Metabolomic Rewiring Promotes Endocrine Therapy Resistance in Breast Cancer.

Approximately one-third of endocrine-treated women with estrogen receptor alpha-positive (ER+) breast cancers are at risk of recurrence due to intrinsic or acquired resistance. Thus, it is vital to understand the mechanisms underlying endocrine therapy resistance in ER+ breast cancer to improve patient treatment. Mitochondrial fatty acid ß-oxidation (FAO) has been shown to be a major metabolic pathway in triple-negative breast cancer (TNBC) that can activate Src signaling. Here, we found metabolic reprogramming that increases FAO in ER+ breast cancer as a mechanism of resistance to endocrine therapy. A metabolically relevant, integrated gene signature was derived from transcriptomic, metabolomic, and lipidomic analyses in TNBC cells following inhibition of the FAO rate-limiting enzyme carnitine palmitoyl transferase 1 (CPT1), and this TNBC-derived signature was significantly associated with endocrine resistance in patients with ER+ breast cancer. Molecular, genetic, and metabolomic experiments identified activation of AMPK-FAO-oxidative phosphorylation (OXPHOS) signaling in endocrine-resistant ER+ breast cancer. CPT1 knockdown or treatment with FAO inhibitors in vitro and in vivo significantly enhanced the response of ER+ breast cancer cells to endocrine therapy. Consistent with the previous findings in TNBC, endocrine therapy-induced FAO activated the Src pathway in ER+ breast cancer. Src inhibitors suppressed the growth of endocrine-resistant tumors, and the efficacy could be further enhanced by metabolic priming with CPT1 inhibition. Collectively, this study developed and applied a TNBC-derived signature to reveal that metabolic reprogramming to FAO activates the Src pathway to drive endocrine resistance in ER+ breast cancer. SIGNIFICANCE: Increased fatty acid oxidation induced by endocrine therapy activates Src signaling to promote endocrine resistance in breast cancer, which can be overcome using clinically approved therapies targeting FAO and Src.

Lysine Demethylase 4A is a Centrosome Associated Protein Required for Centrosome Integrity and Genomic Stability.

Centrosomes play a fundamental role in nucleating and organizing microtubules in the cell and are vital for faithful chromosome segregation and maintenance of genomic stability. Loss of structural or functional integrity of centrosomes causes genomic instability and is a driver of oncogenesis. The lysine demethylase 4A (KDM4A) is an epigenetic 'eraser' of chromatin methyl marks, which we show also localizes to the centrosome with single molecule resolution. We additionally discovered KDM4A demethylase enzymatic activity is required to maintain centrosome homeostasis, and is required for centrosome integrity, a new functionality unlinked to altered expression of genes regulating centrosome number. We find rather, that KDM4A interacts with both mother and daughter centriolar proteins to localize to the centrosome in all stages of mitosis. Loss of KDM4A results in supernumerary centrosomes and accrual of chromosome segregation errors including chromatin bridges and micronuclei, markers of genomic instability. In summary, these data highlight a novel role for an epigenetic 'eraser' regulating centrosome integrity, mitotic fidelity, and genomic stability at the centrosome.

Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map.

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients' data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.