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AS2013444- Y.P. Manawadu
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Problem Background
Breast Cancer
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Breast Cancer
•
“ A kind of cancer that develops from breast tissue
which usually starts off in the inner lining of milk ducts
or the lobules. ”
-NCI-
• one of the major causes of death for women in the last
decade
• occurs in both men and women, although male breast cancer
is rare.
• About one in eight women are diagnosed with breast cancer
during their lifetime
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Breast Cancer
• Breast cancer is a highly heterogeneous disease due to its
– diverse morphological features
– variable clinical outcome
– response to different therapeutic options
• Therefore, it is necessary to devise a clinically meaningful
classification of the disease, which has to be
– scientifically sound
– clinically useful
– widely reproducible.
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Breast Cancer Classification
1. Classification based on pathology
– classified according to its cellular structure and
microanatomy.
2. Classification according to grade
– grade 1 (well differentiated) – the cancer cells look most
like normal cells and are usually slow-growing
– grade 2 (moderately differentiated) – the cancer cells look
less like normal cells are growing faster
– grade 3 (poorly differentiated) – the cancer cells look most
changed and are usually fast-growing.
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Breast Cancer Classification
3. Classification based on stage of cancer
– TNM staging that takes into account the Tumor size,
lymph Node involvement and Metastasis or spread of the
cancer.
4. Classification based on Protein & gene status
– takes into account the estrogen receptor (ER),
progesterone receptor (PR) and HER2 proteins.
– Once the status of these proteins is known, prognosis
can be predicted and certain novel therapies may be
chosen for treatment.
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Breast Cancer Types
1. Endocrine/Hormone receptor-positive (estrogen or
progesterone receptors)
•
About 80% of all breast cancers are “ER-positive.”
–
•
cancer cells grow in response to the hormone estrogen
About 65% of these are “PR-positive.”
–
grow in response to hormone, progesterone.
2. HER2-positive
•
cells make too much of a protein known as HER2.
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Breast Cancer Types
3. Triple negative :
• they don’t have estrogen and progesterone receptors
and don’t overexpress the HER2 protein
• Most breast cancers associated with the gene BRCA1
are triple negative.
4. Triple positive : positive for estrogen receptors,
progesterone receptors, and HER2
• Possible treatments are different.
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Problem Background
Gene Expression Data
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Gene Expression Data
• Numbers in each cell characterize the expression level
of the particular gene in the particular sample.
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Why Gene Expression Data
for breast cancer classification ?
• Gene expression patterns were found to be strongly
associated with estrogen receptor (ER) status and moderately
associated with grade.
(Marc J. van de Vijver et al, 2002)
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Problem Background
Deep Learning
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Deep Learning
• A new area of Machine Learning research, which has been
introduced with the objective of
moving Machine Learning closer to one of its original goals:
Artificial Intelligence.
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Why Deep Learning ?
• perform better for classification than other traditional
Machine Learning methods, because:
– deep learning methods include multi layer processing with
less time and better accuracy performance.
– Sub sampling layers give better result ,by use of CNN and
auto-encoders.
– With the increase number of auto encoders, the accuracy
increases. Similarly increase number of sub sampling too
gives the better.
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Problem , Objectives
& Methodology
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Problem Definition
• Numerous researches available for the analysis of different
data matrices using different algorithms related to breast
cancers.
• No any research done using deep learning applied to gene
expression data for classifying breast cancer.
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Objectives
•
Find whether deep Learning applied to gene expression data
can successfully classify breast cancer than the other
algorithms.
• Comparative analysis of different deep Learning algorithms
applied to gene expression data for classifying breast cancer.
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Methodology
1. Select suitable deep learning algorithms.
2. Select appropriate datasets.
3. Carryout the analysis by employing the selected algorithms.
4. Generate statistics for each of the algorithm.
5. Device conclusions on the performance of each algorithm,
comparatively.
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TimeLine
Study about
• Deep Learning and its algorithms
• Researches related to breast cancer,
Gene Expression data & Deep Learning
• Datasets used in researches related to
deep learning & compare those
datasets with gene expression data
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Related Work
& References
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Related Work
• Breast cancer classification and prognosis based on gene
expression profiles from a population-based study
(Christos Sotiriou et al. , 2003)
– Hierarchical cluster analysis
• Gene expression profiling in breast cancer: classification,
prognostication, and prediction (Prof. Jorge S Reis-Filho et al. ,
2011)
– a molecular classification system and prognostic multigene
classifiers based on microarrays or derivative technologies.
•
Gene Expression Profiling in Breast Cancer: Understanding
the Molecular Basis of Histologic Grade To Improve
Prognosis
(Christos Sotiriou et al. ,2006)
– Kaplan–Meier analysis
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Related Work
• Distinct molecular mechanisms underlying clinically relevant
subtypes of breast cancer: gene expression analyses across
three different platforms. (Therese Sorlie et al. , 2006)
– hierarchical clustering and centroid correlation analysis
• Classification of human breast cancer using gene expression
profiling as a component of the survival predictor algorithm.
(Gennadi V. Glinsky et al. , 2004)
– microarray expression profiling and quantitative reverse
transcription using Kaplan-Meier analysis.
• Gene expression patterns of breast carcinomas distinguish
tumor subclasses with clinical implications. (Therese Sorlie et
al. , 2001)
– Microarray Analysis by Hierarchical Clustering.
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Related Work
• Breast cancer classification using deep belief networks.
(Ahmed M. et al. , 2016)
– Morphological features dataset.
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References
• Christos Sotiriou, Soek-Ying Neo and Lisa M. ‘Breast cancer
classification and prognosis based on gene expression profiles from
a population-based study’ , The National Academy of Sciences , vol.
100 no. 18, 2003.
• Prof. Jorge S Reis-Filho and FRCPath. ‘Gene expression profiling in
breast cancer: classification, prognostication, and prediction’ , The
Lancet , vol. 398 no. 9805, 2011.
• Christos Sotiriou, Pratyaksha Wirapati and Sherene Loi. ‘Gene
Expression Profiling in Breast Cancer: Understanding the Molecular
Basis of Histologic Grade To Improve Prognosis’ , Journal of the
National Cancer Institute, vol. 98 no. 4, 2006.
• Marc J. van de Vijver et al. , A Gene-Expression Signature as a
Predictor of Survival in Breast Cancer, N Engl J Med, Vol. 347 No. 25,
2002
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References
• Therese Sorlie, Yulei Wang, Chunlin Xiao and Hilde Johnsen.
‘Distinct molecular mechanisms underlying clinically relevant
subtypes of breast cancer: gene expression analyses across three
different platforms’ , BMC Genomics, vol. 7 no. 127, 2006.
• Gennadi V. Glinsky, Takuya Higashiyama and Anna B. Glinskii . ’
Classification of human breast cancer using gene expression
profiling as a component of the survival predictor algorithm’,
Clinical Cancer Research, vol. 10 no. 7, 2004.
• Therese Sorlie and Charles M. Peroua. ‘Gene expression patterns of
breast carcinomas distinguish tumor subclasses with clinical
implications’ , The National Academy of Sciences, vol. 98 no. 19,
2001.
• Ahmed M. Abdel-Zaher, Ayman M. Eldeib. ‘Breast cancer
classification using deep belief networks’. Expert Systems With
Applications , vol.46 no. 139, 2016.
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Thank You
Any Questions ?
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