In Vivo 1H-MRS Lipid Signal: Is it Useful for Tumor Response to Neoadjuvant Chemotherapy?

669 Background: Neoadjuvant chemotherapy provides an excellent opportunity for objective assessment of treatment-induced tumor response and for studying biomarkers characteristic of therapy-induced tumor responses. Methods: Proteomic analysis of T3/T4 breast cancer was performed in patients with locally advanced breast cancer in a phase II clinical trial. The breast cancer specimen was obtained before and after four cycles of Taxotere/Carboplatin/±Herceptin treatment. Two proteomic approaches, SELDI mass spectrometry and Clontech Ab Microarray 500, were used to screen for protein biomarkers that predict response of breast cancer to chemotherapy. Results: Five tumors with pathologically complete response (pCR) and 29 tumors with various amounts of residual tumors (Non-pCR) were analyzed by SELDI-TOF using the NP 20 chip. The normalized mass signals were compared between pCR vs Non-pCR at each aligned location by Wilcoxon rank sum test. Statistically significant differences were found at 22 m/z locations using a liberal p <0.20 criterion. The best univariate predictor occurred at m/z 14960 (p=0.004), which correctly classified 5/5 pCR spectra (100%) and 24/29 Non-pCR spectra (83%). A multivariate classification tree developed using m/z 14960 and m/z 12138 intensities correctly classified all 34 spectra. Ab microarray analysis was performed on five pCR tumors and in five tumors with the largest residual cancer. The Internal Normalization Ratio (INR) was calculated and used to compare the difference of protein expression between the two groups. Eight differentially expressed protein biomarkers were selected with the criteria of a statistically significant (Student t, p<0.05) expression change of <0.77 or >1.3 fold. Three proteins (Tat-SF1, PYK2 and PTP1B) were higher, and five (E2F2, IL1b, FEN1, CDC37 and ACM1) were lower in tumors with pCR. The unsupervised hierarchical clustering of the 10 samples by these eight proteins completely separated the pCR tumors from the poor responders. Conclusions: Our study suggests that bothSELDImass spectrometry and antibody microarray may be used to predict the tumor response to neoadjuvant chemotherapy. Proteomic analysis may be useful in developing tailored chemotherapy for breast cancer. [Table: see text]

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Real-time ultrasound elastography for monitoring tumor response to neoadjuvant chemotherapy in primary breast cancer

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.14023 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 14023-14023 ◽

Cited By ~ 1

Author(s):

H. Bando ◽

M. Ishii ◽

E. Tohno ◽

E. Ueno

Keyword(s):

Breast Cancer ◽

Neoadjuvant Chemotherapy ◽

Real Time ◽

Predictive Value ◽

Primary Breast Cancer ◽

Tumor Response ◽

Residual Tumor ◽

Term Survival ◽

Pathological Cr ◽

Response To Neoadjuvant Chemotherapy

14023 Background: Response to neoadjuvant treatment is vital to predict a patient’s long-term survival. Precise detection of residual tumor cells after neoadjuvant chemotherapy would allow a better cosmetic results avoiding over surgery and reduce second operation due to positive margin status. Moreover, accurate prediction of pathological CR will yield no surgical intervention in certain population. Recently, a new generation of ultrasound platforms with real-time freehand elastography that enables the imaging of elasticity of the lesion by using the extended combined autocorrelation method (CAM) has become available. We are currently applying this technology to our patients with primary breast cancer in an attempt to assess response to neoadjuvant chemotherapy in comparison with MRI, conventional ultrasound and pathological findings. Methods: A total of 38 patients with primary breast cancer who underwent neoadjuvant chemotherapy and following surgical resection From May 2005 to Dec 2006 were included in this study. Board certified radiologists assessed the tumor response by MRI, US and US Elastography prior to surgery. Positive predictive value (PPV), and negative predictive value (NPV) for pathological CR (pCR) was assessed. Tsukuba Elastography score was applied for the assessment of Elastography. Results: 11/38 patients (28.9%) achieved a pCR in breast to neoadjuvant chemotherapy while no patients demonstrated progressive disease. The PPV for pCR of MRI and US was 54.5% and 36.3% respectively. The NPV of MRI and US was both 90.9%. None of the residual tumor mass with score 4 or 5 cases diagnosed by Elastography achieved pCR. When residual tumor image was detected by US, pCR was present in all 4 cases with score 1 or 2 Elatography. If the cut-off line is determined between score 3 and 4, the PPV and NPV for pCR by Elastography was 100% and 66.6% respectively. Conclusions: Elastography is easy to perform and it can provide an inexpensive, non-invasive, real-time tool for assessment of response to neoadjuvant chemotherapy among patients with primary breast cancer. In particular, Elastography might more effectively diagnose pathological CR. More patients are needed to evaluate the sensitivity and specificity of this new technology. No significant financial relationships to disclose.

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