Shaheen Sikandar

@ssikandar.bsky.social

Assistant Professor, UC - Santa Cruz Cancer and stem cell biologist! www.sikandarlab.com

Honored to see our work highlighted for #WomensHistoryMonth Thank you @ucscscience.bsky.social for the spotlight

UC Santa Cruz Science @ucscscience.bsky.social · 5mo ago

For #WomensHistoryMonth we spotlight Prof. Shaheen Sikandar. Her research shows how early pregnancy may protect against breast cancer by blocking age-related cell changes. A new @americancancersoc.bsky.social grant will allow her to shift focus from cure to prevention. news.ucsc.edu/2026/01/preg...

Really happy to share a link to our work showing that anti-progestin therapy could help prevent breast cancer before menopause. Published today in Nature, the study suggests this could be a new way to stop breast cancer before it starts: urldefense.com/v3/__https:/... Brilliant team science

Anti-progestin therapy targets hallmarks of breast cancer risk

Nature - Results of an early-phase breast cancer prevention trial demonstrate the potential for breast cancer prevention in premenopausal women with anti-progestin therapy by inducing...

urldefense.com

Are 90% of cancer-related deaths due to metastasis? Apparently the overuse of this percentage in reviews has created confusion and questioning of whether this is true. The answer, which is as always cancer type dependent, may be in these references that also people claim they cannot find.

Are you looking for a reliable strategy to quantify the architecture of mammary tissue using whole mount images?? Well, maybe you should check MaGNet, our new computational based and user friendly approach (and you probably can adapt it to other organs as well). link.springer.com/article/10.1...

MaGNet: A Network-Based Method for Quantitative Analysis of the Mammary Ductal Tree in Developing Female Mice - Journal of Mammary Gland Biology and Neoplasia

The mammary gland is a uniquely dynamic organ with a branching architecture that develops entirely after birth in response to hormonal cues. A common approach in mammary gland biology is the evaluation of branching morphogenesis to characterize the role of developmental, physiological and molecular perturbations on branching tissue invasion, growth, and maintenance. Yet, the field still lacks a fully open-sourced, quantitative framework to analyze whole-mount mammary tissue images, as a commonly utilized methodology. Here, we present MaGNet (Mammary Gland Network analysis tool), a method that leverages network theory to characterize key features of ductal branching during mammary gland development. Applying this pipeline to mammary gland images captured at three pubertal timepoints, we achieved reproducible quantification of ductal tree expansion across development. In addition, this network analysis pipeline captures ductal expansion induced by pregnancy hormones. By providing open-source tools to the research community, this method may increase reproducibility and broad applicability across diverse organ systems, model organisms, and developmental stages.

link.springer.com