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Biology subjects

David Goldstein

Publications and source records attributed to David Goldstein.

2 recordsLinked to original sources

Cancer-associated fibroblasts in pancreatic ductal adenocarcinoma determine response to SLC7A11 inhibition

Cancer-Associated Fibroblasts (CAFs) are major contributors to pancreatic ductal adenocarcinoma (PDAC) progression, through pro-tumour cross-talk and the generation of fibrosis (physical barrier to drugs). CAF inhibition is thus an ideal component of any therapeutic approach for PDAC. SLC7A11 is a cystine transporter that has been identified as a potential therapeutic target in PDAC cells. However, no prior study has evaluated the role of SLC7A11 in PDAC tumour stroma and its prognostic significance. Herein we show that high expression of SLC7A11 in PDAC tumour stroma (but not tumour cells) is independently prognostic of poorer overall survival. We demonstrate using orthogonal approaches that PDAC-derived CAFs are highly dependent on SLC7A11 for cystine uptake and glutathione synthesis, and that SLC7A11 inhibition significantly decreases their proliferation, reduces their resistance to oxidative stress and inhibits their ability to remodel collagen and support PDAC cell growth. Importantly, our paradigm-shifting work demonstrates the need to inhibit SLC7A11 in the PDAC stroma, as genetic ablation of SLC7A11 in PDAC cells alone is not enough to reduce tumour growth. Finally, our work validates that a nano-based gene-silencing drug against SLC7A11, developed by our group, reduces PDAC tumour growth, CAF activation and fibrosis in a mouse model of PDAC.

cancer biology↗

Estimate of disease heritability using 4.7 million familial relationships inferred from electronic health records

Heritability is essential for understanding the biological causes of disease, but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range of clinically relevant data and provide a novel resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified millions of familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically-derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a novel validation of the use of EHRs for genetics and disease research.\n\nOne Sentence SummaryWe demonstrate that next-of-kin information can be used to identify familial relationships in the EHR, providing unique opportunities for precision medicine studies.

Genetics↗