P13.07 Cysteine induces glioblastoma cytotoxicity through mitochondrial reductive stress

BACKGROUND

Glioblastoma (GBM) remains a poorly treatable disease with high mortality. Tumor metabolism in GBM is a critical mechanism responsible for accelerated growth because of upregulation of glucose, amino acid, and fatty acid utilization. However, therapies targeting GBM metabolism, whether through the use of small-molecule compounds or dietary interventions to limit nutrient sources, have failed in clinical trials. Metabolic bypass is an important mechanism that is often overlooked in GBM trials, since many trials have focused instead on combining anti-metabolic therapy with cytotoxic treatments. The goal of this research is to use a multi-pronged treatment approach with targeted drug and dietary therapy to leverage metabolic susceptibilities in GBM.

MATERIALS AND METHODS

We first interrogated the TCGA database and a cancer metabolite database for alterations in glucose and amino acid signatures in GBM relative to other human cancers and relative to low-grade glioma. We identified the amino acid cysteine as contributing to a novel metabolic susceptibility pathway in GBM. To study the role of cysteine in GBM pathogenesis, we treated patient-derived GBM cells with a variety of FDA-approved cysteine-promoting compounds in vitro, including N-acetylcysteine (NAC). We measured cell proliferation, energy production, mitochondrial metabolism, and reactive oxygen species to study mechanisms of oxidoreductive stress. Results: From our TCGA and cancer metabolite database analyses, we found that GBM exhibits the highest levels of cysteine and methionine pathway gene expression of 32 human cancers and that GBM exhibits high levels of cysteine-related metabolites compared to low-grade gliomas. Cysteine compounds, including NAC, reduce growth of GBM cells, which is exacerbated by glucose deprivation. This growth inhibition is associated with reduced mitochondrial metabolism, manifest by reduction in ATP generation, NADPH/NADP+ ratio, mitochondrial membrane potential, and oxygen consumption rate. Through measurement of mitochondrial hydrogen peroxide, we found that NAC-treated cells exhibit a paradoxical increase in mitochondrial hydrogen peroxide levels, likely due to inhibition of thioreductase and glutathione reductase systems. Through genetic and pharmacological studies, we found that induction of thioredoxin-2 rescues NAC-mediated cytotoxicity and that inhibition of thioreductase and glutathione reductase exacerbates mitochondrial toxicity and reductive stress.

CONCLUSIONS

We show that cysteine compounds reduce cell growth and induce mitochondrial toxicity in GBM through reductive stress. This metabolic phenotype is exacerbated by glucose deprivation. This pathway is targetable with FDA-approved cysteine-promoting compounds and could synergize with glucose-lowering treatments, including the ketogenic diet, for GBM.

Abstract P6-07-10: The ERβ4 variant induce transformation of the normal breast mammary epithelial cell line MCF-10A; the ERβ variants ERβ2, ERβ4 and ERβ5 increase aggressiveness of TNBC by regulation of hypoxic signaling

Triple negative breast cancer (TNBC) still remains a challenge to treat in the clinic due to a lack of good targets for treatment. Although TNBC lacks expression of ERα, the expression of ERβ and its variants are detected quite frequently in this cancer type and can represent an avenue for treatment. We show that the variants of ERβ, namely ERβ1, ERβ2, ERβ4, and ERβ5, regulate aggressiveness of TNBC by regulating hypoxic signaling. RNA-seq of patient derived xenografts (PDX) from TNBC show expression of ERβ4 and ERβ5 variants in more than half of the samples. Furthermore, expression of ERβ4 in the immortalized, normal mammary epithelial cell line MCF-10A that is resistant to mammosphere formation caused transformation and development of mammospheres. By contrast, ERβ1, ERβ2 or ERβ5 were unable to support mammosphere formation. We have previously shown that all variants except ERβ1 stabilizes HIF-1α but only ERβ4 appear to have the ability to transform normal mammary epithelial cells, pointing towards a unique property of ERβ4. We propose that ERβ variants may be good diagnostic tools and also serve as novel targets for treatment of breast cancer.

Citation Format: Faria M, Karami S, Granados-Principal S, Dey P, Verma A, Choi DS, Elemento O, Bawa-Khalfe1 T, Chang JC, Gustafsson J-A, Strom AM. The ERβ4 variant induce transformation of the normal breast mammary epithelial cell line MCF-10A; the ERβ variants ERβ2, ERβ4 and ERβ5 increase aggressiveness of TNBC by regulation of hypoxic signaling [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P6-07-10.

Abstract P2-09-01: T-cell receptor beta chain variable region (TRBV) expression patterns predict response to combined trastuzumab/lapatinib treatment in the NeoALTTO/BIG-1-06 trial

Background: Dual anti-HER2 blockade resulted in increased pathologic complete response rate (pCR) in the 3 arm NeoALTTO trial. High immune gene expression and the absence of PIK3CA pathway mutations are predictive of pCR in all treatment arms but no markers were identified that could predict which patients require dual HER2 targeted therapy. The goal of this analysis was to examine if TRBV expression could add to the predictive function of previously identified immune markers.

Patients and Methods: We analyzed RNA and Whole Exome sequencing data from 245 cancers (54% of all patients) included in the trial. The TRBV reference sequences were obtained from the International ImMunoGeneTics information system. Reads were aligned using a custom BLAST mapping pipeline and normalized by the total number of aligned reads in each sample. We calculated 3 T cell receptor metrics for each tumor including (i) total TRBV chain expression level, (ii) Shannon entropy of the normalized unique TRBV-expression frequencies which reflect TCR diversity and (iii) we also used non-negative matrix factorization (NMF) to define TRBV co-expression metagenes (TRBVMG). We evaluated correlation between these metrics and immune and proliferation gene expression signatures and genomic features of the cancer including clonal heterogeneity and mutation load. We assessed association between TRBV and pCR using multivariate logistic regression.

Results: 65 distinct TRBV variants showed heterogeneous expression levels across cancers with strong co-expression patterns. Total TRBV expression correlated strongly with immune metagene expression (Spearman's ρ=0.93, P<0.001), but entropy had a weaker, inverse correlation with immune metagene expression (Spearman's ρ=-0.40, P<0.001). Associations between TRBV metrics and mutation load and clonal heterogeneity were weak. pCR correlated with higher total TRBV expression (Spearman's ρ=0.17, P<0.05). Correlation between entropy and pCR was non-significant (odds ratio (OR) for regressing entropy with pCR was <1). NMF identified 4 distinct TRBVMGs that showed substantial expression variation within immune cell rich cancers. ER-status, proliferation and immune-gene expression adjusted logistic regression analysis including a treatment-arm interaction term revealed that TRBVMG-2, characterized by high expression of TRBV4.3, TRBV6.3 and TRBV7.2 variants, was associated with higher pCR rate in patients treated with trastuzumab plus lapatinib (Interaction OR=3.23 adjusted P=0.03). In immune-rich cancers, TRBVMG-2 expression above the median was associated with higher pCR rate in the dual HER2 targeted treatment arm compared to the other arms (68% vs 21%, Fisher exact test P<0.001). Patients with immune cell rich cancers but TRBVMG-2 expression below the median had similar pCR rates in all arms (42% monotherapy vs. 28% dual therapy, P=0.46).

Conclusions: TRBV expression pattern can provide predictive information beyond known immune gene expression signatures. High expression of TRBV4.3, TRBV6.3 and TRBV7.2 variants is associated with higher pCR rate with dual HER2 targeted and paclitaxel neoadjuvant therapy.

Citation Format: Powles R, Redmond D, Sotiriou C, Loi S, Fumagalli D, Nuciforo P, Harbeck N, de Azambuja E, Sarp S, Di Cosimo S, Huober J, Baselga J, Piccart-Gebhart M, Elemento O, Hatzis C, Pusztai L. T-cell receptor beta chain variable region (TRBV) expression patterns predict response to combined trastuzumab/lapatinib treatment in the NeoALTTO/BIG-1-06 trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-01.

Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies

The transformation of normal cells into cancer cells and maintenance of the malignant state and phenotypes are associated with genetic and epigenetic deregulations, altered cellular signaling responses and aberrant interactions with the microenvironment. These alterations are constantly evolving as tumor cells face changing selective pressures induced by the cells themselves, the microenvironment and drug treatments. Tumors are also complex ecosystems where different, sometime heterogeneous, subclonal tumor populations and a variety of nontumor cells coexist in a constantly evolving manner. The interactions between molecules and between cells that arise as a result of these alterations and ecosystems are even more complex. The cancer research community is increasingly embracing this complexity and adopting a combination of systems biology methods and integrated analyses to understand and predictively model the activity of cancer cells. Systems biology approaches are helping to understand the mechanisms of tumor progression and design more effective cancer therapies. These approaches work in tandem with rapid technological advancements that enable data acquisition on a broader scale, with finer accuracy, higher dimensionality and higher throughput than ever. Using such data, computational and mathematical models help identify key deregulated functions and processes, establish predictive biomarkers and optimize therapeutic strategies. Moving forward, implementing patient-specific computational and mathematical models of cancer will significantly improve the specificity and efficacy of targeted therapy, and will accelerate the adoption of personalized and precision cancer medicine.

Characterization of the human cumulus cell transcriptome during final follicular maturation and ovulation

Cumulus expansion and oocyte maturation are central processes in ovulation. Knowledge gained from rodent and other mammalian models has revealed some of the molecular pathways associated with these processes. However, the equivalent pathways in humans have not been thoroughly studied and remain unidentified. Compact cumulus cells (CCs) from germinal vesicle cumulus oocyte complexes (COCs) were obtained from patients undergoing in vitro maturation (IVM) procedures. Expanded CCs from metaphase 2 COC were obtained from patients undergoing IVF/ICSI. Global transcriptome profiles of the samples were obtained using state-of-the-art RNA sequencing techniques. We identified 1746 differentially expressed (DE) genes between compact and expanded CCs. Most of these genes were involved in cellular growth and proliferation, cellular movement, cell cycle, cell-to-cell signaling and interaction, extracellular matrix and steroidogenesis. Out of the DE genes, we found 89 long noncoding RNAs, of which 12 are encoded within introns of genes known to be involved in granulosa cell processes. This suggests that unique noncoding RNA transcripts may contribute to the regulation of cumulus expansion and oocyte maturation. Using global transcriptome sequencing, we were able to generate a library of genes regulated during cumulus expansion and oocyte maturation processes. Analysis of these genes allowed us to identify important new genes and noncoding RNAs potentially involved in COC maturation and cumulus expansion. These results may increase our understanding of the process of oocyte maturation and could ultimately improve the efficacy of IVM treatment.