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@article{Beers2006Array-CGH,
author = {van Beers, E. and Nederlof, P.},
title = {Array-{CGH} and breast cancer},
journal = {Breast Cancer Research},
year = {2006},
volume = {8},
pages = {210},
number = {3},
abstract = {The introduction of comparative genomic hybridization (CGH) in 1992
opened new avenues in genomic investigation; in particular, it advanced
analysis of solid tumours, including breast cancer, because it obviated
the need to culture cells before their chromosomes could be analyzed.
The current generation of CGH analysis uses ordered arrays of genomic
DNA sequences and is therefore referred to as array-CGH or matrix-CGH.
It was introduced in 1998, and further increased the potential of
CGH to provide insight into the fundamental processes of chromosomal
instability and cancer. This review provides a critical evaluation
of the data published on array-CGH and breast cancer, and discusses
some of its expected future value and developments.},
doi = {10.1186/bcr1510},
pdf = {../local/Beers2006Array-CGH.pdf},
file = {Beers2006Array-CGH.pdf:Beers2006Array-CGH.pdf:PDF},
issn = {1465-5411},
keywords = {breastcancer, cgh},
owner = {jp},
pubmedid = {16817944},
timestamp = {2008.12.08},
url = {http://breast-cancer-research.com/content/8/3/210}
}
@article{Bild2006Oncogenic,
author = {Bild, A. H. and Yao, G. and Chang, J. T. and Wang, Q. and Potti,
A. and Chasse, D. and Joshi, M. B. and Harpole, D. and Lancaster,
J. M. and Berchuck, A. and Olson, J. A., Jr. and Marks, J. R. and
Dressman, H. K. and West, M. and Nevins, J. R.},
title = {Oncogenic pathway signatures in human cancers as a guide to targeted
therapies},
journal = {Nature},
year = {2006},
volume = {439},
pages = {353-7},
number = {7074},
abstract = {The development of an oncogenic state is a complex process involving
the accumulation of multiple independent mutations that lead to deregulation
of cell signalling pathways central to the control of cell growth
and cell fate. {T}he ability to define cancer subtypes, recurrence
of disease and response to specific therapies using {DNA} microarray-based
gene expression signatures has been demonstrated in multiple studies.
{V}arious studies have also demonstrated the potential for using
gene expression profiles for the analysis of oncogenic pathways.
{H}ere we show that gene expression signatures can be identified
that reflect the activation status of several oncogenic pathways.
{W}hen evaluated in several large collections of human cancers, these
gene expression signatures identify patterns of pathway deregulation
in tumours and clinically relevant associations with disease outcomes.
{C}ombining signature-based predictions across several pathways identifies
coordinated patterns of pathway deregulation that distinguish between
specific cancers and tumour subtypes. {C}lustering tumours based
on pathway signatures further defines prognosis in respective patient
subsets, demonstrating that patterns of oncogenic pathway deregulation
underlie the development of the oncogenic phenotype and reflect the
biology and outcome of specific cancers. {P}redictions of pathway
deregulation in cancer cell lines are also shown to predict the sensitivity
to therapeutic agents that target components of the pathway. {L}inking
pathway deregulation with sensitivity to therapeutics that target
components of the pathway provides an opportunity to make use of
these oncogenic pathway signatures to guide the use of targeted therapeutics.},
doi = {10.1038/nature04296},
pdf = {../local/Bild2006Oncogenic.pdf},
file = {Bild2006Oncogenic.pdf:Bild2006Oncogenic.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1038/nature04296}
}
@article{Boyle2005Cancer,
author = {Boyle, P. and Ferlay, J.},
title = {Cancer incidence and mortality in Europe, 2004},
journal = {Ann. Oncol.},
year = {2005},
volume = {16},
pages = {481--488},
number = {3},
month = {Mar},
abstract = {BACKGROUND: There are no recent estimates of the incidence and mortality
from cancer at a European level. Those data that are available generally
refer to the mid-1990s and are of limited use for cancer control
planning. We present estimates of the cancer burden in Europe in
2004, including data for the (25 Member States) European Union. METHODS:
The most recent sources of incidence and mortality data available
in the Descriptive Epidemiology Group at IARC were applied to population
projections to derive the best estimates of the burden of cancer,
in terms of incidence and mortality, for Europe in 2004. RESULTS:
In 2004 in Europe, there were an estimated 2,886,800 incident cases
of cancer diagnosed and 1,711,000 cancer deaths. The most common
incident form of cancer was lung cancer (13.3\% of all incident cases),
followed by colorectal cancer (13.2\%) and breast cancer (13\%).
Lung cancer was also the most common cause of cancer death (341,800
deaths), followed by colorectal (203,700), stomach (137,900) and
breast (129,900). CONCLUSIONS: With an estimated 2.9 million new
cases (54\% occurring in men, 46\% in women) and 1.7 million deaths
(56\% in men, 44\% in women) each year, cancer remains an important
public health problem in Europe, and the ageing of the European population
will cause these numbers to continue to increase even if age-specific
rates remain constant. To make great progress quickly against cancer
in Europe, the need is evident to make a concerted attack on the
big killers: lung, colorectal, breast and stomach cancer. Stomach
cancer rates are falling everywhere in Europe and public health measures
are available to reduce the incidence and mortality of lung cancer,
colorectal cancer and breast cancer.},
doi = {10.1093/annonc/mdi098},
pdf = {../local/Boyle2005Cancer.pdf},
file = {Boyle2005Cancer.pdf:Boyle2005Cancer.pdf:PDF},
institution = {International Agency for Research on Cancer, 150 cours Albert Thomas,
69372 Lyon Cedex 08, France. director@iarc.fr},
keywords = {breastcancer},
owner = {jp},
pii = {mdi098},
pmid = {15718248},
timestamp = {2008.11.26},
url = {http://dx.doi.org/10.1093/annonc/mdi098}
}
@article{Breslin2004Autofluorescence,
author = {Tara M Breslin and Fushen Xu and Gregory M Palmer and Changfang Zhu
and Kennedy W Gilchrist and Nirmala Ramanujam},
title = {Autofluorescence and diffuse reflectance properties of malignant
and benign breast tissues.},
journal = {Ann {S}urg {O}ncol},
year = {2004},
volume = {11},
pages = {65-70},
number = {1},
month = {Jan},
abstract = {B{ACKGROUND}: {F}luorescence spectroscopy is an evolving technology
that can rapidly differentiate between benign and malignant tissues.
{T}hese differences are thought to be due to endogenous fluorophores,
including nicotinamide adenine dinucleotide, flavin adenine dinucleotide,
and tryptophan, and absorbers such as beta-carotene and hemoglobin.
{W}e hypothesized that a statistically significant difference would
be demonstrated between benign and malignant breast tissues on the
basis of their unique fluorescence and reflectance properties. {METHODS}:
{O}ptical measurements were performed on 56 samples of tumor or benign
breast tissue. {A}utofluorescence spectra were measured at excitation
wavelengths ranging from 300 to 460 nm, and diffuse reflectance was
measured between 300 and 600 nm. {P}rincipal component analysis to
dimensionally reduce the spectral data and a {W}ilcoxon ranked sum
test were used to determine which wavelengths showed statistically
significant differences. {A} support vector machine algorithm compared
classification results with the histological diagnosis (gold standard).
{RESULTS}: {S}everal excitation wavelengths and diffuse reflectance
spectra showed significant differences between tumor and benign tissues.
{B}y using the support vector machine algorithm to incorporate relevant
spectral differences, a sensitivity of 70.0\% and specificity of
91.7\% were achieved. {CONCLUSIONS}: {A} statistically significant
difference was demonstrated in the diffuse reflectance and fluorescence
emission spectra of benign and malignant breast tissue. {T}hese differences
could be exploited in the development of adjuncts to diagnostic and
surgical procedures.},
doi = {10.1245/ASO.2004.03.031},
pdf = {../local/Breslin2004Autofluorescence.pdf},
file = {Breslin2004Autofluorescence.pdf:Breslin2004Autofluorescence.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1245/ASO.2004.03.031}
}
@article{Chang2005Automatic,
author = {Ruey-Feng Chang and Wen-Jie Wu and Woo Kyung Moon and Dar-Ren Chen},
title = {Automatic ultrasound segmentation and morphology based diagnosis
of solid breast tumors.},
journal = {Breast {C}ancer {R}es {T}reat},
year = {2005},
volume = {89},
pages = {179-85},
number = {2},
month = {Jan},
abstract = {Ultrasound ({US}) is a useful diagnostic tool to distinguish benign
from malignant masses of the breast. {I}t is a very convenient and
safe diagnostic method. {H}owever, there is a considerable overlap
benignancy and malignancy in ultrasonic images and interpretation
is subjective. {A} high performance breast tumors computer-aided
diagnosis ({CAD}) system can provide an accurate and reliable diagnostic
second opinion for physicians to distinguish benign breast lesions
from malignant ones. {T}he potential of sonographic texture analysis
to improve breast tumor classifications has been demonstrated. {H}owever,
the texture analysis is system-dependent. {T}he disadvantages of
these systems which use texture analysis to classify tumors are they
usually perform well only in one specific ultrasound system. {W}hile
{M}orphological based {US} diagnosis of breast tumor will take the
advantage of nearly independent to either the setting of {US} system
and different {US} machines. {I}n this study, the tumors are segmented
using the newly developed level set method at first and then six
morphologic features are used to distinguish the benign and malignant
cases. {T}he support vector machine ({SVM}) is used to classify the
tumors. {T}here are 210 ultrasonic images of pathologically proven
benign breast tumors from 120 patients and carcinomas from 90 patients
in the ultrasonic image database. {T}he database contains only one
image from each patient. {T}he ultrasonic images are captured at
the largest diameter of the tumor. {T}he images are collected consecutively
from {A}ugust 1, 1999 to {M}ay 31, 2000; the patients' ages ranged
from 18 to 64 years. {S}onography is performed using an {ATL} {HDI}
3000 system with a {L}10-5 small part transducer. {I}n the experiment,
the accuracy of {SVM} with shape information for classifying malignancies
is 90.95\% (191/210), the sensitivity is 88.89\% (80/90), the specificity
is 92.5\% (111/120), the positive predictive value is 89.89\% (80/89),
and the negative predictive value is 91.74\% (111/121).},
doi = {10.1007/s10549-004-2043-z},
pdf = {../local/Chang2005Automatic.pdf},
file = {Chang2005Automatic.pdf:local/Chang2005Automatic.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1007/s10549-004-2043-z}
}
@article{Chang2003Improvement,
author = {Ruey-Feng Chang and Wen-Jie Wu and Woo Kyung Moon and Dar-Ren Chen},
title = {Improvement in breast tumor discrimination by support vector machines
and speckle-emphasis texture analysis.},
journal = {Ultrasound {M}ed {B}iol},
year = {2003},
volume = {29},
pages = {679-86},
number = {5},
month = {May},
abstract = {Recent statistics show that breast cancer is a major cause of death
among women in developed countries. {H}ence, finding an accurate
and effective diagnostic method is very important. {I}n this paper,
we propose a high precision computer-aided diagnosis ({CAD}) system
for sonography. {W}e utilize a support vector machine ({SVM}) to
classify breast tumors according to their texture information surrounding
speckle pixels. {W}e test our system with 250 pathologically-proven
breast tumors including 140 benign and 110 malignant ones. {A}lso
we compare the diagnostic performances of three texture features,
i.e., speckle-emphasis texture feature, nonspeckle-emphasis texture
feature and conventional all pixels texture feature, applied to breast
sonography using {SVM}. {I}n our experiment, the accuracy of {SVM}
with speckle information for classifying malignancies is 93.2\% (233/250),
the sensitivity is 95.45\% (105/110), the specificity is 91.43\%
(128/140), the positive predictive value is 89.74\% (105/117) and
the negative predictive value is 96.24\% (128/133). {B}ased on the
experimental results, speckle phenomenon is a useful tool to be used
in computer-aided diagnosis; its performance is better than those
of the other two features. {S}peckle phenomenon, which is considered
as noise in sonography, can intrude into judgments of a physician
using naked eyes but it is another story for application in a computer-aided
diagnosis algorithm.},
doi = {10.1016/S0301-5629(02)00788-3},
pdf = {../local/Chang2003Improvement.pdf},
file = {Chang2003Improvement.pdf:local/Chang2003Improvement.pdf:PDF},
keywords = {breastcancer},
pii = {S0301562902007883},
url = {http://dx.doi.org/10.1016/S0301-5629(02)00788-3}
}
@article{Chin2007High-resolution,
author = {Chin, S. F. and Teschendorff, A. E. and Marioni, J. C. and Wang,
Y. and Barbosa-Morais, N. L. and Thorne, N. P. and Costa, J. L. and
Pinder, S. E. and van de Wiel, M. A. and Green, A. R. and Ellis,
I. O. and Porter, P. L. and Tavar{\'e}, S. and Brenton, J. D. and
Ylstra, B. and Caldas, C.},
title = {High-resolution {aCGH} and expression profiling identifies a novel
genomic subtype of {ER} negative breast cancer.},
journal = {Genome Biol.},
year = {2007},
volume = {8},
pages = {R215},
number = {10},
abstract = {BACKGROUND: The characterization of copy number alteration patterns
in breast cancer requires high-resolution genome-wide profiling of
a large panel of tumor specimens. To date, most genome-wide array
comparative genomic hybridization studies have used tumor panels
of relatively large tumor size and high Nottingham Prognostic Index
(NPI) that are not as representative of breast cancer demographics.
RESULTS: We performed an oligo-array-based high-resolution analysis
of copy number alterations in 171 primary breast tumors of relatively
small size and low NPI, which was therefore more representative of
breast cancer demographics. Hierarchical clustering over the common
regions of alteration identified a novel subtype of high-grade estrogen
receptor (ER)-negative breast cancer, characterized by a low genomic
instability index. We were able to validate the existence of this
genomic subtype in one external breast cancer cohort. Using matched
array expression data we also identified the genomic regions showing
the strongest coordinate expression changes ('hotspots'). We show
that several of these hotspots are located in the phosphatome, kinome
and chromatinome, and harbor members of the 122-breast cancer CAN-list.
Furthermore, we identify frequently amplified hotspots on 8q22.3
(EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1
(NDUFC2, ALG8, USP35) associated with significantly worse prognosis.
Amplification of any of these regions identified 37 samples with
significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4)
p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p =
0.004) independently of NPI. CONCLUSION: We present strong evidence
for the existence of a novel subtype of high-grade ER-negative tumors
that is characterized by a low genomic instability index. We also
provide a genome-wide list of common copy number alteration regions
in breast cancer that show strong coordinate aberrant expression,
and further identify novel frequently amplified regions that correlate
with poor prognosis. Many of the genes associated with these regions
represent likely novel oncogenes or tumor suppressors.},
doi = {10.1186/gb-2007-8-10-r215},
pdf = {../local/Chin2007High-resolution.pdf},
file = {Chin2007High-resolution.pdf:Chin2007High-resolution.pdf:PDF},
institution = {Breast Cancer Functional Genomics, Cancer Research UK Cambridge Research
Institute and Department of Oncology University of Cambridge, Li
Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. sc10021@cam.ac.uk},
keywords = {breastcancer, cgh},
owner = {jp},
pii = {gb-2007-8-10-r215},
pmid = {17925008},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1186/gb-2007-8-10-r215}
}
@article{Chin2006Using,
author = {Chin, S.-F. and Wang, Y. and Thorne, N. P. and Teschendorff, A. E.
and Pinder, S. E. and Vias, M. and Naderi, A. and Roberts, I. and
Barbosa-Morais, N. L. and Garcia, M. J. and Iyer, N. G. and Kranjac,
T. and Robertson, J. F. R. and Aparicio, S. and Tavare, S. and Ellis,
I. and Brenton, J. D. and Caldas, C.},
title = {Using array-comparative genomic hybridization to define molecular
portraits of primary breast cancers},
journal = {Oncogene},
year = {2006},
volume = {26},
pages = {1959--1970},
number = {13},
month = sep,
doi = {10.1038/sj.onc.1209985},
pdf = {../local/Chin2006Using.pdf},
file = {Chin2006Using.pdf:Chin2006Using.pdf:PDF},
issn = {0950-9232},
keywords = {breastcancer},
owner = {franck},
timestamp = {2007.11.23},
url = {http://dx.doi.org/10.1038/sj.onc.1209985}
}
@article{Chuang2007Network-based,
author = {Chuang, H.-Y. and Lee, E. and Liu, Y.-T. and Lee, D. and Ideker,
T.},
title = {Network-based classification of breast cancer metastasis.},
journal = {Mol. Syst. Biol.},
year = {2007},
volume = {3},
pages = {140},
abstract = {Mapping the pathways that give rise to metastasis is one of the key
challenges of breast cancer research. Recently, several large-scale
studies have shed light on this problem through analysis of gene
expression profiles to identify markers correlated with metastasis.
Here, we apply a protein-network-based approach that identifies markers
not as individual genes but as subnetworks extracted from protein
interaction databases. The resulting subnetworks provide novel hypotheses
for pathways involved in tumor progression. Although genes with known
breast cancer mutations are typically not detected through analysis
of differential expression, they play a central role in the protein
network by interconnecting many differentially expressed genes. We
find that the subnetwork markers are more reproducible than individual
marker genes selected without network information, and that they
achieve higher accuracy in the classification of metastatic versus
non-metastatic tumors.},
doi = {10.1038/msb4100180},
pdf = {../local/Chuang2007Network-based.pdf},
file = {Chuang2007Network-based.pdf:Chuang2007Network-based.pdf:PDF},
institution = {Bioinformatics Program, University of California San Diego, La Jolla,
CA 92093, USA.},
keywords = {breastcancer},
owner = {jp},
pii = {msb4100180},
pmid = {17940530},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1038/msb4100180}
}
@article{Cianfrocca2009New,
author = {Cianfrocca, M. and Gradishar, W.},
title = {New molecular classifications of breast cancer},
journal = {CA Cancer J. Clin.},
year = {2009},
volume = {59},
pages = {303--313},
number = {5},
abstract = {Traditionally, pathologic determinations of tumor size, lymph node
status, endocrine receptor status, and human epidermal growth factor
receptor 2 (HER2) status have driven prognostic predictions and adjuvant
therapy recommendations for patients with early stage breast cancer.
However, these prognostic and predictive factors are relatively crude
measures, resulting in many patients being overtreated or undertreated.
As a result of gene expression assays, there is growing recognition
that breast cancer is a molecularly heterogeneous disease. Evidence
from gene expression microarrays suggests the presence of multiple
molecular subtypes of breast cancer. The recent commercial availability
of gene expression profiling techniques that predict risk of disease
recurrence as well as potential chemotherapy benefit have shown promise
in refining clinical decision making. These techniques will be reviewed
in this article.},
doi = {10.3322/caac.20029},
pdf = {../local/Cianfrocca2009New.pdf},
file = {Cianfrocca2009New.pdf:Cianfrocca2009New.pdf:PDF},
institution = {Division of Hematology/Oncology, Northwestern University, Feinberg
School of Medicine, Robert H. Lurie Comprehensive Cancer Center,
Chicago, IL 60611, USA. m-cianfrocca@northwestern.edu},
keywords = {csbcbook, breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {59/5/303},
pmid = {19729680},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.3322/caac.20029}
}
@article{Ein-Dor2005Outcome,
author = {Ein-Dor, L. and Kela, I. and Getz, G. and Givol, D. and Domany, E.},
title = {Outcome signature genes in breast cancer: is there a unique set?},
journal = {Bioinformatics},
year = {2005},
volume = {21},
pages = {171--178},
number = {2},
month = {Jan},
abstract = {MOTIVATION: Predicting the metastatic potential of primary malignant
tissues has direct bearing on the choice of therapy. Several microarray
studies yielded gene sets whose expression profiles successfully
predicted survival. Nevertheless, the overlap between these gene
sets is almost zero. Such small overlaps were observed also in other
complex diseases, and the variables that could account for the differences
had evoked a wide interest. One of the main open questions in this
context is whether the disparity can be attributed only to trivial
reasons such as different technologies, different patients and different
types of analyses. RESULTS: To answer this question, we concentrated
on a single breast cancer dataset, and analyzed it by a single method,
the one which was used by van't Veer et al. to produce a set of outcome-predictive
genes. We showed that, in fact, the resulting set of genes is not
unique; it is strongly influenced by the subset of patients used
for gene selection. Many equally predictive lists could have been
produced from the same analysis. Three main properties of the data
explain this sensitivity: (1) many genes are correlated with survival;
(2) the differences between these correlations are small; (3) the
correlations fluctuate strongly when measured over different subsets
of patients. A possible biological explanation for these properties
is discussed. CONTACT: eytan.domany@weizmann.ac.il SUPPLEMENTARY
INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/downloads/liate/},
doi = {10.1093/bioinformatics/bth469},
pdf = {../local/Ein-Dor2005Outcome.pdf},
file = {Ein-Dor2005Outcome.pdf:Ein-Dor2005Outcome.pdf:PDF},
institution = {Department of Physics of Complex Systems, Weizmann Institute of Science
Rehovot 76100, Israel.},
keywords = {breastcancer, microarray, featureselection},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bth469},
pmid = {15308542},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1093/bioinformatics/bth469}
}
@article{Fan2006Concordance,
author = {Fan, C. and Oh, D.S. and Wessels, L. and Weigelt, B. and Nuyten,
D.S.A. and Nobel, A.B. and van't Veer, L.J. and Perou, C.M.},
title = {Concordance among gene-expression-based predictors for breast cancer},
journal = {N. Engl. J. Med.},
year = {2006},
volume = {355},
pages = {560},
number = {6},
doi = {10.1056/NEJMoa052933},
pdf = {../local/Fan2006Concordance.pdf},
file = {Fan2006Concordance.pdf:Fan2006Concordance.pdf:PDF},
keywords = {breastcancer, microarray},
owner = {jp},
publisher = {Mass Med Soc},
timestamp = {2011.01.13},
url = {http://dx.doi.org/10.1056/NEJMoa052933}
}
@article{Gusterson2009Do,
author = {Gusterson, B.},
title = {Do 'basal-like' breast cancers really exist?},
journal = {Nat. Rev. Cancer},
year = {2009},
volume = {9},
pages = {128--134},
number = {2},
month = {Feb},
abstract = {It has been proposed that gene expression profiles will revolutionize
the classification of breast cancer, eventually replacing histopathology
with a more reproducible technology. These new approaches, combined
with a better understanding of the cellular origins of breast cancer,
should enable us to identify patient subgroups for more effective
therapy. However, in such a rapidly advancing field it is essential
that initial and thought-provoking results do not become established
as 'facts' without question. This Opinion addresses some of the negatives
and positives generated by the term 'basal-like' breast cancer, and
questions its existence as an entity.},
doi = {10.1038/nrc2571},
pdf = {../local/Gusterson2009Do.pdf},
file = {Gusterson2009Do.pdf:Gusterson2009Do.pdf:PDF},
institution = {gy), Dumbarton Road, Glasgow G11 6NT, Scotland, UK. B.A.Gusterson@clinmed.gla.ac.uk},
keywords = {breastcancer},
owner = {jp},
pii = {nrc2571},
pmid = {19132008},
timestamp = {2009.02.03},
url = {http://dx.doi.org/10.1038/nrc2571}
}
@article{Gusterson2005Basal,
author = {Gusterson, B. A. and Ross, D. T. and Heath, V. J. and Stein, T.},
title = {Basal cytokeratins and their relationship to the cellular origin
and functional classification of breast cancer},
journal = {Breast Cancer Res.},
year = {2005},
volume = {7},
pages = {143--148},
number = {4},
abstract = {Recent publications have classified breast cancers on the basis of
expression of cytokeratin-5 and -17 at the RNA and protein levels,
and demonstrated the importance of these markers in defining sporadic
tumours with bad prognosis and an association with BRCA1-related
breast cancers. These important observations using different technology
platforms produce a new functional classification of breast carcinoma.
However, it is important in developing hypotheses about the pathogenesis
of this tumour type to review the nomenclature that is being used
to emphasize potential confusion between terminology that defines
clinical subgroups and markers of cell lineage. This article reviews
the lineages in the normal breast in relation to what have become
known as the 'basal-like' carcinomas.},
doi = {10.1186/bcr1041},
pdf = {../local/Gusterson2005Basal.pdf},
file = {Gusterson2005Basal.pdf:Gusterson2005Basal.pdf:PDF},
institution = {Division of Cancer Sciences and Molecular Pathology, Western Infirmary,
University of Glasgow, Glasgow, UK. bag5f@clinmed.gla.ac.uk},
keywords = {breastcancer},
owner = {jp},
pii = {bcr1041},
pmid = {15987465},
timestamp = {2009.02.04},
url = {http://dx.doi.org/10.1186/bcr1041}
}
@article{Haibe-Kains2008Comparison,
author = {Haibe-Kains, B. and Desmedt, C. and Piette, F. and Buyse, M. and
Cardoso, F. and Van't Veer, L. and Piccart, M. and Bontempi, G. and
Sotiriou, C.},
title = {Comparison of prognostic gene expression signatures for breast cancer},
journal = {BMC Genomics},
year = {2008},
volume = {9},
pages = {394},
abstract = {BACKGROUND: During the last years, several groups have identified
prognostic gene expression signatures with apparently similar performances.
However, signatures were never compared on an independent population
of untreated breast cancer patients, where risk assessment was computed
using the original algorithms and microarray platforms. RESULTS:
We compared three gene expression signatures, the 70-gene, the 76-gene
and the Gene expression Grade Index (GGI) signatures, in terms of
predicting distant metastasis free survival (DMFS) for the individual
patient. To this end, we used the previously published TRANSBIG independent
validation series of node-negative untreated primary breast cancer
patients. We observed agreement in prediction for 135 of 198 patients
(68\%) when considering the three signatures. When comparing the
signatures two by two, the agreement in prediction was 71\% for the
70- and 76-gene signatures, 76\% for the 76-gene signature and the
GGI, and 88\% for the 70-gene signature and the GGI. The three signatures
had similar capabilities of predicting DMFS and added significant
prognostic information to that provided by the classical parameters.
CONCLUSION: Despite the difference in development of these signatures
and the limited overlap in gene identity, they showed similar prognostic
performance, adding to the growing evidence that these prognostic
signatures are of clinical relevance.},
doi = {10.1186/1471-2164-9-394},
pdf = {../local/Haibe-Kains2008Comparison.pdf},
file = {Haibe-Kains2008Comparison.pdf:Haibe-Kains2008Comparison.pdf:PDF},
institution = {Functional Genomics Unit, Jules Bordet Institute, Université Libre
de Bruxelles, Brussels, Belgium. bhaibeka@ulb.ac.be},
keywords = {breastcancer},
owner = {jp},
pii = {1471-2164-9-394},
pmid = {18717985},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1186/1471-2164-9-394}
}
@article{Hess2006Pharmacogenomic,
author = {Hess, K. R. and Anderson, K. and Symmans, W. F. and Valero, V. and
Ibrahim, N. and Mejia, J. A. and Booser, D. and Theriault, R. L.
and Buzdar, A. U. and Dempsey, P. J. and Rouzier, R. and Sneige,
N. and Ross, J. S. and Vidaurre, T. and G\'omez, H. L. and Hortobagyi,
G. N. and Pusztai, L.},
title = {Pharmacogenomic predictor of sensitivity to preoperative chemotherapy
with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide
in breast cancer.},
journal = {J Clin Oncol},
year = {2006},
volume = {24},
pages = {4236--4244},
number = {26},
month = {Sep},
abstract = {We developed a multigene predictor of pathologic complete response
(pCR) to preoperative weekly paclitaxel and fluorouracil-doxorubicin-cyclophosphamide
(T/FAC) chemotherapy and assessed its predictive accuracy on independent
cases.One hundred thirty-three patients with stage I-III breast cancer
were included. Pretreatment gene expression profiling was performed
with oligonecleotide microarrays on fine-needle aspiration specimens.
We developed predictors of pCR from 82 cases and assessed accuracy
on 51 independent cases.Overall pCR rate was 26\% in both cohorts.
In the training set, 56 probes were identified as differentially
expressed between pCR versus residual disease, at a false discovery
rate of 1\%. We examined the performance of 780 distinct classifiers
(set of genes + prediction algorithm) in full cross-validation. Many
predictors performed equally well. A nominally best 30-probe set
Diagonal Linear Discriminant Analysis classifier was selected for
independent validation. It showed significantly higher sensitivity
(92\% v 61\%) than a clinical predictor including age, grade, and
estrogen receptor status. The negative predictive value (96\% v 86\%)
and area under the curve (0.877 v 0.811) were nominally better but
not statistically significant. The combination of genomic and clinical
information yielded a predictor not significantly different from
the genomic predictor alone. In 31 samples, RNA was hybridized in
replicate with resulting predictions that were 97\% concordant.A
30-probe set pharmacogenomic predictor predicted pCR to T/FAC chemotherapy
with high sensitivity and negative predictive value. This test correctly
identified all but one of the patients who achieved pCR (12 of 13
patients) and all but one of those who were predicted to have residual
disease had residual cancer (27 of 28 patients).},
doi = {10.1200/JCO.2006.05.6861},
pdf = {../local/Hess2006Pharmacogenomic.pdf},
file = {Hess2006Pharmacogenomic.pdf:Hess2006Pharmacogenomic.pdf:PDF},
institution = {Department of Biostatistics and Applied Mathematics, The University
of Texas M.D. Anderson Cancer Center, Houston, TX 77230-1439, USA.},
keywords = {breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {JCO.2006.05.6861},
pmid = {16896004},
timestamp = {2011.11.18},
url = {http://dx.doi.org/10.1200/JCO.2006.05.6861}
}
@article{Jones2004Molecular,
author = {Jones, C. and Ford, E. and Gillett, C. and Ryder, K. and Merrett,
S. and Reis-Filho, J. S. and Fulford, L. G. and Hanby, A. and Lakhani,
S. R.},
title = {Molecular Cytogenetic Identification of Subgroups of Grade III Invasive
Ductal Breast Carcinomas with Different Clinical Outcomes},
journal = {Clin. Cancer Res.},
year = {2004},
volume = {10},
pages = {5988-5997},
number = {18},
abstract = {Tumor grade is an established indicator of breast cancer outcome,
although considerable heterogeneity exists even within-grade. Around
25% of grade III invasive ductal breast carcinomas are associated
with a "basal" phenotype, and these tumors are reported to be a distinct
subgroup. We have investigated whether this group of breast cancers
has a distinguishing pattern of genetic alterations and which of
these may relate to the different clinical outcome of these patients.
We performed comparative genomic hybridization (CGH) analysis on
43 grade III invasive ductal breast carcinomas positive for basal
cytokeratin 14, as well as 43 grade- and age-matched CK14-negative
controls, all with up to 25 years (median, 7 years) of clinical follow-up.
Significant differences in CGH alterations were seen between the
two groups in terms of mean number of changes (CK14+ve - 6.5, CK14-ve
- 10.3; P = 0.0012) and types of alterations at chromosomes 4q, 7q,
8q, 9p, 13q, 16p, 17p, 17q, 19p, 19q, 20p, 20q and Xp. Supervised
and unsupervised algorithms separated the two groups on CGH data
alone with 76% and 74% accuracy, respectively. Hierarchical clustering
revealed distinct subgroups, one of which contained 18 (42%) of the
CK14+ve tumors. This subgroup had significantly shorter overall survival
(P = 0.0414) than other grade III tumors, regardless of CK14 status,
and was an independent prognostic marker (P = 0.031). These data
provide evidence that the "basal" phenotype on its own does not convey
a poor prognosis. Basal tumors are also heterogeneous with only a
subset, identifiable by pattern of genetic alterations, exhibiting
a shorter overall survival. Robust characterization of this basal
group is necessary if it is to have a major impact on management
of patients with breast cancer.},
doi = {10.1158/1078-0432.CCR-03-0731},
eprint = {http://clincancerres.aacrjournals.org/cgi/reprint/10/18/5988.pdf},
pdf = {../local/Jones2004Molecular.pdf},
file = {Jones2004Molecular.pdf:Jones2004Molecular.pdf:PDF},
keywords = {breastcancer, cgh},
owner = {jp},
timestamp = {2008.12.08},
url = {http://clincancerres.aacrjournals.org/cgi/content/abstract/10/18/5988}
}
@article{Kote-Jarai2004Gene,
author = {Zsofia Kote-Jarai and Richard D Williams and Nicola Cattini and Maria
Copeland and Ian Giddings and Richard Wooster and Robert H tePoele
and Paul Workman and Barry Gusterson and John Peacock and Gerald
Gui and Colin Campbell and Ros Eeles},
title = {Gene expression profiling after radiation-induced {DNA} damage is
strongly predictive of {BRCA}1 mutation carrier status.},
journal = {Clin. {C}ancer {R}es.},
year = {2004},
volume = {10},
pages = {958-63},
number = {3},
month = {Feb},
abstract = {P{URPOSE}: {T}he impact of the presence of a germ-line {BRCA}1 mutation
on gene expression in normal breast fibroblasts after radiation-induced
{DNA} damage has been investigated. {EXPERIMENTAL} {DESIGN}: {H}igh-density
c{DNA} microarray technology was used to identify differential responses
to {DNA} damage in fibroblasts from nine heterozygous {BRCA}1 mutation
carriers compared with five control samples without personal or family
history of any cancer. {F}ibroblast cultures were irradiated, and
their expression profile was compared using intensity ratios of the
c{DNA} microarrays representing 5603 {IMAGE} clones. {RESULTS}: {C}lass
comparison and class prediction analysis has shown that {BRCA}1 mutation
carriers can be distinguished from controls with high probability
(approximately 85\%). {S}ignificance analysis of microarrays and
the support vector machine classifier identified gene sets that discriminate
the samples according to their mutation status. {T}hese include genes
already known to interact with {BRCA}1 such as {CDKN}1{B}, {ATR},
and {RAD}51. {CONCLUSIONS}: {T}he results of this initial study suggest
that normal cells from heterozygous {BRCA}1 mutation carriers display
a different gene expression profile from controls in response to
{DNA} damage. {A}daptations of this pilot result to other cell types
could result in the development of a functional assay for {BRCA}1
mutation status.},
pdf = {../local/Kote-Jarai2004Gene.pdf},
file = {Kote-Jarai2004Gene.pdf:local/Kote-Jarai2004Gene.pdf:PDF},
keywords = {biosvm , breastcancer},
url = {http://clincancerres.aacrjournals.org/cgi/content/abstract/10/3/958}
}
@article{Listgarten2004Predictive,
author = {Listgarten, J. and Damaraju, S. and Poulin, B. and Cook, L. and Dufour,
J. and Driga, A. and Mackey, J. and Wishart, D. and Greiner, R. and
Zanke, B.},
title = {Predictive {M}odels for {B}reast {C}ancer {S}usceptibility from {M}ultiple
{S}ingle {N}ucleotide {P}olymorphisms},
journal = {Clin. {C}ancer {R}es.},
year = {2004},
volume = {10},
pages = {2725-2737},
number = {8},
abstract = {Hereditary predisposition and causative environmental exposures have
long been recognized in human malignancies. {I}n most instances,
cancer cases occur sporadically, suggesting that environmental influences
are critical in determining cancer risk. {T}o test the influence
of genetic polymorphisms on breast cancer risk, we have measured
98 single nucleotide polymorphisms ({SNP}s) distributed over 45 genes
of potential relevance to breast cancer etiology in 174 patients
and have compared these with matched normal controls. {U}sing machine
learning techniques such as support vector machines ({SVM}s), decision
trees, and naive {B}ayes, we identified a subset of three {SNP}s
as key discriminators between breast cancer and controls. {T}he {SVM}s
performed maximally among predictive models, achieving 69% predictive
power in distinguishing between the two groups, compared with a 50%
baseline predictive power obtained from the data after repeated random
permutation of class labels (individuals with cancer or controls).
{H}owever, the simpler naive {B}ayes model as well as the decision
tree model performed quite similarly to the {SVM}. {T}he three {SNP}
sites most useful in this model were (a) the +4536{T}/{C} site of
the aldosterone synthase gene {CYP}11{B}2 at amino acid residue 386
{V}al/{A}la ({T}/{C}) (rs4541); (b) the +4328{C}/{G} site of the
aryl hydrocarbon hydroxylase {CYP}1{B}1 at amino acid residue 293
{L}eu/{V}al ({C}/{G}) (rs5292); and (c) the +4449{C}/{T} site of
the transcription factor {BCL}6 at amino acid 387 {A}sp/{A}sp (rs1056932).
{N}o single {SNP} site on its own could achieve more than 60% in
predictive accuracy. {W}e have shown that multiple {SNP} sites from
different genes over distant parts of the genome are better at identifying
breast cancer patients than any one {SNP} alone. {A}s high-throughput
technology for {SNP}s improves and as more {SNP}s are identified,
it is likely that much higher predictive accuracy will be achieved
and a useful clinical tool developed.},
eprint = {http://clincancerres.aacrjournals.org/cgi/reprint/10/8/2725.pdf},
pdf = {../local/Listgarten2004Predictive.pdf},
file = {Listgarten2004Predictive.pdf:local/Listgarten2004Predictive.pdf:PDF},
keywords = {biosvm, breastcancer},
owner = {jeanphilippevert},
url = {http://clincancerres.aacrjournals.org/cgi/content/abstract/10/8/2725}
}
@article{Liu2003Diagnosing,
author = {H. X. Liu and R. S. Zhang and F. Luan and X. J. Yao and M. C. Liu
and Z. D. Hu and B. T. Fan},
title = {Diagnosing breast cancer based on support vector machines},
journal = {J. Chem. Inf. Comput. Sci.},
year = {2003},
volume = {43},
pages = {900-7},
number = {3},
abstract = {The {S}upport {V}ector {M}achine ({SVM}) classification algorithm,
recently developed from the machine learning community, was used
to diagnose breast cancer. {A}t the same time, the {SVM} was compared
to several machine learning techniques currently used in this field.
{T}he classification task involves predicting the state of diseases,
using data obtained from the {UCI} machine learning repository. {SVM}
outperformed k-means cluster and two artificial neural networks on
the whole. {I}t can be concluded that nine samples could be mislabeled
from the comparison of several machine learning techniques.},
doi = {10.1021/ci0256438},
pdf = {../local/Liu2003Diagnosing.pdf},
file = {Liu2003Diagnosing.pdf:local/Liu2003Diagnosing.pdf:PDF},
keywords = {breastcancer},
url = {http://dx.doi.org/10.1021/ci0256438}
}
@article{Mattfeldt2004Prediction,
author = {T. Mattfeldt and H. A. Kestler and H. P. Sinn},
title = {Prediction of the axillary lymph node status in mammary cancer on
the basis of clinicopathological data and flow cytometry.},
journal = {Med {B}iol {E}ng {C}omput},
year = {2004},
volume = {42},
pages = {733-9},
number = {6},
month = {Nov},
abstract = {Axillary lymph node status is a major prognostic factor in mammary
carcinoma. {I}t is clinically desirable to predict the axillary lymph
node status from data from the mammary cancer specimen. {I}n the
study, the axillary lymph node status, routine histological parameters
and flow-cytometric data were retrospectively obtained from 1139
specimens of invasive mammary cancer. {T}he ten variables: age, tumour
type, tumour grade, tumour size, skin infiltration, lymphangiosis
carcinomatosa, p{T}4 category, percentage of tumour cells in {G}2/{M}-
and {S}-phases of the cell cycle, and ploidy index were considered
as predictor variables, and the single variable lymph node metastasis
p{N} (0 for p{N}0, or 1 for p{N}1 or p{N}2) was used as an output
variable. {A} stepwise logistic regression analysis, with the axillary
lymph node as a dependent variable, was used for feature selection.
{O}nly lymphangiosis carcinomatosa and tumour size proved to be significant
as independent predictor variables; the other variables were non-contributory.
{T}hree paradigms with supervised learning rules (multilayer perceptron,
learning vector quantisation and support vector machines) were used
for the purpose of prediction. {I}f any of these paradigms was used
with the information from all ten input variables, 73\% of cases
could be correctly predicted, with specificity ranging from 82 to
84\% and sensitivity ranging from 60 to 63\%. {I}f only the two significant
input variables were used, lymphangiosis carcinomatosa and tumour
diameter, the prediction accuracy was no worse. {N}early identical
results were obtained by two different techniques of cross-validation
(leave-one-out against ten-fold cross validation). {I}t was concluded
that: artificial neural networks can be used for risk stratification
on the basis of routine data in individual cases of mammary cancer;
and lymphangiosis carcinomatosa and tumour size are independent predictors
of axillary lymph node metastasis in mammary cancer.},
keywords = {breastcancer}
}
@article{Michiels2005Prediction,
author = {Michiels, S. and Koscielny, S. and Hill, C.},
title = {Prediction of cancer outcome with microarrays: a multiple random
validation strategy},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {488--492},
number = {9458},
abstract = {BACKGROUND: General studies of microarray gene-expression profiling
have been undertaken to predict cancer outcome. Knowledge of this
gene-expression profile or molecular signature should improve treatment
of patients by allowing treatment to be tailored to the severity
of the disease. We reanalysed data from the seven largest published
studies that have attempted to predict prognosis of cancer patients
on the basis of DNA microarray analysis. METHODS: The standard strategy
is to identify a molecular signature (ie, the subset of genes most
differentially expressed in patients with different outcomes) in
a training set of patients and to estimate the proportion of misclassifications
with this signature on an independent validation set of patients.
We expanded this strategy (based on unique training and validation
sets) by using multiple random sets, to study the stability of the
molecular signature and the proportion of misclassifications. FINDINGS:
The list of genes identified as predictors of prognosis was highly
unstable; molecular signatures strongly depended on the selection
of patients in the training sets. For all but one study, the proportion
misclassified decreased as the number of patients in the training
set increased. Because of inadequate validation, our chosen studies
published overoptimistic results compared with those from our own
analyses. Five of the seven studies did not classify patients better
than chance. INTERPRETATION: The prognostic value of published microarray
results in cancer studies should be considered with caution. We advocate
the use of validation by repeated random sampling.},
doi = {10.1016/S0140-6736(05)17866-0},
institution = {Biostatistics and Epidemiology Unit, Institut Gustave Roussy, Villejuif,
France.},
keywords = {featureselection, breastcancer, microarray},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {S0140-6736(05)17866-0},
pmid = {15705458},
timestamp = {2010.10.12},
url = {http://dx.doi.org/10.1016/S0140-6736(05)17866-0}
}
@article{Nattkemper2005Evaluation,
author = {Tim W Nattkemper and Bert Arnrich and Oliver Lichte and Wiebke Timm
and Andreas Degenhard and Linda Pointon and Carmel Hayes and Martin
O Leach and The UK MARIBS Breast Screening Study},
title = {Evaluation of radiological features for breast tumour classification
in clinical screening with machine learning methods.},
journal = {Artif. {I}ntell. {M}ed.},
year = {2005},
volume = {34},
pages = {129-39},
number = {2},
month = {Jun},
abstract = {O{BJECTIVE}: {I}n this work, methods utilizing supervised and unsupervised
machine learning are applied to analyze radiologically derived morphological
and calculated kinetic tumour features. {T}he features are extracted
from dynamic contrast enhanced magnetic resonance imaging ({DCE}-{MRI})
time-course data. {MATERIAL}: {T}he {DCE}-{MRI} data of the female
breast are obtained within the {UK} {M}ulticenter {B}reast {S}creening
{S}tudy. {T}he group of patients imaged in this study is selected
on the basis of an increased genetic risk for developing breast cancer.
{METHODS}: {T}he k-means clustering and self-organizing maps ({SOM})
are applied to analyze the signal structure in terms of visualization.
{W}e employ k-nearest neighbor classifiers (k-nn), support vector
machines ({SVM}) and decision trees ({DT}) to classify features using
a computer aided diagnosis ({CAD}) approach. {RESULTS}: {R}egarding
the unsupervised techniques, clustering according to features indicating
benign and malignant characteristics is observed to a limited extend.
{T}he supervised approaches classified the data with 74\% accuracy
({DT}) and providing an area under the receiver-operator-characteristics
({ROC}) curve ({AUC}) of 0.88 ({SVM}). {CONCLUSION}: {I}t was found
that contour and wash-out type ({WOT}) features determined by the
radiologists lead to the best {SVM} classification results. {A}lthough
a fast signal uptake in early time-point measurements is an important
feature for malignant/benign classification of tumours, our results
indicate that the wash-out characteristics might be considered as
important.},
keywords = {breastcancer}
}
@article{Perou2000Molecular,
author = {Perou, C M. and S{\o}rlie, T. and Eisen, M. B. and van de Rijn, M.
and Jeffrey, S. S. and Rees, C. A. and Pollack, J. R. and Ross, D.
T. and Johnsen, H. and Akslen, L. A. and Fluge, O. and Pergamenschikov,
A. and Williams, C. and Zhu, S. X. and L{\o}nning, P. E. and B{\o}rresen-Dale,
A. L. and Brown, P. O. and Botstein, D.},
title = {Molecular portraits of human breast tumours},
journal = {Nature},
year = {2000},
volume = {406},
pages = {747--752},
number = {6797},
month = {Aug},
abstract = {Human breast tumours are diverse in their natural history and in their
responsiveness to treatments. Variation in transcriptional programs
accounts for much of the biological diversity of human cells and
tumours. In each cell, signal transduction and regulatory systems
transduce information from the cell's identity to its environmental
status, thereby controlling the level of expression of every gene
in the genome. Here we have characterized variation in gene expression
patterns in a set of 65 surgical specimens of human breast tumours
from 42 different individuals, using complementary DNA microarrays
representing 8,102 human genes. These patterns provided a distinctive
molecular portrait of each tumour. Twenty of the tumours were sampled
twice, before and after a 16-week course of doxorubicin chemotherapy,
and two tumours were paired with a lymph node metastasis from the
same patient. Gene expression patterns in two tumour samples from
the same individual were almost always more similar to each other
than either was to any other sample. Sets of co-expressed genes were
identified for which variation in messenger RNA levels could be related
to specific features of physiological variation. The tumours could
be classified into subtypes distinguished by pervasive differences
in their gene expression patterns.},
doi = {10.1038/35021093},
pdf = {../local/Perou2000Molecular.pdf},
file = {Perou2000Molecular.pdf:Perou2000Molecular.pdf:PDF},
institution = {Department of Genetics, Stanford University School of Medicine, California
94305, USA.},
keywords = {breastcancer, csbcbook, csbcbook-ch3},
owner = {jp},
pmid = {10963602},
timestamp = {2009.02.04},
url = {http://dx.doi.org/10.1038/35021093}
}
@phdthesis{Reyal2009Analyse,
author = {Reyal, F.},
title = {Analyse du profil d'expression par la technique des puces {\`a} ADN.
Application \`a la caract\'erisation mol\'eculaire et \`a la d\'etermination
du pronostic des cancers canalaires infiltrants du sein.},
school = {Universit\'e Paris 11},
year = {2009},
keywords = {breastcancer, microarray},
owner = {jp},
timestamp = {2009.10.31}
}
@article{Sotiriou2003Breast,
author = {Sotiriou, C. and Neo, S.-Y. and McShane, L. M. and Korn, E. L. and
Long, P. M. and Jazaeri, A. and Martiat, P. and Fox, S. B. and Harris,
A. L. and Liu, E. T.},
title = {Breast cancer classification and prognosis based on gene expression
profiles from a population-based study.},
journal = {Proc. Natl. Acad. Sci. U. S. A.},
year = {2003},
volume = {100},
pages = {10393--10398},
number = {18},
month = {Sep},
abstract = {Comprehensive gene expression patterns generated from cDNA microarrays
were correlated with detailed clinico-pathological characteristics
and clinical outcome in an unselected group of 99 node-negative and
node-positive breast cancer patients. Gene expression patterns were
found to be strongly associated with estrogen receptor (ER) status
and moderately associated with grade, but not associated with menopausal
status, nodal status, or tumor size. Hierarchical cluster analysis
segregated the tumors into two main groups based on their ER status,
which correlated well with basal and luminal characteristics. Cox
proportional hazards regression analysis identified 16 genes that
were significantly associated with relapse-free survival at a stringent
significance level of 0.001 to account for multiple comparisons.
Of 231 genes previously reported by others [van't Veer, L. J., et
al. (2002) Nature 415, 530-536] as being associated with survival,
93 probe elements overlapped with the set of 7,650 probe elements
represented on the arrays used in this study. Hierarchical cluster
analysis based on the set of 93 probe elements segregated our population
into two distinct subgroups with different relapse-free survival
(P < 0.03). The number of these 93 probe elements showing significant
univariate association with relapse-free survival (P < 0.05) in the
present study was 14, representing 11 unique genes. Genes involved
in cell cycle, DNA replication, and chromosomal stability were consistently
elevated in the various poor prognostic groups. In addition, glutathione
S-transferase M3 emerged as an important survival marker in both
studies. When taken together with other array studies, our results
highlight the consistent biological and clinical associations with
gene expression profiles.},
doi = {10.1073/pnas.1732912100},
pdf = {../local/Sotiriou2003Breast.pdf},
file = {Sotiriou2003Breast.pdf:Sotiriou2003Breast.pdf:PDF},
institution = {Division of Clinical Sciences, National Cancer Institute, Advanced
Technology Center, 8717 Grovemont Circle, Gaithersburg, MD 20877,
USA.},
keywords = {breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {1732912100},
pmid = {12917485},
timestamp = {2010.07.02},
url = {http://dx.doi.org/10.1073/pnas.1732912100}
}
@article{Su2001Molecular,
author = {Su, A. I. and Welsh, J. B. and Sapinoso, L. M. and Kern, S. G. and
Dimitrov, P. and Lapp, H. and Schultz, P. G. and Powell, S. M. and
Moskaluk, C. A. and Frierson, H. F.Jr. and Hampton, G. M.},
title = {Molecular {C}lassification of {H}uman {C}arcinomas by {U}se of {G}ene
{E}xpression {S}ignatures},
journal = {Cancer {R}es.},
year = {2001},
volume = {61},
pages = {7388-7393},
number = {20},
abstract = {Classification of human tumors according to their primary anatomical
site of origin is fundamental for the optimal treatment of patients
with cancer. {H}ere we describe the use of large-scale {RNA} profiling
and supervised machine learning algorithms to construct a first-generation
molecular classification scheme for carcinomas of the prostate, breast,
lung, ovary, colorectum, kidney, liver, pancreas, bladder/ureter,
and gastroesophagus, which collectively account for [~]70% of all
cancer-related deaths in the {U}nited {S}tates. {T}he classification
scheme was based on identifying gene subsets whose expression typifies
each cancer class, and we quantified the extent to which these genes
are characteristic of a specific tumor type by accurately and confidently
predicting the anatomical site of tumor origin for 90% of 175 carcinomas,
including 9 of 12 metastatic lesions. {T}he predictor gene subsets
include those whose expression is typical of specific types of normal
epithelial differentiation, as well as other genes whose expression
is elevated in cancer. {T}his study demonstrates the feasibility
of predicting the tissue origin of a carcinoma in the context of
multiple cancer classes.},
pdf = {../local/Su2001Molecular.pdf.html},
file = {Su2001Molecular.pdf.html:local/Su2001Molecular.pdf.html:PDF},
keywords = {biosvm, breastcancer},
owner = {jeanphilippevert},
url = {http://cancerres.aacrjournals.org/cgi/content/abstract/61/20/7388}
}
@article{Soerlie2001Gene,
author = {S{\o}rlie, T. and Perou, C. M. and Tibshirani, R. and Aas, T. and
Geisler, S. and Johnsen, H. and Hastie, T. and Eisen, M. B. and van
de Rijn, M. and Jeffrey, S. S. and Thorsen, T. and Quist, H. and
Matese, J. C. and Brown, P. O. and Botstein, D. and Eystein L{\o}nning,
P. and B{\o}rresen-Dale, A. L.},
title = {Gene expression patterns of breast carcinomas distinguish tumor subclasses
with clinical implications},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2001},
volume = {98},
pages = {10869--10874},
number = {19},
month = {Sep},
abstract = {The purpose of this study was to classify breast carcinomas based
on variations in gene expression patterns derived from cDNA microarrays
and to correlate tumor characteristics to clinical outcome. A total
of 85 cDNA microarray experiments representing 78 cancers, three
fibroadenomas, and four normal breast tissues were analyzed by hierarchical
clustering. As reported previously, the cancers could be classified
into a basal epithelial-like group, an ERBB2-overexpressing group
and a normal breast-like group based on variations in gene expression.
A novel finding was that the previously characterized luminal epithelial/estrogen
receptor-positive group could be divided into at least two subgroups,
each with a distinctive expression profile. These subtypes proved
to be reasonably robust by clustering using two different gene sets:
first, a set of 456 cDNA clones previously selected to reflect intrinsic
properties of the tumors and, second, a gene set that highly correlated
with patient outcome. Survival analyses on a subcohort of patients
with locally advanced breast cancer uniformly treated in a prospective
study showed significantly different outcomes for the patients belonging
to the various groups, including a poor prognosis for the basal-like
subtype and a significant difference in outcome for the two estrogen
receptor-positive groups.},
doi = {10.1073/pnas.191367098},
pdf = {../local/Soerlie2001Gene.pdf},
file = {Soerlie2001Gene.pdf:Soerlie2001Gene.pdf:PDF},
institution = {cs, The Norwegian Radium Hospital, Montebello, N-0310 Oslo, Norway.},
keywords = {breastcancer, csbcbook, csbcbook-ch2},
owner = {jp},
pii = {98/19/10869},
pmid = {11553815},
timestamp = {2008.11.15},
url = {http://dx.doi.org/10.1073/pnas.191367098}
}
@article{Veer2002Gene,
author = {van 't Veer, L. J. and Dai, H. and van de Vijver, M. J. and He, Y.
D. and Hart, A. A. M. and Mao, M. and Peterse, H. L. and van der
Kooy, K. and Marton, M. J. and Witteveen, A. T. and Schreiber, G.
J. and Kerkhoven, R. M. and Roberts, C. and Linsley, P. S. and Bernards,
R. and Friend, S. H.},
title = {Gene expression profiling predicts clinical outcome of breast cancers},
journal = {Nature},
year = {2002},
volume = {415},
pages = {530--536},
number = {6871},
month = {Jan},
abstract = {Breast cancer patients with the same stage of disease can have markedly
different treatment responses and overall outcome. The strongest
predictors for metastases (for example, lymph node status and histological
grade) fail to classify accurately breast tumours according to their
clinical behaviour. Chemotherapy or hormonal therapy reduces the
risk of distant metastases by approximately one-third; however, 70-80\%
of patients receiving this treatment would have survived without
it. None of the signatures of breast cancer gene expression reported
to date allow for patient-tailored therapy strategies. Here we used
DNA microarray analysis on primary breast tumours of 117 young patients,
and applied supervised classification to identify a gene expression
signature strongly predictive of a short interval to distant metastases
('poor prognosis' signature) in patients without tumour cells in
local lymph nodes at diagnosis (lymph node negative). In addition,
we established a signature that identifies tumours of BRCA1 carriers.
The poor prognosis signature consists of genes regulating cell cycle,
invasion, metastasis and angiogenesis. This gene expression profile
will outperform all currently used clinical parameters in predicting
disease outcome. Our findings provide a strategy to select patients
who would benefit from adjuvant therapy.},
doi = {10.1038/415530a},
pdf = {../local/Veer2002Gene.pdf},
file = {Veer2002Gene.pdf:Veer2002Gene.pdf:PDF},
institution = {Division of Diagnostic Oncology, The Netherlands Cancer Institute,
121 Plesmanlaan, 1066 CX Amsterdam, The Netherlands.},
keywords = {breastcancer, csbcbook, csbcbook-ch3},
owner = {jp},
pii = {415530a},
pmid = {11823860},
timestamp = {2008.11.16},
url = {http://dx.doi.org/10.1038/415530a}
}
@article{Vijver2002gene-expression,
author = {van de Vijver, M. J. and He, Y. D. and van't Veer, L. J. and Dai,
H. and Hart, A. A. M. and Voskuil, D. W. and Schreiber, G. J. and
Peterse, J. L. and Roberts, C. and Marton, M. J. and Parrish, M.
and Atsma, D. and Witteveen, A. and Glas, A. and Delahaye, L. and
van der Velde, T. and Bartelink, H. and Rodenhuis, S. and Rutgers,
E. T. and Friend, S. H. and Bernards, R.},
title = {A gene-expression signature as a predictor of survival in breast
cancer},
journal = {N. Engl. J. Med.},
year = {2002},
volume = {347},
pages = {1999--2009},
number = {25},
month = {Dec},
abstract = {BACKGROUND: A more accurate means of prognostication in breast cancer
will improve the selection of patients for adjuvant systemic therapy.
METHODS: Using microarray analysis to evaluate our previously established
70-gene prognosis profile, we classified a series of 295 consecutive
patients with primary breast carcinomas as having a gene-expression
signature associated with either a poor prognosis or a good prognosis.
All patients had stage I or II breast cancer and were younger than
53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive
disease. We evaluated the predictive power of the prognosis profile
using univariable and multivariable statistical analyses. RESULTS:
Among the 295 patients, 180 had a poor-prognosis signature and 115
had a good-prognosis signature, and the mean (+/-SE) overall 10-year
survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively.
At 10 years, the probability of remaining free of distant metastases
was 50.6+/-4.5 percent in the group with a poor-prognosis signature
and 85.2+/-4.3 percent in the group with a good-prognosis signature.
The estimated hazard ratio for distant metastases in the group with
a poor-prognosis signature, as compared with the group with the good-prognosis
signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001).
This ratio remained significant when the groups were analyzed according
to lymph-node status. Multivariable Cox regression analysis showed
that the prognosis profile was a strong independent factor in predicting
disease outcome. CONCLUSIONS: The gene-expression profile we studied
is a more powerful predictor of the outcome of disease in young patients
with breast cancer than standard systems based on clinical and histologic
criteria.},
doi = {10.1056/NEJMoa021967},
pdf = {../local/Vijver2002gene-expression.pdf},
file = {Vijver2002gene-expression.pdf:local/Vijver2002gene-expression.pdf:PDF},
institution = {Division of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam,
The Netherlands.},
keywords = {breastcancer, csbcbook, csbcbook-ch3},
owner = {jp},
pii = {347/25/1999},
pmid = {12490681},
timestamp = {2008.11.16},
url = {http://dx.doi.org/10.1056/NEJMoa021967}
}
@article{Vincent-Salomon2008Integrated,
author = {Vincent-Salomon, A. and Lucchesi, C. and Gruel, N. and Raynal, V.
and Pierron, G. and Goudefroye, R. and Reyal, F. and Radvanyi, F.
and Salmon, R. and Thiery, J.-P. and Sastre-Garau, X. and Sigal-Zafrani,
B. and Fourquet, A. and Delattre, A.},
title = {Integrated genomic and transcriptomic analysis of ductal carcinoma
in situ of the breast},
journal = {Clin. Cancer Res.},
year = {2008},
volume = {14},
pages = {1956--1965},
number = {7},
month = {Apr},
abstract = {PURPOSE: To gain insight into genomic and transcriptomic subtypes
of ductal carcinomas in situ of the breast (DCIS). EXPERIMENTAL DESIGN:
We did a combined phenotypic and genomic analysis of a series of
57 DCIS integrated with gene expression profile analysis for 26 of
the 57 cases. RESULTS: Thirty-two DCIS exhibited a luminal phenotype;
21 were ERBB2 positive, and 4 were ERBB2/estrogen receptor (ER) negative
with 1 harboring a bona fide basal-like phenotype. Based on a CGH
analysis, genomic types were identified in this series of DCIS with
the 1q gain/16q loss combination observed in 3 luminal DCIS, the
mixed amplifier pattern including all ERBB2, 12 luminal and 2 ERBB2(-)/ER(-)
DCIS, and the complex copy number alteration profile encompassing
14 luminal and 1 ERBB2(-)/ER(-) DCIS. Eight cases (8 of 57; 14\%)
presented a TP53 mutation, all being amplifiers. Unsupervised analysis
of gene expression profiles of 26 of the 57 DCIS showed that luminal
and ERBB2-amplified, ER-negative cases clustered separately. We further
investigated the effect of high and low copy number changes on gene
expression. Strikingly, amplicons but also low copy number changes
especially on 1q, 8q, and 16q in DCIS regulated the expression of
a subset of genes in a very similar way to that recently described
in invasive ductal carcinomas. CONCLUSIONS: These combined approaches
show that the molecular heterogeneity of breast ductal carcinomas
exists already in in situ lesions and further indicate that DCIS
and invasive ductal carcinomas share genomic alterations with a similar
effect on gene expression profile.},
doi = {10.1158/1078-0432.CCR-07-1465},
pdf = {../local/Vincent-Salomon2008Integrated.pdf},
file = {Vincent-Salomon2008Integrated.pdf:Vincent-Salomon2008Integrated.pdf:PDF},
institution = {Institut Curie, Department of Tumor Biology, Paris, France.},
keywords = {breastcancer, cgh},
owner = {jp},
pii = {14/7/1956},
pmid = {18381933},
timestamp = {2008.12.09},
url = {http://dx.doi.org/10.1158/1078-0432.CCR-07-1465}
}
@article{Wang2005Gene-expression,
author = {Wang, Y. and Klijn, J.G.M. and Zhang, Y. and Sieuwerts, A.M. and
Look, M.P. and Yang, F. and Talantov, D. and Timmermans, M. and Meijer-van
Gelder, M.E. and Yu, J. and Jatkoe, T. and Berns, E.M.J.J. and Atkins,
D. and Foekens, J.A.},
title = {Gene-expression profiles to predict distant metastasis of lymph-node-negative
primary breast cancers},
journal = {Lancet},
year = {2005},
volume = {365},
pages = {671--679},
number = {9460},
abstract = {BACKGROUND: Genome-wide measures of gene expression can identify patterns
of gene activity that subclassify tumours and might provide a better
means than is currently available for individual risk assessment
in patients with lymph-node-negative breast cancer. METHODS: We analysed,
with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts
from total RNA of frozen tumour samples from 286 lymph-node-negative
patients who had not received adjuvant systemic treatment. FINDINGS:
In a training set of 115 tumours, we identified a 76-gene signature
consisting of 60 genes for patients positive for oestrogen receptors
(ER) and 16 genes for ER-negative patients. This signature showed
93\% sensitivity and 48\% specificity in a subsequent independent
testing set of 171 lymph-node-negative patients. The gene profile
was highly informative in identifying patients who developed distant
metastases within 5 years (hazard ratio 5.67 [95\% CI 2.59-12.4]),
even when corrected for traditional prognostic factors in multivariate
analysis (5.55 [2.46-12.5]). The 76-gene profile also represented
a strong prognostic factor for the development of metastasis in the
subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal
patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20
mm (14.1 [3.34-59.2]), a group of patients for whom prediction of
prognosis is especially difficult. INTERPRETATION: The identified
signature provides a powerful tool for identification of patients
at high risk of distant recurrence. The ability to identify patients
who have a favourable prognosis could, after independent confirmation,
allow clinicians to avoid adjuvant systemic therapy or to choose
less aggressive therapeutic options.},
doi = {10.1016/S0140-6736(05)17947-1},
pdf = {../local/Wang2005Gene-expression.pdf},
file = {Wang2005Gene-expression.pdf:local/Wang2005Gene-expression.pdf:PDF},
keywords = {microarray, breastcancer},
owner = {jp},
pii = {S0140673605179471},
pmid = {15894094},
timestamp = {2006.07.06},
url = {http://dx.doi.org/10.1016/S0140-6736(05)17947-1}
}
@article{Wellings1973origin,
author = {Wellings, S. R. and Jensen, H. M.},
title = {On the origin and progression of ductal carcinoma in the human breast},
journal = {J. Natl. Cancer Inst.},
year = {1973},
volume = {50},
pages = {1111--1118},
number = {5},
month = {May},
keywords = {breastcancer},
owner = {jp},
pmid = {4123242},
timestamp = {2009.02.04}
}
@article{Wirapati2008Meta-analysis,
author = {Wirapati, P. and Sotiriou, C. and Kunkel, S. and Farmer, P. and Pradervand,
S. and Haibe-Kains, B. and Desmedt, C. and Ignatiadis, M. and Sengstag,
T. and Sch\"utz, F. and Goldstein, D. R. and Piccart, M. and Delorenzi,
M.},
title = {Meta-analysis of gene expression profiles in breast cancer: toward
a unified understanding of breast cancer subtyping and prognosis
signatures.},
journal = {Breast Cancer Res.},
year = {2008},
volume = {10},
pages = {R65},
number = {4},
abstract = {INTRODUCTION: Breast cancer subtyping and prognosis have been studied
extensively by gene expression profiling, resulting in disparate
signatures with little overlap in their constituent genes. Although
a previous study demonstrated a prognostic concordance among gene
expression signatures, it was limited to only one dataset and did
not fully elucidate how the different genes were related to one another
nor did it examine the contribution of well-known biological processes
of breast cancer tumorigenesis to their prognostic performance. METHOD:
To address the above issues and to further validate these initial
findings, we performed the largest meta-analysis of publicly available
breast cancer gene expression and clinical data, which are comprised
of 2,833 breast tumors. Gene coexpression modules of three key biological
processes in breast cancer (namely, proliferation, estrogen receptor
[ER], and HER2 signaling) were used to dissect the role of constituent
genes of nine prognostic signatures. RESULTS: Using a meta-analytical
approach, we consolidated the signatures associated with ER signaling,
ERBB2 amplification, and proliferation. Previously published expression-based
nomenclature of breast cancer 'intrinsic' subtypes can be mapped
to the three modules, namely, the ER-/HER2- (basal-like), the HER2+
(HER2-like), and the low- and high-proliferation ER+/HER2- subtypes
(luminal A and B). We showed that all nine prognostic signatures
exhibited a similar prognostic performance in the entire dataset.
Their prognostic abilities are due mostly to the detection of proliferation
activity. Although ER- status (basal-like) and ERBB2+ expression
status correspond to bad outcome, they seem to act through elevated
expression of proliferation genes and thus contain only indirect
information about prognosis. Clinical variables measuring the extent
of tumor progression, such as tumor size and nodal status, still
add independent prognostic information to proliferation genes. CONCLUSION:
This meta-analysis unifies various results of previous gene expression
studies in breast cancer. It reveals connections between traditional
prognostic factors, expression-based subtyping, and prognostic signatures,
highlighting the important role of proliferation in breast cancer
prognosis.},
doi = {10.1186/bcr2124},
pdf = {../local/Wirapati2008Meta-analysis.pdf},
file = {Wirapati2008Meta-analysis.pdf:Wirapati2008Meta-analysis.pdf:PDF},
institution = {Swiss Institute of Bioinformatics, 'Batiment Genopode', University
of Lausanne, 1015 Lausanne, Switzerland. Pratyaksha.Wirapati@isb-sib.ch},
keywords = {microarray, breastcancer},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {bcr2124},
pmid = {18662380},
timestamp = {2010.10.13},
url = {http://dx.doi.org/10.1186/bcr2124}
}
@comment{{jabref-meta: selector_author:}}
@comment{{jabref-meta: selector_journal:Adv. Drug Deliv. Rev.;Am. J. Hu
m. Genet.;Am. J. Pathol.;Ann. Appl. Stat.;Ann. Math. Statist.;Ann. N.
Y. Acad. Sci.;Ann. Probab.;Ann. Stat.;Artif. Intell. Med.;Bernoulli;Bi
ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
.;Clin. Cancer Res.;Combinator. Probab. Comput.;Comm. Pure Appl. Math.
;Comput. Chem.;Comput. Comm. Rev.;Comput. Stat. Data An.;Curr. Genom.;
Curr. Opin. Chem. Biol.;Curr. Opin. Drug Discov. Devel.;Data Min. Know
l. Discov.;Electron. J. Statist.;Eur. J. Hum. Genet.;FEBS Lett.;Found.
Comput. Math.;Genome Biol.;IEEE T. Neural Networ.;IEEE T. Pattern. An
al.;IEEE T. Signal. Proces.;IEEE Trans. Inform. Theory;IEEE Trans. Kno
wl. Data Eng.;IEEE/ACM Trans. Comput. Biol. Bioinf.;Int. J. Comput. Vi
sion;Int. J. Data Min. Bioinform.;Int. J. Qantum Chem.;J Biol Syst;J.
ACM;J. Am. Soc. Inf. Sci. Technol.;J. Am. Stat. Assoc.;J. Bioinform. C
omput. Biol.;J. Biol. Chem.;J. Biomed. Inform.;J. Cell. Biochem.;J. Ch
em. Inf. Comput. Sci.;J. Chem. Inf. Model.;J. Clin. Oncol.;J. Comput.
Biol.;J. Comput. Graph. Stat.;J. Eur. Math. Soc.;J. Intell. Inform. Sy
st.;J. Mach. Learn. Res.;J. Med. Chem.;J. Mol. BIol.;J. R. Stat. Soc.
Ser. B;Journal of Statistical Planning and Inference;Mach. Learn.;Math
. Program.;Meth. Enzymol.;Mol. Biol. Cell;Mol. Biol. Evol.;Mol. Cell.
Biol.;Mol. Syst. Biol.;N. Engl. J. Med.;Nat. Biotechnol.;Nat. Genet.;N
at. Med.;Nat. Methods;Nat. Rev. Cancer;Nat. Rev. Drug Discov.;Nat. Rev
. Genet.;Nature;Neural Comput.;Neural Network.;Neurocomputing;Nucleic
Acids Res.;Pattern Anal. Appl.;Pattern Recognit.;Phys. Rev. E;Phys. Re
v. Lett.;PLoS Biology;PLoS Comput. Biol.;Probab. Theory Relat. Fields;
Proc. IEEE;Proc. Natl. Acad. Sci. USA;Protein Eng.;Protein Eng. Des. S
el.;Protein Sci.;Protein. Struct. Funct. Genet.;Random Struct. Algorit
hm.;Rev. Mod. Phys.;Science;Stat. Probab. Lett.;Statistica Sinica;Theo
r. Comput. Sci.;Trans. Am. Math. Soc.;Trends Genet.;}}
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