Novel mutations in actionable breast cancer genes by targeted sequencing in an ethnically homogenous cohort.

BACKGROUND: Genetic testing is becoming an essential tool for breast cancer (BC) diagnosis and treatment pathway, and particularly important for early detection and cancer prevention. The purpose of this study was to explore the diagnostic yield of targeted sequencing of the high priority BC genes. METHODS: We have utilized a cost-effective targeted sequencing approach of high priority actionable BC genes (BRCA1, BRCA2, ERBB2 and TP53) in a homogeneous patient cohort from Bangladesh (n = 52) by using tumor and blood samples. RESULTS: Blood derived targeted sequencing revealed 25.58% (11/43) clinically relevant mutations (both pathogenic and variants of uncertain significance (VUS)), with 13.95% (6/43) of samples carrying a pathogenic mutations. We have identified and validated five novel pathogenic germline mutations in this cohort, comprising of two frameshift deletions in BRCA2, and missense mutations in BRCA1, BRCA2 and ERBB2 gene respectively. Furthermore, we have identified three pathogenic mutations and a VUS within three tumor samples, including a sample carrying pathogenic mutations impacting both TP53 (c.322dupG; a novel frameshift insertion) and BRCA1 genes (c.116G > A). 22% of tissue samples had a clinically relevant TP53 mutation. Although the cohort is small, we have found pathogenic mutations to be enriched in BRCA2 (9.30%, 4/43) compare to BRCA1 (4.65%, 2/43). The frequency of germline VUS mutations found to be similar in both BRCA1 (4.65%; 2/43) and BRCA2 (4.65%; 2/43) compared to ERBB2 (2.32%; 1/43). CONCLUSIONS: This is the first genetic study of BC predisposition genes in this population, implies that genetic screening through targeted sequencing can detect clinically significant and actionable BC-relevant mutations.

Bimodal Multiparameter-Based Approach for Benign-Malignant Classification of Breast Tumors.

Proposed here is a breast tumor classification technique using conventional ultrasound B-mode imaging and a new elasticity imaging-based bimodal multiparameter index. A set of conventional ultrasound (US) and ultrasound elastography (UE) parameters are studied, and among those, the effective ones whose independent as well as combined performance is found satisfactory are selected. To improve the combined US performance, two new US parameters are proposed: edge diffusivity, which assesses edge blurriness to differentiate malignant from benign lesions, and the shape asymmetry factor, which quantifies tumor shape irregularity by comparing the tumor boundary with an ellipse fitted to the lesion. Then a new bimodal multiparameter characterization index is defined to discriminate 201 pathologically confirmed breast tumors of which 56 are malignant lesions, 79 are fibroadenomas, 42 are cysts and 24 are inflammatory lesions. The weights of the multiparameter bimodal index are optimally computed using a genetic algorithm (GA). To evaluate the performance variation of the index on different data sets, the tumors are categorized into three classes: malignant lesion versus fibroadenoma, malignant lesion versus fibroadenoma and cyst and malignant lesion versus fibroadenoma, cyst and inflammation. The test results reveal that the proposed bimodal index achieves satisfactory quality metrics (e.g., 94.64%-98.21% sensitivity, 97.24%-100.00% specificity and 96.52%-99.44% accuracy) for classification of the aforementioned three classes of breast tumors. Its performance is also observed to be better in totality of the quality metrics sensitivity, specificity, accuracy, positive predictive value and negative predictive value as compared with that of a conventional bimodal index as well as unimodal multiparameter indices based on US or UE. It is suggested that the proposed simple bimodal linear classifier may assist radiologists in better diagnosis of breast tumors and help reduce the number of unnecessary biopsies.

Lesion edge preserved direct average strain estimation for ultrasound elasticity imaging.

Elasticity imaging techniques with built-in or regularization-based smoothing feature for ensuring strain continuity are not intelligent enough to prevent distortion or lesion edge blurring while smoothing. This paper proposes a novel approach with built-in lesion edge preservation technique for high quality direct average strain imaging. An edge detection scheme, typically used in diffusion filtering is modified here for lesion edge detection. Based on the extracted edge information, lesion edges are preserved by modifying the strain determining cost function in the direct-average-strain-estimation (DASE) method. The proposed algorithm demonstrates approximately 3.42-4.25 dB improvement in terms of edge-mean-square-error (EMSE) than the other reported regularized or average strain estimation techniques in finite-element-modeling (FEM) simulation with almost no sacrifice in elastographic-signal-to-noise-ratio (SNRe) and elastographic-contrast-to-noise-ratio (CNRe) metrics. The efficacy of the proposed algorithm is also tested for the experimental phantom data and in vivo breast data. The results reveal that the proposed method can generate a high quality strain image delineating the lesion edge more clearly than the other reported strain estimation techniques that have been designed to ensure strain continuity. The computational cost, however, is little higher for the proposed method than the simpler DASE and considerably higher than that of the 2D analytic minimization (AM2D) method.

Phase-based direct average strain estimation for elastography.

In this paper, a phase-based direct average strain estimation method is developed. A mathematical model is presented to calculate axial strain directly from the phase of the zero-lag cross-correlation function between the windowed precompression and stretched post-compression analytic signals. Unlike phase-based conventional strain estimators, for which strain is computed from the displacement field, strain in this paper is computed in one step using the secant algorithm by exploiting the direct phase-strain relationship. To maintain strain continuity, instead of using the instantaneous phase of the interrogative window alone, an average phase function is defined using the phases of the neighboring windows with the assumption that the strain is essentially similar in a close physical proximity to the interrogative window. This method accounts for the effect of lateral shift but without requiring a prior estimate of the applied strain. Moreover, the strain can be computed both in the compression and relaxation phases of the applied pressure. The performance of the proposed strain estimator is analyzed in terms of the quality metrics elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), and mean structural similarity (MSSIM), using a finite element modeling simulation phantom. The results reveal that the proposed method performs satisfactorily in terms of all the three indices for up to 2.5% applied strain. Comparative results using simulation and experimental phantom data, and in vivo breast data of benign and malignant masses also demonstrate that the strain image quality of our method is better than the other reported techniques.