Phenotypic heterogeneity drives differential disease outcome in a mouse model of triple negative breast cancer

Front Oncol. 2023 Sep 29:13:1230647. doi: 10.3389/fonc.2023.1230647. eCollection 2023.

Abstract

The triple negative breast cancer (TNBC) subtype is one of the most aggressive forms of breast cancer that has poor clinical outcome and is an unmet clinical challenge. Accumulating evidence suggests that intratumoral heterogeneity or the presence of phenotypically distinct cell populations within a tumor play a crucial role in chemoresistance, tumor progression and metastasis. An increased understanding of the molecular regulators of intratumoral heterogeneity is crucial to the development of effective therapeutic strategies in TNBC. To this end, we used an unbiased approach to identify a molecular mediator of intratumoral heterogeneity in breast cancer by isolating two tumor cell populations (T1 and T2) from the 4T1 TNBC model. Phenotypic characterization revealed that the cells are different in terms of their morphology, proliferation and self-renewal ability in vitro as well as primary tumor formation and metastatic potential in vivo. Bioinformatic analysis followed by Kaplan Meier survival analysis in TNBC patients identified Metastasis associated colon cancer 1 (Macc1) as one of the top candidate genes mediating the aggressive phenotype in the T1 tumor cells. The role of Macc1 in regulating the proliferative phenotype was validated and taken forward in a therapeutic context with Lovastatin, a small molecule transcriptional inhibitor of Macc1 to target the T1 cell population. This study increases our understanding of the molecular underpinnings of intratumoral heterogeneity in breast cancer that is critical to improve the treatment of women currently living with the highly aggressive TNBC subtype.

Keywords: Lovastatin; MACC1; TNBC; intratumoral heterogeneity; phenotypic heterogeneity.

Grants and funding

RN is the recipient of the Ramanujan Fellowship from the Government of India (SERB) (SB/S2/RJN/182/2014) and intramural funding from Rajiv Gandhi Centre for Biotechnology, Kerala, India. MJ was supported by Ramanujan Fellowship (SB/S2/RJN-049/2018) provided by SERB, DST, Government of India, and by InfoSys Foundation, Bangalore. AT was supported by CSIR senior research fellowship and SR was supported by fellowship from University Grants Commission (UGC), Government of India.