COMPARISON OF SPARSE CODING AND KERNEL METHODS FOR HISTOPATHOLOGICAL CLASSIFICATION OF GLIOBASTOMA MULTIFORME.

This paper compares performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven to be an effective technique for restoration, and has recently been extended to classification. The main issue with classification of histology sections is inherent heterogeneity as a result of technical and biological variations. Technical variations originate from sample preparation, fixation, and staining from multiple laboratories, where biological variations originate from tissue content. Image patches are represented with invariant features at local and global scales, where local refers to responses measured with Laplacian of Gaussians, and global refers to measurements in the color space. Experiments are designed to learn dictionaries, through sparse coding, and to train classifiers through kernel methods with normal, necorotic, apoptotic, and tumor with with characteristics of high cellularity. Two different kernel methods of support vector machine (SVM) and kernel discriminant analysis (KDA) are used for comparative analysis. Preliminary investigation on histological samples of Glioblastoma multiforme (GBM) indicates that kernel methods perform as good if not better than sparse coding with redundant representation.

Predicting response to primary chemotherapy: gene expression profiling of paraffin-embedded core biopsy tissue.

PURPOSE: Primary chemotherapy provides an ideal opportunity to correlate gene expression with response to treatment. We used paraffin-embedded core biopsies from a completed phase II trial to identify genes that correlate with response to primary chemotherapy. PATIENTS AND METHODS: Patients with newly diagnosed stage II or III breast cancer were treated with sequential doxorubicin 75 mg/M2 q2 wks x 3 and docetaxel 40 mg/M2 weekly x 6; treatment order was randomly assigned. Pretreatment core biopsy samples were interrogated for genes that might correlate with pathologic complete response (pCR). In addition to the individual genes, the correlation of the Oncotype DX Recurrence Score with pCR was examined. RESULTS: Of 70 patients enrolled in the parent trial, core biopsies samples with sufficient RNA for gene analyses were available from 45 patients; 9 (20%) had inflammatory breast cancer (IBC). Six (14%) patients achieved a pCR. Twenty-two of the 274 candidate genes assessed correlated with pCR (p < 0.05). Genes correlating with pCR could be grouped into three large clusters: angiogenesis-related genes, proliferation related genes, and invasion-related genes. Expression of estrogen receptor (ER)-related genes and Recurrence Score did not correlate with pCR. In an exploratory analysis we compared gene expression in IBC to non-inflammatory breast cancer; twenty-four (9%) of the genes were differentially expressed (p < 0.05), 5 were upregulated and 19 were downregulated in IBC. CONCLUSION: Gene expression analysis on core biopsy samples is feasible and identifies candidate genes that correlate with pCR to primary chemotherapy. Gene expression in IBC differs significantly from noninflammatory breast cancer.