The association between the amino acid transporter LAT1, tumor immunometabolic and proliferative features and menopausal status in breast cancer

Gautham Ramshankar; Ryan Liu; Rachel J Perry

doi:10.1371/journal.pone.0292678

. 2023 Oct 11;18(10):e0292678. doi: 10.1371/journal.pone.0292678

The association between the amino acid transporter LAT1, tumor immunometabolic and proliferative features and menopausal status in breast cancer

Gautham Ramshankar ^1,², Ryan Liu ^2,³, Rachel J Perry ^2,^*

Editor: Pankaj K Singh⁴

PMCID: PMC10566702 PMID: 37819900

Abstract

L-type Amino Acid Transporter 1 (LAT1) facilitates the uptake of specific essential amino acids, and due to this quality, it has been correlated to worse patient outcomes in various cancer types. However, the relationship between LAT1 and various clinical factors, including menopausal status, in mediating LAT1’s prognostic effects remains incompletely understood. This is particularly true in the unique subset of tumors that are both obesity-associated and responsive to immunotherapy, including breast cancer. To close this gap, we employed 6 sets of transcriptomic data using the Kaplan-Meier model in the Xena Functional Genomics Explorer, demonstrating that higher LAT1 expression diminishes breast cancer patients’ survival probability. Additionally, we analyzed 3′-Deoxy-3′-¹⁸F-Fluorothymidine positron emission tomography-computed tomography (¹⁸F-FLT PET-CT) images found on The Cancer Imaging Archive (TCIA). After separating all patients based on menopausal status, we correlated the measured ¹⁸F-FLT uptake with various clinical parameters quantifying body composition, tumor proliferation, and immune cell infiltration. By analyzing a wealth of deidentified, open-access data, the current study investigates the impact of LAT1 expression on breast cancer prognosis, along with the menopausal status-dependent associations between tumor proliferation, immunometabolism, and systemic metabolism.

Introduction

As the second leading cause of cancer deaths in women, breast cancer has become a major clinical and social burden, with annual out-of-pocket costs for breast cancer care in the U.S. exceeding $3 billion in 2019 [1]. Because breast cancer has high economic and social costs, it has become increasingly necessary to identify potential risk factors, biomarkers, and treatments. Nearly 30% of breast cancer cases are caused by modifiable risk factors like excess body weight and alcohol consumption [2]. Several of the modifiable risk factors that predispose to breast cancer converge on metabolism. Consequently, a key priority in the cancer field has been to investigate tumor metabolism and how it can be affected by a patient’s lifestyle. In the 1920s, Otto Warburg discovered that in order to sustain their energetic needs while prioritizing generating the biomass and nucleotides required for rapid proliferation and growth, cancer cells have greater metabolic demands than their benign counterparts. Because of this, oncogenic metabolism is characterized by heightened glycolytic metabolism, which necessitates greater uptake of glucose. This phenomenon is now called the Warburg Effect and has greatly shaped the field of tumor metabolism [3]. However, many years after Warburg’s groundbreaking work identifying glucose metabolism as a key contributor to tumor pathogenesis, there remains relatively less investigation into the role of amino acid metabolism in tumor progression. The same can be said about amino acid metabolic reprogramming, the abnormal changes to amino acid uptake or metabolic pathways caused by tumor progression. However, past literature has shown that low concentrations of amino acids in the tumor microenvironment inhibit nearby immune cells, weakening immune responses to tumor cells and contributing to tumor progression [4, 5]. These data beg further investigation of the tumor- and/or immune cell-centric metabolic role of amino acids in the tumor microenvironment.

In order to leave the tumor interstitial compartment and undergo metabolism by tumor cells, amino acids must cross the plasma membrane with the help of amino acid transporters. Amino acid transporters can thus facilitate the uptake of amino acids to meet the metabolic needs of cancer cells, explaining why the expression of these transporters has been associated with the proliferation of cancer cells. One such transporter, L-type amino acid transporter 1 (LAT1) is particularly important in the amino acid transport process [4]. Encoded by the gene Solute Carrier Family 7 Member 5 (SLC7A5), LAT1 is a light-chain protein that heterodimerizes with its heavy-chain partner 4F2hc (SLC3A2) through a conserved disulfide bridge, forming the human LAT1-4F2hc complex. A sodium-independent transporter, LAT1 is an integral membrane protein that mediates the transport of large neutral amino acids like methionine, leucine, and histidine by exchanging them with intracellular glutamine [6]. LAT1 is unique in that it transports multiple essential amino acids, which cannot be synthesized by the human body and must be obtained through diet [7, 8]. Considering the dietary dependence of its transported molecules, LAT1 is a particularly intriguing target to participate in the links between lifestyle, systemic metabolism, and cancer.

Positron emission tomography-computed tomography (PET-CT) is a powerful tool in cancer metabolism research due to its ability to visualize thin slices of tissue in vivo and quantify cells’ metabolic activity by measuring radiotracers like 3′-Deoxy-3′-¹⁸F-Fluorothymidine (¹⁸F-FLT). An analog of the nucleoside thymidine, ¹⁸F-FLT is phosphorylated by the cytosolic enzyme thymidine kinase 1 (TK1) and taken up into the cell. During the S-phase of the cell cycle, TK1 is overexpressed nearly tenfold and ¹⁸F-FLT uptake is at its highest. In this way, concentrations of ¹⁸F-FLT and TK1 are elevated in cancer cells, making ¹⁸F-FLT uptake a quantitative marker for tumor proliferation [9–12]. Ki-67 is a nuclear nonhistone protein, and because it is only expressed in cells that are not in the G₀ phase of the cell cycle, it can only be observed in actively-proliferating cells. This quality has made Ki-67 a classic proliferative marker for tumor cells [13], and is included in the datasets analyzed in the current report.

Past studies have demonstrated that menopausal status affects to what extent obesity is a risk factor for developing breast cancer. In multiple studies, obesity has been observed to have a protective relationship with breast cancer risk in premenopausal patients whereas it is a risk factor for breast cancer in postmenopausal patients [14]. Because of this, we segmented our analyses based on patients’ menopausal statuses. We used body mass index (BMI) in kg/m² as a metric for obesity. By analyzing PET-CT scans of 58 patients from The National Cancer Imaging Archive (TCIA), we correlate patients’ calculated ¹⁸F-FLT uptake and Ki-67 index values to their BMIs to study the relationship between obesity and breast cancer [10, 15, 16].

To demonstrate the relationship between LAT1 and poorer health outcomes with a larger sample size, we leveraged RNA-seq data in the UCSC Xena Functional Genomics Explorer [17]. This allowed us to visualize the effect of LAT1 expression on breast cancer prognosis in premenopausal and postmenopausal patients. Ultimately, we used a similar workflow to our prior published work to examine the impact of SLC7A5, a gene with a drastically different role in metabolism, in breast cancer [18]. Our analyses reveal new insights into the associations between clinical variables (obesity, menopausal status), cell proliferation, infiltration with multiple immune cell subtypes, tumor LAT1 expression, and survival in breast cancer patients, which deepen our understanding of the bidirectional relationships that may inform interventional studies targeting these variables in individuals with breast cancer.

Methods

¹⁸F-FLT PET-CT quantitative image analysis

Deidentified PET-CT images produced during the ACRIN 6688 clinical trial [10] were obtained from The Cancer Imaging Archive (TCIA). This dataset, “ACRIN-FLT-Breast (ACRIN 6688)”, can be found here: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=30671268. Because only publicly available, deidentified data were analyzed, and participants, all of whom were adults, gave written consent for their data to be used, deidentified, in public repositories, separate ethical approval is not required for these or other datasets analyzed in this manuscript. Data sharing via TCIA is approved under the supervision of the University of Arkansas for Medical Sciences (UAMS) Institutional Review Board (IRB # 205568), and informed consent was provided by the patients for their data to be shared with TCIA; however, the details of the consent process are not available to us. Because only publicly available, deidentified data were analyzed, and we had no information about the patients whose data were analyzed, separate ethical approval was not sought. All data are submitted to the TGCA in accordance with the submitter’s institutional policies, including IRB approval and informed consent provided by the patients for their data to be submitted, deidentified, to the TCGA. However, the details of the consent process are not available to us. We did not seek separate IRB approval because our use of these deidentified data are covered under these IRB approvals.

All data were accessed for research purposes between 2/3/23 and 7/1/2023. We analyzed the scans of all patients with a menopausal status, height, weight, and 5 clear CT slices (i.e., slices in which the primary breast tumor could be identified and its corresponding SUV values could be generated) present in the dataset. 58 of the 90 enrolled patients in the ACRIN clinical trial met these criteria, and all were analyzed. Of these 58 patients, 26 were premenopausal and 32 were postmenopausal. Scans taken at 3 different dates were available for most patients, and we used the earliest scan (from the baseline scanning which was defined to be 4 weeks before any treatment was administered) to minimize the chemotherapeutic effect of the treatment used in the clinical trial. Likewise, heights and weights measured on patients’ first visits were used. These data were selected for analysis because breast cancer treatment often causes some weight gain [19–22], which may obscure differences in BMI that could promote proliferation.

The patients’ images were uploaded to Fiji ImageJ and we used the PET-CT Viewer plugin to view and analyze them. After identifying the primary breast tumor on the PET image, we selected the tumor and used the Brown Fat Volume tool to draw fixed-volume spheres around the interior regions of interest (ROIs) on the CT slice. 5 slices were used from each patient’s scan. SUV parameters were set at 2 to 15, and ¹⁸F-FLT uptake was calculated in the tumor tissue in the specified ROI. ¹⁸F-FLT uptake on PET-CT scans is measured by calculating and recording lean body mass-corrected standardized uptake values (SUV) of which there are 3 types: SUV_Mean, SUV_Max, and SUV_Peak. After positioning a fixed-volume sphere on a tumor, within the ROI, SUV_Mean represents the average SUV, SUV_Max indicates the maximum SUV, and SUV_Peak corresponds to the SUV derived from a localized cluster of voxels with high uptake [10, 23]. The primary endpoint of image analysis was BMI (kg/m²) correlated to the 3 types of tumor SUV (g/mL).

LAT1 prognostic analysis

Using the UCSC Xena Functional Genomics Browser (https://xenabrowser.net/), we accessed the “TCGA Breast Cancer (BRCA) cohort” (found here: https://xenabrowser.net/datapages/?cohort=TCGA%20Breast%20Cancer%20(BRCA)&removeHub=http%3A%2F%2F127.0.0.1%3A7222) which included 2 datasets. The BRCA cohort had 1247 total patients, and all of them had menopausal statuses recorded, which we accessed through the “Phenotypes” dataset: https://xenabrowser.net/datapages/?dataset=TCGA.BRCA.sampleMap%2FBRCA_clinicalMatrix&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443. 1236 of the 1247 patients had survival data recorded. The “IlluminaHiSeq” dataset was used to study LAT1 expression and it can be found here: https://xenabrowser.net/datapages/?dataset=TCGA.BRCA.sampleMap%2FHiSeqV2&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=http%3A%2F%2F127.0.0.1%3A7222. The “IlluminaHiSeq” dataset used fragments per kilobase of exon per million mapped fragments (FPKM) to measure gene expression. 1218 of the 1247 patients had LAT1 expression data. These datasets were used alongside the Kaplan-Meier model in the Xena visualization suite to analyze LAT1 and its effect on breast cancer prognosis.

To analyze LAT1 expression, the following workflow was used: the 1247 patients were added to Column A. SLC7A5 was added to Column B as a genomic variable with the gene expression dataset selected, and menopause status was added to Column C as a phenotypic variable. After removing null and duplicate samples, a Kaplan-Meier (KM) plot was generated in Column B to show LAT1 expression and its effect on prognosis in 1005 of the 1247 patients. Next, low and high-expression groups were created from these patients. After 34 patients with indeterminate menopausal statuses were removed from the dataset, 10.46 FPKM was calculated by Xena to be the median for LAT1 expression. Patients were divided at the median: 485 patients were in the low expression group (< 10.46 FPKM), and 486 patients were in the high expression group (> = 10.46 FPKM). A KM plot was generated for each group using Column C, creating 2 KM plots with the premenopausal, perimenopausal, and postmenopausal patients in each expression group.

In addition, after selecting a menopausal status (premenopausal or postmenopausal patients), a breast cancer subtype (ER+ or HER2-) was chosen, and survival was observed in low and high LAT1 expression groups. Data from patients with ER+ and HER2- tumors were also analyzed and a KM plot was created for each subtype’s high and low expression groups, separately for pre- and postmenopausal patients. Patients were divided at the calculated median expression level for each group: 10.09 FPKM for the ER+ patients and 10.38 FPKM for the HER2- patients.

In addition to the BRCA dataset, the following datasets were used to access breast cancer patients’ gene expression data: “RSEM norm-count” from the “TCGA TARGET GTEx” cohort (https://xenabrowser.net/datapages/?dataset=TcgaTargetGtex_RSEM_Hugo_norm_count&host=https%3A%2F%2Ftoil.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443), “Desmedt 76 Gene Node-Neg Gene Exp” from the “node-negative breast cancer (Desmedt 2007)” cohort (https://xenabrowser.net/datapages/?dataset=desmedt2007_public%2Fdesmedt2007_genomicMatrix&host=https%3A%2F%2Fucscpublic.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443), “gene expression RNAseq—US projects” from the “ICGC (donor centric)” cohort (https://xenabrowser.net/datapages/?dataset=donor%2Fexp_seq.all_projects.donor.USonly.xena.tsv&host=https%3A%2F%2Ficgc.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443), “Gene Expression” from the “Breast Cancer (Chin 2006)” cohort (https://xenabrowser.net/datapages/?dataset=chin2006_public%2Fchin2006Exp_genomicMatrix&host=https%3A%2F%2Fucscpublic.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443), and “Miller TP53 Gene Exp” from the “Breast Cancer (Miller 2005)” cohort (https://xenabrowser.net/datapages/?dataset=miller2005_public%2Fmiller2005_genomicMatrix&host=https%3A%2F%2Fucscpublic.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443). To measure expression, the “TCGA TARGET GTEx” dataset used FPKM, the “ICGC (donor centric)” dataset used normalized read count, and the “Node-negative breast cancer (Desmedt 2007)” and “Breast Cancer (Miller 2005)” datasets used log2 units. Although these gene expression datasets did not include menopausal status as a possible phenotypic variable, our selection criteria were to include breast cancer datasets that had RNA-seq data on SLC7A5 and survival data from the same patients that could be used to produce Kaplan-Meier plots on the Xena platform. Each dataset was used in the same way: SLC7A5 was selected as a genomic variable in Column B, and after null and duplicate samples were removed, a KM plot was generated. For the “TCGA TARGET GTEx” and “ICGC (donor centric)” datasets, only patients with breast tumors were selected for the analysis.

All of the KM plots were created with Overall Survival as the dependent variable unless otherwise specified. After patients were split at the median for the gene expression analyses, some groups had an unequal number of patients because patients with the same expression levels were put in the same group. Some patients were at the expression median, and the median was calculated to ensure they were placed in the high-expression group while keeping the sizes of each expression group roughly the same.

Statistical analysis

Correlation tests were performed between patients’ SUV and BMI values. 26 premenopausal patients’ BMIs ranged from 23.829 to 142.822 kg/m² (mean [SD] = 33.972 [22.584]), and 32 postmenopausal patients’ BMIs ranged from 17.940 to 199.219 kg/m² (mean [SD] = 40.865 [36.818]). The unusually high BMI values are driven by unusually low heights reported for these patients; however, only one premenopausal and postmenopausal patient had a reported BMI above 100. Because 5 slices were used per patient, each patient had 5 SUV_Max values and 5 SUV_Peak values but only 1 BMI. In order to correlate BMI and SUV, we needed the same number of values for each. In order to get one SUV for each patient, we took the mean of the SUVs produced from all 5 slices. For SUV_Mean, the calculated SUV had a margin of error indicated by a plus-minus sign. This meant that the calculation of each SUV_Mean yielded 2 numerical values, one being the high value and the other being the low value, so 5 slices yield 10 SUV_Mean values per patient. We took the mean of these 10 values for each patient. Each of these individual SUVs was then correlated with each patient’s BMI.

We also correlated each patient’s 3 types of SUVs to their Ki-67 values to further inform the validity of ¹⁸F-FLT uptake as a metric for tumor proliferation. BMI was also correlated to Ki-67. All correlations were two-tailed Pearson correlation tests performed after patients’ data were segmented by menopausal status. Shapiro-Wilk tests were also performed to determine if any groups of data were normally distributed. Student’s t-tests and Mann-Whitney U tests were performed on parametric and nonparametric data, respectively, to assess difference. For both tests, all data were transformed using log2 fold changes of the mean.

Unless otherwise specified, statistical analysis was done and graphs were made in Python 3.9 using the pandas (version 1.5) and SciPy (version 1.10) libraries. The two-tailed Pearson correlation tests were conducted using the “pearsonr” function from the scipy.stats module. The Mann-Whitney U tests were conducted using the “mannwhitney” function, the Student’s t-tests were conducted using the “ttest_ind” function, and the Shapiro-Wilk tests were performed using the “shapiro” function, all from the scipy.stats module. All Python code can be found here: https://github.com/gramshankar/LAT1BreastCancer. For each of the KM plots, a log-rank test was conducted by Xena to compare the curves in the graph. Test statistics and p-values were calculated. Statistical significance was indicated by p-values less than 0.05, and marginally significant results have p-values greater than 0.05 but less than 0.10.

Results

Correlation analysis between proliferative markers, obesity, and immune cells by menopausal status

In premenopausal patients, Ki-67 insignificantly positively correlated with SUV_Mean, SUV_Peak (marginal significance), and SUV_Max (marginal significance) (Fig 1A). However, the relationship between Ki-67 and tumor ¹⁸F-FLT uptake was statistically stronger in postmenopausal patients, in whom Ki-67 significantly positively correlated with SUV_Mean, SUV_Peak, and SUV_Max (Fig 1B).

In premenopausal patients, BMI insignificantly negatively correlated with SUV_Mean, SUV_Peak, and SUV_Max (Fig 1A). In postmenopausal patients, BMI insignificantly positively correlated with SUV_Mean (marginal significance), SUV_Peak, and SUV_Max (Fig 1B). In premenopausal and postmenopausal patients, BMI insignificantly positively correlated with Ki-67 (Fig 1A and 1B).

In premenopausal patients, basophil, eosinophil, neutrophil, monocyte, and lymphocyte counts insignificantly negatively correlated with SUV_Mean, SUV_Peak, and SUV_Max. All immune cells insignificantly negatively correlated with Ki-67 (Fig 1A).

In postmenopausal patients, basophil, eosinophil, monocyte, and lymphocyte counts insignificantly positively correlated with SUV_Mean, SUV_Peak, and SUV_Max. Neutrophil counts insignificantly negatively correlated with SUV_Mean, SUV_Peak, and SUV_Max. Basophil, eosinophil, monocyte, and lymphocyte counts insignificantly negatively correlated with Ki-67. Neutrophil counts insignificantly positively correlated with Ki-67 (Fig 1B).

In premenopausal patients, BMI negatively correlated with basophil, eosinophil, monocyte, and lymphocyte counts and positively correlated with neutrophil counts (Fig 1A). Opposite relationships were observed in postmenopausal patients, in whom BMI positively correlated with basophil, eosinophil, monocyte, and lymphocyte counts and negatively correlated with neutrophil counts (Fig 1B).

These correlation tests’ p-values are presented in Tables 1–4.

Table 1. P–values from correlations between Ki–67 and ¹⁸F–FLT uptake.

Significant results are bolded and marginally significant results are bolded and italicized.

	Premenopausal Patients	Postmenopausal Patients
SUV_Mean	0.114	0.035
SUV_Peak	0.081	0.045
SUV_Max	0.078	0.029

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Table 4. P–values from correlations between immune cells and proliferative markers in postmenopausal patients.

	Basophils	Eosinophils	Neutrophils	Monocytes	Lymphocyte
SUV_Mean	0.239	0.237	0.671	0.235	0.233
SUV_Peak	0.560	0.563	0.586	0.560	0.561
SUV_Max	0.682	0.685	0.741	0.679	0.681
Ki-67	0.781	0.737	0.986	0.723	0.762

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Table 2. P–values from correlations between BMI and proliferative markers.

Marginally significant results are bolded and italicized.

	Premenopausal Patients	Postmenopausal Patients
SUV_Mean	0.279	0.095
SUV_Peak	0.335	0.342
SUV_Max	0.268	0.437
Ki-67	0.767	0.691

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Table 3. P–values from correlations between immune cells and proliferative markers in premenopausal patients.

	Basophils	Eosinophils	Neutrophils	Monocytes	Lymphocyte
SUV_Mean	0.395	0.539	0.334	0.190	0.472
SUV_Peak	0.401	0.620	0.420	0.262	0.404
SUV_Max	0.519	0.691	0.520	0.342	0.537
Ki-67	0.394	0.712	0.626	0.417	0.132

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LAT1 expression and survival probability

In the TCGA BRCA gene expression dataset, patients in the high LAT1 expression group experienced lower overall survival than patients in the low expression group until 4000 days after initial treatment. From that point until 6500 days and again from 6600 days until 7500 days, the low LAT1 expression group had a worse overall survival rate (Fig 2A). Similarly, patients with high LAT1 expression had a lower disease-specific survival rate than patients with low LAT1 expression until 4400 days; after that point until the end of the study, patients in the low expression group had a lower disease-specific survival rate (Fig 2B). We recognize that the long follow-up period prevents us from concluding with certainty that mortality is breast cancer-related, but even if mortality were unrelated to cancer at this time point, the utility of LAT1 as a prognostic factor remains important.

Fig 2 — Prognosis in patients from the TCGA BRCA gene expression dataset. Patients were separated into high (> = 10.47 FPKM) and low (< 10.47 FPKM) LAT1 expression groups, and **(A)** overall survival and **(B)** disease–specific survival were observed up to 8605 days after initial treatment. The median for expression level is slightly different from the median stated earlier because patients with indeterminate menopausal status were included in this analysis.

In the “TCGA TARGET GTEx” gene expression dataset, patients in the high LAT1 expression group (> = 10.65 FPKM) experienced lower survival rates than the low LAT1 expression group (< 10.65 FPKM) until 4000 days after initial treatment. From 4000 days until the end of the study, the low-expression group had a lower survival rate (Fig 3A). In the “Node-negative breast cancer (Desmedt 2007)” gene expression dataset, after the first 500 days, the high LAT1 expression group (> = 0.1221 log2) experienced a lower survival rate than the low LAT1 expression group (< 0.1221 log2) for the remainder of the study (Fig 3B). In the “ICGC (donor centric)” gene expression dataset, the high LAT1 expression group (> = 0.00002400) had lower survival than the low LAT1 expression group (< 0.00002400) for the entire study. The high expression group’s survival probability reached 0% near 4000 days (Fig 3C). In the “Breast Cancer (Chin 2006)” gene expression dataset, except from 1.2 to 1.4 years, the high LAT1 expression group experienced a lower survival rate than the low LAT1 expression group. Units were not given for this study but it most likely used log2 units (Fig 3D). In the “Breast Cancer (Miller 2005)” gene expression dataset, overall survival data were not available so disease-specific survival was observed. The high LAT1 expression group (> = -0.1707 log2) experienced worse survival than the low LAT1 expression group (< -0.1707 log2) for the entire study (Fig 3E). Overall, 2 of the gene expression datasets showed that low LAT1 expression conferred a poorer prognosis in breast cancer patients than high LAT expression, while 4 others showed the opposite.

Fig 3 — After patients were separated into high and low LAT1 expression groups, survival was observed in patients from the **(A)** “TCGA TARGET GTEx”, **(B)** “Node–negative breast cancer (Desmedt 2007)”, **(C)** “ICGC (donor centric)”, **(D)** “Breast Cancer (Chin 2006)”, and **(E)** “Breast Cancer (Miller 2005)” cohorts.

Survival probability with high LAT1 expression TCGA BRCA patients by menopausal status

The impact of menopausal status on survival in patients with high LAT1 expression is shown in Fig 4. After 1000 days, postmenopausal patients had the lowest survival rates. Premenopausal patients had the highest survival rates among the 3 groups until approximately 2500 days, from which point peri-menopausal patients had the highest survival until 3600 days (Fig 4); however, our ability to draw conclusions regarding survival in peri-menopausal patients is limited by the relatively low number of patients in this group.

Survival probability with low LAT1 expression TCGA BRCA patients by menopausal status

The impact of menopausal status on survival in patients with low LAT1 expression is shown in Fig 5. Premenopausal patients had a higher survival rate than postmenopausal patients until 3200 days. Postmenopausal patients, after 3200 days and until the end of the available survival data for premenopausal patients at approximately 3800 days, exhibited a higher survival rate compared to premenopausal patients. The few peri-menopausal patients in this study maintained a 100% survival probability throughout the duration that they were monitored.

Survival probability of premenopausal vs. postmenopausal patients with ER+ tumors by LAT1 expression level

Fig 6 shows the survival of ER+ patients by menopausal status and LAT1 expression level. Among the premenopausal patients, the low expression group (< 10.26 FPKM) experienced a lower survival rate than the high expression group (> = 10.26 FPKM), excluding a short interval from approximately 2500 to 2900 days after initial treatment. Among the postmenopausal patients, the high expression group (> = 10.07 FPKM) experienced lower survival until approximately 3900 days after initial treatment, from which point the low expression group (< 10.07 FPKM) has a lower survival rate. The low expression group reached a 0% survival rate 4275 days after initial treatment.

Survival probability of premenopausal vs. postmenopausal patients with HER2- tumors by LAT1 expression level

Fig 7 shows the survival of breast cancer patients with HER2- tumors by menopausal status and LAT1 expression level. Among premenopausal patients, the high expression group (> = 10.43 FPKM) experienced worse survival until nearly 3300 days after initial treatment. After then, the low expression group (< 10.43 FPKM) experienced worse survival until the end of the study. Among postmenopausal patients, the high expression group (> = 10.37 FPKM) experienced lower survival until approximately 3900 days after initial treatment, from which point the low expression group (< 10.37 FPKM) had worse survival until the end of the survival.

Survival probability with low and high LAT1 expression in ER+ and HER2- tumors of TCGA BRCA patients by menopausal status

Next, we reanalyzed the same data shown in Figs 6 and 7 with different groupings, in order to demonstrate the impact of menopausal status on survival probability in patients with tumors expressing LAT1 at low (<10.09 FPKM) and high (> = 10.09 FPKM) levels. In patients with ER+ tumors with low LAT1 expression, postmenopausal patients experienced lower survival than their premenopausal counterparts, excluding a short interval from approximately 3200 to 3800 days after initial treatment (S1A Fig). The survival rate for postmenopausal patients reached 0% 6593 days after the start of the study. In the group with ER+ tumors and high LAT1 expression, postmenopausal patients experienced a considerably lower survival rate than premenopausal patients (S1B Fig). In both expression groups, the small number of peri-menopausal patients with data available had a 100% survival rate throughout the study.

S2 Fig shows the survival of patients with HER2- breast tumors by menopausal status and tumor LAT1 expression level. Among patients with low LAT1 expression (< 10.38 FPKM), postmenopausal patients with HER2- tumors had a lower survival rate than premenopausal patients, excluding a short interval from approximately 3300 to 3700 days after initial treatment. Among patients with high LAT1 expression (> = 10.38 FPKM), postmenopausal patients with HER2- tumors experienced a lower survival throughout the study. In both expression groups, peri-menopausal patients had a 100% survival rate.

Discussion

Increasing interest in the relationship between systemic metabolism, tumor metabolism, immunometabolism, and cancer outcomes, alongside evolving technologies expanding both the available data and the community’s ability to mine it to develop new insights. To that end, in this study, we utilized multiple publicly available breast cancer datasets, including “ACRIN-FLT-Breast (ACRIN 6688)”, TCGA BRCA “Phenotypes”, TCGA BRCA “IlluminaHiSeq”, “TCGA TARGET GTEx”, “Node-negative breast cancer (Desmedt 2007)”, “ICGC (donor centric)”, “Breast Cancer (Chin 2006)”, and “Breast Cancer (Miller 2005)”, aiming to better understand the intersection between parameters of systemic metabolic health, tumor gene expression, and immune cell infiltration, and outcomes in individuals with breast cancer (Fig 8).

Fig 8 — Figure created with BioRender.com. The current study analyzed ¹⁸F–FLT uptake, immune infiltrate levels, body weights, LAT1 expression, and tumor proliferative factors observed in breast cancer patients.

As opposed to genes or metabolic fluxes involved in glucose [24–31] or lipid metabolism [31–39], there exists a relative paucity of studies exploring the impact of expression of genes regulating amino acid uptake in breast cancer. Therefore, we elected to focus the current study on the expression of LAT1, which transports large amino acids including leucine, isoleucine, valine, phenylalanine, methionine, tyrosine, histidine, and tryptophan into the cell, and its relationships with body weight, tumor cell proliferation, and immune infiltration. Prior literature indicates that LAT1 is involved in protein synthesis [40, 41] and mTORC1 activity [42, 43], and may also modulate the anti-tumor immune response [44–47]. Overexpression of LAT1 has been observed in a plethora of tumor types ranging from lung to endometrial to liver, but fewer studies of the relationship between LAT1 and breast cancer exist [48]. Furthermore, LAT1 has been less frequently associated with a poor long-term clinical prognosis in breast cancer than in other cancers. Our data, too, provide mixed evidence: while some datasets showed that high LAT1 expression was worse for prognosis, others showed the opposite. Namely, the BRCA dataset showed that low LAT1 expression conferred worse survival at some points. This is likely because when compared to the high-expression group, a greater proportion of low-expression patients in the BRCA dataset had a positive margin status. Also, a greater percentage of the low-expression group had a distant metastasis present. The fact that the low LAT1 expression group tended to have more positive margin status and distant metastases than patients in the high-expression group may contribute to the discrepancy between our data on the predictive value of LAT1, as these have shown to be poor prognostic factors in breast cancer [49, 50]. Additionally, in the “TCGA TARGET GTEx” dataset, the low-expression group also had lower survival than the high-expression group. Margin status and the presence of distant metastases could also be confounding variables in this dataset, but data were not available to determine that. In this way, we show that the relationship between LAT1 expression and survival in breast cancer patients may be more complicated than previously appreciated.

Past analyses on LAT1 are not stratified by menopausal status, another unique quality of our study. In breast cancer patients with high tumor LAT1 expression, we observed worse survival in postmenopausal individuals as compared to peri- or premenopausal, but interestingly, these relationships were not observed in patients with low LAT1 expression. This discrepancy may reflect the fact that LAT1 has been shown to be estrogen-dependent in endocrine-responsive cells [51, 52]. Therefore, it is likely that more of the tumors in the low LAT1 group were triple-negative breast cancers, which generally have a poor prognosis independently of menopausal status. We recognize that worse survival is expected over the more than 10-year duration of follow-up in the datasets analyzed in postmenopausal patients, who are older and at greater risk for numerous conditions than their younger counterparts. Thus, the fact that survival differences were not observed in the LAT1 group implies that a regulator of LAT1 expression—such as estrogen—may obscure expected differences in survival. Additionally, we observed a positive correlation (r < 0.5) between BMI and basophil, eosinophil, monocyte, and lymphocyte counts in postmenopausal patients, a finding not seen in premenopausal patients. On the contrary, the opposite was observed in premenopausal patients. Premenopausal patients experience heightened 17β-estradiol levels, dampening obesity-induced inflammation, whereas postmenopausal patients (and those with obesity) have higher levels of estrone, stimulating inflammation. The imbalance between estrone and 17β-estradiol levels that occurs after menopause results in the release of cytokines and the recruitment of nearby immune cells, which likely explains this correlation only being observed in postmenopausal patients [53]. A limitation of our survival data is that LAT1 expression was measured, but the patients’ levels of inflammatory cytokines were not. Because of this, we were unable to observe the association between LAT1 expression and cytokine circulation, but future studies should pursue this.

Past literature has shown LAT1 to be involved in endocrine therapy resistance in ER+ breast cancer patients [51, 54, 55]. In addition, LAT1 expression in HER2- patients has been shown to contribute to treatment resistance [56]. Because of the significance of these two molecular subtypes, we sought to observe if menopausal status and LAT1 expression impacted patient survival. We did not observe significant differences in survival in any of the breast cancer subtypes between low and high LAT1 expression; however, low LAT1 expression strongly tended to be a favorable prognostic factor in postmenopausal patients with ER+ (p = 0.05) and HER2- tumors (p = 0.10) In contrast, LAT1 did not approach significance as a prognostic factor in premenopausal patients with ER+ (p = 0.34) or HER2- tumors (p = 0.65). These data highlight the importance of considering molecular subtype and menopausal status when examining LAT1 as a prognostic factor in breast cancer. These are considerations future efforts should make as there exists minimal work outside the current study on how both molecular subtype and menopausal status stratify how LAT1 expression affects breast cancer survival. If so, interventions targeting specific LAT1—such as JPH203, a LAT1 inhibitor that has recently shown to be tolerated in patients with advanced solid tumors [57]—could be administered using precision medicine approaches in patients with breast cancer and potentially other tumors.

¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) has been the traditional radiotracer utilized in cancer research, and it is still used in the majority of tumor radiotracer analyses. Indeed, the Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) is based on the use of ¹⁸F-FDG as the radiotracer [56, 58]. However, although high ¹⁸F-FDG uptake correlates with poor prognosis in numerous tumor types, including breast cancer [59–64], it is not a direct readout of tumor proliferative activity. For this, it is necessary to utilize a tracer such as ¹⁸F-FLT, an analog of thymidine which is phosphorylated by thymidine kinase prior to incorporation into DNA during cell replication. Because our study employs ¹⁸F-FLT imaging as a more direct readout of tumor proliferation rather than ¹⁸F-FDG, we provide an analysis that has previously been insufficiently explored. We observe differences in the strength of the correlation between Ki-67 and ¹⁸F-FLT uptake in pre- and postmenopausal patients: in postmenopausal patients, Ki-67 significantly positively correlated with ¹⁸F-FLT SUV_Mean, SUV_Peak, and SUV_Max, whereas in premenopausal patients, Ki-67 insignificantly positively correlated with ¹⁸F-FLT. These data are consistent with prior studies in which the correlation between Ki-67 and ¹⁸F-FLT was found to be relatively weak and dependent on clinical variables (pre- or post-treatment timing, hormone receptor status) [62, 63]. Surprisingly, BMI barely correlated with either Ki-67 or ¹⁸F-FLT, which may indicate that obesity is more involved in the appearance—and potentially recurrence—of cancer rather than its progression once a tumor is already established. Further work will be required to better understand the nuanced relationships between these clinical variables. Additionally, it will be important to understand the relationship between LAT1 expression, ¹⁸F-FLT uptake, and clinical variables including BMI and—better yet [64]—adiposity, as well as additional molecular factors that were not available in the datasets analyzed. In fact, to our knowledge, there are no studies correlating LAT1 expression to all 3 types of ¹⁸F-FLT SUVs. We recognize that a limitation of our study is that BMI is not the best metric for obesity. In the datasets analyzed, there were no clinical data including possible alternatives for BMI like visceral adiposity, so we did not have an alternative to relying on BMI. This limitation exists largely because breast cancer imaging occurs at levels that typically do not allow calculation of visceral adipose tissue mass. Correlating ¹⁸F-FLT uptake to both gene expression and a broad range of anthropometric indices, including visceral adiposity, will be of great interest in future studies. Also, ¹⁸F-FLT uptake and Ki-67 values should be correlated to LAT1 expression to explore its role in tumor proliferation. This would make sense considering LAT1 expression has already been established in the activation of the mTOR pathway, promoting cell proliferation in breast cancer [43]. Finally, future clinical trials will be required to establish the utility of LAT1 as a biomarker for breast cancer prognosis, particularly in association with other clinical factors: survival data in the datasets analyzed were limited, but will be important to examine in forthcoming studies.

Conclusion

Through our analyses, we show that although the extent to which this occurs is stratified by menopausal status, LAT1 expression worsens breast cancer prognosis, bolstering the role of amino acid metabolism in tumor energetics, an aspect of the literature that has been underexplored. Using various clinical variables, we correlated tumor proliferation, body composition, and immune cell populations to identify the complex relationships underlying metabolism, immune surveillance, and cancer progression. Future studies should aim to utilize a wider variety of immune cell types and metrics for body composition, while further segmenting patients based on breast cancer subtype and menopausal status, to gain a more comprehensive understanding of the findings we establish here. In addition, we speculate that future studies should target LAT1 or its heavy chain partner 4F2hc to inhibit the LAT1-4F2hc complex, interventions that may plausibly improve patient outcomes.

Supporting information

S1 Fig. Prognosis in TCGA BRCA patients with ER+ tumors.

Patients were separated into (A) low (< 10.09 FPKM) and (B) high (> = 10.09 FPKM) LAT1 expression groups. Survival is observed for each menopausal status: premenopausal, postmenopausal, and peri-menopausal.

(TIF)

Click here for additional data file.^{(426.5KB, tif)}

S2 Fig. Prognosis in TCGA BRCA patients with HER2- tumors.

Patients were separated into (A) low (< 10.38 FPKM) and (B) high (> = 10.38 FPKM) expression groups. Survival is observed for each menopausal status: premenopausal, postmenopausal, and peri-menopausal.

(TIF)

Click here for additional data file.^{(406.9KB, tif)}

S1 Table. Mann-Whitney U tests identify no significant differences between the log2 fold change in Ki-67 and ¹⁸F-FLT uptake in pre- or postmenopausal patients.

P-values are shown.

(DOCX)

Click here for additional data file.^{(12.7KB, docx)}

S2 Table. Mann-Whitney U tests identify differences between the log2 fold change in BMI and SUV_Mean in postmenopausal patients, but no other proliferative markers differed in pre- or postmenopausal patients.

(DOCX)

Click here for additional data file.^{(12.9KB, docx)}

S3 Table. Mann-Whitney U and student’s t-tests identify differences between immune cells and proliferative markers in premenopausal patients.

The log2 fold change of each parameter was compared. The comparisons’ p-values are shown above. Shapiro-Wilk tests were used to assess the data’s normality. Based on these results, normally distributed data were compared using the Student’s t-test, and all other analyses used the Mann-Whitney test. ^a These comparisons use the Student’s t-test.

(DOCX)

Click here for additional data file.^{(13.3KB, docx)}

S4 Table. Mann-Whitney U tests identify differences between immune cells and proliferative markers in postmenopausal patients.

The log2 fold change of each parameter was compared. The comparisons’ p-values are shown above. Significant results are bolded, and marginally significant results are bolded and italicized.

(DOCX)

Click here for additional data file.^{(13.2KB, docx)}

Acknowledgments

The authors thank Dr. Gang Peng for helpful discussions lending his biostatistical expertise to the analysis of data in this manuscript.

Data Availability

Funding Statement

The authors are grateful for awards from the Lion Heart Foundation and from the Yale Cancer Center (both to R.J.P.), which supported this research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0292678.r001

Decision Letter 0

Pankaj K Singh

18 Aug 2023

PONE-D-23-21115The Influence of the Amino Acid Transporter LAT1 on Patient Prognosis and the Relationships between Tumor Immunometabolic and Proliferative Features Depend on Menopausal Status in Breast CancerPLOS ONE

Dear Dr. Perry,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Pankaj K Singh, Ph.D.

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PLOS ONE

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this article, using transcriptomic data analysis, the authors demonstrate that high tumor LAT1 expression predicts abbreviated survival in postmenopausal breast cancer patients compared to peri- or premenopausal patients. The authors also discuss about tumor immunometabolism and proliferation and show that these features are associated with pre and postmenopausal statuses of breast cancer patients. They show that tumor Ki-67 staining significantly and positively correlated with 18F-FLT uptake in postmenopausal patients.

LAT1 expression is already well established as an independent poor prognostic factor in invasive breast cancer and other cancer types. While there are many shortcomings in the article which makes conclusions rather confounding, like the use of BMI as an obesity marker and including weak insignificant correlations of immune cells with SUV, and Ki-67, the correlation of LAT1 expression with the menopausal status is clinically relevant and significant. However, the lack of inclusion of other molecular factors in this study raises concerns, see below, and undermines the clinical association and utility of this work.

1. The authors do not show if LAT1 expression correlates with menopausal status in breast cancer subtypes especially in ER+, and HER2- tumors. There is a huge body of literature suggesting LAT1 as an estrogen responsive gene and that LAT1 is responsible for endocrine therapy resistance. Likewise, HER2- tumors have been shown to have high expression of LAT1 and do not respond to current therapies. It would be interesting to see if LAT1 expression can stratify pre and post-menopausal breast cancer patient survival in these subtypes. If so, these cohorts can benefit from drugs like JPH203, currently in clinical trial, targeting LAT1 activity.

2. Postmenopausal patients have increased systemic circulation of inflammatory cytokines such as IL-6, IL-1beta, and TNFa. Coincidently, these cytokines are also known to regulate the expression of LAT1 and can promote tumor-immune infiltration. The authors do not show if any of these post-menopausal cytokines correlate with LAT1 expression in the same data sets as a casual mechanistic link for LAT1 dependent abbreviated survival? If so, this rationally explains the significant worse survival association of LAT1 in postmenopausal patients.

3. Both Ki-67 and 81F-FLT uptake are good quantitative markers of tumor proliferation. The authors show a strong association of Ki-67 with 18F-FLT uptake in postmenopausal patients. Does Ki-67 and 18F-FLT uptake show a significantly positive association with LAT1 expression in postmenopausal patients in the same dataset? This is because LAT1 is known to activate mTORC1 through uptake of leucine and mTOR activation drives cell proliferation.

4. In the title, the authors declare that the relationships between tumor immunometabolic and proliferative features depend on the menopausal status in breast cancer. While the metabolic and proliferative features do significantly correlate with menopausal status, the immune cells demonstrate only weak correlation and is not significant. The weak positive correlation in postmenopausal patients could again be because of the high levels of systemic cytokines in these cohort.

The authors need to address these concerns to strengthen the use of LAT1 as a clinical biomarker for breast cancer patient prognosis.

Reviewer #2: The manuscript by Ramshankar et al. deals with the relationship among various clinical and genetic factors including fluorothymidine uptake, immune cell infiltration, body weight, LAT1 expression, and cell proliferation markers (e.g. ki67). This manuscript is unclear in term of its scientific significance and message. All data are not connected as one story. Thus it’s very hard to understand the authors’ opinion. In addition, some findings are not novel. For example, the correlation between LAT1 expression and poor prognosis has been reported in breast cancer patients (PMID: 35177712, PMID: 29566741). Most of all, the main reason why I decide to reject this manuscript is that the study was not scientifically sound, and the data interpretation was flawed.

• In Figure 1, the authors used the Pearson correlation to analyze the association among various clinical factors. The Pearson correlation is for measuring the linear relationship between two variables. Correlation can be biased. In my opinion, multivariate approach is more suitable in clinical data. The author should consider multicollinearity.

• Regarding to the data analysis in Figure 1, the author stated “basophil, eosinophil, neutrophil, monocyte, and lymphocyte counts insignificantly negatively correlated” in line 262, “White blood cell counts insignificantly positively correlated” in line 263. I can’t understand this statement. White blood cell is consisted of five different subtypes of cells – basophil, eosinophil, neutrophil, monocyte, and lymphocyte. I am deeply concerned that the author may have provided ambiguous information to the academic community or patients based on data that was interpreted with unclear statistical methods with limited medical knowledge. This kind of analysis was repeated in Line 267 – 277.

• In Figure 2 and 3, the author monitored the survival rate up to 8,605 days. It’s almost 23 years after the initial treatment. I am not an expert in survival monitoring, but wondering it’s too long to interpret the event is the disease-related.

• It is not clear that the authors included and excluded the appropriate patients for this study.

• The introduction contains unclear or inaccurate information. For example, “Nearly 30% of breast cancer deaths are caused by modifiable risk factors like excess body weight and alcohol consumption [2]” in Line 37. Please check the reference [2] and the original reference again.

Overall, I do not think this manuscript is suitable for publication.

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Reviewer #1: Yes: Surajit Sinha

Reviewer #2: No

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Attachment

Submitted filename: Plos One comments.docx

Click here for additional data file.^{(17KB, docx)}

PLoS One. 2023 Oct 11;18(10):e0292678. doi: 10.1371/journal.pone.0292678.r002

Author response to Decision Letter 0

31 Aug 2023

We thank the editors and reviewers for their comments, which have prompted us to make edits that, in our view, substantially improve this manuscript. All comments are reproduced verbatim in bold in our response to reviewers, with our responses in unbolded text, but unfortunately this does not come through on the website.

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Thank you for these links. We have followed these templates to ensure that our manuscript meets the journal’s style requirements.

We have edited the Methods to include the following statement:

“Because only publicly available, deidentified data were analyzed, and participants, all of whom were adults, gave written consent for their data to be used, deidentified, in public repositories, separate ethical approval is not required for these or other datasets analyzed in this manuscript. Data sharing via TCIA is approved under the supervision of the University of Arkansas for Medical Sciences (UAMS) Institutional Review Board (IRB # 205568), and informed consent was provided by the patients for their data to be shared with TCIA; however, the details of the consent process are not available to us. Because only publicly available, deidentified data were analyzed, and we had no information about the patients whose data were analyzed, separate ethical approval was not sought. All data are submitted to the TGCA in accordance with the submitter’s institutional policies, including IRB approval and informed consent provided by the patients for their data to be submitted, deidentified, to the TCGA. However, the details of the consent process are not available to us. We did not seek separate IRB approval because our use of these deidentified data are covered under these IRB approvals.”

This text has been included in the Ethics Statement field of the submission form.

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

Thank you for providing this helpful information.

3. Thank you for stating the following in the Acknowledgments Section of your manuscript:

“The authors are grateful for awards from the Lion Heart Foundation and from the Yale Cancer Center, which supported this research.”

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

We have removed the funding-related text from the acknowledgments in the manuscript, and have included the funding statement (which we do not intend to change) in our cover letter. The tool to build the funding statement in the online submission system does not provide a place to comment that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript, so we have removed this.

We have uploaded new copies of each of these figures at substantially higher resolution.

Reviewer Comments:

We thank the reviewer for their time spent examining this manuscript and for their helpful comments. Although discussed in more detail in response to the specific comments below, we agree with the reviewer’s important point that BMI as an obesity marker has shortcomings, as we had commented in the manuscript:

“We recognize that a limitation of our study is that BMI is not the best metric for obesity. In the datasets analyzed, there were no clinical data including possible alternatives for BMI like visceral adiposity, so we did not have an alternative to relying on BMI[…] Correlating 18F-FLT uptake to both gene expression and a broad range of anthropometric indices, including visceral adiposity, will be of great interest in future studies.”

To clarify the reason we did not include these analyses in the current study - an important point highlighted by the reviewer - we have added an additional comment within the statement above, clarifying that without imaging studies that allow quantification of visceral adiposity, BMI is the best parameter we can analyze with data available from patients with breast cancer:

“This limitation exists largely because breast cancer imaging occurs at levels that typically do not allow calculation of visceral adipose tissue mass.”

With regard to the inclusion of other molecular factors, we certainly agree that this is a limitation of the study that results from the fact that we are restricted to data available in the extant datasets, and we have included a comment (following “as well as”) about this in the revised manuscript:

“Additionally, it will be important to understand the relationship between LAT1 expression, 18F-FLT uptake, and clinical variables including BMI and - better yet - adiposity, as well as additional molecular factors that were not available in the datasets analyzed.”

Thank you for this helpful suggestion. Using the TCGA BRCA dataset, we have assessed the impact of LAT1 expression on prognosis in premenopausal and postmenopausal patients with ER+ and HER2- tumors as 4 new Kaplan-Meier plots in Supplementary Figures 1 and 2:

Supplementary Figure 1

Supplementary Figure 2

In addition, we analyzed the aforementioned data separately to compare survival in patients with low and high LAT1 expression specifically in ER+ and HER2- negative tumors; these data, now shown in the new Figures 6 and 7, demonstrate that while there was no statistically significant difference in survival, high LAT1 expression much more strongly tended to predict poor prognosis in postmenopausal women with ER+ breast cancer (new Fig 6)

Figure 6

as well as in postmenopausal women with HER2- breast cancer (new Fig 7):

Figure 7

Taken together these data suggest that menopausal status affects the impact of LAT1 on survival in patients with both ER+ and HER2- tumors, though prospective studies will be required to confirm or refute this. TCGA BRCA is the only dataset from those we used that includes patients’ menopausal statuses, thus limiting the data available for these analyses. It is possible that some of the strong tendencies without reaching statistical significance in the aforementioned analyses may be affected by the relatively low sample numbers in these analyses.

The survival datasets we utilized on Xena Functional Genomics Browser did not measure patients’ inflammatory cytokines. We agree that this would have been a valuable correlation to explore so we have added a comment addressing this idea to the Discussion section, which can urge future studies to analyze this:

“A limitation of our survival data is that LAT1 expression was measured, but the patients’ levels of inflammatory cytokines were not. Because of this, we were unable to observe the association between LAT1 expression and cytokine circulation but future studies should pursue this.”

The “ACRIN-FLT-Breast (ACRIN 6688)” dataset which offered patients’ Ki-67 values and tumor 18F-FLT uptake did not include LAT1 expression data. Likewise, the survival datasets which offered LAT1 expression data did not include patients’ Ki-67 values and tumor 18F-FLT uptake. Because of this, we were unable to correlate tumor proliferative factors to LAT1 expression. However, we have added a comment in the manuscript to highlight that this would be a worthwhile correlation to perform in the future:

Also, 18F-FLT uptake and Ki-67 values should be correlated to LAT1 expression to explore its role in tumor proliferation. This would make sense considering LAT1 expression has already been established in the activation of the mTOR pathway, promoting cell proliferation in breast cancer [43].

We agree with the reviewer’s insightful point, and therefore have changed the title to more accurately reflect the findings of this analysis: “The Association between the Amino Acid Transporter LAT1, Tumor Immunometabolic and Proliferative Features and Menopausal Status in Breast Cancer.” Additionally, we have added a comment to the Discussion regarding the need for future studies to correlate clinical variables and, specifically, inflammatory cytokines to outcomes:

“Additionally, future clinical trials will be required to establish the utility of LAT1 as a biomarker for breast cancer prognosis, particularly in association with other clinical factors: survival data in the datasets analyzed were limited, but will be of great interest in forthcoming studies.”

We hope that these revisions will help to position this manuscript as scientifically sound and well communicated, in keeping with the criteria for publication in PLOS One.

The authors need to address these concerns to strengthen the use of LAT1 as a clinical biomarker for breast cancer patient prognosis.

Unfortunately, as mentioned in our point-by-point responses to each comment, we are limited by the available data and are not able to perform all of the analyses recommended because of the lack of available data required to establish the use of LAT1 as a clinical biomarker for breast cancer prognosis. Indeed, this would require a clinical trial! Instead, we intend for this work to clarify the association between LAT1 and other prognostic and clinical factors in breast cancer, and have added a comment to the discussion to address this point as well as the point immediately above from Reviewer 1. We respectfully hope that these revisions will, in the reviewer’s view, be sufficient to justify the manuscript as appropriate for publication according to PLOS One’s criteria: “Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail,” and “Conclusions are presented in an appropriate fashion and are supported by the data,” as well as that the work be original and the data presentation be clear.

We sincerely thank the reviewer for their time spent on this manuscript, and for their comments which have helped us to improve the manuscript for resubmission as invited by the editor. In keeping with PLOS One’s criteria, we aim to improve the manuscript to ensure that it is scientifically sound and well-presented.

We used the Pearson correlation because we hoped to observe the linear relationship between each pair of clinical variables, as Reviewer 2 described. The reviewer’s concern that clinical data may have multicollinearity is certainly reasonable, so we consulted Dr. Gang Peng, formerly a Yale Cancer Center biostatistician (now faculty at Indiana University), who agreed with the use of the Pearson correlation test in this context. To quote his email:

“If you want to show the association between two variables (x, y), correlation test is enough (Pearson for linear and Spearman for nonlinear).”

In the interest of full transparency, the complete email exchange between Dr. Peng and the corresponding author is shown at the end of this response to reviewer comments on the following page of this rebuttal.

In addition to basophil, eosinophil, neutrophil, monocyte, and lymphocyte counts, the clinical data we used offered white blood cell counts as a separate measured value from the basophil, eosinophil, neutrophil, monocyte, and lymphocyte counts, but we have removed WBC counts for clarity:

In these figures, we monitored the survival rate up to 8605 days because that is where patient survival data ends in the datasets used. 10-year survival in breast cancer patients is 80%: this is a good problem to have, and means that long-term tracking of outcomes is important. However, as the reviewer highlights we cannot be sure that mortality at this late timepoint is cancer-related, and have added a comment addressing this important point:

“We recognize that the long followup period prevents us from concluding with certainty that mortality is breast cancer-related, but even if mortality were unrelated to cancer at this time point, the utility of LAT1 as a prognostic factor remains important.”

• It is not clear that the authors included and excluded the appropriate patients for this study.

The inclusion criteria for the 18F-FLT PET-CT image analysis, as mentioned in the Methods section, are the following:

“We analyzed the scans of all patients with a menopausal status, height, weight, and 5 clear CT slices (i.e., slices in which the primary breast tumor could be identified and its corresponding SUV values could be generated) present in the dataset. 58 of the 90 enrolled patients in the ACRIN clinical trial met these criteria, and all were analyzed.”

No patients whose data included menopausal status, height, weight, and 5 clear CT slices were excluded. We are unsure as to Reviewer 2’s specific concern and would be happy to provide more information to clarify as needed.

We appreciate the reviewer for identifying this mistake. We have corrected this: “Nearly 30% of breast cancer cases are caused by modifiable risk factors like excess body weight and alcohol consumption [2].”

Overall, I do not think this manuscript is suitable for publication.

We thank the reviewer for his/her comments, which have catalyzed edits that substantially improve the manuscript. We hope the reviewer will believe that the manuscript is suitable for publication after these revisions in keeping with PLOS One’s editorial criteria.

Attachment

Submitted filename: LAT1 Breast Cancer Paper_ Rebuttal.docx

Click here for additional data file.^{(646.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0292678.r003

Decision Letter 1

Pankaj K Singh

25 Sep 2023

PONE-D-23-21115R1The Association between the Amino Acid Transporter LAT1, Tumor Immunometabolic and Proliferative Features and Menopausal Status in Breast CancerPLOS ONE

Dear Dr. Perry,

Please submit your revised manuscript by Nov 09 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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We look forward to receiving your revised manuscript.

Kind regards,

Pankaj K Singh, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

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1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: N/A

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: The authors have addressed the concerns arisen from data analysis and interpretation. First, statistical analysis in this manuscript were revised by the authors, remaining a few concerns that all studies have. The authors explained properly on the analysis. Second, the authors revised the ambiguous medical interpretations by describing clearly and adding the limitations and shortcomings about their result and interpretation. This study suggests that LAT1 expression status can be a possible biomarker in breast cancer patients. They also suggested a few clinical factors that highly related to breast cancer incidence such as a proliferation status, obesity, immune / inflammation status. Despite the limitation of available clinical data representing the risk factors, the authors demonstrated the best way predicting the prognosis of breast cancer patients with factors including LAT1 expression. Since the authors mentioned the necessity of future studies to validate this finings, the readers can interpret and assume the intention and purpose of this article. The article also suggested the correlation among the factors before / after menopause, indicating this article has a novelty in this field. In addition, this article is of importance to describe the method and result of early biomarker study in breast cancer research. I recommend to publish this article with the minor revision.

1. The purpose of Figure 1 is to show the association between two factors. Is it necessary to show how much different between two factors in Supple Table 5-8? Also, the authors performed the U-test (non-parametric) instead of T-test (parametric), however in Figure 1, the authors performed the pearson test (parametric) instead of spearman (non-parametric) with the same data. In my suggestion, Table 5-8 is not necessary.

2. I think it's better to describe more details in the legend for figure 8 . Please add conclusions or explains with the fig 8 legend title for the readers who read the summary figure first.

**********

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Reviewer #1: Yes: Surajit Sinha

Reviewer #2: No

**********

PLoS One. 2023 Oct 11;18(10):e0292678. doi: 10.1371/journal.pone.0292678.r004

Author response to Decision Letter 1

26 Sep 2023

We thank the reviewers and editors for their time spent evaluating this manuscript. We are delighted that the reviewers and editor find the manuscript of interest, and that Reviewer 1 feels that we have addressed all of their comments. We have edited the manuscript in response to Reviewer 2’s remaining comments as described below.

We greatly appreciate the reviewer’s many positive comments on our revision, indicating that we have addressed most of the issues they raised. We are delighted that the reviewer believes “this article is of importance to describe the method and result of early biomarker study in breast cancer research.” We have revised according to each of the reviewer’s comments as described below, and after these revisions hope the article will be deemed acceptable for publication.

We thank the reviewer for these comments, and in accordance, have removed Supplemental Tables 5-8. We agree that considering the presentation of data in Figure 1, Supplemental Tables 5-8 are not necessary.

2. I think it's better to describe more details in the legend for figure 8 . Please add conclusions or explains with the fig 8 legend title for the readers who read the summary figure first.

We have added additional description as follows: “The current study analyzed 18F-FLT uptake, immune infiltrate levels, body weights, LAT1 expression, and tumor proliferative factors observed in breast cancer patients.”

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Submitted filename: LAT1 rebuttal 2.docx

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PLoS One. doi: 10.1371/journal.pone.0292678.r005

Decision Letter 2

Pankaj K Singh

26 Sep 2023

The Association between the Amino Acid Transporter LAT1, Tumor Immunometabolic and Proliferative Features and Menopausal Status in Breast Cancer

PONE-D-23-21115R2

Dear Dr. Perry,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Pankaj K Singh, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0292678.r006

Acceptance letter

Pankaj K Singh

3 Oct 2023

PONE-D-23-21115R2

The Association between the Amino Acid Transporter LAT1, Tumor Immunometabolic and Proliferative Features and Menopausal Status in Breast Cancer

Dear Dr. Perry:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Pankaj K Singh

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Prognosis in TCGA BRCA patients with ER+ tumors.

(TIF)

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S2 Fig. Prognosis in TCGA BRCA patients with HER2- tumors.

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S1 Table. Mann-Whitney U tests identify no significant differences between the log2 fold change in Ki-67 and ¹⁸F-FLT uptake in pre- or postmenopausal patients.

P-values are shown.

(DOCX)

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(DOCX)

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S3 Table. Mann-Whitney U and student’s t-tests identify differences between immune cells and proliferative markers in premenopausal patients.

(DOCX)

Click here for additional data file.^{(13.3KB, docx)}

S4 Table. Mann-Whitney U tests identify differences between immune cells and proliferative markers in postmenopausal patients.

The log2 fold change of each parameter was compared. The comparisons’ p-values are shown above. Significant results are bolded, and marginally significant results are bolded and italicized.

(DOCX)

Click here for additional data file.^{(13.2KB, docx)}

Attachment

Submitted filename: Plos One comments.docx

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Attachment

Submitted filename: LAT1 Breast Cancer Paper_ Rebuttal.docx

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Submitted filename: LAT1 rebuttal 2.docx

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Data Availability Statement

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PERMALINK

The association between the amino acid transporter LAT1, tumor immunometabolic and proliferative features and menopausal status in breast cancer

Gautham Ramshankar

Ryan Liu

Rachel J Perry

Roles

Abstract

Introduction

Methods

18F-FLT PET-CT quantitative image analysis

LAT1 prognostic analysis

Statistical analysis

Results

Correlation analysis between proliferative markers, obesity, and immune cells by menopausal status

Fig 1. Correlations between clinical variables.

Table 1. P–values from correlations between Ki–67 and 18F–FLT uptake.

Table 4. P–values from correlations between immune cells and proliferative markers in postmenopausal patients.

Table 2. P–values from correlations between BMI and proliferative markers.

Table 3. P–values from correlations between immune cells and proliferative markers in premenopausal patients.

LAT1 expression and survival probability

Fig 2. LAT1 expression and prognosis in TCGA BRCA patients.

Fig 3. LAT1 expression and prognosis in patients from other datasets.

Survival probability with high LAT1 expression TCGA BRCA patients by menopausal status

Fig 4. High expression and prognosis in TCGA BRCA patients.

Survival probability with low LAT1 expression TCGA BRCA patients by menopausal status

Fig 5. Low expression and prognosis in TCGA BRCA patients.

Survival probability of premenopausal vs. postmenopausal patients with ER+ tumors by LAT1 expression level

Fig 6. Prognosis in TCGA BRCA patients with ER+ tumors.

Survival probability of premenopausal vs. postmenopausal patients with HER2- tumors by LAT1 expression level

Fig 7. Prognosis in TCGA BRCA patients with HER2– tumors.

Survival probability with low and high LAT1 expression in ER+ and HER2- tumors of TCGA BRCA patients by menopausal status

Discussion

Fig 8. Summary of factors correlated in this analysis.

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Pankaj K Singh

Roles

Author response to Decision Letter 0

Decision Letter 1

Pankaj K Singh

Roles

Author response to Decision Letter 1

Decision Letter 2

Pankaj K Singh

Roles

Acceptance letter

Pankaj K Singh

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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Links to NCBI Databases

¹⁸F-FLT PET-CT quantitative image analysis

Table 1. P–values from correlations between Ki–67 and ¹⁸F–FLT uptake.