Overexpressed Genes/ESTs and Characterization of Distinct Amplicons on 17823 in Breast Cancer Cells

Abstract17823 is a frequent site of gene amplification in breast cancer. Several lines of evidence suggest the presence of multiple amplicons on 17823. To characterize distinct amplicons on 17823 and localize putative oncogenes, we screened genes and expressed sequence tags (ESTs) in existing physical and radiation hybrid maps for amplification and overexpression in breast cancer cell lines by semiquantitative duplex PCR, semiquantitative duplex RT-PCR, Southern blot, Northern blot analyses. We identified two distinct amplicons on 17823, one including TBX2 and another proximal region including RPS6KB1 (PS6K) and MUL. In addition to these previously reported overexpressed genes, we also identified amplification and overexpression of additional uncharacterized genes and ESTs, some of which suggest potential oncogenic activity. In conclusion, we have further defined two distinct regions of gene amplification and overexpression on 17823 with identification of new potential oncogene candidates. Based on the amplification and overexpression patterns of known and as of yet unrecognized genes on 17823, it is likely that some of these genes mapping to the discrete amplicons function as oncogenes and contribute to tumor progression in breast cancer cells

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype

The Breast Cancer Screening and Timing of Breast MRI—Experience in a Genetic High-Risk Screening Clinic in a Comprehensive Cancer Center

For women with genetic risk of breast cancer, the addition of screening breast MRI to mammography has become a standard. The order and interval of annual imaging can be variable among providers. To evaluate the clinical implications related to the timing, we conducted a chart review on a cohort of women (N = 276) with high-risk (BRCA1, BRCA2, CDH1, PTEN and TP53) and moderate high-risk (ATM and CHEK2) predisposition to breast cancer in a 48-month follow up. The estimated MRI detection rate in the entire group is 1.75% (18 per 1000 MRI tests). For the high-risk group, the estimated rate is 2.98% (30 per 1000 MRI tests). Many women discovered their genetic risk at an age much older (average age of the high-risk group was 48 years) than the age recommended to initiate enhanced screening (age 20 to 25 years). In total, 4 of the 11 primary breast cancers detected were identified by screening MRI within the first month after initial visit, which were not detected by previous mammography, suggesting the benefit of initiating MRI immediately after the discovery of genetic risk. Breast screening findings for women with Lynch syndrome and neurofibromatosis type 1 were also included in this report

Genetic Anthropology of the Colorectal Cancer–Susceptibility Allele APC I1307K: Evidence of Genetic Drift within the Ashkenazim

The adenomatous polyposis coli (APC) I1307K allele is found in 6% of the Ashkenazi Jewish population and in 1%–2% of Sephardi Jews; it confers a relative risk of 1.5–2.0 for colorectal cancer (CRC) on all carriers. Within the Ashkenazim, the existence of numerous high-prevalence mutations, including I1307K, has sparked controversy over whether genetic drift or selection is the underlying cause. For the present population-based case-control study of CRC in Israel, we tested whether selection has operated at I1307K. We also estimated the age of the I1307K allele, to understand its origin in the context of the Jewish diasporas and subsequent founder events. We genotyped 83 matched pairs, in which one or both members of the pair carried I1307K, at three microsatellites and two SNPs. Haplotypes were statistically constructed using PHASE software. Single-marker age estimates for I1307K were calculated using the approach described by Risch et al. A common progenitor haplotype spanned across APC I1307K from the centromeric marker D5S135 to the telomeric marker D5S346 and was observed in individuals of Ashkenazi, Sephardi, and Arab descent. The ancestor of modern I1307K alleles existed 87.9–118 generations ago (∼2,200–2,950 years ago). This age estimate indicates that I1307K existed at about the time of the beginning of the Jewish diaspora, explaining its presence in non-Ashkenazi populations. Our data do not indicate that selection operated at I1307K (D5S346, P=.114; D5S135, P=.373), providing compelling evidence that the high frequency of disease-susceptibility alleles in the Ashkenazim is due to genetic drift, not selection. This research underscores the importance of the migratory patterns of ancestral populations in the ethnic and geographic distribution of APC I1307K

Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set.

Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article