term survival with co-expression of AR in
ER positive breast cancers [16].
The survival curves from time of diagnosis initially
overlap and then diverge after profiling occurs. This
may suggest that basing therapy on tumor profiling has
an effect on selecting optimal therapies and improving
outcome. Combined with the increase in survival and
lowering of death rates, this leads to the conclusion that
there is a beneficent role of tumor molecular profiling in
this cohort.
MATERIALS AND METHODS
The Caris CODE database (Comprehensive
Oncology Database Explorer) contains tumor molecular
profile data for 841 patients with solid tumors in version
1.0. It also contains demographic information about the
patients, their drug treatments that they received before
and after molecular profiling, and records of their clinical
outcomes. There are 92 breast cancer patients recorded,
and this breast cancer cohort was mined after web scraping
the data from the Caris website, to determine if molecular
characterization recommendations influenced drug
selection by their physicians after the time of profiling,
and if any molecular subsets had different outcomes. Table
1 describes the clinical characteristics of the patients in
this breast cancer cohort. According to Caris, 33% of
the samples were from metastatic samples; 50% of these

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metastatic breast samples were from the lymph nodes, and
the rest were from other sites.
The amount of time that patients were monitored
varied, as shown in Figure 1. On average patients’
treatment records were available for 1327 days after
diagnosis (1342 for matched treatment patients and 131
for unmatched), and on average the time of monitoring
after profiling was 583 days. The longest period of
monitoring after tumor profiling (the patient represented
on the furthest right of Figure 1) was 1317 days; this was
1407 days after diagnosis. The longest amount of time that
records were available, i.e. after diagnosis up until the last
contact day, was 9427 days.
Abbreviations
AR: androgen receptor; HER2: human epidermal
growth factor receptor 2; IHC: immunohistochemistry;
ER: estrogen receptor; OS: overall survival; PR:
progesterone receptor.
Author contributions
All authors contributed equally to the work
described in this paper and were involved in writing it.
ACKNOWLEDGMENTS
Thanks to Caris Life Sciences for the provision
of their CODE database and the data contained within,
and assistance by Dr David Spetzler, Todd Maney, Dr Jia
Zeng, Dr Tina Lui, Dr Nick Xiao and Stephanie Ratliff in
the use of this resource. We are also grateful to the patients
involved in this study.
CONFLICTS OF INTEREST
We have no conflicts of interest.
FUNDING
This work was supported by the Action Against
Cancer charity (http://www.aacancer.org/). Infrastructure
support was provided by the Imperial Experimental
Cancer Medicine Centre, the CRUK Imperial Centre, and
the Imperial NIHR Biomedical Research Centre.
REFERENCES
1.
Stephens, P.J., Stephens P.J., Tarpey P.S., Davies H., Van
Loo P., Greenman C., Wedge D.C., Nik-Zainal S., Martin
S., Varela I., Bignell G.R., Yates L.R., Papaemmanuil E., et
al., The landscape of cancer genes and mutational processes
in breast cancer. Nature, 2012. 486: p. 400-4.
2.
Alexandrov L.B., Nik-Zainal S., Wedge D.C., Aparicio
S.A., Behjati S., Biankin A.V., Bignell G.R., Bolli N., Borg
A., Børresen-Dale A.L., Boyault S., Burkhardt B., Butler
A.P., et al., Signatures of mutational processes in human
cancer. Nature, 2013. 500: p. 415-21.
3.
Nik-Zainal S., Davies H., Staaf J., Ramakrishna M., Glodzik
D., Zou X., Martincorena I., Alexandrov L.B., Martin S.,
Wedge D.C., Van Loo P., Ju Y.S., Smid M., et al., Landscape
of somatic mutations in 560 breast cancer whole-genome
sequences. Nature, 2016. 534: p. 47-54.
4.
Pereira B., Chin S.F., Rueda O.M., Vollan H.K., Provenzano
E., Bardwell H.A., Pugh M., Jones L., Russell R., Sammut
S.J., Tsui D.W., Liu B., Dawson S.J., et al., The somatic
mutation profiles of 2,433 breast cancers refines their
genomic and transcriptomic landscapes. Nat Commun,
2016. 7: p. 11479.
5.
Nik-Zainal, S., Alexandrov L.B., Wedge D.C., Van Loo
P.,Greenman C.D., Raine K., Jones D., Hinton J., Marshall
J.,Stebbings L.A., Menzies A., Martin S., Leung K., et al.,
Mutational processes molding the genomes of 21 breast
cancers. Cell, 2012. 149: p. 979-93.
6.
Shlien A., Raine K., Fuligni F., Arnold R., Nik-Zainal S.,
Dronov S., Mamanova L., Rosic A., Ju Y.S., Cooke S.L.,
Ramakrishna M., Papaemmanuil E., Davies H.R., et al.,
Direct Transcriptional Consequences of Somatic Mutation
in Breast Cancer. Cell Rep, 2016. 16: p. 2032-46.
7.
Smid M., Rodríguez-González F.G., Sieuwerts A.M.,
Salgado R., Prager-Van der Smissen W.J., Vlugt-Daane
M.V., van Galen A., Nik-Zainal S., Staaf J., Brinkman
A.B., van de Vijver M.J., Richardson A.L., Fatima A.,
et al., Breast cancer genome and transcriptome integration
implicates specific mutational signatures with immune cell
infiltration. Nat Commun, 2016. 7: p. 12910.
8.
Ward, H.W., Anti-oestrogen therapy for breast cancer: a trial
of tamoxifen at two dose levels. Br Med J, 1973. 1: p. 13-4.
9.
Bozovic-Spasojevic I., Azim H.A. Jr, Paesmans M., Suter
T., Piccart M.J., de Azambuja E., Neoadjuvant anthracycline
and trastuzumab for breast cancer: is concurrent treatment
safe? Lancet Oncol, 2011. 12: p. 209-11.
10.
Baxter E., Gondara L., Lohrisch C., Chia S., Gelmon K.,
Hayes M., Davidson A., Tyldesley S., Using proliferative
markers and Oncotype DX in therapeutic decision-making
for breast cancer: the B.C. experience. Curr Oncol, 2015.
22: p. 192-8.
11.
Garnett M.J., Edelman E.J., Heidorn S.J., Greenman C.D.,
Dastur A., Lau K.W., Greninger P., Thompson I.R., Luo
X., Soares J., Liu Q., Iorio F., Surdez D., et al., Systematic
identification of genomic markers of drug sensitivity in
cancer cells. Nature, 2012. 483: p. 570-5.
12.
Shaw A.T., Kim D.W., Nakagawa K., Seto T., Crinó L., Ahn
M.J., De Pas T., Besse B., Solomon B.J., Blackhall F., Wu
Y.L., Thomas M., O'Byrne K.J., et al., Crizotinib versus
chemotherapy in advanced ALK-positive lung cancer. N
Engl J Med, 2013. 368: p. 2385-94.

Oncotarget8
www.impactjournals.com/oncotarget
13.
Frampton G.M., Fichtenholtz A., Otto G.A., Wang
K., Downing S.R., He J., Schnall-Levin M., White J.,
Sanford E.M., An P., Sun J., Juhn F., Brennan K., et al.,
Development and validation of a clinical cancer genomic
profiling test based on massively parallel DNA sequencing.
Nat Biotechnol, 2013. 31: p. 1023-31.
14.
Khirade, M.F., G. Lal, S.A. Bapat, Derivation of a fifteen gene
prognostic panel for six cancers. Sci Rep, 2015. 5: p. 13248.
15.
Corcoran, R.B. New therapeutic strategies for BRAF
mutant colorectal cancers. J Gastrointest Oncol. 2015
Dec;6:650-9
16.
Hu R., Dawood S., Holmes M.D., Collins L.C., Schnitt
S.J., Cole K., Marotti J.D., Hankinson S.E., Colditz G.A.,
Tamimi R.M. Androgen receptor expression and breast
cancer survival in postmenopausal women. Clin Cancer
Res, 2011. 17: p. 1867-74.