@comment{{This file has been generated by bib2bib 1.97}}
@comment{{Command line: bib2bib ../bibli.bib -c 'subject:"csbcbook-ch3" or keywords:"csbcbook-ch3"' -ob tmp.bib}}
@article{Veer2008Enabling,
author = {{van't Veer}, L. J. and Bernards, R.},
title = {Enabling personalized cancer medicine through analysis of gene-expression
patterns.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {564--570},
number = {7187},
month = {Apr},
abstract = {Therapies for patients with cancer have changed gradually over the
past decade, moving away from the administration of broadly acting
cytotoxic drugs towards the use of more-specific therapies that are
targeted to each tumour. To facilitate this shift, tests need to
be developed to identify those individuals who require therapy and
those who are most likely to benefit from certain therapies. In particular,
tests that predict the clinical outcome for patients on the basis
of the genes expressed by their tumours are likely to increasingly
affect patient management, heralding a new era of personalized medicine.},
doi = {10.1038/nature06915},
pdf = {../local/Veer2008Enabling.pdf},
file = {Veer2008Enabling.pdf:Veer2008Enabling.pdf:PDF},
institution = {Agendia BV, Louwesweg 6, 1066 EC Amsterdam, The Netherlands.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature06915},
pmid = {18385730},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature06915}
}
@article{Billerey1996Etude,
author = {Billerey, C. and Boccon-Gibod, L.},
title = {Etude des variations inter-pathologistes dans l'{\'e}valuation du
grade et du stade des tumeurs v{\'e}sicales},
journal = {Progr{\`e}s en Urologie},
year = {1996},
volume = {6},
pages = {49--57},
pdf = {../local/Billerey1996Etude.pdf},
file = {Billerey1996Etude.pdf:Billerey1996Etude.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3},
url = {http://www.urofrance.org/fileadmin/documents/data/PU/1996/PU-1996-00070049/TEXF-PU-1996-00070049.PDF}
}
@article{Buyse2006Validation,
author = {Buyse, M. and Loi, S. and van't Veer, S. and Viale, G. and Delorenzi,
M. and Glas, A. M. and Saghatchian d'Assignies, M. and Bergh, J.
and Lidereau, R. and Ellis, P. and Harris, A. and Bogaerts, J. and
Therasse, P. and Floore, A. and Amakrane, M. and Piette, F. and Rutgers,
E. and Sotiriou, C. and Cardoso, F. and Piccart, M. J. and T. R.
A. N. S. B. I. G. Consortium},
title = {Validation and clinical utility of a 70-gene prognostic signature
for women with node-negative breast cancer.},
journal = {J. Natl. Canc. Inst.},
year = {2006},
volume = {98},
pages = {1183--1192},
number = {17},
month = {Sep},
abstract = {BACKGROUND: A 70-gene signature was previously shown to have prognostic
value in patients with node-negative breast cancer. Our goal was
to validate the signature in an independent group of patients. METHODS:
Patients (n = 307, with 137 events after a median follow-up of 13.6
years) from five European centers were divided into high- and low-risk
groups based on the gene signature classification and on clinical
risk classifications. Patients were assigned to the gene signature
low-risk group if their 5-year distant metastasis-free survival probability
as estimated by the gene signature was greater than 90\%. Patients
were assigned to the clinicopathologic low-risk group if their 10-year
survival probability, as estimated by Adjuvant! software, was greater
than 88\% (for estrogen receptor [ER]-positive patients) or 92\%
(for ER-negative patients). Hazard ratios (HRs) were estimated to
compare time to distant metastases, disease-free survival, and overall
survival in high- versus low-risk groups. RESULTS: The 70-gene signature
outperformed the clinicopathologic risk assessment in predicting
all endpoints. For time to distant metastases, the gene signature
yielded HR = 2.32 (95\% confidence interval [CI] = 1.35 to 4.00)
without adjustment for clinical risk and hazard ratios ranging from
2.13 to 2.15 after adjustment for various estimates of clinical risk;
clinicopathologic risk using Adjuvant! software yielded an unadjusted
HR = 1.68 (95\% CI = 0.92 to 3.07). For overall survival, the gene
signature yielded an unadjusted HR = 2.79 (95\% CI = 1.60 to 4.87)
and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic
risk yielded an unadjusted HR = 1.67 (95\% CI = 0.93 to 2.98). For
patients in the gene signature high-risk group, 10-year overall survival
was 0.69 for patients in both the low- and high-clinical risk groups;
for patients in the gene signature low-risk group, the 10-year survival
rates were 0.88 and 0.89, respectively. CONCLUSIONS: The 70-gene
signature adds independent prognostic information to clinicopathologic
risk assessment for patients with early breast cancer.},
doi = {10.1093/jnci/djj329},
pdf = {../local/Buyse2006Validation.pdf},
file = {Buyse2006Validation.pdf:Buyse2006Validation.pdf:PDF},
institution = {International Drug Development Institute, Brussels, Belgium.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {16954471},
timestamp = {2009.10.17},
url = {http://dx.doi.org/10.1093/jnci/djj329}
}
@article{Calin2006MicroRNA,
author = {Calin, G. A. and Croce, C. M.},
title = {Micro{RNA} signatures in human cancers},
journal = {Nat. Rev. Cancer},
year = {2006},
volume = {6},
pages = {857--866},
number = {11},
month = {Nov},
abstract = {MicroRNA (miRNA) alterations are involved in the initiation and progression
of human cancer. The causes of the widespread differential expression
of miRNA genes in malignant compared with normal cells can be explained
by the location of these genes in cancer-associated genomic regions,
by epigenetic mechanisms and by alterations in the miRNA processing
machinery. MiRNA-expression profiling of human tumours has identified
signatures associated with diagnosis, staging, progression, prognosis
and response to treatment. In addition, profiling has been exploited
to identify miRNA genes that might represent downstream targets of
activated oncogenic pathways, or that target protein-coding genes
involved in cancer.},
doi = {10.1038/nrc1997},
pdf = {../local/Calin2006MicroRNA.pdf},
file = {Calin2006MicroRNA.pdf:Calin2006MicroRNA.pdf:PDF},
institution = {Department of Molecular Virology, Immunology and Medical Genetics
and Comprehensive Cancer Center, Ohio State University, Columbus,
Ohio 43210, USA.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nrc1997},
pmid = {17060945},
timestamp = {2009.10.17},
url = {http://dx.doi.org/10.1038/nrc1997}
}
@article{Chen2002Gene,
author = {Chen, X. and Cheung, S. T. and So, S. and Fan, S. T. and Barry, C.
and Higgins, J. and Lai, K.-M. and Ji, J. and Dudoit, S. and Ng,
I. O L. and {Van De Rijn}, M. and Botstein, D. and Brown, P. O.},
title = {Gene expression patterns in human liver cancers.},
journal = {Mol. Biol. Cell},
year = {2002},
volume = {13},
pages = {1929--1939},
number = {6},
month = {Jun},
abstract = {Hepatocellular carcinoma (HCC) is a leading cause of death worldwide.
Using cDNA microarrays to characterize patterns of gene expression
in HCC, we found consistent differences between the expression patterns
in HCC compared with those seen in nontumor liver tissues. The expression
patterns in HCC were also readily distinguished from those associated
with tumors metastatic to liver. The global gene expression patterns
intrinsic to each tumor were sufficiently distinctive that multiple
tumor nodules from the same patient could usually be recognized and
distinguished from all the others in the large sample set on the
basis of their gene expression patterns alone. The distinctive gene
expression patterns are characteristic of the tumors and not the
patient; the expression programs seen in clonally independent tumor
nodules in the same patient were no more similar than those in tumors
from different patients. Moreover, clonally related tumor masses
that showed distinct expression profiles were also distinguished
by genotypic differences. Some features of the gene expression patterns
were associated with specific phenotypic and genotypic characteristics
of the tumors, including growth rate, vascular invasion, and p53
overexpression.},
doi = {10.1091/mbc.02-02-0023},
pdf = {../local/Chen2002Gene.pdf},
file = {Chen2002Gene.pdf:Chen2002Gene.pdf:PDF},
institution = {Department of Biochemistry, Stanford University School of Medicine,
Stanford, California 94305, USA.},
keywords = {csbcbook-ch3, csbcbook},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {12058060},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1091/mbc.02-02-0023}
}
@article{Chin2008Translating,
author = {Chin, L. and Gray, J. W.},
title = {Translating insights from the cancer genome into clinical practice.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {553--563},
number = {7187},
month = {Apr},
abstract = {Cancer cells have diverse biological capabilities that are conferred
by numerous genetic aberrations and epigenetic modifications. Today's
powerful technologies are enabling these changes to the genome to
be catalogued in detail. Tomorrow is likely to bring a complete atlas
of the reversible and irreversible alterations that occur in individual
cancers. The challenge now is to work out which molecular abnormalities
contribute to cancer and which are simply 'noise' at the genomic
and epigenomic levels. Distinguishing between these will aid in understanding
how the aberrations in a cancer cell collaborate to drive pathophysiology.
Past successes in converting information from genomic discoveries
into clinical tools provide valuable lessons to guide the translation
of emerging insights from the genome into clinical end points that
can affect the practice of cancer medicine.},
doi = {10.1038/nature06914},
pdf = {../local/Chin2008Translating.pdf},
file = {Chin2008Translating.pdf:Chin2008Translating.pdf:PDF},
institution = {Dana-Farber Cancer Institute and Harvard Medical School, 44 Binney
Street, Boston, Massachusetts 02115, USA. lynda_chin@dfci.harvard.edu},
keywords = {csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature06914},
pmid = {18385729},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature06914}
}
@article{Cianfrocca2004Prognostic,
author = {Cianfrocca, M. and Goldstein, L. J.},
title = {Prognostic and predictive factors in early-stage breast cancer},
journal = {Oncologist},
year = {2004},
volume = {9},
pages = {606--616},
number = {6},
abstract = {Breast cancer is the most common malignancy among American women.
Due to increased screening, the majority of patients present with
early-stage breast cancer. The Oxford Overview Analysis demonstrates
that adjuvant hormonal therapy and polychemotherapy reduce the risk
of recurrence and death from breast cancer. Adjuvant systemic therapy,
however, has associated risks and it would be useful to be able to
optimally select patients most likely to benefit. The purpose of
adjuvant systemic therapy is to eradicate distant micrometastatic
deposits. It is essential therefore to be able to estimate an individual
patient's risk of harboring clinically silent micrometastatic disease
using established prognostic factors. It is also beneficial to be
able to select the optimal adjuvant therapy for an individual patient
based on established predictive factors. It is standard practice
to administer systemic therapy to all patients with lymph node-positive
disease. However, there are clearly differences among node-positive
women that may warrant a more aggressive therapeutic approach. Furthermore,
there are many node-negative women who would also benefit from adjuvant
systemic therapy. Prognostic factors therefore must be differentiated
from predictive factors. A prognostic factor is any measurement available
at the time of surgery that correlates with disease-free or overall
survival in the absence of systemic adjuvant therapy and, as a result,
is able to correlate with the natural history of the disease. In
contrast, a predictive factor is any measurement associated with
response to a given therapy. Some factors, such as hormone receptors
and HER2/neu overexpression, are both prognostic and predictive.},
doi = {10.1634/theoncologist.9-6-606},
pdf = {../local/Cianfrocca2004Prognostic.pdf},
file = {Cianfrocca2004Prognostic.pdf:Cianfrocca2004Prognostic.pdf:PDF},
institution = {D.O., Fox Chase Cancer Center, 333 Cottman Ave, Philadelphia, Pennsylvania
19111, USA. M_Cianfrocca@fccc.edu},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {9/6/606},
pmid = {15561805},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1634/theoncologist.9-6-606}
}
@article{Ellis1992Pathological,
author = {Ellis, I. O. and Galea, M. and Broughton, N. and Locker, A. and Blamey,
R. W. and Elston, C. W.},
title = {Pathological prognostic factors in breast cancer. II. Histological
type. Relationship with survival in a large study with long-term
follow-up.},
journal = {Histopathology},
year = {1992},
volume = {20},
pages = {479--489},
number = {6},
month = {Jun},
abstract = {The histological tumour type determined by current criteria has been
investigated in a consecutive series of 1621 women with primary operable
breast carcinoma, presenting between 1973 and 1987. All women underwent
definitive surgery with node biopsy and none received adjuvant systemic
therapy. Special types, tubular, invasive cribriform and mucinous,
with a very favourable prognosis can be identified. A common type
of tumour recognized by our group and designated tubular mixed carcinoma
is shown to be prognostically distinct from carcinomas of no special
type; it has a characteristic histological appearance and is the
third most common type in this series. Analysis of subtypes of lobular
carcinoma confirms differing prognoses. The classical, tubulo-lobular
and lobular mixed types are associated with a better prognosis than
carcinomas of no special type; this is not so for the solid variant.
Tubulo-lobular carcinoma in particular has an extremely good prognosis
similar to tumours included in the 'special type' category above.
Neither medullary carcinoma nor atypical medullary carcinoma are
found to carry a survival advantage over carcinomas of no special
type. The results confirm that histological typing of human breast
carcinoma can provide useful prognostic information.},
doi = {10.1111/j.1365-2559.1992.tb01032.x},
pdf = {../local/Ellis1992Pathological.pdf},
file = {Ellis1992Pathological.pdf:Ellis1992Pathological.pdf:PDF},
institution = {Department of Histopathology, City Hospital, Nottingham, UK.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {1607149},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1111/j.1365-2559.1992.tb01032}
}
@article{Elston1991Pathological,
author = {Elston, C. W. and Ellis, I. O.},
title = {Pathological prognostic factors in breast cancer. I. The value of
histological grade in breast cancer: experience from a large study
with long-term follow-up.},
journal = {Histopathology},
year = {1991},
volume = {19},
pages = {403--410},
number = {5},
month = {Nov},
abstract = {Morphological assessment of the degree of differentiation has been
shown in numerous studies to provide useful prognostic information
in breast cancer, but until recently histological grading has not
been accepted as a routine procedure, mainly because of perceived
problems with reproducibility and consistency. In the Nottingham/Tenovus
Primary Breast Cancer Study the most commonly used method, described
by Bloom & Richardson, has been modified in order to make the criteria
more objective. The revised technique involves semiquantitative evaluation
of three morphological features--the percentage of tubule formation,
the degree of nuclear pleomorphism and an accurate mitotic count
using a defined field area. A numerical scoring system is used and
the overall grade is derived from a summation of individual scores
for the three variables: three grades of differentiation are used.
Since 1973, over 2200 patients with primary operable breast cancer
have been entered into a study of multiple prognostic factors. Histological
grade, assessed in 1831 patients, shows a very strong correlation
with prognosis; patients with grade I tumours have a significantly
better survival than those with grade II and III tumours (P less
than 0.0001). These results demonstrate that this method for histological
grading provides important prognostic information and, if the grading
protocol is followed consistently, reproducible results can be obtained.
Histological grade forms part of the multifactorial Nottingham prognostic
index, together with tumour size and lymph node stage, which is used
to stratify individual patients for appropriate therapy.},
doi = {10.1111/j.1365-2559.1991.tb00229.x},
pdf = {../local/Elston1991Pathological.pdf},
file = {Elston1991Pathological.pdf:Elston1991Pathological.pdf:PDF},
institution = {Department of Histopathology, City Hospital, Nottingham, UK.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {1757079},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1111/j.1365-2559.1991.tb00229.x}
}
@article{Finetti2008Sixteen-kinase,
author = {Finetti, P. and Cervera, N. and Charafe-Jauffret, E. and Chabannon,
C. and Charpin, C. and Chaffanet, M. and Jacquemier, J. and Viens,
P. and Birnbaum, D. and Bertucci, F.},
title = {Sixteen-kinase gene expression identifies luminal breast cancers
with poor prognosis},
journal = {Cancer Res.},
year = {2008},
volume = {68},
pages = {767--776},
number = {3},
month = {Feb},
abstract = {Breast cancer is a heterogeneous disease made of various molecular
subtypes with different prognosis. However, evolution remains difficult
to predict within some subtypes, such as luminal A, and treatment
is not as adapted as it should be. Refinement of prognostic classification
and identification of new therapeutic targets are needed. Using oligonucleotide
microarrays, we profiled 227 breast cancers. We focused our analysis
on two major breast cancer subtypes with opposite prognosis, luminal
A (n = 80) and basal (n = 58), and on genes encoding protein kinases.
Whole-kinome expression separated luminal A and basal tumors. The
expression (measured by a kinase score) of 16 genes encoding serine/threonine
kinases involved in mitosis distinguished two subgroups of luminal
A tumors: Aa, of good prognosis and Ab, of poor prognosis. This classification
and its prognostic effect were validated in 276 luminal A cases from
three independent series profiled across different microarray platforms.
The classification outperformed the current prognostic factors in
univariate and multivariate analyses in both training and validation
sets. The luminal Ab subgroup, characterized by high mitotic activity
compared with luminal Aa tumors, displayed clinical characteristics
and a kinase score intermediate between the luminal Aa subgroup and
the luminal B subtype, suggesting a continuum in luminal tumors.
Some of the mitotic kinases of the signature represent therapeutic
targets under investigation. The identification of luminal A cases
of poor prognosis should help select appropriate treatment, whereas
the identification of a relevant kinase set provides potential targets.},
doi = {10.1158/0008-5472.CAN-07-5516},
pdf = {../local/Finetti2008Sixteen-kinase.pdf},
file = {Finetti2008Sixteen-kinase.pdf:Finetti2008Sixteen-kinase.pdf:PDF},
institution = {UMR599 Inserm, Institut Paoli-Calmettes, Laboratoire d'Oncologie
Moléculaire, Centre de Recherche en Cancérologie de Marseille, Marseille,
France.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {68/3/767},
pmid = {18245477},
timestamp = {2009.10.18},
url = {http://dx.doi.org/10.1158/0008-5472.CAN-07-5516}
}
@article{Golub1999Molecular,
author = {Golub, T. R. and Slonim, D. K. and Tamayo, P. and Huard, C. and Gaasenbeek,
M. and Mesirov, J. P. and Coller, H. and Loh, M. L. and Downing,
J. R. and Caligiuri, M. A. and Bloomfield, C. D. and Lander, E. S.},
title = {Molecular classification of cancer: class discovery and class prediction
by gene expression monitoring},
journal = {Science},
year = {1999},
volume = {286},
pages = {531--537},
abstract = {Although cancer classification has improved over the past 30 years,
there has been no general approach for identifying new cancer classes
(class discovery) or for assigning tumors to known classes (class
prediction). Here, a generic approach to cancer classification based
on gene expression monitoring by DNA microarrays is described and
applied to human acute leukemias as a test case. A class discovery
procedure automatically discovered the distinction between acute
myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without
previous knowledge of these classes. An automatically derived class
predictor was able to determine the class of new leukemia cases.
The results demonstrate the feasibility of cancer classification
based solely on gene expression moni- toring and suggest a general
strategy for discovering and predicting cancer classes for other
types of cancer, independent of previous biological knowledge.},
doi = {10.1126/science.286.5439.531},
pdf = {../local/Golub1999Molecular.pdf},
file = {Golub1999Molecular.pdf:Golub1999Molecular.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3, csbcbook-ch4},
subject = {microarray},
url = {http://dx.doi.org/10.1126/science.286.5439.531}
}
@article{Lowery2008MicroRNAs,
author = {Lowery, A. J. and Miller, N. and McNeill, R. E. and Kerin, M. J.},
title = {{MicroRNAs} as prognostic indicators and therapeutic targets: potential
effect on breast cancer management.},
journal = {Clin. Cancer Res.},
year = {2008},
volume = {14},
pages = {360--365},
number = {2},
month = {Jan},
abstract = {The discovery of microRNAs (miRNA) as novel modulators of gene expression
has resulted in a rapidly expanding repertoire of molecules in this
family, as reflected in the concomitant expansion of scientific literature.
MiRNAs are a category of naturally occurring RNA molecules that play
important regulatory roles in plants and animals by targeting mRNAs
for cleavage or translational repression. Characteristically, miRNAs
are noncoding, single-stranded short (18-22 nucleotides) RNAs, features
which possibly explain why they had not been intensively investigated
until recently. Accumulating experimental evidence indicates that
miRNAs play a pivotal role in many cellular functions via the regulation
of gene expression. Furthermore, their dysregulation and/or mutation
has been shown in carcinogenesis. We provide a brief review of miRNA
biogenesis and discuss the technical challenges of modifying experimental
techniques to facilitate the identification and characterization
of these small RNAs. MiRNA function and their involvement in malignancy,
particularly their putative role as oncogenes or tumor suppressors
is also discussed, with a specific emphasis on breast cancer. Finally,
we comment on the potential role of miRNAs in breast cancer management,
particularly in improving current prognostic tools and achieving
the goal of individualized cancer treatment.},
doi = {10.1158/1078-0432.CCR-07-0992},
pdf = {../local/Lowery2008MicroRNAs.pdf},
file = {Lowery2008MicroRNAs.pdf:Lowery2008MicroRNAs.pdf:PDF},
institution = {Department of Surgery, National University of Ireland, Galway, Ireland.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {14/2/360},
pmid = {18223209},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1158/1078-0432.CCR-07-0992}
}
@article{Lu2005MicroRNA,
author = {Lu, J. and Getz, G. and Miska, E. A. and Alvarez-Saavedra, E. and
Lamb, J. and Peck, D. and Sweet-Cordero, A. and Ebert, D. L. and
Mak, R. H. and Ferrando, A. A. and Downing, J. R. and Jacks, T. and
Horvitz, H. R. and Golub, T. R.},
title = {MicroRNA expression profiles classify human cancers.},
journal = {Nature},
year = {2005},
volume = {435},
pages = {834--838},
number = {7043},
month = {Jun},
abstract = {Recent work has revealed the existence of a class of small non-coding
RNA species, known as microRNAs (miRNAs), which have critical functions
across various biological processes. Here we use a new, bead-based
flow cytometric miRNA expression profiling method to present a systematic
expression analysis of 217 mammalian miRNAs from 334 samples, including
multiple human cancers. The miRNA profiles are surprisingly informative,
reflecting the developmental lineage and differentiation state of
the tumours. We observe a general downregulation of miRNAs in tumours
compared with normal tissues. Furthermore, we were able to successfully
classify poorly differentiated tumours using miRNA expression profiles,
whereas messenger RNA profiles were highly inaccurate when applied
to the same samples. These findings highlight the potential of miRNA
profiling in cancer diagnosis.},
doi = {10.1038/nature03702},
pdf = {../local/Lu2005MicroRNA.pdf},
file = {Lu2005MicroRNA.pdf:Lu2005MicroRNA.pdf:PDF},
institution = {Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141,
USA.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature03702},
pmid = {15944708},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature03702}
}
@article{Loenning2007Breast,
author = {L{\o}nning, P. E.},
title = {Breast cancer prognostication and prediction: are we making progress?},
journal = {Ann. Oncol.},
year = {2007},
volume = {18 Suppl 8},
pages = {viii3--viii7},
month = {Sep},
abstract = {Currently, much effort is being invested in the identification of
new, accurate prognostic and predictive factors in breast cancer.
Prognostic factors assess the patient's risk of relapse based on
indicators such as intrinsic tumor biology and disease stage at diagnosis,
and are traditionally used to identify patients who can be spared
unnecessary adjuvant therapy based only on the risk of relapse. Lymph
node status and tumor size are accepted as well-defined prognostic
factors in breast cancer. Predictive factors, in contrast, determine
the responsiveness of a particular tumor to a specific treatment.
Despite recent advances in the understanding of breast cancer biology
and changing practices in disease management, with the exception
of hormone receptor status, which predicts responsiveness to endocrine
treatment, no predictive factor for response to systemic therapy
in breast cancer is widely accepted. While gene expression studies
have provided important new information with regard to tumor biology
and prognostication, attempts to identify predictive factors have
not been successful so far. This article will focus on recent advances
in prognostication and prediction, with emphasis on findings from
gene expression profiling studies.},
doi = {10.1093/annonc/mdm260},
pdf = {../local/Loenning2007Breast.pdf},
file = {Loenning2007Breast.pdf:Loenning2007Breast.pdf:PDF},
institution = {Institute of Medicine, University of Bergen, Department of Oncology,
Haukeland University Hospital, Bergen, Norway. per.lonning@helse-bergen.no},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {18/suppl_8/viii3},
pmid = {17890212},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1093/annonc/mdm260}
}
@article{Miller2007Expression,
author = {Miller, L.D. and Liu, E.T.},
title = {Expression genomics in breast cancer research: microarrays at the
crossroads of biology and medicine},
journal = {Breast Cancer Res.},
year = {2007},
volume = {9},
pages = {206},
abstract = {Genome-wide expression microarray studies have revealed that the biological
and clinical heterogeneity of breast cancer can be partly explained
by information embedded within a complex but ordered transcriptional
architecture. Comprising this architecture are gene expression networks,
or signatures, reflecting biochemical and behavioral properties of
tumors that might be harnessed to improve disease subtyping, patient
prognosis and prediction of therapeutic response. Emerging 'hypothesis-driven'
strategies that incorporate knowledge of pathways and other biological
phenomena in the signature discovery process are linking prognosis
and therapy prediction with transcriptional readouts of tumorigenic
mechanisms that better inform therapeutic options.},
doi = {10.1186/bcr1662},
pdf = {../local/Miller2007Expression.pdf},
file = {Miller2007Expression.pdf:Miller2007Expression.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3},
url = {http://dx.doi.org/10.1186/bcr1662}
}
@article{Perou1999Distinctive,
author = {Perou, C. M. and Jeffrey, S. S. and {van de Rijn}, M. and Rees, C.
A. and Eisen, M. B. and Ross, D. T. and Pergamenschikov, A. and Williams,
C. F. and Zhu, S. X. and Lee, J. C. and Lashkari, D. and Shalon,
D. and Brown, P. O. and Botstein, D.},
title = {Distinctive gene expression patterns in human mammary epithelial
cells and breast cancers.},
journal = {Proc. Natl. Acad. Sci. U S A},
year = {1999},
volume = {96},
pages = {9212--9217},
number = {16},
month = {Aug},
abstract = {cDNA microarrays and a clustering algorithm were used to identify
patterns of gene expression in human mammary epithelial cells growing
in culture and in primary human breast tumors. Clusters of coexpressed
genes identified through manipulations of mammary epithelial cells
in vitro also showed consistent patterns of variation in expression
among breast tumor samples. By using immunohistochemistry with antibodies
against proteins encoded by a particular gene in a cluster, the identity
of the cell type within the tumor specimen that contributed the observed
gene expression pattern could be determined. Clusters of genes with
coherent expression patterns in cultured cells and in the breast
tumors samples could be related to specific features of biological
variation among the samples. Two such clusters were found to have
patterns that correlated with variation in cell proliferation rates
and with activation of the IFN-regulated signal transduction pathway,
respectively. Clusters of genes expressed by stromal cells and lymphocytes
in the breast tumors also were identified in this analysis. These
results support the feasibility and usefulness of this systematic
approach to studying variation in gene expression patterns in human
cancers as a means to dissect and classify solid tumors.},
doi = {10.1073/pnas.96.16.9212},
pdf = {../local/Perou1999Distinctive.pdf},
file = {Perou1999Distinctive.pdf:Perou1999Distinctive.pdf:PDF},
institution = {Department of Genetics, Stanford University School of Medicine, Stanford,
CA 94305, USA.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {10430922},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1073/pnas.96.16.9212}
}
@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}
}
@article{Sawyers2008cancer,
author = {Sawyers, C. L.},
title = {The cancer biomarker problem.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {548--552},
number = {7187},
month = {Apr},
abstract = {Genomic technologies offer the promise of a comprehensive understanding
of cancer. These technologies are being used to characterize tumours
at the molecular level, and several clinical successes have shown
that such information can guide the design of drugs targeted to a
relevant molecule. One of the main barriers to further progress is
identifying the biological indicators, or biomarkers, of cancer that
predict who will benefit from a particular targeted therapy.},
doi = {10.1038/nature06913},
pdf = {../local/Sawyers2008cancer.pdf},
file = {Sawyers2008cancer.pdf:Sawyers2008cancer.pdf:PDF},
institution = {Howard Hughes Medical Institute, Human Oncology and Pathogenesis
Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue,
New York, New York 10065, USA.},
keywords = {csbcbook-ch3, csbcbook},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nature06913},
pmid = {18385728},
timestamp = {2011.11.30},
url = {http://dx.doi.org/10.1038/nature06913}
}
@article{Soerlie2006Gene,
author = {S{\o}rlie, T. and Perou, C. M. and Fan, C. and Geisler, S. and Aas,
T. and Nobel, A. and Anker, G. and Akslen, L. A. and Botstein, D.
and B{\o}rresen-Dale, A.-L. and L{\o}nning, P. E.},
title = {Gene expression profiles do not consistently predict the clinical
treatment response in locally advanced breast cancer},
journal = {Mol. Cancer Ther.},
year = {2006},
volume = {5},
pages = {2914--2918},
number = {11},
month = {Nov},
abstract = {Neoadjuvant treatment offers an opportunity to correlate molecular
variables to treatment response and to explore mechanisms of drug
resistance in vivo. Here, we present a statistical analysis of large-scale
gene expression patterns and their relationship to response following
neoadjuvant chemotherapy in locally advanced breast cancers. We analyzed
cDNA expression data from 81 tumors from two patient series, one
treated with doxorubicin alone (51) and the other treated with 5-fluorouracil
and mitomycin (30), and both were previously studied for correlations
between TP53 status and response to therapy. We observed a low frequency
of progressive disease within the luminal A subtype from both series
(2 of 36 versus 13 of 45 patients; P = 0.0089) and a high frequency
of progressive disease among patients with luminal B type tumors
treated with doxorubicin (5 of 8 patients; P = 0.0078); however,
aside from these two observations, no other consistent associations
between response to chemotherapy and tumor subtype were observed.
These specific associations could possibly be explained by covariance
with TP53 mutation status, which also correlated with tumor subtype.
Using supervised analysis, we could not uncover a gene profile that
could reliably (>70\% accuracy and specificity) predict response
to either treatment regimen.},
doi = {10.1158/1535-7163.MCT-06-0126},
pdf = {../local/Soerlie2006Gene.pdf},
file = {Soerlie2006Gene.pdf:Soerlie2006Gene.pdf:PDF},
institution = {Department of Medicine, Section of Oncology, Haukeland University
Hospital, N-5021 Bergen, Norway.},
keywords = {csbcbook, csbcbook-ch3},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {5/11/2914},
pmid = {17121939},
timestamp = {2011.04.07},
url = {http://dx.doi.org/10.1158/1535-7163.MCT-06-0126}
}
@article{Sorlie2003Repeated,
author = {S{\o}rlie, T. and Tibshirani, R. and Parker, J. and Hastie, T. and
Marron, J.S. and Nobel, A. and Deng, S. and Johnsen, H. and Pesich,
R. and Geisler, S. and Demeter, J. and Perou, C.M. and Lønning, P.E.
and Brown, P.O. and Børresen-Dale, A.L. and Botstein, D.},
title = {Repeated observation of breast tumor subtypes in independent gene
expression data sets},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {8418--8423},
number = {14},
month = {Jul},
abstract = {Characteristic patterns of gene expression measured by DNA microarrays
have been used to classify tumors into clinically relevant subgroups.
In this study, we have refined the previously defined subtypes of
breast tumors that could be distinguished by their distinct patterns
of gene expression. A total of 115 malignant breast tumors were analyzed
by hierarchical clustering based on patterns of expression of 534
"intrinsic" genes and shown to subdivide into one basal-like, one
ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like
subgroup. The genes used for classification were selected based on
their similar expression levels between pairs of consecutive samples
taken from the same tumor separated by 15 weeks of neoadjuvant treatment.
Similar cluster analyses of two published, independent data sets
representing different patient cohorts from different laboratories,
uncovered some of the same breast cancer subtypes. In the one data
set that included information on time to development of distant metastasis,
subtypes were associated with significant differences in this clinical
feature. By including a group of tumors from BRCA1 carriers in the
analysis, we found that this genotype predisposes to the basal tumor
subtype. Our results strongly support the idea that many of these
breast tumor subtypes represent biologically distinct disease entities.},
doi = {10.1073/pnas.0932692100},
pdf = {../local/Sorlie2003Repeated.pdf},
file = {Sorlie2003Repeated.pdf:Sorlie2003Repeated.pdf:PDF},
keywords = {csbcbook, csbcbook-ch3},
url = {http://dx.doi.org/10.1073/pnas.0932692100}
}
@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}
}
@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.;}}
@comment{{jabref-meta: selector_keywords:biogm;biosvm;breastcancer;cgh;
chemogenomics;chemoinformatics;csbcbook;csbcbook-ch1;csbcbook-ch2;csbc
book-ch3;csbcbook-ch4;csbcbook-ch5;csbcbook-ch6;csbcbook-ch7;csbcbook-
ch8;csbcbook-ch9;csbcbook-mustread;dimred;featureselection;glycans;her
g;hic;highcontentscreening;image;immunoinformatics;kernel-theory;kerne
lbook;lasso;microarray;ngs;nlp;plasmodium;proteomics;PUlearning;rnaseq
;segmentation;sirna;}}
@comment{{jabref-meta: selector_booktitle:Adv. Neural. Inform. Process
Syst.;}}
This file was generated by bibtex2html 1.97.