The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left-Right Functional Differences

Even though not addressed in this study, it is important to consider here that lipids play a crucial role in the cellular membrane potential during cancer processes. Alterations in lipid metabolism and membrane composition are commonly observed in cancer cells, with significant implications for the membrane potential. Lipids, such as phospholipids, influence membrane fluidity and impact the activity of ion channels and signaling pathways [53]. Lipid rafts, specialized regions enriched with cholesterol and sphingolipids, organize membrane proteins involved in signaling and affect ion channel localization and activity [54]. Certain lipid molecules participate in signaling cascades that regulate the membrane potential [55]. Altered lipid metabolism in cancer cells can affect the composition and properties of the cell membrane, influencing ion channel function and membrane potential. Understanding the interplay between lipids and membrane potential offers valuable insights for research and potential therapeutic interventions.

Our finding that differences between L-R breast tumors are related to ICHs suggests possible therapeutic opportunities by applying already approved drugs used in other diseases to modulate channels. Dysregulation of ICH has been implicated in various diseases. In recent years, ICH modulating drugs have gained attention as potential cancer therapeutics. Drug repositioning is a strategy that involves the repurposing of existing drugs for new therapeutic indications. In the context of cancer treatment, repositioning ICH modulating drugs has emerged as a promising approach to identify new treatments for cancer [56] and the use of ICH drugs in cancer is increasingly being studied, as reviewed in [57]. An oncology-focused drug repurposing database has been developed, comprising drugs that show potential for repurposing in cancer treatment (https://www.anticancerfund.org/en/redo-db, accessed on 4 May 2023). Among the 369 drugs listed to date, there are 20 that target some of the six channels included in our 6-ICH signature, which sounds very encouraging. By repositioning these drugs for cancer treatment, the development timeline and costs associated with drug development can be reduced, making it an attractive option for drug discovery.

Finally, another evidence of our study is that the ICH signature was found inversely associated with DNMT expression, and not with other epigenetic modulators (e.g., histone deacetylases, acetylases, histone methyl transferases, DNA methyl-erasers). It is now well established that epigenetic dysregulations play pivotal roles in cancer onset and progression, including DNA methylation. To occur, these epigenetic events need to be triggered by genetic alterations or transcription shifts of the epigenetic regulators. In this, the TME plays a fundamental role. Therefore, it is valid to reason that the ICH signature is regulated by DNA methylation, and further studies should be performed to confirm this, by for example inhibiting in vitro DNMTs function with 5-azacytidine or deoxycytidine.

In conclusion, our study suggests that the anatomical location of breast tumors may influence their bioelectric state, potentially mediated by ICH. The 6-ICH signature identified in our study may play a role in modulating key biological processes involved in cancer progression, and further studies are warranted to validate these findings. The potential impact of anatomical location on BC biology and clinical outcomes should also be further investigated, suggesting differential treatment options with repurposed ICH drugs.

RNA and tumor tissue from our patient’s tumor tissue bank was used for this study. Informed consent had been originally obtained from all subjects’ tissue included in the bank [58]. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the School of Medical Sciences, National University of Cuyo, Mendoza, Argentina. (Protocol code: 15856/2016, date of approval: 16 September 2016).

All animal procedures were reviewed and approved by the Tufts University Institutional Animal Care and Use Committee. Five MMTV-PyMT (002374, Jackson Laboratory, Bar Harbor, ME, USA) female mice were housed together in the same cage in an on-site housing facility, with access to standard food, water, and a 12/12 light cycle. At 13 weeks of age, the MMTV-PyMT mice were euthanized using CO2, and paired L-R breast tumors from 5 mice, all from the 4th gland, were excised. Tumors were fractionated into 5 pieces for measuring membrane potential by confocal microscopy and flow cytometry, and for DNA and RNA extraction.

Tumors were desegregated in a Petri dish with 1–2 mL of disaggregation solution containing 1 mL collagenase 2 mg/mL in PBS, 150 µL of hyaluronidase 10 mg/mL in PBS (Sigma, #H4272, St. Louis, MO, USA), 100 µL antibiotic (penicillin-streptomycin-glutamine 100×, Gibco, #10378016, Life Technologies, Carlsbad, CA, USA) and 8.75 mL of DMEM/F12 media (Gibco, #21041025, Life Technologies, Carlsbad, CA, USA) with FBS 2% (SH30071.03, Cytiva, Marlborough, MA, USA). Tumors were cut into small pieces using vesture and all the remaining fat/normal mammary gland/skin was removed. The mix of tumor tissue and disaggregation solution was transferred to a tube and digested at 37 °C with agitation for 2–4 h. Cells were filtered with 40 µm cell strainers, then centrifuged for 5 min at 10,000 rpm and washed 4 times with cold PBS. Afterwards, cells were resuspended in 200 µL DMEM FluoroBrite (Gibco, #A1896701, Life Technologies, Carlsbad, CA, USA) with 2% FBS. DiBAC4(3) 2 µM (positively charged, DB) (Invitrogen, #B438, Life Technologies, Carlsbad, CA, USA) and Mytotracker Deep Red FM 500 nM (negatively charged, MT) (Cell Signaling Technology, #8778, Danvers, MA, USA) was added and incubated for 30 min at 37 °C. We used the MT probe instead of rhodamine 6G (which is a potentiometric dye [59]), because they have similar functionality and to preclude rhodamine 6G’s spectral overlap with DIBAC4(3). Fluorescence was measured by flow cytometry (Attune NxT flow cytometer®) using a 530/30 emission filter for DB measurement and a 670/14 emission filter for MT measurement. The autofluorescence of each tumor cell was measured and subtracted. Results were analyzed using FlowJo v X.0.7® software 10.8.1 (RRID: SCR_008520).

Thin slices of the paired L-R 4th gland tumors were cut and placed in Petri dishes with DMEM FluoroBrite (Gibco, #A1896701, Life Technologies, Carlsbad, CA, USA) plus 2% FBS. For measuring membrane potential, 2–3 slices per tumor were used, incubating 15 min at 37 °C with media containing DB 2 μM and MT 500 nm in Fluorobrite DMEM. One slice was used to measure autofluorescence of the cells. Images were captured using a Leica Stellaris Sp8 confocal microscope at 10× magnification, at 37 °C and 3–5 images were taken per tumor, with 3 z-slices per image. As control for normalized measurements, cells were treated with a high concentrated KCl (65 mM) solution as depolarizing agent, for 15 min at 37 °C. Afterwards, slices were incubated with Fluorobrite DMEM containing DB (2 μM) and MT (500 nM). The images were processed using Image J software (ImageJ 1.53t/Java 1.8.0_322, National Institutes of Health, USA, RRID: SCR_003070). The sum of the z-slices was used for image projection. A ratio of MT/DB integrated density was calculated for each image, and this ratio was normalized to the same ratio obtained from the depolarized control images.

RNA was extracted from mice tumors using the Quick-RNA Miniprep Plus Kit (Zymo Research, #R1057, Irvine, CA, USA), following the manufacturer’s protocol. RNA from human breast tumors was extracted with a Trizol (Life Technologies, Carlsbad, CA, USA) based protocol.

Purified RNA from mice tumors was converted to cDNA using M-MLV Reverse Transcriptase (Inbio #K1600, Tandil, Buenos Aires, Argentina), and cDNA was stored at −20 °C until use. For each reaction, 1 ng was used plus ddPCR Supermix for Probes (No dUTP) (BioRad, #1863023, Hercules, CA, USA) and PrimeTime® Mini qPCR Assay probes for KI67, CD44 and HPRT (housekeeping) genes with FAM fluorescence for detection. Sequences for KI67 probe: 5′-/56-FAM/TGGCCTACC/ZEN/TGGTCTTAGTTCCGT/31ABkFQ/-3′, primer 1: 5′-TTCCTTCAGCAAGCCTGAG-3′, primer 2: 5′-CTTCATAGGCATTCCCTCACTC-3′. For CD44 probe: 5′-/56-FAM/ACCCATACC/ZEN/TGCATGTTTCAAAACCC/31ABkFQ/-3′, primer 1: 5′-GCTTTCAACAGTACCTTACCCA-3′, primer 2: 5′-GGATGAATCCTCGGAATTACCA-3′. For HPRT probe: 5′-/56-FAM/CTTGCTGGT/ZEN/GAAAAGGACCTCTCGAA/31ABkFQ/-3, primer 1: 5′-CCCCAAAATGGTTAAGGTTGC-3′, primer 2: 5′-AACAAAGTCTGGCCTGTATCC-3′. We used the following probe concentrations to improve the multiplexed reaction: probe CD44 1.5X with HPRT 1X; and probe KI67 1X with HPRT 2X. For each assay, droplets were generated using droplet generation oil for probes (Bio-Rad, #1863005, Hercules, CA, USA) on the QX200 Droplet Generator (Bio-Rad, #17005227, Hercules, CA, USA) according to the manufacturer’s protocol adding the specific primers. Droplets were cycled on the C1000 Touch Thermal Cycler (Bio-Rad, Hercules, CA, USA) for 40 cycles, with a 58 °C annealing temperature. Droplets were read using the QX200 Droplet Reader (BioRad, Hercules, CA, USA). Data were analyzed with QuantaSoft software version 1.7 (BioRad, Hercules, CA, USA).

One µg of total RNA was used for the synthesis of cDNA using M-MLV retro-transcriptase (Inbio #K1600, Tandil, Buenos Aires, Argentina) and Random Hexamers (Qiagen #79236, Germantown, MD, USA) primers. An amount of 15 ng was used to perform Real-Time PCR using specific primers for CD44, KI67 and β-actin (housekeeping) in an AriaMx Real-time PCR System (Agilent, Santa Clara, CA, USA). The sequences of the primers for CD44 were as follows: forward 5-TGCCGCTTTGCAGGTGTATT-3 and reverse 5-CCGATGCTCAGAGCTTTCTCC-3; for KI67: forward 5-TGACCCTGATGAGAAAGCTCAA-3 and reverse 5-CCCTGAGCAACACTGTCTTTT-3; for β-actin: forward 5-TGACGTGGACATCCGCAAAG-3 and reverse 5-CTGGAAGGTGGACAGCGAGG-3. For the reaction, a Master Mix qPCR 2X with SYBR was used (Inbio, Tandil, Buenos Aires, Argentina). The amplification program consisted of 40 cycles of 15 s at 95 °C, 30 s at 60 °C, and 30 s at 72 °C, followed by a final melting curve step. The analysis was performed using AriaMx Software version 2.0 (Agilent, Santa Clara, CA, USA). Relative expression normalization of genes of interest was carried out using β-actin gene expression as endogenous reference control by the ΔCq method.

Human ICH genes were obtained from the HUGO Gene Nomenclature Committee (HGNC) database (https://www.genenames.org/data/genegroup/#!/group/177, accessed on 10 February 2023).

Gene expression and laterality data of primary breast tumors were downloaded from The Cancer Genome Atlas (TCGA)-Xena Functional Genomics Explorer-UCSC (https://xenabrowser.net/, accessed on 16 June 2023) (TCGA-Breast Cancer dataset) and exploration tools for differential gene expression (DGE), enrichment analyses, and Kaplan-Meier curves were used.

Dataset enrichment score analyses to compare L-R tumors were performed using the computational method gene set enrichment analysis (GSEA). GSEA allows it to be determined whether pre-defined gene sets show significant differences between two phenotypes (in this case, L- and R-sided tumors). Based on gene expression data from these two biological states, GSEA calculates and ranks the DGE, searches for the gene sets in the Human Molecular Signature Database (MSigDB) containing 33,591 gene sets and identifies the top ones that significantly contribute to the differences between the phenotypes. Leading-edge analysis using the GSEA tool, was used to find associated gene signatures.

Statistical comparisons were performed using GraphPad Prism version 5.03 for Windows, GraphPad Software (San Diego, CA, USA, www.graphpad.com). Un-paired or paired Student’s t-test were used according to the experiment type, to compare the means of the fluorescence ratios. Data from different experiments were normalized to a maximum depolarized state by using high concentrated KCl solution. All results are means of 3 independent experiments with 2–3 technical replications each. Correlation analyses between two variables were performed using Spearman rank coefficient calculations. Sensitivity/specificity values of the 6-ICH signature to distinguish between high vs. low proliferation and stemness markers were calculated using the receiver operating characteristic curve (ROC). Overall survival, disease free survival, progression free survival, and disease specific survival rates were analyzed by Kaplan-Meier plots and p values < 0.05 were considered statistically significant, using FDR correction (<0.1) when needed for repetitive comparisons.