Monitoring breast tumor progression by photoacoustic measurements: a xenograft mice model study

Abstract. The current study reports the photoacoustic spectroscopy-based assessment of breast tumor progression in a nude mice xenograft model. The tumor was induced through subcutaneous injection of MCF-7 cells in female nude mice and was monitored for 20 days until the tumor volume reached 1000  mm3. The tumor tissues were extracted at three different time points (days 10, 15, and 20) after tumor inoculation and subjected to photoacoustic spectral recordings in time domain ex vivo at 281 nm pulsed laser excitations. The spectra were converted into the frequency domain using the fast Fourier transformed tools of MATLAB® algorithms and further utilized to extract seven statistical features (mean, median, area under the curve, variance and standard deviation, skewness and kurtosis) from each time point sample to assess the tumor growth with wavelet principal component analysis based logistic regression analysis performed on the data. The prediction accuracies of the analysis for day 10 versus day 15, day 15 versus day 20, and day 10 versus day 20 were found to be 92.31, 87.5, and 95.2%, respectively. Also, receiver operator characteristics area under the curve analysis for day 10 versus day 15, day 15 versus day 20, and day 10 versus day 20 were found to be 0.95, 0.85, and 0.93, respectively. The ability of photoacoustic measurements in the objective assessment of tumor progression has been clearly demonstrated, indicating its clinical potential.

[1]  H. White,et al.  Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. , 2001, Journal of clinical epidemiology.

[2]  A. Rice,et al.  A NOD/SCID Model of Primary Human Breast Cancer , 2008 .

[3]  Zoya I. Volynskaya,et al.  Diagnostic power of diffuse reflectance spectroscopy for targeted detection of breast lesions with microcalcifications , 2012, Proceedings of the National Academy of Sciences.

[4]  T. Hagemann,et al.  The tumor microenvironment at a glance , 2012, Journal of Cell Science.

[5]  H. Abramczyk,et al.  Raman spectroscopy and imaging: applications in human breast cancer diagnosis. , 2012, The Analyst.

[6]  Jin Ho Chang,et al.  Photoacoustic-based nanomedicine for cancer diagnosis and therapy. , 2015, Journal of controlled release : official journal of the Controlled Release Society.

[7]  Hui Li,et al.  Characterization of photoacoustic signal using wavelet analysis , 2010, SPIE/COS Photonics Asia.

[8]  Gerhard Christofori,et al.  Mouse models of breast cancer metastasis , 2006, Breast Cancer Research.

[9]  J. Mourant,et al.  Predictions and measurements of scattering and absorption over broad wavelength ranges in tissue phantoms. , 1997, Applied optics.

[10]  Minghua Xu,et al.  Thermoacoustic and Photoacoustic Tomography of Thick Biological Tissues toward Breast Imaging , 2005, Technology in cancer research & treatment.

[11]  R. Kiessling,et al.  Tumor-dependent increase of serum amino acid levels in breast cancer patients has diagnostic potential and correlates with molecular tumor subtypes , 2013, Journal of Translational Medicine.

[12]  Lloyd D. Fisher,et al.  Biostatistics: A Methodology for the Health Sciences , 1993 .

[13]  M. O'hare,et al.  Models of breast cancer: is merging human and animal models the future? , 2003, Breast Cancer Research.

[14]  Lihong V. Wang,et al.  Deep reflection-mode photoacoustic imaging of biological tissue. , 2007, Journal of biomedical optics.

[15]  C. Patel,et al.  Optical absorption coefficients of water , 1979, Nature.

[16]  Dae-Ki Kang,et al.  Regularization parameter tuning optimization approach in logistic regression , 2013, 2013 15th International Conference on Advanced Communications Technology (ICACT).

[17]  N. Ramanujam Fluorescence spectroscopy of neoplastic and non-neoplastic tissues. , 2000, Neoplasia.

[18]  Valery V Tuchin,et al.  In vivo photoacoustic flow cytometry for monitoring of circulating single cancer cells and contrast agents. , 2006, Optics letters.

[19]  T. Nakamura,et al.  Growth and angiogenesis of human breast cancer in a nude mouse tumour model is reduced by NK4, a HGF/SF antagonist. , 2003, Carcinogenesis.

[20]  Irene Georgakoudi,et al.  Trimodal spectroscopy for the detection and characterization of cervical precancers in vivo. , 2002, American journal of obstetrics and gynecology.

[21]  Pietro Liò,et al.  Wavelets in bioinformatics and computational biology: state of art and perspectives , 2003, Bioinform..

[22]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook Introductory Theory And Applications In Science , 2002 .

[23]  E. Feleppa,et al.  Theoretical framework for spectrum analysis in ultrasonic tissue characterization. , 1983, The Journal of the Acoustical Society of America.

[24]  Jie Yuan,et al.  The functional pitch of an organ: quantification of tissue texture with photoacoustic spectrum analysis. , 2014, Radiology.

[25]  J. Pollard,et al.  Microenvironmental regulation of metastasis , 2009, Nature Reviews Cancer.

[26]  Satadru Ray,et al.  Photoacoustic spectroscopy of ovarian normal, benign, and malignant tissues: a pilot study. , 2011, Journal of biomedical optics.

[27]  Qifa Zhou,et al.  Optimal ultraviolet wavelength for in vivo photoacoustic imaging of cell nuclei. , 2012, Journal of biomedical optics.

[28]  Kornelia Polyak,et al.  Microenvironmental regulation of cancer development. , 2008, Current opinion in genetics & development.

[29]  Zoya I. Volynskaya,et al.  Diagnosing breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence spectroscopy. , 2008, Journal of biomedical optics.

[30]  Mette Jensen,et al.  Tumor volume in subcutaneous mouse xenografts measured by microCT is more accurate and reproducible than determined by 18F-FDG-microPET or external caliper , 2008, BMC Medical Imaging.

[31]  Vadim Backman Erratum: Detection of preinvasive cancer cells (Nature (2000) 406 (35-36)) , 2000 .

[32]  S. Emelianov,et al.  Photoacoustic imaging in cancer detection, diagnosis, and treatment guidance. , 2011, Trends in biotechnology.

[33]  S. Gambhir,et al.  Light in and sound out: emerging translational strategies for photoacoustic imaging. , 2014, Cancer research.

[34]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[35]  Takashi Ishikawa,et al.  Plasma Free Amino Acid Profiling of Five Types of Cancer Patients and Its Application for Early Detection , 2011, PloS one.

[36]  Cheri X Deng,et al.  Frequency-domain analysis of photoacoustic imaging data from prostate adenocarcinoma tumors in a murine model. , 2011, Ultrasound in medicine & biology.

[37]  Krishna Kishore Mahato,et al.  Photoacoustic spectroscopy in the monitoring of breast tumor development: a pre-clinical study , 2014, Photonics West - Biomedical Optics.

[38]  À. Sierra Animal models of breast cancer for the study of pathogenesis and therapeutic insights , 2009, Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico.

[39]  Lorenzo Bruzzone,et al.  Active Learning for Domain Adaptation in the Supervised Classification of Remote Sensing Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[40]  D. Euhus,et al.  Tumor measurement in the nude mouse , 1986, Journal of surgical oncology.

[41]  Satadru Ray,et al.  Autofluorescence of normal, benign, and malignant ovarian tissues: a pilot study. , 2009, Photomedicine and laser surgery.

[42]  Qifa Zhou,et al.  In vivo label-free photoacoustic microscopy of cell nuclei by excitation of DNA and RNA. , 2010, Optics letters.

[43]  Ashok N. Oza,et al.  Distinguishing autofluorescence of normal, benign, and cancerous breast tissues through wavelet domain correlation studies. , 2011, Journal of biomedical optics.

[44]  Baogang Xu,et al.  Tryptophan as the fingerprint for distinguishing aggressiveness among breast cancer cell lines using native fluorescence spectroscopy , 2014, Journal of biomedical optics.

[45]  Krishna Kishore Mahato,et al.  Photoacoustic spectroscopy based evaluation of breast cancer condition , 2015, Photonics West - Biomedical Optics.

[46]  A. Tam,et al.  PULSED OPTO-ACOUSTICS : THEORY AND APPLICATIONS , 1983 .

[47]  High Frequency Acoustic Properties of Tumor Tissue , 1996 .

[48]  Michael C. Kolios,et al.  Optoacoustic characterization of prostate cancer in an in vivo transgenic murine model , 2014, Journal of biomedical optics.

[49]  Peter C Austin,et al.  Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. , 2004, Journal of clinical epidemiology.

[50]  A. Rosencwaig,et al.  Photoacoustic spectroscopy. , 1980, Annual review of biophysics and bioengineering.

[51]  S. Shapshay,et al.  Detection of preinvasive cancer cells , 2000, Nature.

[52]  Molly Brewer,et al.  Potential role of coregistered photoacoustic and ultrasound imaging in ovarian cancer detection and characterization. , 2011, Translational oncology.

[53]  Daniele Zink,et al.  Nuclear structure in cancer cells , 2004, Nature Reviews Cancer.

[54]  K. Schulmeister,et al.  Review of exposure limits and experimental data for corneal and lenticular damage from short pulsed UV and IR laser radiation , 2008 .

[55]  J. Pennings,et al.  Identification of breast cancer biomarkers in transgenic mouse models: A proteomics approach , 2010, Proteomics. Clinical applications.

[56]  Valerie M. Weaver,et al.  The extracellular matrix at a glance , 2010, Journal of Cell Science.

[57]  Guan Xu,et al.  Photoacoustic spectrum analysis for microstructure characterization in biological tissue: analytical model. , 2015, Ultrasound in medicine & biology.

[58]  M. Tomayko,et al.  Determination of subcutaneous tumor size in athymic (nude) mice , 2004, Cancer Chemotherapy and Pharmacology.

[59]  K. K. Mahato,et al.  Prediction of absorption coefficients by pulsed laser induced photoacoustic measurements. , 2014, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[60]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[61]  A. Kinney,et al.  Applications of Photoacoustic Spectroscopy , 1982 .

[62]  Laura A. Sordillo,et al.  Optical Spectral Fingerprints of Tissues from Patients with Different Breast Cancer Histologies Using a Novel Fluorescence Spectroscopic Device , 2013, Technology in cancer research & treatment.