Download Detecting Breast Cancer Using Raman Spectroscopy

Detecting Breast Cancer Using Raman Spectroscopy
The Project
The overall goal of this research project is to develop near-infrared (NIR) Raman
spectroscopy as a histochemical/morphological tool for detection and diagnosis of breast
cancer. We strive to characterize the Raman spectra of normal and diseased tissue types,
correlate spectroscopic features with biochemistry and tissue morphology, establish
diagnostic decision schemes, and develop instrumentation needed for rapid and accurate
data collection and analysis both in vitro and in vivo.
Background
Breast cancer is the most common malignant tumor among women in the western world.
In the US, approximately 180,000 new cases are diagnosed each year and 44,000 women
die from the disease. Mammography is the most common technique for detecting nonpalpable, highly curable breast cancer. In cases where the tissue is particularly dense,
ultrasound may also be used. Mammography quantitatively probes the density changes
in breast tissue. However, these density changes are not uniquely correlated with the
probability of breast cancer. Because of this, mammography serves as a screening
technique rather than a diagnostic tool. This is evidenced by the fact that 70-90% of
mammographically detected lesions are found to be benign upon biopsy; additionally,
mammography fails to detect 20% of all malignant lesions often due to their small size or
diffuse nature. The desire to reduce patient trauma, time delay and the high medical costs
associated with biopsy has encouraged researchers to develop minimally invasive optical
methods for diagnosing malignant lesions in the breast.
Figure 1. H&E images of A) normal breast tissue and B) intraductal carcinoma
A pathologist looks for certain morphological features or patterns to determine
malignancy, each of which is associated with biochemical changes in the tissue, figure 1.
Many changes in biochemical composition occur between normal tissue and malignant
neoplasms. For example, changes in the extracellular matrix during invasion, such as
fibrosis, are due to changes in collagen and glycosaminoglycan content. Increased
cellular proliferation is accompanied by a decrease in triglycerides and an increase in
NADH, flavins, and ATP. Cellular changes, such as differentiation and nuclear
pleomorphism, correspond to increased DNA content and concentration. Each of these
changes, or possibly a combination of these changes, may provide a spectral marker for
identifying pre-malignant lesions and malignant tumors in biological tissue using Raman
spectroscopy.
With an appropriate model, Raman spectroscopy can provide quantitative biochemical
and morphological information about tissue composition in situ comparable to the
information used by a pathologist.1-2 We have previously shown that using a
combination of principal component analysis and logistic regression one can distinguish
between benign and malignant tumors based on the samples macroscopic Raman
spectrum.3 However, the chemical basis for this differentiation remains unknown.
Using our confocal Raman microscope, we are able to collect spectra from individual
morphological features. Using this system, we have recently developed a morphological
model, shown in figure 2, in an effort to determine the features responsible for the
spectral differentiation between benign and malignant breast lesions.4 This model fits
macroscopic tissue spectra with a linear combination of basis spectra derived from
Cell cytoplasm
Intensity (a.u.)
Cell nucleus
Fat
-carotene
Collagen
Calcium hydroxyapatite
Calcium oxalate
Cholesterol-like
600
800
1000
1200
1400
1600
1800
Raman shift (cm-1)
Figure 2. Raman morphological model of breast tissue
spectra of the cell cytoplasm, the cell nucleus, fat, -carotene, collagen, calcium
hydroxyapatite, calcium oxalate dihydrate, and cholesterol-like lipid deposits. Each basis
spectrum represents data acquired from multiple patients and, when appropriate, from a
variety of normal and diseased states. Modeling is based on the assumptions that the
Raman spectrum of a mixture is a linear combination of the spectra of its components and
that signal intensity and chemical concentration are linearly related. Least-squares fitting
of the macroscopic Raman spectrum of tissue yields the contribution of each basis
spectrum to the entire tissue spectrum. To understand the relationship between a tissue
sample’s Raman spectrum and its disease state we examine the contribution of each basis
spectrum to a variety of pathologies.
Intensity (a.u.)
Intensity (a.u.)
Intensity (a.u.)
Intensity (a.u.)
Intensity (a.u.)
Fits of normal, benign, and malignant samples of breast tissue are shown in figure 3.
Using the morphological
model, the spectral features
Normal:
of a range of tissue samples
83% Fat
can be explained in terms of
1% Collagen
each sample’s morphological
0% Cell nucleus
0% Cell cytoplasm
composition.
The fit
10% Cholesterol-like
coefficients given by the
2% -carotene
3% Calcium Hydroxyapatite
model normalized to sum to
one, represent percentage
contributions of chemicals
600
800
1000 1200 1400 1600 1800
and morphological features to
Raman shift (cm-1)
the bulk tissue spectrum. As
expected from pathology,
Fibrosis:
when we analyze the fit
49% Fat
coefficients of the normal
32% Collagen
Fibrous
+ Cysts:
0% Cell
nucleus
sample, it is primarily
4%2%
FatCell cytoplasm
60.5%
composed of fat. The sample
14%Collagen
Cholesterol-like
0%2%
Cell
nucleus
diagnosed as fibrosis, a
-carotene
15% Cell cytoplasm
1% Calcium Hydroxyapatite
benign
condition
16% Cholesterol-like
4% -carotene
characterized by scarring,
4.5% Calcium Hydroxyapatite
600
800
1000
1200
1400
1600
1800
exhibits an increase in the
600
800
1000
1400
1800
-1) 1600
Raman1200
shift (cm
amount of collagen present.
Raman shift (cm-1)
Again, the composition is
consistent with pathology as
Adenosis:
scar tissue is formed through
Fibroadenoma:
48% Fat
26%
FatCollagen
23%
collagen
accumulation.
16%
2%Collagen
Cell nucleus
Furthermore,
the
5%8%
Cell
nucleus
Cell
cytoplasm
31%
Cell
cytoplasm
fibroadenoma and malignant
14%
Cholesterol-like
15%
3%Cholesterol-like
-carotene
samples both have a large
0%2%
-carotene
Calcium Hydroxyapatite
cell
cytoplasm
content
7% Calcium Hydroxyapatite
because they are pathologies
600 800
800 1000
1000 1200
1200 1400
1400 1600
1600 1800
600
1800
which
exhibit
cellular
Ramanshift
shift (cm
(cm-1-1))
Raman
proliferation.
Infiltrating Ductal Carcinoma:
2% Fat
40% Collagen
5% Cell nucleus
34% Cell cytoplasm
19% Cholesterol-like
0% -carotene
0% Calcium Hydroxyapatite
Intensity (a.u.)
We are currently in the
process of collecting a library
of bulk Raman data in order
to assess the ability of the
morphological model to
predict breast tissue disease
state.
600
800
1000
1200
1400
1600
1800
Raman shift (cm-1)
Figure
3. 2.Examples
of morphological
with
corresponding
Figure
Examples of morphological
fits to fibrosisfits
+ cysts,
fibroadenoma,
and
ductal carcinoma
with–corresponding
fit(residual
contributions.plotted
• Data – Model
fit
fit infiltrating
contributions.
• Data
Model
fit
below).
(residual plotted below).
REFERENCES
1) Manoharan R, Baraga JJ, Feld MS, “Quantitative histochemical analysis of
human artery using Raman spectroscopy”, J. Photochem. Photobiol. 16: 211-233
1992.
2) Hanlon EB, Manoharan R, Koo TW, Shafer KE, Motz JT, Fitzmaurice M, Kramer
JR, Itzkan I, Dasari RR, Feld MS, “Prospects of in vivo Raman spectroscopy”,
Phys. Med. Biol. 45: R1-R59 2000.
3) Manoharan R, Shafer K, Perelman L, Wu J, Chen K, Deinum G, Fitzmaurice M,
Myles J, Crowe J, Dasari RR, Feld MS, “Raman spectroscopy and fluorescence
photon migration for breast cancer diagnosis and imaging”, Photochem.
Photobiol. 67(1): 15-22 1998.
4) Shafer-Peltier K, Haka AS, Fitzmaurice M, Crowe J, Dasari RR and Feld MS
“Raman microspectroscopic model of human breast tissue: implications for breast
cancer diagnosis in vivo”, J. Raman Spectrosc. in press.
RESEARCH GROUP
 Core-Investigator: Michael S. Feld PhD
 Graduate Studen: Abigail S. Haka
 Collaborators: Joseph Crowe MD, Cleveland Clinic Foundation, Maryann
Fitzmaurice
MD, University Hospitals Cleveland/Case Western Reserve University