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- Breast cancer (1)
- Target identification (1)
The goal of this study was to identify a novel target for antibody-drug conjugate (ADC) development in triple negative breast cancer (TNBC), which has limited treatment options, using gene expression datasets and in vitro siRNA/CRISPR and in vivo functional assays. We analyzed 4467 breast cancers and identified GABRP as top expressed gene in TNBC with low expression in most normal tissues. GABRP protein was localized to cell membrane with broad range of receptors/cell (815–53,714) and expressed by nearly half of breast cancers tissues. GABRP gene knockdown inhibited TNBC cell growth and colony formation in vitro and growth of MDA-MB-468 xenografts in nude mice. Commercially available anti-GABRP antibody (5–100 μg/ml) or de novo generated Fabs (20 μg/ml) inhibited TNBC cell growth in vitro. The same antibody conjugated to mertansine (DM1) also showed significant anticancer activity at nanomolar concentrations. Our results indicate that GABRP is a potential novel therapeutic target for ADC development.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.