Rapid intraoperative assessment of breast excision specimens is clinically important because up to 40% of patients undergoing breast-conserving cancer surgery require reexcision for positive or close margins. We demonstrate nonlinear microscopy (NLM) for the assessment of benign and malignant breast pathologies in fresh surgical specimens. A total of 179 specimens from 50 patients was imaged with NLM using rapid extrinsic nuclear staining with acridine orange and intrinsic second harmonic contrast generation from collagen. Imaging was performed on fresh, intact specimens without the need for fixation, embedding, and sectioning required for conventional histopathology. A visualization method to aid pathological interpretation is presented that maps NLM contrast from two-photon fluorescence and second harmonic signals to features closely resembling histopathology using hematoxylin and eosin staining. Mosaicking is used to overcome trade-offs between resolution and field of view, enabling imaging of subcellular features over square-centimeter specimens. After NLM examination, specimens were processed for standard paraffin-embedded histology using a protocol that coregistered histological sections to NLM images for paired assessment. Blinded NLM reading by three pathologists achieved 95.4% sensitivity and 93.3% specificity, compared with paraffin-embedded histology, for identifying invasive cancer and ductal carcinoma in situ versus benign breast tissue. Interobserver agreement was ? = 0.88 for NLM and ? = 0.89 for histology. These results show that NLM achieves high diagnostic accuracy, can be rapidly performed on unfixed specimens, and is a promising method for intraoperative margin assessment.
Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin ?4 and ?10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.
The progression from preinvasive lesion to invasive carcinoma is a critical step contributing to breast cancer lethality. We identified downregulation of milk fat globule-EGF factor 8 (MFG-E8) as a contributor to breast cancer progression using microarray analysis of laser capture microdissected (LCM) tissues. We first identified MFG-E8 downregulation in invasive lesions in transgenic mammary tumor models, which were confirmed in LCM-isolated human invasive ductal carcinomas compared with patient-matched normal tissues. In situ analyses of MFG-E8 expression in estrogen receptor (ER) positive cases confirmed its downregulation during breast cancer progression and small inhibitory MFG-E8 RNAs accelerated ER(+) breast cancer cell proliferation. MFG-E8 also decreased in erbB2(+) human cancers and erbB2 transgenic mice lacking MFG-E8 showed accelerated tumor formation. In contrast, MFG-E8 expression was present at high levels in triple-negative (ER(-), PgR(-), erbB2(-)) breast cancers, cell lines, and patient sera. Knockdown, chromatin immunoprecipitation, and reporter assays all showed that p63 regulates MFG-E8 expression, and MFG-E8 knockdowns sensitized triple-negative breast cancers to cisplatin treatment. Taken together, our results show that MFG-E8 is expressed in triple-negative breast cancers as a target gene of the p63 pathway, but may serve a suppressive function in ER(+) and erbB2(+) breast cancers. Its potential use as a serum biomarker that contributes to the pathogenesis of triple-negative breast cancers urges continued evaluation of its differential functions.
The GATA family members are zinc finger transcription factors involved in cell differentiation and proliferation. GATA3 in particular is necessary for mammary gland maturation, and its loss has been implicated in breast cancer development. Our goal was to validate the ability of GATA3 expression to predict survival in breast cancer patients. Protein expression of GATA3 was analyzed on a high-density tissue microarray consisting of 242 cases of breast cancer. We associated GATA3 expression with patient outcomes and clinicopathologic variables. Expression of GATA3 was significantly increased in breast cancer, in situ lesions, and hyperplastic tissue compared with normal breast tissue. GATA3 expression decreased with increasing tumor grade. Low GATA3 expression was a significant predictor of disease-related death in all patients, as well as in subgroups of estrogen receptor-positive or low-grade patients. In addition, low GATA3 expression correlated with increased tumor size and estrogen and progesterone receptor negativity. GATA3 is an important predictor of disease outcome in breast cancer patients. This finding has been validated in a diverse set of populations. Thus, GATA3 expression has utility as a prognostic indicator in breast cancer.
Chemotherapy is often used for breast cancer treatment, but individual outcome varies widely. We hypothesized that tumor proteomic profiles obtained prior to chemotherapy may predict the individual tumor response to treatment. The goal of our study was to explore feasibility of using proteomic profiling to preselect patients for an effective chemotherapeutic regimen. Tumors from 52 patients with T2-T4 breast cancer were prospectively collected before neoadjuvant chemotherapy, and were analyzed using surface-enhanced laser desorption ionization/time of flight (SELDI) mass spectrometry. Mass spectral profiles were obtained from tumors with various sensitivities to chemotherapy. Both non-supervised hierarchical clustering and supervised neural network-based classification approaches were employed to compare the profiles. The first two thirds of the enrolled cases (35) were allocated to a training set to select peaks characteristic of resistant tumors. The candidate peaks were used to develop a predicting rule to evaluate the remaining 17 specimens in the validation set. In the training set, the most prominent differences were found between drug resistant and drug susceptible tumors by non-supervised hierarchical clustering. In the validation set, the supervised classification with the K nearest neighbor (KNN) model correctly classified most tumor responses with an accuracy rate of 92.3% [100% of resistant tumors (4/4), and 84.6% of the tumors with favorable response (11/13)]. In the entire group, a single peak at m/z 16,906 correctly separated 88.9% of the tumors with pathologically complete response, and 91.7% of the resistant tumors. The data suggest that breast cancer protein biomarkers may be used to pre-select patients for optimal chemotherapeutic treatment.
Previous research in the Nurses Health Study (NHS) and the NHSII observed that, among women diagnosed with benign breast disease (BBD), those with predominant type 1/no type 3 lobules (a marker of complete involution) versus other lobule types were at lower risk of subsequent breast cancer. Studies in animal models suggest that insulin-like growth factor-1 (IGF-1) may inhibit involution of lobules in the breast; however, this has not been studied in humans.
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