Evaluation of a developmental hierarchy for breast cancer cells to assess risk-based patient selection for targeted treatment

Sarah A. Bliss, Sunirmal Paul, Piotr W. Pobiarzyn, Seda Ayer, Garima Sinha, Saumya Pant, Holly Hilton, Neha Sharma, Maria F. Cunha, Daniel J. Engelberth, Steven J. Greco, Margarette Bryan, Magdalena J. Kucia, Sham S. Kakar, Mariusz Z. Ratajczak, Pranela Rameshwar

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


This study proposes that a novel developmental hierarchy of breast cancer (BC) cells (BCCs) could predict treatment response and outcome. The continued challenge to treat BC requires stratification of BCCs into distinct subsets. This would provide insights on how BCCs evade treatment and adapt dormancy for decades. We selected three subsets, based on the relative expression of octamer-binding transcription factor 4 A (Oct4A) and then analysed each with Affymetrix gene chip. Oct4A is a stem cell gene and would separate subsets based on maturation. Data analyses and gene validation identified three membrane proteins, TMEM98, GPR64 and FAT4. BCCs from cell lines and blood from BC patients were analysed for these three membrane proteins by flow cytometry, along with known markers of cancer stem cells (CSCs), CD44, CD24 and Oct4, aldehyde dehydrogenase 1 (ALDH1) activity and telomere length. A novel working hierarchy of BCCs was established with the most immature subset as CSCs. This group was further subdivided into long- and short-term CSCs. Analyses of 20 post-treatment blood indicated that circulating CSCs and early BC progenitors may be associated with recurrence or early death. These results suggest that the novel hierarchy may predict treatment response and prognosis.

Original languageEnglish (US)
Article number367
JournalScientific reports
Issue number1
StatePublished - Dec 1 2018

All Science Journal Classification (ASJC) codes

  • General

Fingerprint Dive into the research topics of 'Evaluation of a developmental hierarchy for breast cancer cells to assess risk-based patient selection for targeted treatment'. Together they form a unique fingerprint.

Cite this