Baseline tumor oxygen saturation correlates with a pathologic complete response in breast cancer patients undergoing neoadjuvant chemotherapy.
Journal: 2012/November - Cancer Research
ISSN: 1538-7445
Abstract:
Tissue hemoglobin oxygen saturation (i.e., oxygenation) is a functional imaging endpoint that can reveal variations in tissue hypoxia, which may be predictive of pathologic response in subjects undergoing neoadjuvant chemotherapy. In this study, we used diffuse optical spectroscopic imaging (DOSI) to measure concentrations of oxyhemoglobin (ctO(2)Hb), deoxy-hemoglobin (ctHHb), total Hb (ctTHb = ctO(2)Hb + ctHHb), and oxygen saturation (stO(2) = ctO(2)Hb/ctTHb) in tumor and contralateral normal tissue from 41 patients with locally advanced primary breast cancer. Measurements were acquired before the start of neoadjuvant chemotherapy. Optically derived parameters were analyzed separately and in combination with clinical biomarkers to evaluate correlations with pathologic response. Discriminant analysis was conducted to determine the ability of optical and clinical biomarkers to classify subjects into response groups. Twelve (28.6%) of 42 tumors achieved pathologic complete response (pCR) and 30 (71.4%) were non-pCR. Tumor measurements in pCR subjects had higher stO(2) levels (median 77.8%) than those in non-pCR individuals (median 72.3%, P = 0.01). There were no significant differences in baseline ctO(2)Hb, ctHHb, and ctTHb between response groups. An optimal tumor oxygenation threshold of stO(2) = 76.7% was determined for pCR versus non-pCR (sensitivity = 75.0%, specificity = 73.3%). Multivariate discriminant analysis combining estrogen receptor staining and stO(2) further improved the classification of pCR versus non-pCR (sensitivity = 100%, specificity = 85.7%). These results show that elevated baseline tumor stO(2) are correlated with a pCR. Noninvasive DOSI scans combined with histopathology subtyping may aid in stratification of individual patients with breast cancer before neoadjuvant chemotherapy.
Relations:
Content
Citations
(37)
References
(45)
Grants
(93)
Diseases
(1)
Chemicals
(2)
Organisms
(1)
Processes
(1)
Affiliates
(1)
Similar articles
Articles by the same authors
Discussion board
Cancer Res 72(17): 4318-4328

Baseline tumor oxygen saturation correlates with a pathologic complete response in breast cancer patients undergoing neoadjuvant chemotherapy

+3 authors

INTRODUCTION

Neoadjuvant chemotherapy (NCT) has been recommended as a standard treatment for locally advanced breast cancer and is currently accepted for patients having operable breast cancer (1, 2). In the neoadjuvant setting, a complete pathologic response (pCR) is an important surrogate endpoint since it is correlated with a favorable prognosis including longer disease free survival (DFS) and overall survival (OS) (35). There is currently an effort to explore biomarkers associated with the mechanisms of chemosensitivity and response in breast cancer in order to aid in treatment planning and prognosis (68). Histologic markers utilized to determine tumor grade, proliferation, and biological receptor status have been shown to be useful prognostic indicators (9). To date, there are no clinically accepted prognostic imaging endpoints available prior to treatment that can provide insight into the likelihood of an individual patient responding to neoadjuvant chemotherapy. Functional metrics that quantify tissue oxygen saturation, vascular supply and drainage, and tumor metabolism have been largely unexplored in this context and may be closely associated with chemosensitivity and therapy response.

Diffuse Optical Spectroscopic Imaging (DOSI) is a non-invasive functional imaging modality that utilizes near infrared (NIR) light to provide metabolic and hemodynamic information from thick tissues. DOSI is capable of measuring tissue concentrations (ct) of oxygenated hemoglobin (ctO2Hb), deoxygenated hemoglobin (ctHHb), water (ctH2O), and lipid (10). These measurements are directly related to tumor metabolism and vascular characteristics. For example, high levels of tumor ctO2Hb are considered to be a surrogate marker for elevated vascular supply potentially due to angiogenesis. High levels of ctHHb reflect high oxygen consumption and tissue metabolism due to tumor proliferation and/or poor vascular drainage. Total hemoglobin concentration (ctTHb) corresponds to the total blood volume in tumors and has been validated as an index that corresponds to increased vascular density (11). Low oxygen saturation (stO2, defined as ctO2Hb / (ctO2Hb + ctHHb)) is an indication of tumor hypoxia or necrosis. Several investigators have reported a relationship between hypoxic tumors and lower partial pressure of oxygen (pO2) (1214), and stO2 has been demonstrated as a surrogate of pO2 by several studies conducted using erythrocyte-containing phantoms (1517).

Previous studies by our group and others have shown that DOSI and similar techniques are able to localize and characterize functional properties of breast tumors at baseline (18) and during neoadjuvant treatment (1922). Significant changes in hemoglobin, water, and lipid over the first weeks of therapy have been shown to correlate with overall pathologic response. These studies all examined the impact of NCT on tumor physiology in individual patients over the course of lengthy regimens.

In this research we investigate, for the first time, the relationship between baseline tumor properties determined by DOSI and final post-surgical pathologic response. We hypothesize that DOSI functional measurements prior to surgery can provide information that correlates with clinical outcome. This is due to the direct relevance of DOSI-measured properties on tumor perfusion and metabolism, which, in turn impact the delivery and utilization of chemotherapeutic drugs. Similar attempts have been made to predict neoadjuvant therapy response based on imaging endpoints obtained prior to treatment using MRI (23, 24) , diffusion-weighted MRI (DW-MRI) (25, 26) , and molecular imaging techniques, e.g. PET (27, 28). These studies were designed to assess either pretreatment anatomic features of tumors (MRI), the diffusion of water in tumor tissue (DW-MRI), or the uptake of glucose and magnitude of blood flow (FDG-PET and O-Water). Because DOSI parameters reveal intrinsic physiological properties of tumors such as metabolism and perfusion, they may be broadly applicable to different chemotherapeutic strategies and have the added advantage of not requiring exogenous contrast agents.

This retrospective study examines 41 primary breast cancer patients who received neoadjuvant chemotherapy (NCT) and enrolled in a University of California, Irvine Institutional Review Board (IRB) optical imaging clinical protocol over a period of 7 years. Baseline DOSI measurements demonstrate that pre-therapy tumor tissue oxygen saturation, stO2, (also known as oxygenation) is the single best DOSI-derived predictor of pathologic complete response. In addition, we show that DOSI imaging endpoints can enhance the utility of conventional biomarkers, such as hormone receptor status, potentially providing new insight for treatment planning and optimization prior to initiating therapy.

METHODS

DOSI Instrumentation

Details of the instrumentation are provided elsewhere (10). Briefly, DOSI uses near infrared (NIR) light (650 – 1000 nm) from six laser diodes and a broadband lamp to determine the optical scattering and absorption properties of thick tissue such as the breast. Laser diode output is amplitude modulated between 50 and 600 MHz. Amplitude and phase delay of detected signals are used as inputs into an analytical model of diffuse light transport to determine tissue scattering and absorption coefficients at the laser wavelengths. A broadband lamp is also used to illuminate tissue and the detected reflectance spectrum is scaled so that absorption is determined continuously over the entire spectral range. Absolute tissue concentrations of ctHbO2, ctHHb, ctH2O, and lipid are calculated by fitting known absorption spectra from these quantities to the measured absorption spectrum. ctTHb is defined as ctHbO2 + ctHHb, and stO2 is ctHbO2/(ctHbO2+ ctHHb).

A hand-held probe is used to acquire measurements in patients. The probe houses illumination optical fibers which transport light from the laser diodes and broadband lamp, an avalanche photodiode detector (APD) that detects laser light, as well as the distal end of an optical fiber which transports broadband light to a spectrometer. Measurements are performed by placing the handheld probe on the tissue with light pressure so that there is adequate optical contact. We have shown in previous studies that this procedure does not require compression, does not influence tissue optical property measurements, and optical contrast is available even from small (<15mm) tumors embedded millimeters or centimeters below the surface of the skin (18, 29).

Patient Measurements

This is a retrospective analysis of forty one patients with newly diagnosed, operative, and primary breast cancer measured between October 2004 and March 2010. All patients provided informed consent and participated in this study under a clinical study approved by institutional review committee of the University of California, Irvine. Women were excluded if they were pregnant or were less than 21 years old or more than 75 years old. Subjects were included in this analysis if they 1. received neoadjuvant chemotherapy before surgical resection of tumors, 2. were measured with the DOSI system at least 14 days after core biopsy and prior to NCT treatment (average 31.3 days, [14 to 110]), and 3. had evaluable DOSI measurements. Of the 48 subjects measured during the study period, 6 subjects were determined to be non-evaluable. Non-evaluable measurements include measurements in which a laser diode was malfunctioning (n=3), a subject had an implant in close proximity to the tumor (n=1), or when the tumor was retroareolar and it was not possible to distinguish contrast from tumor and contrast from areola (n=2). One additional subject was excluded due to a diagnosis of inflammatory breast cancer. All subjects included in the study were histologically diagnosed with invasive ductal or lobular carcinoma before neoadjuvant treatment. Age, tumor location, tumor size, clinical stage, and histological grading were obtained from patients’ medical records. Estrogen receptor (ER), progesterone receptor (PR), Ki67 staining, and c-erbB2 (HER2) were immunohistochemically assessed in the specimens obtained by core biopsy.

Patients were measured in a supine position using a handheld probe placed against the breast tissue. Sequential measurements were taken in a rectangular grid pattern marked on the breast with each point separated by 10 mm. Measurements were taken to include the area of the underlying tumor as determined by ultrasound and palpation, as well as a margin of surrounding normal tissue. Contralateral normal breast measurements were collected from patients with unilateral breast cancer. A point measurement was acquired in fewer than 30 seconds and total measurement time varied between 20 min and 1 hour per patient. A detailed video description of the patient measurement procedure developed for an ongoing American College of Radiology Imaging Networks (ACRIN) clinical trial (ACRIN 6691) is available (30).

Mean values of tumor ctO2Hb, ctHHb, ctTHb, and stO2 were calculated by averaging DOSI measurements taken on breast tissue corresponding to regions-of-interest (ROIs) using a previously described method (31). Briefly, ROIs were determined by palpation, mammography, ultrasound and MRI (if available) to contain tumor. Tumor locations were additionally confirmed by an increase in ctHHb and water and a decrease in lipids measured with DOSI. This combination of chromophore values, designated as the “tissue optical index (TOI)”, has been previously shown to be a consistent indicator of tumor location (32). Optical parameters from contralateral normal breast tissue were also measured in corresponding “mirror image” locations. The measurement procedure and example optical property maps are shown in figure 1.

An external file that holds a picture, illustration, etc.
Object name is nihms393837f1.jpg
DOSI measurement procedure and optical property maps

Measurements are taken using a hand-held probe that is moved in a grid or line pattern over tumor and normal breast tissue. Dots indicate measurement locations. In this example, an 8 cm by 7 cm region of tissue was measured containing a clinical stage 2 IDC measured to be 27 mm in the greatest dimension. Maps of optical properties are made by interpolating data values between measurement points. In this example, both deoxyhemoglobin (ctHHb) and oxygen saturation (stO2) are shown. In both maps, which are from identical tissue locations, the dotted circle indicates the approximate tumor location determined by ultrasound and palpation. This subject was non-pCR. Note the relatively low oxygen saturation in the tumor region compared to surrounding normal tissue.

For the relatively large tumors measured in this study (mean 3.5cm in largest dimension), ROIs included at least 3 and typically 5 discrete measurement points. Each optical parameter value reported is therefore a mean of the ~5 values within the ROI, and the standard deviation is a reflection of the physiological variation for that patient. This contrasts with the intrinsic instrument precision (relative standard deviation) which has been well-characterized based on repetitive measurements of homogeneous tissue phantoms and human breast tissue to less than 5% for all relevant parameters (29).

Neoadjuvant Chemotherapy Regimen

Twenty four (58.5%) of 41 patients received a chemotherapy regimen that consisted of doxorubicin (60mg/m) intravenously and cyclophosphamide (AC regimen) (600mg/m) intravenously every 14 days for 2 to 4 cycles with pegfilgrastim support, depending on clinical response. This treatment was followed sequentially with paclitaxel or nab-particle paclitaxel (100mg/m), as well as weekly carboplatin (area under the curve =2) for 3 weeks followed by 1 week of rest, for 9 to 12 doses. Concurrent trastuzumab therapy was administered at 4 mg/kg loading dose, followed by a maintenance dose of 2 mg/kg weekly for 10 to 12 cycles in four patients with HER2 positive tumor. Concurrent bevacizumab therapy was administered at 10mg/kg every 2 weeks for 5 to 6 doses in eleven patients.

Seventeen (41.5%) patients received a concurrent regimen of carboplatin and nab-paclitaxel combined with trastuzumab for 10 to 12 cycles or with bevacizumab for 10 to 12 cycles, respectively. This was sequentially followed by AC regimens depending on clinical response.

Histological grading system

The histological grading system was based on three morphologic features consisting of nuclear pleomorphism of tumor cells, degree of tumor tubule formation and mitotic activity (33). Scores of all three components are added together to give the “grade”, score 3 to 7. Grade was classified into low grade (3 to 5) and high grade (6 to 9).

Immunohistochemistry

Formalin fixed, paraffin embedded tissues obtained for diagnostic core biopsy were utilized for this study. Immunohistochemistry was performed on 5-μm thick tissue sections. After deparaffination and rehydration, endogenous peroxidase activity was blocked for 10 minutes in a methanol solution that contained 0.3% hydrogen peroxidase. After antigen retrieval with 10-minute microwave in 10 mM sodium citrate (pH 6.0), a cooling–off period of 20 minutes preceded the incubation of the primary antibodies. Briefly, ER antibody (1D5, Dako, Denmark), PR antibody (PgR636, Dako, Denmark), and Ki67 (MIB1, Dako, Demmark) were ready-to-use agents with overnight incubation in a cold room. All antibodies were detected with the standard streptavidin-biotin complex method with 3,3’-diaminobenzidine as the chromogen. All stainings were developed with anti-polyvalent, HRP/DAB detection system and counterstained for 15 minutes with hematoxylin and eosin. HER2 testing was done according to the protocol of Herceptest kit (Dako, Denmark). Breast cancer tissues previously determined to have positive results were used as positive controls.

Evaluation of immunohistochemical biomarkers

Percentage of ER and PR immunostaining was calculated based on the fraction of positive tumor cells to whole tumor cells (less than 5% of nuclei staining = negative, 5 to 100% of nuclei staining = positive). For Ki67, the index was estimated by counting the percentage of Ki67 positive cell nuclei per a minimum of 400 cancer cells in areas with the highest mitotic activity at low-power fields (×40) in representative sections of the tumors. HER2 status was determined by immunohistochemistry or fluorescence in situ hybridization (FISH) analysis. Tumors with a score 3+ (entire circumference of the cell membrane was strongly stained) or tumors that amplified Her2/neu gene by FISH were defined as positive.

Pathological Response

All specimens excised from the breast were sectioned into 5 μm-thick slices and were microscopically analyzed for the presence of residual tumor by a board certified pathologist. Pathological complete response (pCR) was defined as microscopic evidence that invasive components of cancer cells had entirely disappeared in all inspected pathologic specimens. Regional lymph node involvement was not evaluated in this study. The tumors that did not achieve pCR were considered to be non-pCR.

Surgery

Surgery was planned 3–5 weeks after the final course of chemotherapy was delivered. Conservative surgery with lumpectomy or segmentectomy was chosen depending on tumor size and its location after tumor shrinkage. In patients who achieved a significant response to chemotherapy, an ultrasonographic examination was performed prior to surgery to assist in confirming the location of the lesion. The remaining patients underwent a modified radical mastectomy. In all patients, axillary dissection was performed.

Statistical analysis

Statistical comparisons between response groups were computed using JMP software (Cary, North Carolina). Unpaired 2-sided student’s t-tests and the Wilxocon test were used to compare differences in tumor values (T), contralateral normal values (N), and normalized (T-N) values of ctO2Hb, ctHHb, ctTHb, stO2 as well as ER staining, PR staining, Ki67 staining, and tumor size between pCR and non-pCR groups. Fisher’s exact test was used to compare HER2 status and Grade between response groups. P-values less than 0.05 were considered significant.

Discriminant analysis was performed using Matlab (MathWorks, Natick, MA). Two classification algorithms were utilized, a linear classifier based on Bayesian parameter estimation which assumed multivariate normal densities and equal covariances for each group, and a ordinal logistic regression model with does not assume the data is normally distributed. A priori probabilities for the linear discriminant classifier were based on the relative proportion of each group. 5 fold cross-validation was used for all classifiers to mitigate potential over-performance in this relatively small data set. Receiver-operating characteristic (ROC) curves were constructed using computed posterior probabilities calculated from the classifiers. The area under the curve (AUC) of the ROC curve was utilized as an overall performance metric for the classifier. Sensitivity, specificity, negative predictive values, and positive predictive values were also reported at the optimal threshold or Q-point. The Q-point is the operating point on the ROC curve that has the minimum geometric distance from the upper left hand corner of the plot. For multivariate discrimination analysis, AUCs were compared from classifiers using all possible combinations (two at a time) of the following features: ctO2Hb, ctHHb, ctTHb, stO2, ER staining, PR staining, Ki67 staining, and tumor size.

A one-way analysis of variance was performed to estimate the inter- and intra- tumor variance for each optical parameter and the F-test was applied to test the null hypothesis of no difference between these variances.

DOSI Instrumentation

Details of the instrumentation are provided elsewhere (10). Briefly, DOSI uses near infrared (NIR) light (650 – 1000 nm) from six laser diodes and a broadband lamp to determine the optical scattering and absorption properties of thick tissue such as the breast. Laser diode output is amplitude modulated between 50 and 600 MHz. Amplitude and phase delay of detected signals are used as inputs into an analytical model of diffuse light transport to determine tissue scattering and absorption coefficients at the laser wavelengths. A broadband lamp is also used to illuminate tissue and the detected reflectance spectrum is scaled so that absorption is determined continuously over the entire spectral range. Absolute tissue concentrations of ctHbO2, ctHHb, ctH2O, and lipid are calculated by fitting known absorption spectra from these quantities to the measured absorption spectrum. ctTHb is defined as ctHbO2 + ctHHb, and stO2 is ctHbO2/(ctHbO2+ ctHHb).

A hand-held probe is used to acquire measurements in patients. The probe houses illumination optical fibers which transport light from the laser diodes and broadband lamp, an avalanche photodiode detector (APD) that detects laser light, as well as the distal end of an optical fiber which transports broadband light to a spectrometer. Measurements are performed by placing the handheld probe on the tissue with light pressure so that there is adequate optical contact. We have shown in previous studies that this procedure does not require compression, does not influence tissue optical property measurements, and optical contrast is available even from small (<15mm) tumors embedded millimeters or centimeters below the surface of the skin (18, 29).

Patient Measurements

This is a retrospective analysis of forty one patients with newly diagnosed, operative, and primary breast cancer measured between October 2004 and March 2010. All patients provided informed consent and participated in this study under a clinical study approved by institutional review committee of the University of California, Irvine. Women were excluded if they were pregnant or were less than 21 years old or more than 75 years old. Subjects were included in this analysis if they 1. received neoadjuvant chemotherapy before surgical resection of tumors, 2. were measured with the DOSI system at least 14 days after core biopsy and prior to NCT treatment (average 31.3 days, [14 to 110]), and 3. had evaluable DOSI measurements. Of the 48 subjects measured during the study period, 6 subjects were determined to be non-evaluable. Non-evaluable measurements include measurements in which a laser diode was malfunctioning (n=3), a subject had an implant in close proximity to the tumor (n=1), or when the tumor was retroareolar and it was not possible to distinguish contrast from tumor and contrast from areola (n=2). One additional subject was excluded due to a diagnosis of inflammatory breast cancer. All subjects included in the study were histologically diagnosed with invasive ductal or lobular carcinoma before neoadjuvant treatment. Age, tumor location, tumor size, clinical stage, and histological grading were obtained from patients’ medical records. Estrogen receptor (ER), progesterone receptor (PR), Ki67 staining, and c-erbB2 (HER2) were immunohistochemically assessed in the specimens obtained by core biopsy.

Patients were measured in a supine position using a handheld probe placed against the breast tissue. Sequential measurements were taken in a rectangular grid pattern marked on the breast with each point separated by 10 mm. Measurements were taken to include the area of the underlying tumor as determined by ultrasound and palpation, as well as a margin of surrounding normal tissue. Contralateral normal breast measurements were collected from patients with unilateral breast cancer. A point measurement was acquired in fewer than 30 seconds and total measurement time varied between 20 min and 1 hour per patient. A detailed video description of the patient measurement procedure developed for an ongoing American College of Radiology Imaging Networks (ACRIN) clinical trial (ACRIN 6691) is available (30).

Mean values of tumor ctO2Hb, ctHHb, ctTHb, and stO2 were calculated by averaging DOSI measurements taken on breast tissue corresponding to regions-of-interest (ROIs) using a previously described method (31). Briefly, ROIs were determined by palpation, mammography, ultrasound and MRI (if available) to contain tumor. Tumor locations were additionally confirmed by an increase in ctHHb and water and a decrease in lipids measured with DOSI. This combination of chromophore values, designated as the “tissue optical index (TOI)”, has been previously shown to be a consistent indicator of tumor location (32). Optical parameters from contralateral normal breast tissue were also measured in corresponding “mirror image” locations. The measurement procedure and example optical property maps are shown in figure 1.

An external file that holds a picture, illustration, etc.
Object name is nihms393837f1.jpg
DOSI measurement procedure and optical property maps

Measurements are taken using a hand-held probe that is moved in a grid or line pattern over tumor and normal breast tissue. Dots indicate measurement locations. In this example, an 8 cm by 7 cm region of tissue was measured containing a clinical stage 2 IDC measured to be 27 mm in the greatest dimension. Maps of optical properties are made by interpolating data values between measurement points. In this example, both deoxyhemoglobin (ctHHb) and oxygen saturation (stO2) are shown. In both maps, which are from identical tissue locations, the dotted circle indicates the approximate tumor location determined by ultrasound and palpation. This subject was non-pCR. Note the relatively low oxygen saturation in the tumor region compared to surrounding normal tissue.

For the relatively large tumors measured in this study (mean 3.5cm in largest dimension), ROIs included at least 3 and typically 5 discrete measurement points. Each optical parameter value reported is therefore a mean of the ~5 values within the ROI, and the standard deviation is a reflection of the physiological variation for that patient. This contrasts with the intrinsic instrument precision (relative standard deviation) which has been well-characterized based on repetitive measurements of homogeneous tissue phantoms and human breast tissue to less than 5% for all relevant parameters (29).

Neoadjuvant Chemotherapy Regimen

Twenty four (58.5%) of 41 patients received a chemotherapy regimen that consisted of doxorubicin (60mg/m) intravenously and cyclophosphamide (AC regimen) (600mg/m) intravenously every 14 days for 2 to 4 cycles with pegfilgrastim support, depending on clinical response. This treatment was followed sequentially with paclitaxel or nab-particle paclitaxel (100mg/m), as well as weekly carboplatin (area under the curve =2) for 3 weeks followed by 1 week of rest, for 9 to 12 doses. Concurrent trastuzumab therapy was administered at 4 mg/kg loading dose, followed by a maintenance dose of 2 mg/kg weekly for 10 to 12 cycles in four patients with HER2 positive tumor. Concurrent bevacizumab therapy was administered at 10mg/kg every 2 weeks for 5 to 6 doses in eleven patients.

Seventeen (41.5%) patients received a concurrent regimen of carboplatin and nab-paclitaxel combined with trastuzumab for 10 to 12 cycles or with bevacizumab for 10 to 12 cycles, respectively. This was sequentially followed by AC regimens depending on clinical response.

Histological grading system

The histological grading system was based on three morphologic features consisting of nuclear pleomorphism of tumor cells, degree of tumor tubule formation and mitotic activity (33). Scores of all three components are added together to give the “grade”, score 3 to 7. Grade was classified into low grade (3 to 5) and high grade (6 to 9).

Immunohistochemistry

Formalin fixed, paraffin embedded tissues obtained for diagnostic core biopsy were utilized for this study. Immunohistochemistry was performed on 5-μm thick tissue sections. After deparaffination and rehydration, endogenous peroxidase activity was blocked for 10 minutes in a methanol solution that contained 0.3% hydrogen peroxidase. After antigen retrieval with 10-minute microwave in 10 mM sodium citrate (pH 6.0), a cooling–off period of 20 minutes preceded the incubation of the primary antibodies. Briefly, ER antibody (1D5, Dako, Denmark), PR antibody (PgR636, Dako, Denmark), and Ki67 (MIB1, Dako, Demmark) were ready-to-use agents with overnight incubation in a cold room. All antibodies were detected with the standard streptavidin-biotin complex method with 3,3’-diaminobenzidine as the chromogen. All stainings were developed with anti-polyvalent, HRP/DAB detection system and counterstained for 15 minutes with hematoxylin and eosin. HER2 testing was done according to the protocol of Herceptest kit (Dako, Denmark). Breast cancer tissues previously determined to have positive results were used as positive controls.

Evaluation of immunohistochemical biomarkers

Percentage of ER and PR immunostaining was calculated based on the fraction of positive tumor cells to whole tumor cells (less than 5% of nuclei staining = negative, 5 to 100% of nuclei staining = positive). For Ki67, the index was estimated by counting the percentage of Ki67 positive cell nuclei per a minimum of 400 cancer cells in areas with the highest mitotic activity at low-power fields (×40) in representative sections of the tumors. HER2 status was determined by immunohistochemistry or fluorescence in situ hybridization (FISH) analysis. Tumors with a score 3+ (entire circumference of the cell membrane was strongly stained) or tumors that amplified Her2/neu gene by FISH were defined as positive.

Pathological Response

All specimens excised from the breast were sectioned into 5 μm-thick slices and were microscopically analyzed for the presence of residual tumor by a board certified pathologist. Pathological complete response (pCR) was defined as microscopic evidence that invasive components of cancer cells had entirely disappeared in all inspected pathologic specimens. Regional lymph node involvement was not evaluated in this study. The tumors that did not achieve pCR were considered to be non-pCR.

Surgery

Surgery was planned 3–5 weeks after the final course of chemotherapy was delivered. Conservative surgery with lumpectomy or segmentectomy was chosen depending on tumor size and its location after tumor shrinkage. In patients who achieved a significant response to chemotherapy, an ultrasonographic examination was performed prior to surgery to assist in confirming the location of the lesion. The remaining patients underwent a modified radical mastectomy. In all patients, axillary dissection was performed.

Statistical analysis

Statistical comparisons between response groups were computed using JMP software (Cary, North Carolina). Unpaired 2-sided student’s t-tests and the Wilxocon test were used to compare differences in tumor values (T), contralateral normal values (N), and normalized (T-N) values of ctO2Hb, ctHHb, ctTHb, stO2 as well as ER staining, PR staining, Ki67 staining, and tumor size between pCR and non-pCR groups. Fisher’s exact test was used to compare HER2 status and Grade between response groups. P-values less than 0.05 were considered significant.

Discriminant analysis was performed using Matlab (MathWorks, Natick, MA). Two classification algorithms were utilized, a linear classifier based on Bayesian parameter estimation which assumed multivariate normal densities and equal covariances for each group, and a ordinal logistic regression model with does not assume the data is normally distributed. A priori probabilities for the linear discriminant classifier were based on the relative proportion of each group. 5 fold cross-validation was used for all classifiers to mitigate potential over-performance in this relatively small data set. Receiver-operating characteristic (ROC) curves were constructed using computed posterior probabilities calculated from the classifiers. The area under the curve (AUC) of the ROC curve was utilized as an overall performance metric for the classifier. Sensitivity, specificity, negative predictive values, and positive predictive values were also reported at the optimal threshold or Q-point. The Q-point is the operating point on the ROC curve that has the minimum geometric distance from the upper left hand corner of the plot. For multivariate discrimination analysis, AUCs were compared from classifiers using all possible combinations (two at a time) of the following features: ctO2Hb, ctHHb, ctTHb, stO2, ER staining, PR staining, Ki67 staining, and tumor size.

A one-way analysis of variance was performed to estimate the inter- and intra- tumor variance for each optical parameter and the F-test was applied to test the null hypothesis of no difference between these variances.

RESULTS

Baseline characteristics of tumor

Forty-one patients were measured in this study. One patient had bilateral disease so a total of 42 tumors were evaluated for this study. There was at least a 14-day interval (average 31.3 days, [14 to 110]) between the diagnostic core biopsy and the baseline DOSI measurements prior to the beginning of neoadjuvant chemotherapy. The average number of days between the baseline DOSI measurement and the first infusion was 11.7 ± 10.7 days. Table 1 displays subject and tumor characteristics. Twelve (28.6%) tumors achieved pCR and thirty (71.4%) tumors were defined as non-pCR. pCR rate did not differ between subjects who received chemotherapy alone and those who received monoclonal antibody combination regimens (p = 0.4 student’s t-test).

Table 1

Patient and tumor characteristics

VariablesTotals
n = 42(%)
Age, yearsMean ± SD49.2 ± 11.2
Tumor size, cmMean ± SD3.5 ± 2.1
MenopausePre23(54.8)
Post19(45.2)
LocationLeft21(50)
Right21(50)
HistologyIDC36(85.7)
ILC5(11.9)
IDC+ILC1(2.4)
Histological gradeScore 3–614(33.3)
Score 7–925(59.5)
unknown3(7.1)
T-stageI7(16.7)
II25(59.5)
III7(16.7)
IV3(7.1)
Nodal status8(19)
+34(81)
ER status (cutoff 5%)12(28.6)
+28(66.7)
unknown2(5)
PR status (cutoff 5%)14(33.3)
+26(61.9)
unknown2(5)
HER2 status0,1+,2+(FISH−)29(69)
3+ or FISH+10(23.8)
unknown3(7.1)
Chemotherapy regimenChemotherapy alone13(31)
Trastuzumab combo9(21.4)
Bevacizumab combo20(47.6)
Surgical procedureMastectomy29(69)
Segmentectomy4(9.5)
Lumpectomy9(21.4)
Pathologic responsenon-pCR30(71.4)
pCR12(28.6)

SD, Standard derivation; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; HR, hormone receptor; pCR, pathological complete response; Monoclonal antibody combinations include trastzumab or bevacizumab with chemotherapy.

Comparison of optical properties and biomarkers in subjects achieving pCR and non-pCR

Table 2 shows the mean and median of tumor, normal, and tumor-normal (T-N) values of ctO2Hb, ctHHb, ctTHb, stO2, as well as tumor size, ER staining, PR staining, Ki67 staining, tumor grade, and HER2 status for both the pCR and non-pCR groups. stO2-T measured in subjects achieving pCR was higher than non-pCR (median 77.8%, vs. median 72.3%, p = 0.02 student’s t-test, p = 0.01 Wilcoxon). There were no significant differences between response groups for the optical parameters ctO2Hb-T (p = 0.4 student’s t-test, p = 0.3 Wilcoxon), ctHHb (p = 0.2 student’s t-test, p = 0.3 Wilcoxon), and ctTHb (p = 0.7 student’s t-test, p = 0.5 Wilcoxon).

Table 2

Baseline optical and tissue biomarker values for tumors achieving pCR and non-pCR. For continuous variables, p-values from both a Student’s t-test and a Wilcoxon test are shown. For binary variables, p-values from Fisher’s Exact test are shown.

A) Continuous parameter
VariablesnumberpCR
numbernon-pCR
Student t-test p-valueWilcoxon test p-value
mean [95%CI]medianmean [95%CI]median


ctO2Hb (μM)-T1226.1 [20.4, 31.7]24.73023.4 [19.8, 26.9]20.70.40.3
ctHHb (μM)-T127.32 [5.39, 9.24]6.67308.64 [7.42, 9.85]7.640.20.3
ctTHb (μM)-T1233.4 [26.3, 40.4]31.33032.0 [27.6, 36.5]28.90.70.5
stO2 (%)-T1277.8 [74.0, 81.5]77.83072.3 [69.9, 74.7]73.80.02*0.01*
TOI-T124.75 [2.38, 7.12]3.18305.47 [3.97, 6.97]4.290.6.5


ctO2Hb (μM)-N1115.2 [12.1, 18.3]14.82216.5 [14.2, 18.7]15.40.50.6
ctHHb (μM)-N114.22 [3.24, 5.20]4.22224.7 [3.99, 5.38]4.150.40.8
ctTHb (μM)-N1119.4 [15.7, 23.2]18.82221.1 [18.5, 23.8]19.60.50.5
stO2 (%)-N1177.4 [74.1, 80.8]77.72277.5[75.2, 79.9]78.10.970.98
TOI-N111.14 [0.39, 1.88]0.91221.66 [1.14, 2.19]1.170.30.6


ctO2Hb (μM)-T-N1110.0 [4.21, 15.8]8.67227.59 [3.48, 11.7]5.350.50.2
ctHHb (μM)-T-N112.70 [0.67, 4.74]2.13223.65 [2.21, 5.09]2.290.40.98
ctTHb (μM)-T-N1112.7 [5.36, 20.1]10.72211.2 [6.03, 16.45]8.120.70.4
stO2 (%)-T-N110.56 [−3.24, 4.36]0.3022−3.97 [−6.66, −1.29]−3.220.060.05
TOI-T-N112.75 [0.74, 4.76]1.67223.24 [1.82, 4.66]2.370.70.8


Tumor size (cm)122.89 [1.66, 4.12]2.40303.70 [2.92, 4.48]2.950.30.1
ER (%)1218.3 [0.02, 36.5]0.002883.5 [71.6, 95.4]95.0< 0.0001*< 0.0001*
PR (%)1216.3 [−7.56, 40.1]0.002853.2 [37.6, 68.8]70.00.01*0.002*
Ki67 (%)958.0 [41.5, 74.5]60.02128.5 [17.7, 39.3]20.00.005*0.02*

B) Binary parameter
VariablespCRnon-pCRFisher's exact test p-value

GradeHigh10150.2
Low212
unknown03


HER2 statusPositive640.04*
Negative623
unknown03

There were significant differences between response groups in ER (p < 0.0001 student’s t-test, p < 0.0001 Wilcoxon), PR (p = 0.01 student’s t-test, p = 0.002 Wilcoxon), and Ki67 (p = 0.005 student’s t-test, p = 0.02 Wilcoxon), HER2 status (p = 0.04 Fisher’s exact test). Tumor size and grade were not significantly different between response groups (p = 0.3 student’s t-test, p = 0.1 Wilcoxon for tumor size; p = 0.2 Fisher’s exact test for tumor grade).

Figure 2 shows box-and-whisker plots of stO2 levels in pCR and non-pCR in both tumor and contralateral normal tissue. As described above, stO2-T measurements were significantly higher in tumor tissue measured in subjects achieving pCR compared to non-pCR (p = 0.02, student’s t-test, p = 0.01 Wilcoxon). stO2 measurements from contralateral normal tissue did not show significant differences between pCR and non-pCR response groups (median 77.4%, versus median 77.5%, p = 0.97 student’s t-test, p = 0.98 Wilcoxon). When stO2 measured in normal tissue was subtracted from stO2 measured in paired tumor tissue (stO2-T-N), no statistical differences were detected between pCR and non-pCR groups (median 0.3% versus median - 3.22%, p = 0.06 student’s t-test, p = 0.05 Wilcoxon).

An external file that holds a picture, illustration, etc.
Object name is nihms393837f2.jpg

Box-and-whisker plots showing the difference in tumor stO2 levels between pCR and non-pCR tumors (left) (median 77.8%, vs. median 72.3%, p = 0.01, Wicoxon) and the lack of difference in stO2 levels between contralateral normal tissues (middle) (median 77.7%, vs. median 78.1%, p = 0.98 Wilcoxon).

StO2-T levels in pCR and non-pCR tumors were stratified by either chemotherapy alone, trastuzumab combined with chemotherapy or bevacizumab combined with chemotherapy. There were significant differences between tumor stO2 in the pCR and non-pCR groups when chemotherapy was used alone using t-tests but not with the Wilcoxon test (median 80.5% versus median 76.1%, p = 0.05 student’s t-test, p = 0.06, Wilcoxon)). Although not significant, the same trend occurred when chemotherapy was combined with trastuzumab (median 77.4%, versus median 71.7%, p = 0.2 student’s t-test, p = 0.3 Wilcoxon). There was an insufficient number of tumors treated with bevacizumab that achieved pCR (n=3) to statistically compare StO2 values in this treatment subgroup.

It is of note that the mean intra-tumor variation was significantly lower than the inter-tumor variation for stO2, ctO2Hb, ctHHb, ctTHb, and TOI. Model-based estimates of the inter-and intra-tumor standard deviations and the P-values for the F-test with 41 and 186 degrees of freedom are as follows: stO2 (16.4, 3.2, p<0.0001), ctO2Hb (20.5, 7.4, p<00001), ctHHb (6.7, 1.4, p<0.0001), ctTHb (24.9, 7.8, p<0.0001), T01 (7.8, 2.6, p<0.0001).

Discriminant analysis

Performance of the linear discriminant classifier and the ordinal logistic classifier were compared and the AUC values for both classifiers are shown in Table 3. Because the difference between the models did not change the outcome or our conclusions, thresholds, sensitivity, specificity, positive predictive values, and negative predictive values are only shown for the linear discriminant classifier.

Table 3

Predictive performance of optical and tissue biomarkers using univariate and multivariate discriminant analyses.

ParameternThresholdAUC (Logistic)AUC (LDA)SeSpPPVNPV
Optical Properties

StO2-T4276.7%.733.71975.0% (42.8 – 93.3%)73.3% (53.8 – 87.0%)52.9% (28.5 – 76.1%)88.0% (67.7 – 96.8%)
ctHbO2-T4225.4μM.506.53640.0% (22.3 – 77.7%)66.7% (47.1 – 82.1%)37.5% (16.3 – 64.1%)76.9% (55.9 – 90.2%)
ctHHb-T427.51μM.576.59966.7% (35.4 – 88.7%)53.3% (34.6 – 71.2%)36.4% (18.0 – 59.2%)80.0% (55.7 – 93.4%)
ctTHb-T4233.4μM.419.50650.0% (22.3 – 77.7%)53.3% (34.6 – 71.2%)30.0% (12.8 – 54.3%)72.7% (49.6 – 88.4%)

StO2-T-N33−2.51%.692.66981.8% (47.8 – 96.8%)59.1% (36.7 – 78.5%)50.0% (26.8 – 73.2%)86.7% (58.4 – 97.7%)
ctHbO2-T-N338.40μM.545.50245.5% (18.1 – 75.4%)77.3% (54.2 – 91.3%)50.0% (20.1 – 79.9%)73.9% (51.3 – 88.9%)
ctHHb-T-N332.46 μM.471.45072.7% (39.3 – 92.7%)50.0% (28.8 – 71.2%)42.1% (21.1 – 66.0%)78.6% (48.8 – 94.3%)
ctTHb-T-N3310.9 μM.488.39636.4% (12.4 – 68.4%)77.3% (54.2 – 91.3%)44.4% (15.3 – 77.3%)70.8% (48.8 – 86.6%)
Biomarkers

ER4079.6%.854.86091.7% (59.8 – 99.6%)82.1% (62.4 – 93.2%)68.8% (41.5 – 87.9%)95.8% (76.9 – 99.8%)
PR407.61%.707.70583.3% (50.9 – 97.1%)67.9% (47.6 – 83.4%)52.6% (29.5 – 47.8%)90.5% (68.2 – 98.3%)
Ki673048.2%.672.68566.7% (30.9 – 91.0%)76.2% (52.4 – 90.9%)54.5% (24.6 – 81.9%)84.2% (59.5 – 95.8%)
Tumor Size422.64cm.607.66175.0% (42.8 – 93.3%)63.3% (43.9 – 79.5%)45.0% (23.8 – 68.0%)86.4% (64.0 – 96.4%)
Combination

StO2-T + ER40NA.955.933100% (69.9 – 100%)85.7% (66.4 – 95.3%)75.0% (47.4 – 91.7%)100% (82.8 – 100%)
StO2-T + PR40NA.813.80275.0% (42.8 – 93.3%)78.6% (58.5 – 91.0%)60.0% (32.9 – 82.5%)88.0% (67.7 – 96.8%)
ctHHb-T + Ki6730NA.773.73577.8% (40.2 – 96.1%)81.0% (57.4 – 93.7%)63.6% (31.6 – 87.6%)89.5% (65.5 – 98.2%)
StO2-T + Tumor Size42NA.681.69666.7% (35.4 – 88.7%)66.7% (47.1 – 82.1%)44.4% (22.4 – 68.7%)83.3% (61.8 – 94.5%)

StO2-T-N + ER32NA.896.89090.9% (57.1 – 99.5%)90.5% (68.2 – 98.3%)83.3% (50.9 – 97.1%)95.0% (73.1 – 99.7%)
StO2-T-N + PR32NA.706.71063.6% (31.6 – 87.6%)81.0% (57.4 – 93.7%)63.6% (31.6 – 87.6%)81.0% (57.4 – 93.7%)
ctHHb-T-N + Ki6725NA.750.76575.0% (35.6 – 95.5%)88.2% (62.3 – 97.9%)75.0% (35.6 – 95.5%)88.2% (62.3 – 97.9%)
StO2-T-N + Tumor Size33NA.682.66972.7% (39.3 – 92.7%)77.3% (54.2 – 91.3%)61.5% (32.3 – 84.9%)85.0% (61.1 – 96.0%)

Tumor Size + ER40NA.827.86691.7% (59.8 – 99.6%)77.3% (85.7 – 66.4%)73.3% (44.8 – 91.1%)96.0% (77.7 – 99.8%)
ER + PR40NA.811.83983.3% (50.9 – 97.1%)89.3% (70.6 – 97.2%)76.9% (46.0 – 93.8%)92.6% (74.2 – 98.7%)

Table 3 shows classification results from optically derived parameters (ctO2Hb, ctHHb, ctTHb, stO2 from tumor site, normal site, and tumor-normal (T-N)) and clinical biomarkers (ER, PR, Ki67, and tumor size). ER staining was the best performing parameter with an AUC of 0.854, sensitivity of 91.7%, specificity of 82.1%, positive predictive value of 68.8%, and negative predictive value of 95.8%. The optimal threshold for ER staining was 79.6% although it is of note that similar performance could be achieved over a wide range of threshold values since subjects generally had none, very low staining (<10%) or very high staining (>85%). stO2-T was the best performing optically derived parameter and the second best parameter overall with an AUC of 0.733, sensitivity of 75.0%, specificity of 73.3%, positive predictive value of 52.9%, and negative predictive value of 88.0%. The optimal threshold for stO2 was 76.7%. stO2- (T-N) (i.e. normalized tumor value) was the second best performing optically derived parameter and the forth best parameter overall with an AUC of 0.692, sensitivity of 81.8%, specificity of 59.1%, positive predictive value of 50.0%, and negative predictive value of 86.7%.

The best performing combination of two parameters in the multivariate discriminant analysis was ER staining and stO2-T. When these parameters were used together to discriminate response groups, an AUC of 0.955 was achieved with a sensitivity of 100%, specificity of 85.7%, positive predictive value of 75.0%, and negative predictive value of 100%. This combination provides an 11.8% increase in AUC over using ER staining alone and a 30.3% increase over stO2-T alone. Table 3 also shows the highest performing combinations of optical and clinical biomarkers for each biomarker. Other combinations that achieved a high classification accuracy included ER and BRS Grade (AUC 0.897), ER and ctHHb (AUC 0.871), and ER and ctO2Hb (AUC 0.859).

Statistical significance between ROC curves was determined using the methods described in Vergara et al. (34). Posterior probabilities output from the discriminant analysis for the predictors of ctO2Hb, ctHHb, ctTHb, stO2, ER, PR, tumor size, stO2 + ER, stO2 + PR, and stO2 + tumor size were used as inputs for the comparison. The method requires sample sizes for each predictor to be equivalent and two data points were removed from several of the predictors in order to meet this criterion. Ki67 was not compared with the other predictors as only 30 samples were available. The combination of StO2 + ER was statistically different (p < .05) from all other predictors except for ER (p = .23) implying that this combination provides information not available from the other predictors.

Baseline characteristics of tumor

Forty-one patients were measured in this study. One patient had bilateral disease so a total of 42 tumors were evaluated for this study. There was at least a 14-day interval (average 31.3 days, [14 to 110]) between the diagnostic core biopsy and the baseline DOSI measurements prior to the beginning of neoadjuvant chemotherapy. The average number of days between the baseline DOSI measurement and the first infusion was 11.7 ± 10.7 days. Table 1 displays subject and tumor characteristics. Twelve (28.6%) tumors achieved pCR and thirty (71.4%) tumors were defined as non-pCR. pCR rate did not differ between subjects who received chemotherapy alone and those who received monoclonal antibody combination regimens (p = 0.4 student’s t-test).

Table 1

Patient and tumor characteristics

VariablesTotals
n = 42(%)
Age, yearsMean ± SD49.2 ± 11.2
Tumor size, cmMean ± SD3.5 ± 2.1
MenopausePre23(54.8)
Post19(45.2)
LocationLeft21(50)
Right21(50)
HistologyIDC36(85.7)
ILC5(11.9)
IDC+ILC1(2.4)
Histological gradeScore 3–614(33.3)
Score 7–925(59.5)
unknown3(7.1)
T-stageI7(16.7)
II25(59.5)
III7(16.7)
IV3(7.1)
Nodal status8(19)
+34(81)
ER status (cutoff 5%)12(28.6)
+28(66.7)
unknown2(5)
PR status (cutoff 5%)14(33.3)
+26(61.9)
unknown2(5)
HER2 status0,1+,2+(FISH−)29(69)
3+ or FISH+10(23.8)
unknown3(7.1)
Chemotherapy regimenChemotherapy alone13(31)
Trastuzumab combo9(21.4)
Bevacizumab combo20(47.6)
Surgical procedureMastectomy29(69)
Segmentectomy4(9.5)
Lumpectomy9(21.4)
Pathologic responsenon-pCR30(71.4)
pCR12(28.6)

SD, Standard derivation; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; HR, hormone receptor; pCR, pathological complete response; Monoclonal antibody combinations include trastzumab or bevacizumab with chemotherapy.

Comparison of optical properties and biomarkers in subjects achieving pCR and non-pCR

Table 2 shows the mean and median of tumor, normal, and tumor-normal (T-N) values of ctO2Hb, ctHHb, ctTHb, stO2, as well as tumor size, ER staining, PR staining, Ki67 staining, tumor grade, and HER2 status for both the pCR and non-pCR groups. stO2-T measured in subjects achieving pCR was higher than non-pCR (median 77.8%, vs. median 72.3%, p = 0.02 student’s t-test, p = 0.01 Wilcoxon). There were no significant differences between response groups for the optical parameters ctO2Hb-T (p = 0.4 student’s t-test, p = 0.3 Wilcoxon), ctHHb (p = 0.2 student’s t-test, p = 0.3 Wilcoxon), and ctTHb (p = 0.7 student’s t-test, p = 0.5 Wilcoxon).

Table 2

Baseline optical and tissue biomarker values for tumors achieving pCR and non-pCR. For continuous variables, p-values from both a Student’s t-test and a Wilcoxon test are shown. For binary variables, p-values from Fisher’s Exact test are shown.

A) Continuous parameter
VariablesnumberpCR
numbernon-pCR
Student t-test p-valueWilcoxon test p-value
mean [95%CI]medianmean [95%CI]median


ctO2Hb (μM)-T1226.1 [20.4, 31.7]24.73023.4 [19.8, 26.9]20.70.40.3
ctHHb (μM)-T127.32 [5.39, 9.24]6.67308.64 [7.42, 9.85]7.640.20.3
ctTHb (μM)-T1233.4 [26.3, 40.4]31.33032.0 [27.6, 36.5]28.90.70.5
stO2 (%)-T1277.8 [74.0, 81.5]77.83072.3 [69.9, 74.7]73.80.02*0.01*
TOI-T124.75 [2.38, 7.12]3.18305.47 [3.97, 6.97]4.290.6.5


ctO2Hb (μM)-N1115.2 [12.1, 18.3]14.82216.5 [14.2, 18.7]15.40.50.6
ctHHb (μM)-N114.22 [3.24, 5.20]4.22224.7 [3.99, 5.38]4.150.40.8
ctTHb (μM)-N1119.4 [15.7, 23.2]18.82221.1 [18.5, 23.8]19.60.50.5
stO2 (%)-N1177.4 [74.1, 80.8]77.72277.5[75.2, 79.9]78.10.970.98
TOI-N111.14 [0.39, 1.88]0.91221.66 [1.14, 2.19]1.170.30.6


ctO2Hb (μM)-T-N1110.0 [4.21, 15.8]8.67227.59 [3.48, 11.7]5.350.50.2
ctHHb (μM)-T-N112.70 [0.67, 4.74]2.13223.65 [2.21, 5.09]2.290.40.98
ctTHb (μM)-T-N1112.7 [5.36, 20.1]10.72211.2 [6.03, 16.45]8.120.70.4
stO2 (%)-T-N110.56 [−3.24, 4.36]0.3022−3.97 [−6.66, −1.29]−3.220.060.05
TOI-T-N112.75 [0.74, 4.76]1.67223.24 [1.82, 4.66]2.370.70.8


Tumor size (cm)122.89 [1.66, 4.12]2.40303.70 [2.92, 4.48]2.950.30.1
ER (%)1218.3 [0.02, 36.5]0.002883.5 [71.6, 95.4]95.0< 0.0001*< 0.0001*
PR (%)1216.3 [−7.56, 40.1]0.002853.2 [37.6, 68.8]70.00.01*0.002*
Ki67 (%)958.0 [41.5, 74.5]60.02128.5 [17.7, 39.3]20.00.005*0.02*

B) Binary parameter
VariablespCRnon-pCRFisher's exact test p-value

GradeHigh10150.2
Low212
unknown03


HER2 statusPositive640.04*
Negative623
unknown03

There were significant differences between response groups in ER (p < 0.0001 student’s t-test, p < 0.0001 Wilcoxon), PR (p = 0.01 student’s t-test, p = 0.002 Wilcoxon), and Ki67 (p = 0.005 student’s t-test, p = 0.02 Wilcoxon), HER2 status (p = 0.04 Fisher’s exact test). Tumor size and grade were not significantly different between response groups (p = 0.3 student’s t-test, p = 0.1 Wilcoxon for tumor size; p = 0.2 Fisher’s exact test for tumor grade).

Figure 2 shows box-and-whisker plots of stO2 levels in pCR and non-pCR in both tumor and contralateral normal tissue. As described above, stO2-T measurements were significantly higher in tumor tissue measured in subjects achieving pCR compared to non-pCR (p = 0.02, student’s t-test, p = 0.01 Wilcoxon). stO2 measurements from contralateral normal tissue did not show significant differences between pCR and non-pCR response groups (median 77.4%, versus median 77.5%, p = 0.97 student’s t-test, p = 0.98 Wilcoxon). When stO2 measured in normal tissue was subtracted from stO2 measured in paired tumor tissue (stO2-T-N), no statistical differences were detected between pCR and non-pCR groups (median 0.3% versus median - 3.22%, p = 0.06 student’s t-test, p = 0.05 Wilcoxon).

An external file that holds a picture, illustration, etc.
Object name is nihms393837f2.jpg

Box-and-whisker plots showing the difference in tumor stO2 levels between pCR and non-pCR tumors (left) (median 77.8%, vs. median 72.3%, p = 0.01, Wicoxon) and the lack of difference in stO2 levels between contralateral normal tissues (middle) (median 77.7%, vs. median 78.1%, p = 0.98 Wilcoxon).

StO2-T levels in pCR and non-pCR tumors were stratified by either chemotherapy alone, trastuzumab combined with chemotherapy or bevacizumab combined with chemotherapy. There were significant differences between tumor stO2 in the pCR and non-pCR groups when chemotherapy was used alone using t-tests but not with the Wilcoxon test (median 80.5% versus median 76.1%, p = 0.05 student’s t-test, p = 0.06, Wilcoxon)). Although not significant, the same trend occurred when chemotherapy was combined with trastuzumab (median 77.4%, versus median 71.7%, p = 0.2 student’s t-test, p = 0.3 Wilcoxon). There was an insufficient number of tumors treated with bevacizumab that achieved pCR (n=3) to statistically compare StO2 values in this treatment subgroup.

It is of note that the mean intra-tumor variation was significantly lower than the inter-tumor variation for stO2, ctO2Hb, ctHHb, ctTHb, and TOI. Model-based estimates of the inter-and intra-tumor standard deviations and the P-values for the F-test with 41 and 186 degrees of freedom are as follows: stO2 (16.4, 3.2, p<0.0001), ctO2Hb (20.5, 7.4, p<00001), ctHHb (6.7, 1.4, p<0.0001), ctTHb (24.9, 7.8, p<0.0001), T01 (7.8, 2.6, p<0.0001).

Discriminant analysis

Performance of the linear discriminant classifier and the ordinal logistic classifier were compared and the AUC values for both classifiers are shown in Table 3. Because the difference between the models did not change the outcome or our conclusions, thresholds, sensitivity, specificity, positive predictive values, and negative predictive values are only shown for the linear discriminant classifier.

Table 3

Predictive performance of optical and tissue biomarkers using univariate and multivariate discriminant analyses.

ParameternThresholdAUC (Logistic)AUC (LDA)SeSpPPVNPV
Optical Properties

StO2-T4276.7%.733.71975.0% (42.8 – 93.3%)73.3% (53.8 – 87.0%)52.9% (28.5 – 76.1%)88.0% (67.7 – 96.8%)
ctHbO2-T4225.4μM.506.53640.0% (22.3 – 77.7%)66.7% (47.1 – 82.1%)37.5% (16.3 – 64.1%)76.9% (55.9 – 90.2%)
ctHHb-T427.51μM.576.59966.7% (35.4 – 88.7%)53.3% (34.6 – 71.2%)36.4% (18.0 – 59.2%)80.0% (55.7 – 93.4%)
ctTHb-T4233.4μM.419.50650.0% (22.3 – 77.7%)53.3% (34.6 – 71.2%)30.0% (12.8 – 54.3%)72.7% (49.6 – 88.4%)

StO2-T-N33−2.51%.692.66981.8% (47.8 – 96.8%)59.1% (36.7 – 78.5%)50.0% (26.8 – 73.2%)86.7% (58.4 – 97.7%)
ctHbO2-T-N338.40μM.545.50245.5% (18.1 – 75.4%)77.3% (54.2 – 91.3%)50.0% (20.1 – 79.9%)73.9% (51.3 – 88.9%)
ctHHb-T-N332.46 μM.471.45072.7% (39.3 – 92.7%)50.0% (28.8 – 71.2%)42.1% (21.1 – 66.0%)78.6% (48.8 – 94.3%)
ctTHb-T-N3310.9 μM.488.39636.4% (12.4 – 68.4%)77.3% (54.2 – 91.3%)44.4% (15.3 – 77.3%)70.8% (48.8 – 86.6%)
Biomarkers

ER4079.6%.854.86091.7% (59.8 – 99.6%)82.1% (62.4 – 93.2%)68.8% (41.5 – 87.9%)95.8% (76.9 – 99.8%)
PR407.61%.707.70583.3% (50.9 – 97.1%)67.9% (47.6 – 83.4%)52.6% (29.5 – 47.8%)90.5% (68.2 – 98.3%)
Ki673048.2%.672.68566.7% (30.9 – 91.0%)76.2% (52.4 – 90.9%)54.5% (24.6 – 81.9%)84.2% (59.5 – 95.8%)
Tumor Size422.64cm.607.66175.0% (42.8 – 93.3%)63.3% (43.9 – 79.5%)45.0% (23.8 – 68.0%)86.4% (64.0 – 96.4%)
Combination

StO2-T + ER40NA.955.933100% (69.9 – 100%)85.7% (66.4 – 95.3%)75.0% (47.4 – 91.7%)100% (82.8 – 100%)
StO2-T + PR40NA.813.80275.0% (42.8 – 93.3%)78.6% (58.5 – 91.0%)60.0% (32.9 – 82.5%)88.0% (67.7 – 96.8%)
ctHHb-T + Ki6730NA.773.73577.8% (40.2 – 96.1%)81.0% (57.4 – 93.7%)63.6% (31.6 – 87.6%)89.5% (65.5 – 98.2%)
StO2-T + Tumor Size42NA.681.69666.7% (35.4 – 88.7%)66.7% (47.1 – 82.1%)44.4% (22.4 – 68.7%)83.3% (61.8 – 94.5%)

StO2-T-N + ER32NA.896.89090.9% (57.1 – 99.5%)90.5% (68.2 – 98.3%)83.3% (50.9 – 97.1%)95.0% (73.1 – 99.7%)
StO2-T-N + PR32NA.706.71063.6% (31.6 – 87.6%)81.0% (57.4 – 93.7%)63.6% (31.6 – 87.6%)81.0% (57.4 – 93.7%)
ctHHb-T-N + Ki6725NA.750.76575.0% (35.6 – 95.5%)88.2% (62.3 – 97.9%)75.0% (35.6 – 95.5%)88.2% (62.3 – 97.9%)
StO2-T-N + Tumor Size33NA.682.66972.7% (39.3 – 92.7%)77.3% (54.2 – 91.3%)61.5% (32.3 – 84.9%)85.0% (61.1 – 96.0%)

Tumor Size + ER40NA.827.86691.7% (59.8 – 99.6%)77.3% (85.7 – 66.4%)73.3% (44.8 – 91.1%)96.0% (77.7 – 99.8%)
ER + PR40NA.811.83983.3% (50.9 – 97.1%)89.3% (70.6 – 97.2%)76.9% (46.0 – 93.8%)92.6% (74.2 – 98.7%)

Table 3 shows classification results from optically derived parameters (ctO2Hb, ctHHb, ctTHb, stO2 from tumor site, normal site, and tumor-normal (T-N)) and clinical biomarkers (ER, PR, Ki67, and tumor size). ER staining was the best performing parameter with an AUC of 0.854, sensitivity of 91.7%, specificity of 82.1%, positive predictive value of 68.8%, and negative predictive value of 95.8%. The optimal threshold for ER staining was 79.6% although it is of note that similar performance could be achieved over a wide range of threshold values since subjects generally had none, very low staining (<10%) or very high staining (>85%). stO2-T was the best performing optically derived parameter and the second best parameter overall with an AUC of 0.733, sensitivity of 75.0%, specificity of 73.3%, positive predictive value of 52.9%, and negative predictive value of 88.0%. The optimal threshold for stO2 was 76.7%. stO2- (T-N) (i.e. normalized tumor value) was the second best performing optically derived parameter and the forth best parameter overall with an AUC of 0.692, sensitivity of 81.8%, specificity of 59.1%, positive predictive value of 50.0%, and negative predictive value of 86.7%.

The best performing combination of two parameters in the multivariate discriminant analysis was ER staining and stO2-T. When these parameters were used together to discriminate response groups, an AUC of 0.955 was achieved with a sensitivity of 100%, specificity of 85.7%, positive predictive value of 75.0%, and negative predictive value of 100%. This combination provides an 11.8% increase in AUC over using ER staining alone and a 30.3% increase over stO2-T alone. Table 3 also shows the highest performing combinations of optical and clinical biomarkers for each biomarker. Other combinations that achieved a high classification accuracy included ER and BRS Grade (AUC 0.897), ER and ctHHb (AUC 0.871), and ER and ctO2Hb (AUC 0.859).

Statistical significance between ROC curves was determined using the methods described in Vergara et al. (34). Posterior probabilities output from the discriminant analysis for the predictors of ctO2Hb, ctHHb, ctTHb, stO2, ER, PR, tumor size, stO2 + ER, stO2 + PR, and stO2 + tumor size were used as inputs for the comparison. The method requires sample sizes for each predictor to be equivalent and two data points were removed from several of the predictors in order to meet this criterion. Ki67 was not compared with the other predictors as only 30 samples were available. The combination of StO2 + ER was statistically different (p < .05) from all other predictors except for ER (p = .23) implying that this combination provides information not available from the other predictors.

DISCUSSION

In this retrospective analysis of 41 patients measured over a period of 7 years using a standardized optical imaging technology, we observed that patients who exhibited a complete pathologic response (pCR) to neoadjuvant chemotherapy had higher tumor tissue hemoglobin oxygen saturation (stO2) values than non-pCR subjects. Additionally, tumor stO2 levels measured in subjects achieving pCR were similar or higher than levels measured in paired normal tissue. In contrast, non-pCR tumors had lower stO2 compared to normal tissue. Finally, stO2 levels were not different between response groups in contralateral normal breast tissue. These findings support the idea that pCR and non-pCR tumors have differential oxygen delivery and utilization. These results highlight the potential utility of DOSI measurements for understanding in vivo tumor biology and represent one of the first applications of functional imaging for chemotherapy response prediction using baseline measurements alone.

Discriminant analyses revealed the significance of tumor stO2 as a prognostic indicator of chemotherapy responsiveness. ROC analysis demonstrated that tumor stO2 alone was sufficient to separate pCR from non-pCR response groups (AUC 0.733) with comparable accuracy to established predictive markers such as ER (AUC 0.854), PR (AUC 0.707), Ki67 (AUC 0.672), and tumor size (AUC 0.607). A sensitivity of 75.0% and specificity of 73.3% was shown for the classification of pCR using an optimal stO2 threshold of 76.7%.

We also explored the prognostic capability of more traditional biomarkers to predict pCR alone and in combination with optical markers. In confirmation of well-established trends, we observed that lower hormone receptor expression and higher proliferation as measured by Ki67 were both correlated with pCR (3537). Furthermore, when stO2 and ER were used together in a multivariate discriminant analysis, classification of response groups improved (AUC 0.955). The combination of stO2 and ER was the best pairing of the measured parameters. This finding suggests that the non-invasive optical measurements explored in this study provide independent prognostic information that may be able to supplement current “standard of care” tissue molecular biomarkers. It should be noted that only two parameters were tested at a time for the discriminant analysis due to a relatively small sample size. This did not allow for control of multiple other covariates simultaneously. It is also of note that the predictive value of ER alone in this study was excellent (Se: 92% Sp: 82%) and other studies have reported more modest correlations with response. For example, in three separate studies of > 200 subjects receiving NCT for breast cancer with chemotherapy regimens similar to those in this study, the predictive value of ER negativity was Se:88% Sp:50%; Se:64% Sp:63%; and Se:90% Sp:61% (35, 38, 39). The discrepancy is likely due to the small sample size in this study.

In contrast to our previous work in which we demonstrated a correlation between normalized baseline oxyhemoglobin concentration and NCT response in a small cohort of patients (n=11) all receiving the same drug regimen (19), this study shows that non-normalized stO2 measurements are of prognostic significance in a much larger group of patients (n=41) receiving various chemotherapy regimens. Absolute measurements are advantageous because they do not rely on a choice of a normal tissue site and could potentially be implemented into clinical practice as a tumor measurement with a clearly defined saturation threshold (76.7%). Furthermore, we have demonstrated here that combining optical endpoints with clinical biomarkers significantly improves discrimination between responders and non-responders.

The biological basis for the observed associations between tumor oxygenation and chemo-sensitivity are potentially explained by several factors including the extent and condition of tumor vasculature, hypoxia, drug delivery and proliferation/metabolism (4042). Elevated levels of stO2 could be an indication of an efficient blood supply to the tumor. This allows for better delivery of drugs and nutrients necessary to maintain replication and cell division (13, 43). Additionally, in the presence of oxygen, cytotoxic drugs generate free radicals that damage DNA of cancer cells (44). Differences in stO2 may reveal differential metabolic pathways between pCR and non-pCR tumors. Lower stO2 in non-pCR tumors is associated with hypoxia and subsequent buildup of ctHHb. Higher stO2 in pCR tumors reflects diminished oxygen extraction and tumor cells that are in a more proliferative state (42). This may significantly enhance chemotherapeutic efficacy because rapidly proliferating cells are more sensitive to chemotherapy (45). Our data show that neither baseline ctO2Hb nor ctHHb alone correlate with response and that a combination of supply and metabolism (stO2) are necessary to explain the differences observed between pCR and non-pCR tumors.

Our observations broadly concur with the small number of other studies conducted using PET to correlate metabolic tumor properties with NCT response. For example, Mankoff et al. found that a low metabolic rate of FFDG relative to blood flow (measured with O-Water PET) was a predictor of complete response (27). It was hypothesized that this ratio of glucose metabolism to blood flow represents the efficiency of glucose extraction by tumors and that non-responding, hypoxic tumors may be particularly adept at extracting glucose even in the context of poor delivery. Our observation that non-responding tumors have lower stO2 and relatively higher levels of ctHHb is consistent with the idea that non-pCR tumors are adept at extracting oxygen even in the presence of poor delivery. Similar trends were shown by Specht et al. in which tumors were categorized by molecular subtypes (28).

Although this is the first published study that demonstrates a correlation between breast cancer NCT response and baseline tissue oxygen saturation measurements, it is important to note that caution should be used when comparing oxygen saturation values derived using different instruments. Technical details such as the separation between the source and detector fibers, the mathematical models used for computing light propagation, whether measurements are time-dependent, time-independent, or a combination of both, and the number and selection of optical wavelengths will affect measurement accuracy and precision. More specifically, these factors will determine whether a given device is capable of adequately resolving light absorption from scattering and whether physiological property estimates, including stO2, are sufficiently accurate and reliable to compare different individuals. We have shown that the DOSI technology used in this study combining multi-frequency, frequency-domain photon migration with spectrally broadband data has high information content and is well-suited for quantitative tissue optical and physiological property measurements (46, 47). Inter-patient comparison that employs tissue saturation devices designed for relative “trending” measurements may not have similar performance. Because of these challenges, methods to standardize optical tissue measurements remain an important and ongoing area of investigation.

A single hand-held probe design employing a fixed source-detector separation (2.8 cm) was used for all measurements in this study. It is of note that a variety of tumor sizes and depths were investigated and that relative contributions of tumor and normal tissue, also known as “partial volume effects”, are an inevitable consequence of this type of analysis. DOSI measurements convey a smaller fraction of the optical and functional properties of deep vs. shallow tumors given equivalent optical properties of tumor and surrounding normal tissue, tumor size and geometry.

Despite these limitations, stO2 measured with DOSI was a significant predictor of response. Comparable efforts have been made to assess tumor oxygenation in breast cancer using invasive microelectrodes and immunohistochemistry in order to derive prognostic value from determining tumor hypoxia (13, 48). Although direct measurements of tumor oxygen tension and related molecular pathways are desirable, electrodes and histochemical analyses probe highly localized tissue volumes and they require multiple insertions and fields of view, respectively, to adequately sample large tumors. These methods are susceptible to undersampling errors that can be particularly challenging in the case of microenvironmental heterogeneity. Thus DOSI can potentially provide a non-invasive prognostic alternative by rapidly measuring global levels of tumor and normal tissue oxygenation

In summary, this is the first study to report stO2 as a prognostic functional optical imaging endpoint for breast cancer neoadjuvant chemotherapy prior to drug administration. When considered in conjunction with previous molecular imaging studies, these findings suggest a general framework for predicting individual response to chemotherapy based on the need for adequate perfusion and metabolism to support drug delivery and utilization, respectively. With continued standardization of the measurement and analysis technology, these features could be rapidly evaluated in the clinical oncologic work flow and may be relevant for other types of large solid tumors. Ultimately, the combination of non-invasive functional imaging endpoints and tissue-specific biomarkers may provide a promising strategy for predicting individual patient chemotherapy responsiveness and guiding clinical decision-making. This information could be used, for example, in subjects who are likely to be non-responders where neoadjuvant chemotherapy would offer no benefit and subjects might endure significant side-effects. These individuals could immediately undergo surgical resection with no change in overall outcome. DOSI may also have utility in devising new treatment strategies by providing oncologists with feedback on drugs that enhance tumor perfusion prior to the administration of cytotoxic agents.

Acknowledgments

Grant Support

This work was supported by the National Institutes of Health under grants P41-EB015890 (Laser Microbeam and Medical Program: LAMMP), U54-CA136400, R01-CA142989, P30-CA62203 (University of California, Irvine Cancer Center Support Grant), and the American College of Radiology Imaging Networks (ACRIN). DMR acknowledge support from the DOD Era of Hope Fellowship Program (W81XWH-10-1-0972). DMR and TO acknowledge support from the UCI Cancer Research Institute Training grant (NCI-T32CA009054). BLI programmatic support from the Beckman Foundation is acknowledged.

The authors wish to thank Montana Compton for her assistance and the patients who generously volunteered their time for this study. We would also to thank Christine McLaren and Wen-Pin Chen for their assistance with statistical analysis.

Laser Microbeam and Medical Program (LAMMP), Beckman Laser Institute and Medical Clinic, University of California, Irvine
Chao Family Comprehensive Cancer Center, University of California, Irvine Medical Center, Orange, CA
Department of Breast Oncology, Saitama International Medical Center, Saitama Medical University, Saitama, Japan
Corresponding Author: Bruce J. Tromberg, Beckman Laser Institute and Medical Clinic, University of California, Irvine, 1002 Health Sciences Road, Irvine, California 92612. Phone: 949-824-8705; Fax: 949-824-8413 ude.icu@ebmortjb
S. Ueda and D. Roblyer contributed equally to this manuscript

Abstract

Tissue hemoglobin oxygen saturation (i.e. oxygenation) is a functional imaging endpoint that can reveal variations in tissue hypoxia which may be predictive of pathological response in subjects undergoing Neoadjuvant Chemotherapy (NCT). In this study we used Diffuse Optical Spectroscopic Imaging (DOSI) to measure concentrations of oxyhemoglobin (ctO2Hb), deoxy-hemoglobin (ctHHb), total Hb (ctTHb = ctO2Hb + ctHHb) and oxygen saturation (stO2=ctO2Hb/ctTHb) in tumor and contralateral normal tissue from forty-one patients with locally advanced primary breast cancer. Measurements were acquired prior to the start of neoadjuvant chemotherapy. Optically derived parameters were analyzed separately and in combination with clinical biomarkers to evaluate correlations with pathologic response. Discriminant analysis was performed to determine the ability of optical and clinical biomarkers to classify subjects into response groups. Twelve (28.6%) of 42 tumors achieved pCR and 30 (71.4%) were non-pCR. Tumor measurements in pCR subjects had higher stO2 levels (median 77.8%) than those in non-pCR individuals (median 72.3%, p = 0.01). There were no significant differences in baseline ctO2Hb, ctHHb, and ctTHb between response groups. An optimal tumor oxygenation threshold of stO2 = 76.7% was determined for pCR vs. non-pCR (sensitivity = 75.0%, specificity = 73.3%). Multivariate discriminant analysis combining estrogen receptor (ER) staining and stO2 further improved the classification of pCR vs. non-pCR (sensitivity = 100% specificity = 85.7%). These results demonstrate that elevated baseline tumor stO2 are correlated with a pathologic complete response. Non-invasive DOSI scans combined with histopathology subtyping may aid in stratification of individual breast cancer patients prior to NCT.

Keywords: Neoadjuvant Chemotherapy, Tumor Oxygenation, Optics, Oxygen Saturation, Breast Cancer
Abstract

Footnotes

Disclosure of Potential Conflicts of Interest: B.T. and A.C. report patents, which are owned by the University of California, that are related to the technology and analysis methods described in this study. The diffuse optical spectroscopic imaging instrumentation used in this study was constructed in a university laboratory using federal grant support (National Institutes of Health). The University of California has licensed diffuse optical spectroscopic imaging technology and analysis methods to two companies, FirstScan, Inc. and Volighten, Inc., for different fields of use, including breast cancer (FirstScan, Inc.). This research was completed without participation, knowledge, or financial support of either company, and data were acquired and processed from patients by coauthors unaffiliated with either entity. The Institutional Review Board and Conflict of Interest Office of the University of California, Irvine, have reviewed both patent and corporate disclosures and did not find any concerns.

Footnotes

References

  • 1. Smith IC, Heys SD, Hutcheon AW, Miller ID, Payne S, Gilbert FJ, et al Neoadjuvant chemotherapy in breast cancer: significantly enhanced response with docetaxel. J Clin Oncol. 2002;20(6):1456–66.[PubMed][Google Scholar]
  • 2. Kaufmann M, von Minckwitz G, Bear HD, Buzdar A, McGale P, Bonnefoi H, et al Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: new perspectives 2006. Ann Oncol. 2007;18(12):1927–34.[PubMed][Google Scholar]
  • 3. Fisher B, Bryant J, Wolmark N, Mamounas E, Brown A, Fisher ER, et al Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J Clin Oncol. 1998;16(8):2672–85.[PubMed][Google Scholar]
  • 4. Sachelarie I, Grossbard ML, Chadha M, Feldman S, Ghesani M, Blum RHPrimary systemic therapy of breast cancer. Oncologist. 2006;11(6):574–89.[PubMed][Google Scholar]
  • 5. Rastogi P, Anderson SJ, Bear HD, Geyer CE, Kahlenberg MS, Robidoux A, et al Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. J Clin Oncol. 2008;26(5):778–85.[PubMed][Google Scholar]
  • 6. Guarneri V, Broglio K, Kau SW, Cristofanilli M, Buzdar AU, Valero V, et al Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. J Clin Oncol. 2006;24(7):1037–44.[PubMed][Google Scholar]
  • 7. Andre F, Broglio K, Roche H, Martin M, Mackey JR, Penault-Llorca F, et al Estrogen receptor expression and efficacy of docetaxel-containing adjuvant chemotherapy in patients with node-positive breast cancer: results from a pooled analysis. J Clin Oncol. 2008;26(16):2636–43.[PubMed][Google Scholar]
  • 8. Sanchez-Munoz A, Garcia-Tapiador AM, Martinez-Ortega E, Duenas-Garcia R, Jaen-Morago A, Ortega-Granados AL, et al Tumour molecular subtyping according to hormone receptors and HER2 status defines different pathological complete response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Clin Transl Oncol. 2008;10(10):646–53.[PubMed][Google Scholar]
  • 9. Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, et al American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol. 2007;25(33):5287–312.[PubMed][Google Scholar]
  • 10. Bevilacqua F, Berger AJ, Cerussi AE, Jakubowski D, Tromberg BJBroadband absorption spectroscopy in turbid media by combined frequency-domain and steady-state methods. Applied optics. 2000;39(34):6498–507.[PubMed][Google Scholar]
  • 11. Pakalniskis MG, Wells WA, Schwab MC, Froehlich HM, Jiang S, Li Z, et al Tumor angiogenesis change estimated by using diffuse optical spectroscopic tomography: demonstrated correlation in women undergoing neoadjuvant chemotherapy for invasive breast cancer? Radiology. 259(2):365–74.[Google Scholar]
  • 12. Denko NCHypoxia, HIF1 and glucose metabolism in the solid tumour. Nat Rev Cancer. 2008[PubMed][Google Scholar]
  • 13. Vaupel P, Schlenger K, Knoop C, Hockel MOxygenation of human tumors: evaluation of tissue oxygen distribution in breast cancers by computerized O2 tension measurements. Cancer Res. 1991;51(12):3316–22.[PubMed][Google Scholar]
  • 14. Okunieff P, Hoeckel M, Dunphy EP, Schlenger K, Knoop C, Vaupel POxygen tension distributions are sufficient to explain the local response of human breast tumors treated with radiation alone. Int J Radiat Oncol Biol Phys. 1993;26(4):631–6.[PubMed][Google Scholar]
  • 15. Finlay JC, Foster THHemoglobin oxygen saturations in phantoms and in vivo from measurements of steady-state diffuse reflectance at a single, short source-detector separation. Med Phys. 2004;31(7):1949–59.[PubMed][Google Scholar]
  • 16. Finlay JC, Foster THRecovery of hemoglobin oxygen saturation and intrinsic fluorescence with a forward-adjoint model. Appl Opt. 2005;44(10):1917–33.[PubMed][Google Scholar]
  • 17. Wang HW, Zhu TC, Putt ME, Solonenko M, Metz J, Dimofte A, et al Broadband reflectance measurements of light penetration, blood oxygenation, hemoglobin concentration, and drug concentration in human intraperitoneal tissues before and after photodynamic therapy. J Biomed Opt. 2005;10(1):14004.[PubMed][Google Scholar]
  • 18. Cerussi A, Shah N, Hsiang D, Durkin A, Butler J, Tromberg BJIn vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy. J Biomed Opt. 2006;11(4):044005.[PubMed][Google Scholar]
  • 19. Cerussi A, Hsiang D, Shah N, Mehta R, Durkin A, Butler J, et al Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy. Proc Natl Acad Sci U S A. 2007;104(10):4014–9.[Google Scholar]
  • 20. Soliman H, Gunasekara A, Rycroft M, Zubovits J, Dent R, Spayne J, et al Functional imaging using diffuse optical spectroscopy of neoadjuvant chemotherapy response in women with locally advanced breast cancer. Clin Cancer Res. 16(9):2605–14.[PubMed][Google Scholar]
  • 21. Zhu Q, Tannenbaum S, Hegde P, Kane M, Xu C, Kurtzman SHNoninvasive monitoring of breast cancer during neoadjuvant chemotherapy using optical tomography with ultrasound localization. Neoplasia. 2008;10(10):1028–40.[Google Scholar]
  • 22. Jiang S, Pogue BW, Carpenter CM, Poplack SP, Wells WA, Kogel CA, et al Evaluation of breast tumor response to neoadjuvant chemotherapy with tomographic diffuse optical spectroscopy: case studies of tumor region-of-interest changes. Radiology. 2009;252(2):551–60.[Google Scholar]
  • 23. Esserman L, Kaplan E, Partridge S, Tripathy D, Rugo H, Park J, et al MRI phenotype is associated with response to doxorubicin and cyclophosphamide neoadjuvant chemotherapy in stage III breast cancer. Ann Surg Oncol. 2001;8(6):549–59.[PubMed][Google Scholar]
  • 24. Uematsu T, Kasami M, Yuen SNeoadjuvant chemotherapy for breast cancer: correlation between the baseline MR imaging findings and responses to therapy. Eur Radiol. 20(10):2315–22.[PubMed][Google Scholar]
  • 25. Li XR, Cheng LQ, Liu M, Zhang YJ, Wang JD, Zhang AL, et al DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Med Oncol [PubMed]
  • 26. Nilsen L, Fangberget A, Geier O, Olsen DR, Seierstad TDiffusion-weighted magnetic resonance imaging for pretreatment prediction and monitoring of treatment response of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Acta Oncol. 49(3):354–60.[PubMed][Google Scholar]
  • 27. Mankoff DA, Dunnwald LK, Gralow JR, Ellis GK, Schubert EK, Tseng J, et al Changes in blood flow and metabolism in locally advanced breast cancer treated with neoadjuvant chemotherapy. J Nucl Med. 2003;44(11):1806–14.[PubMed][Google Scholar]
  • 28. Specht JM, Kurland BF, Montgomery SK, Dunnwald LK, Doot RK, Gralow JR, et al Tumor metabolism and blood flow as assessed by positron emission tomography varies by tumor subtype in locally advanced breast cancer. Clin Cancer Res. 16(10):2803–10.[Google Scholar]
  • 29. Cerussi A, Siavoshi S, Durkin A, Chen C, Tanamai W, Hsiang D, et al Effect of contact force on breast tissue optical property measurements using a broadband diffuse optical spectroscopy handheld probe. Appl Opt. 2009;48(21):4270–7.[Google Scholar]
  • 30. Acrin 6691 Training Videos. 2012.[PubMed]
  • 31. Roblyer D, Ueda S, Cerussi A, Tanamai W, Durkin A, Mehta R, et al Optical imaging of breast cancer oxyhemoglobin flare correlates with neoadjuvant chemotherapy response one day after starting treatment. Proc Natl Acad Sci U S A. 108(35):14626–31.[Google Scholar]
  • 32. Tromberg BJ, Cerussi A, Shah N, Compton M, Durkin A, Hsiang D, et al Imaging in breast cancer: diffuse optics in breast cancer: detecting tumors in pre-menopausal women and monitoring neoadjuvant chemotherapy. Breast Cancer Res. 2005;7(6):279–85.[Google Scholar]
  • 33. Bloom HJ, Richardson WWHistological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years. British journal of cancer. 1957;11(3):359–77.[Google Scholar]
  • 34. Vergara IA, Norambuena T, Ferrada E, Slater AW, Melo FStAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinformatics. 2008;9:265.[Google Scholar]
  • 35. Tan MC, Al Mushawah F, Gao F, Aft RL, Gillanders WE, Eberlein TJ, et al Predictors of complete pathological response after neoadjuvant systemic therapy for breast cancer. Am J Surg. 2009;198(4):520–5.[Google Scholar]
  • 36. Jones RL, Salter J, A'Hern R, Nerurkar A, Parton M, Reis-Filho JS, et al Relationship between oestrogen receptor status and proliferation in predicting response and long-term outcome to neoadjuvant chemotherapy for breast cancer. Breast Cancer Res Treat. 119(2):315–23.[PubMed][Google Scholar]
  • 37. Colleoni M, Viale G, Goldhirsch ALessons on responsiveness to adjuvant systemic therapies learned from the neoadjuvant setting. Breast. 2009;18 (Suppl 3):S137–40.[PubMed][Google Scholar]
  • 38. Kuerer HM, Newman LA, Smith TL, Ames FC, Hunt KK, Dhingra K, et al Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. J Clin Oncol. 1999;17(2):460–9.[PubMed][Google Scholar]
  • 39. Straver ME, Rutgers EJ, Rodenhuis S, Linn SC, Loo CE, Wesseling J, et al The relevance of breast cancer subtypes in the outcome of neoadjuvant chemotherapy. Ann Surg Oncol. 17(9):2411–8.[Google Scholar]
  • 40. Warburg OOn respiratory impairment in cancer cells. Science. 1956;124(3215):269–70.[PubMed][Google Scholar]
  • 41. Racker EWarburg effect revisited. Science. 1981;213(4514):1313.[PubMed][Google Scholar]
  • 42. Vander Heiden MG, Cantley LC, Thompson CBUnderstanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324(5930):1029–33.[Google Scholar]
  • 43. Vaupel P, Thews O, Kelleher DK, Hoeckel MOxygenation of human tumors: the Mainz experience. Strahlenther Onkol. 1998;174 (Suppl 4):6–12.[PubMed][Google Scholar]
  • 44. Tredan O, Galmarini CM, Patel K, Tannock IFDrug resistance and the solid tumor microenvironment. J Natl Cancer Inst. 2007;99(19):1441–54.[PubMed][Google Scholar]
  • 45. Huang LE, Bindra RS, Glazer PM, Harris ALHypoxia-induced genetic instability--a calculated mechanism underlying tumor progression. J Mol Med. 2007;85(2):139–48.[PubMed][Google Scholar]
  • 46. Fujimoto JG, Farkas DL, editors. Biomedical Optical Imaging. New York: Oxford University Press; 2009. [PubMed]
  • 47. Boas DA, Pitris C, Ramanujam N, editors. Handbook of Biomedical Optics. Boca Raton: CRC Press; 2011. [PubMed]
  • 48. Hohenberger P, Felgner C, Haensch W, Schlag PMTumor oxygenation correlates with molecular growth determinants in breast cancer. Breast Cancer Res Treat. 1998;48(2):97–106.[PubMed][Google Scholar]
Collaboration tool especially designed for Life Science professionals.Drag-and-drop any entity to your messages.