Extremely Red Objects from the Hubble Space Telescope NICMOS Parallel Imaging Survey

We present a catalog of extremely red objects (EROs) discovered using the Hubble Space Telescope Near Infrared Camera and Multi-Object Spectrometer (NICMOS) parallel imaging database and ground-based optical follow-up observations. Within an area of 16 arcmin^2, we detect 15 objects with R-F160W > 5 and F160W 6. Our objects have F110W-F160W colors in the range 1.3–2.1, redder than the cluster elliptical galaxies at z ~ 0.8 and nearly 1 mag redder than the average population selected from the F160W images at the same depth. In addition, among only 22 NICMOS pointings, we detected two groups or clusters in two fields; each contains three or more EROs, suggesting that extremely red galaxies may be strongly clustered. At bright magnitudes with F160W < 19.5, the ERO surface density is similar to what has been measured by other surveys. At the limit of our sample, F160W = 21.5, our measured surface density is 0.94 ± 0.24 arcmin^(-2). Excluding the two possible groups or clusters and the one apparently stellar object reduces the surface density to 0.38 ± 0.15 arcmin^(-2)

Molecular gas and star formation in early-type galaxies

We present new mm interferometric and optical integral-field unit (IFU) observations and construct a sample of 12 E and S0 galaxies with molecular gas which have both CO and optical maps. The galaxies contain 2 x 10^7 to 5 x 10^9 M\odot of molecular gas distributed primarily in central discs or rings (radii 0.5 to 4 kpc). The molecular gas distributions are always coincident with distributions of optically-obscuring dust that reveal tightly-wound spiral structures in many cases. The ionised gas always approximately corotates with the molecular gas, evidencing a link between these two gas components, yet star formation is not always the domi- nant ionisation source. The galaxies with less molecular gas tend to have [O III]/H{\beta} emission-line ratios at high values not expected for star formation. Most E/S0s with molecular gas have young or intermediate age stellar populations based on optical colours, ultraviolet colours and absorption linestrengths. The few that appear purely old lie close to the limit where such populations would be undetectable based on the mass fractions of expected young to observed old stars. The 8{\mu}m polycyclic aromatic hydrocarbon (PAH) and 24{\mu}m emission yield similar star formation rate estimates of E/S0s, but the total infrared overpredicts the rate due to a contribution to dust heating from older stars. The radio-far infrared relation also has much more scatter than for other star-forming galaxies. However, despite these biases and additional scatter, the derived star formation rates locate the E/S0 galaxies within the large range of the Schmidt-Kennicutt and constant efficiency star formation laws. Thus the star formation process in E/S0s is not overwhelmingly different than in other star-forming galaxies, although one of the more reliable tracers (24{\mu}m) points to a possible lower star-formation efficiency at a given gas surface density.Comment: submitted to MNRA

Intrinsic Breast Tumor Subtypes, Race, and Long-Term Survival in the Carolina Breast Cancer Study

Previous research identified differences in breast cancer-specific mortality across four "intrinsic" tumor subtypes: luminal A, luminal B, basal-like, and human epidermal growth factor receptor 2 positive/estrogen receptor negative (HER2+/ER−)

Managing Incidental Genomic Findings in Clinical Trials: Fulfillment of the Principle of Justice

<p>Managing Incidental Genomic Findings in Clinical Trials: Fulfillment of the Principle of Justice</p

The molecular portraits of breast tumors are conserved acress microarray platforms

Background Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. Results A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. Conclusion This study validates the breast tumor intrinsic subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

The molecular portraits of breast tumors are conserved across microarray platforms

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

CARs derived from broadly neutralizing, human monoclonal antibodies identified by single B cell sorting target hepatitis B virus-positive cells

To design new CARs targeting hepatitis B virus (HBV), we isolated human monoclonal antibodies recognizing the HBV envelope proteins from single B cells of a patient with a resolved infection. HBV-specific memory B cells were isolated by incubating peripheral blood mononuclear cells with biotinylated hepatitis B surface antigen (HBsAg), followed by single-cell flow cytometry-based sorting of live, CD19+ IgG+ HBsAg+ cells. Amplification and sequencing of immunoglobulin genes from single memory B cells identified variable heavy and light chain sequences. Corresponding immunoglobulin chains were cloned into IgG1 expression vectors and expressed in mammalian cells. Two antibodies named 4D06 and 4D08 were found to be highly specific for HBsAg, recognized a conformational and a linear epitope, respectively, and showed broad reactivity and neutralization capacity against all major HBV genotypes. 4D06 and 4D08 variable chain fragments were cloned into a 2nd generation CAR format with CD28 and CD3zeta intracellular signaling domains. The new CAR constructs displayed a high functional avidity when expressed on primary human T cells. CAR-grafted T cells proved to be polyfunctional regarding cytokine secretion and killed HBV-positive target cells. Interestingly, background activation of the 4D08-CAR recognizing a linear instead of a conformational epitope was consistently low. In a preclinical model of chronic HBV infection, murine T cells grafted with the 4D06 and the 4D08 CAR showed on target activity indicated by a transient increase in serum transaminases, and a lower number of HBV-positive hepatocytes in the mice treated. This study demonstrates an efficient and fast approach to identifying pathogen-specific monoclonal human antibodies from small donor cell numbers for the subsequent generation of new CARs

Race, Breast Cancer Subtypes, and Survival in the Carolina Breast Cancer Study

Context: Gene expression analysis has identified several breast cancer subtypes, including basal-like, human epidermal growth factor receptor-2 positive/estrogen receptor negative (HER2+/ER–), luminal A, and luminal B. Objectives: To determine population-based distributions and clinical associations for breast cancer subtypes. Design, Setting, and Participants: Immunohistochemical surrogates for each subtype were applied to 496 incident cases of invasive breast cancer from the Carolina Breast Cancer Study (ascertained between May 1993 and December 1996), a population based, case-control study that oversampled premenopausal and African American women. Subtype definitions were as follows: luminal A (ER+ and/or progesterone receptor positive [PR+], HER2−), luminal B (ER+ and/or PR+, HER2+), basal-like (ER−, PR−, HER2−, cytokeratin 5/6 positive, and/or HER1+), HER2+/ER− (ER−, PR−, and HER2+), and unclassified (negative for all 5 markers). Main Outcome Measures: We examined the prevalence of breast cancer subtypes within racial and menopausal subsets and determined their associations with tumor size, axillary nodal status, mitotic index, nuclear pleomorphism, combined grade, p53 mutation status, and breast cancer–specific survival. Results The basal-like breast cancer subtype was more prevalent among premenopausal African American women (39%) compared with postmenopausal African American women (14%) and non–African American women (16%) of any age (P<.001), whereas the luminal A subtype was less prevalent (36% vs 59% and 54%, respectively). The HER2+/ER− subtype did not vary with race or menopausal status (6%-9%). Compared with luminal A, basal-like tumors had more TP53 mutations (44% vs 15%, P<.001), higher mitotic index (odds ratio [OR], 11.0; 95% confidence interval [CI], 5.6-21.7), more marked nuclear pleomorphism (OR, 9.7; 95% CI, 5.3-18.0), and higher combined grade (OR, 8.3; 95% CI, 4.4-15.6). Breast cancer–specific survival differed by subtype (P<.001), with shortest survival among HER2+/ER− and basal-like subtypes. Conclusions: Basal-like breast tumors occurred at a higher prevalence among premenopausal African American patients compared with postmenopausal African American and non–African American patients in this population-based study. A higher prevalence of basal-like breast tumors and a lower prevalence of luminal A tumors could contribute to the poor prognosis of young African American women with breast cancer