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[ "<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850445</article-id><article-id pub-id-type=\"pmc\">PMC7431518</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01383</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Prognostic Molecular Signatures for Metastatic Potential in Clinically Low-Risk Stage I and II Clear Cell Renal Cell Carcinomas</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Shih</surname><given-names>Andrew J.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup></sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/538851/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Murphy</surname><given-names>Neal</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kozel</surname><given-names>Zachary</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/984403/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Shah</surname><given-names>Paras</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yaskiv</surname><given-names>Oksana</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/954327/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Khalili</surname><given-names>Houman</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Liew</surname><given-names>Anthony</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kavoussi</surname><given-names>Louis</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hall</surname><given-names>Simon</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Vira</surname><given-names>Manish</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/954305/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhu</surname><given-names>Xin-Hua</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup></sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1019214/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lee</surname><given-names>Annette T.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/982435/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Feinstein Institutes for Medical Research</institution>, <addr-line>Manhasset, NY</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Donald and Barbara Zucker School of Medicine at Hofstra/Northwell</institution>, <addr-line>Hempstead, NY</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Division of Hospital Medicine, LIJ Medical Center</institution>, <addr-line>New Hyde Park, NY</addr-line>, <country>United States</country></aff><aff id=\"aff4\"><sup>4</sup><institution>The Smith Institute for Urology</institution>, <addr-line>New Hyde Park, NY</addr-line>, <country>United States</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Department of Urology, Mayo Clinic</institution>, <addr-line>Rochester, MN</addr-line>, <country>United States</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Northwell Health Department of Pathology</institution>, <addr-line>New Hyde Park, NY</addr-line>, <country>United States</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Northwell Health Cancer Institute</institution>, <addr-line>Lake Success, NY</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Ronald M. Bukowski, Cleveland Clinic, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Dharmesh Gopalakrishnan, University at Buffalo, United States; Moshe C. Ornstein, Cleveland Clinic, United States</p></fn><corresp id=\"c001\">Correspondence: Andrew J. Shih <email>[email protected]</email></corresp><corresp id=\"c002\">Xin-Hua Zhu <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Genitourinary Oncology, a section of the journal Frontiers in Oncology</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work and share first authorship</p></fn><fn fn-type=\"other\" id=\"fn003\"><p>‡These authors have contributed equally to this work and share last authorship</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1383</elocation-id><history><date date-type=\"received\"><day>15</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Shih, Murphy, Kozel, Shah, Yaskiv, Khalili, Liew, Kavoussi, Hall, Vira, Zhu and Lee.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Shih, Murphy, Kozel, Shah, Yaskiv, Khalili, Liew, Kavoussi, Hall, Vira, Zhu and Lee</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Introduction:</bold> For patients with localized node-negative (Stage I and II) clear cell renal cell carcinomas (ccRCC), current clinicopathological staging has limited predictive capability because of their low risk. Analyzing molecular signatures at the time of nephrectomy can aid in understanding future metastatic potential.</p><p><bold>Objective:</bold> Develop a molecular signature that can stratify patients who have clinically low risk ccRCC, but have high risk genetic changes driving an aggressive metastatic phenotype.</p><p><bold>Patients, Materials, and Methods:</bold> Presented is the differential expression of mRNA and miRNA in 44 Stage I and Stage II patients, 21 who developed metastasis within 5 years of nephrectomy, compared to 23 patients who remained disease free for more than 5 years. Extracted RNA from nephrectomy specimens preserved in FFPE blocks was sequenced using RNAseq. MiRNA expression was performed using the TaqMan OpenArray qPCR protocol.</p><p><bold>Results:</bold> One hundred thirty one genes and 2 miRNA were differentially expressed between the two groups. Canonical correlation (CC) analysis was applied and four CCs (CC32, CC20, CC9, and CC7) have an AUC > 0.65 in our dataset with similar predictive power in the TCGA-KIRC dataset. Gene set enrichment showed CC9 as kidney development/adhesion, CC20 as oxidative phosphorylation pathway, CC32 as RNA binding/spindle and CC7 as immune response. In a multivariate Cox model, the four CCs were able to identify high/low risk groups for metastases in the TCGA-KIRC (<italic>p</italic> < 0.05) with odds ratios of CC32 = 5.7, CC20 = 4.4, CC9 = 3.6, and CC7 = 2.7.</p><p><bold>Conclusion:</bold> These results identify molecular signatures for more aggressive tumors in clinically low risk ccRCC patients who have a higher potential of metastasis than would be expected.</p></abstract><kwd-group><kwd>clear cell</kwd><kwd>molecular biomarker</kwd><kwd>renal cell carcinoma</kwd><kwd>gene expression</kwd><kwd>miRNA</kwd><kwd>canonical correlation analysis</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"51\"/><page-count count=\"9\"/><word-count count=\"6456\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>In 2020, the American Cancer Society estimates 74,000 new cases of renal cell carcinoma will be diagnosed and account for ~15,000 deaths, putting renal cell carcinoma in the top ten leading cause of cancer deaths in the United States (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Of the three major subtypes (clear cell, papillary, and chromophobe) clear cell renal cell carcinoma (ccRCC) is the most prevalent comprising 75% of diagnosed cases and resulting in more deaths per year than other subtypes (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>).</p><p>For most ccRCC patients with early stage localized tumor, stage I or II, surgical resection offers achievable survival rates over 95% (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). However, for a small percentage of clinically low risk patients disease recurrence ensues in 5–10% stage I and 15–20% stage II patients. Several models exist to assess risk for disease recurrence after nephrectomy including: MSKCC postoperative prognostic nomogram (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>), UCLA UISS score (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>) and Mayo clinic SSIGN score (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). These scoring systems rely on clinical parameters such as tumor grade, size, presence of necrosis, and patient functional status that often do not accurately differentiate early stage clinically low risk disease. Only tumor size has the highest prognostic accuracy for future development of metastatic disease (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>).</p><p>While new treatments have increased survival, <20% of patients diagnosed with metastatic disease have a median survival of >27 months (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Kuijpers et al. estimated that 71% of stage I and 52% of stage II RCC patients who developed metastatic disease following surgery were potentially curable, reasoning that early detection of metastatic disease would lead to more successful treatment (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). However, Dabestani et al. reported that intensive follow-up after nephrectomy does not improve overall survival after recurrence (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>).</p><p>Therefore, patients with clinicopathologically defined low risk ccRCC (stage I and II) who develop a distant recurrence within 5 years represent a unique cohort of patients, where understanding the biology of the key molecular factors driving progression to metastatic disease can help supplement clinical parameters. Given that there are a relatively small number of people who develop metastasis in early stage ccRCC, we chose to match a similar number to have a balanced dataset that can capture as many molecular signals as possible. Here we present an analysis of a similar number of tumor samples from stage I and II ccRCC patients who developed metastatic disease within 5 years of surgery and from patients who did not develop metastatic disease in more than 5 years to capture the molecular signatures that associate with metastatic potential at an early time point as possible. Furthermore, we validated these signatures in The Cancer Genome Atlas ccRCC (TCGA-KIRC) cohort.</p></sec><sec id=\"s2\"><title>Patients, Materials, and Methods</title><sec><title>Patient Selection</title><p>Patients from 2008-2012 who had metachronous metastases within 5 years with a primary diagnosis of stage I or II ccRCC in the Northwell Health tumor bank were selected. A similar number of patients were selected who did not have recurrence as a comparison group to maximize the molecular signatures captured. The ccRCC tissue samples in the discovery set were obtained from the Northwell Health pathology department as formalin-fixed, paraffin-embedded (FFPE) tissue blocks. Samples were initially matched on size, gender and age with two samples removed for QC issues following RNA extraction.</p><p>TNM classification and the Fuhrman grading system were used to stage and grade tumor samples (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). The Northwell Health System Regional Ethics Committee granted research approval for the study with waivers of HIPAA Authorization and informed consent. The validation set consisted of 218 Stage I and 45 Stage II patients from the TCGA-KIRC Data Resource (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>).</p></sec><sec><title>RNA Extraction</title><p>A genitourinary pathologist (O. Yaskiv) reviewed FFPE tissue blocks and their corresponding H&E slides. Slides were matched up with tissue blocks and ~35 mg of the pre-identified tumor were excised. RNA was extracted using the RecoverAll<sup>TM</sup> Total Nucleic Acid Isolation Kit according to manufacturer's instructions. RNA was evaluated using an AB Bioanalyzer.</p></sec><sec><title>mRNA Sequencing and Expression Analysis</title><p>Libraries were prepared using the Illumina TruSeq RNA Access Library kit and sequenced on a NextSeq 500 following the manufacturer's instructions. Sequenced segments were aligned with STAR2 (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>) to the Human GENCODE reference (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Gene counts were assessed using ht-seq counts (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Differential expression was calculated using DESeq2 which is designed to analyze digital count data generated by sequencing (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Log fold changes were shrunk using apeglm (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Pathway enrichment analysis was done using GAGE (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>).</p></sec><sec><title>Cell Subset Deconvolution</title><p>Composition of cell subsets of our bulk RNA-seq ccRCC was determined with CIBERSORT (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>) using a previously published ccRCC single cell RNA-seq dataset (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>) as a reference. Only cell populations present in >1% of ccRCC samples were considered. Correlations of cell populations to metastatic phenotype was done using MASC (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>).</p></sec><sec><title>miRNA RT-qPCR and Expression Analysis</title><p>Quantification of miRNA was done using the TaqMan OpenArray, which profiles 754 known miRNAs using qPCR. A Ct threshold of 30 was used for detectable level of expression. MiRNAs were further filtered by removing those with no expression in any samples and samples were filtered that had <10% of miRNAs detected. Housekeeping miRNAs were the top 10 most stable miRNAs from the NormqPCR package (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>), which were used to normalize raw Ct values using ΔCt normalization (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Differential expression was calculated using limma which is designed to analyze log2 based expression assays like qPCR (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>).</p></sec><sec><title>Canonical Correlation Analysis and Validation</title><p><italic>De novo</italic> mRNA and miRNA modules or Canonical Components (CCs) were identified in the discovery set using sparse canonical correlation analysis (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>) with AUC to metastatic status calculated using pROC (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Data analysis was done using R (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>) and tidyverse (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). RNA-seq and microRNA-seq count data for validation were done in the TCGA-KIRC dataset which was downloaded, then normalized using DESeq2. Phenotypes were defined using the TCGA Pan-Cancer Clinical Data Resource (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). The specific gene and microRNA weights calculated for CCs in the discovery set were applied to the validation set. Thresholds for risk stratification in the discovery set on the discovered CCs were done using rpart (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>) and then applied to the validation set to create high and low risk groups. Cox regression of the high and low risk groups in the validation set to their disease free survival time was done using survminer (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>).</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Demographics</title><p>The discovery set consisted of 24 patients with Stage I and 20 patients with Stage II ccRCC who underwent either partial or radical nephrectomy. In Stage I, 13 patients had more than 5.5 years of follow-up with no evidence of metastasis while 11 developed metastatic relapse within 5 years; for Stage II, 10 patients did not develop metastases, while 10 patients developed metastatic disease (see <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Two samples did not meet QC metrics for microRNA. The population was 16% female (16.7% in stage I and 15% in stage II). The average age at surgery for the entire group was 61.3 ± 10.3 years with no significant differences between stage I (62.2 ± 8.4 years) and stage II (60.3 ± 12.5 years).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographics of patients in this study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Stage I</bold></th><th valign=\"top\" align=\"center\" colspan=\"2\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Stage II</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Metastatic (11)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Non-metastatic (13)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Metastatic (10)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Non-metastatic (10)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Women</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (9.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (23.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (20%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (20%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Size (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.7 ± 1.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.9 ± 1.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.7 ± 3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 ± 3.2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Low grade (I–II)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (81.8%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (84.6%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (40%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (50%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age at surgery</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63.9 ± 6.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.8 ± 9.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.8 ± 11.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64 ± 13.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MSKCC score</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.5 ± 1.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.3 ± 1.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69.7 ± 10.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">68.5 ± 11.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">UISS score</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">89.2 ± 4.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">89.5 ± 4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.4 ± 0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.4 ± 0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Recurrence time</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29.7 ± 20.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.1 ± 19.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Radical Nephrectomy<xref ref-type=\"table-fn\" rid=\"TN1\"><sup></sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><break/>2/9 (22.2%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><break/>3/13 (23.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><break/>6/6 (100%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><break/>4/4 (100%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Margins<xref ref-type=\"table-fn\" rid=\"TN1\"><sup></sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Neg (9/9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Neg (13/13)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Neg (6/6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Neg (4/4)</td></tr></tbody></table><table-wrap-foot><fn id=\"TN1\"><label></label><p><italic>Nephrectomy and margin status were not available for all patients</italic>.</p></fn></table-wrap-foot></table-wrap><p>For stage I patients, metastases were evident at 29.7 ± 20.3 months, and the average tumor size of Stage I was 4.78 ± 1.14 cm with 83.3% low grade (I–II) and 16.7% high grade (III–IV). While patients with stage II disease recurred in 17.1 ± 19.8 months, with an average tumor size of 10.82 ± 2.98 cm with 45% low grade and 55% high grade.</p><p>There was no significant difference in age, tumor size, grade, radical vs. partial nephrectomy, margin status, UISS and MSKCC recurrence score in patients who developed metastases compared to those who did not: age at surgery (<italic>p</italic>-value stage I=0.54 and stage II=0.36), tumor size (<italic>p</italic>-value stage I=0.71 and stage II=0.82), tumor grade (<italic>p</italic>-value stage I=0.57 and stage II=0.75), radical vs. partial nephrectomy (<italic>p</italic>-value stage I=1 and stage II=1), margin status (<italic>p</italic>-value stage I=1 and stage II=1), UISS recurrence score (<italic>p</italic>-value stage I=0.86 and stage II=1), or MSKCC recurrence score (<italic>p</italic>-value stage I=0.81 and stage II=0.82).</p></sec><sec><title>RNAseq Gene Expression Analysis of Stage I and II ccRCC Tumors</title><p>Group comparison of patients who developed metastatic disease vs. those who did not showed 131 genes that were significant after multiple testing correction (see <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 1</xref>). Of those genes, the majority of them were seen in patients that had developed metastases (125 genes) as opposed to patients cured by surgery (6 genes). Seventy two of the 125 genes upregulated in tumors from patients who developed metastatic disease were immunoglobulin genes (IGH, IGK, and IGL).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Volcano plot of patients who developed metastases within 5 years vs. those that did not. The x-axis is log 2 fold change between groups while the y-axis is -log 10 raw p-value. The top 20 genes by p-value are labeled.</p></caption><graphic xlink:href=\"fonc-10-01383-g0001\"/></fig><p>The model used to develop the molecular signature of metastasis in ccRCC used variables that took into account the contribution of stage and gender to remove those covariates from contributing to the signal for metastases. There were 267 stage specific genes with adjusted <italic>p</italic> < 0.05 with absolute value log 2 fold change > 1; 253 of these were seen up-regulated in stage II while 14 genes were seen to be up-regulated in stage I, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 2</xref>. In gender, 166 genes were found to pass similar thresholds, with 150 genes higher in males with 16 genes higher in females (see <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 3</xref>). Notably, many of the genes found are gender specific like Y chromosome specific genes (i.e., UTY, DDX3Y, USP9Y, and RPS4Y1) being higher in males or XIST, the X-inactive specific transcript, being higher in females.</p><p>Sub-group analysis focused on male-specific or female-specific signals found a high correlation between test statistics of the male subgroup vs. the full data (y ~ 0.94x, R<sup>2</sup>= 0.86) compared to those of the female sub-group (y~0.18x, R<sup>2</sup>= 0.039), see <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>. In the male-specific subgroup analysis 8 genes had an adjusted p-value threshold of 0.05 that was higher than 0.1 in the full group analysis: CNDB2, COL8A2, EPHB2, KRT16, PRSS21, SOGA3, SPINK5, and TMEM63C. In female-specific subgroup analysis, only three genes matched the same criteria, CTHRC1, BCL2L14, and AGBL4.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Comparison and correlation of the metastatic test statistic in the full dataset in a sub-group analysis for gender: (left) males and (right) females.</p></caption><graphic xlink:href=\"fonc-10-01383-g0002\"/></fig><p>Deconvolution of the RNA-seq data into cell subsets resulted in 28 distinct cell types (see <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 4</xref>. None of the cell subpopulations were associated with patients who developed metastases with <italic>p</italic> < 0.05.</p></sec><sec><title>miRNA Expression Analysis of Stage I and II ccRCC Tumors</title><p>Since miRNA is much sparser (only 214 miRNA passed QC thresholds, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 5</xref>) the adjusted p-value threshold for miRNA significance was lowered to 0.25. Only two miRNAs were found to be significant at this threshold. MiR-18a and miR-301 were found to be upregulated in patients who developed metastatic disease. Gender and stage were also taken into account in differential expression analysis of miRNA, see <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Tables 6, 7</xref>. For gender, there were no miRNA that passed adjusted <italic>p</italic> < 0.25. For stage, 157 total miRNA had adjusted <italic>p</italic> < 0.25 with 121 associated with stage II and 29 associated with stage I.</p></sec><sec><title>Canonical Correlation Analysis With mRNA and miRNA Expression Datasets</title><p>Sparse canonical correlation analysis (CCA) is able to identify similar correlation structures between two orthogonal datasets and redefine them as canonical components (CCs). It is analogous to Principal Component Analysis (PCA) with two orthogonal multi-dimensional datasets; making linear combinations of variables that have the highest correlation across datasets. CCs can be thought of as <italic>de novo</italic> modules of genes and miRNAs that have correlated expression patterns with 42 total CCs identified where expression of both can be combined into a single score (see <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 8</xref>). Univariate logistic regression was performed on canonical components to classify patients who developed metastatic disease. The top performing canonical component (CC32) had an AUC of 0.77 (see <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). To assign potential function to CCs, a gene enrichment analysis, GAGE, was used on Gene Ontology (GO) terms with an adjusted p-value threshold of 0.2. CC32 was enriched in GO terms related to RNA binding and spindle function, CC20 was enriched in oxidative-phosphorylation genes, CC9 was enriched in kidney development while CC7 was enriched in immune terms.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Canonical components (CCs) with AUC > 0.65 in both the discovery and validation datasets.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Canonical Component</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Discovery AUC</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TCGA AUC</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>GO terms</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Adjusted P-Value</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RNA BINDING</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.36E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SPINDLE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.36E-01</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC20</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.69</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.76</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">MITOCHONDRION</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">3.64E-19</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC20</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.69</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.76</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">MITOCHONDRIAL PART</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">1.29E-16</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC20</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.69</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.76</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CELLULAR RESPIRATION</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">8.69E-15</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC20</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.69</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.76</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">SMALL MOLECULE METABOLIC PROCESS</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">1.44E-14</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC20</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.69</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.76</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">OXIDATION REDUCTION PROCESS</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">1.61E-13</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MESONEPHROS DEVELOPMENT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.68E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BIOLOGICAL ADHESION</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.68E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HOMOPHILIC CELL ADHESION VIA PLASMA MEMBRANE ADHESION MOLECULES</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.68E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CC9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KIDNEY EPITHELIUM DEVELOPMENT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.68E-01</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC7</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.65</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">REGULATION OF IMMUNE RESPONSE</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">8.15E-05</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC7</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.65</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">EXTERNAL SIDE OF PLASMA MEMBRANE</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">2.42E-04</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC7</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.65</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">REGULATION OF IMMUNE SYSTEM PROCESS</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">2.42E-04</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC7</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.65</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">ADAPTIVE IMMUNE RESPONSE</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">3.02E-04</td></tr><tr><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">CC7</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.65</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"left\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">IMMUNOGLOBULIN COMPLEX</td><td valign=\"top\" align=\"center\" style=\"background-color:#f8f8f6\" rowspan=\"1\" colspan=\"1\">7.04E-04</td></tr></tbody></table></table-wrap></sec><sec><title>Validation in TCGA-KIRC</title><p>A subset of the publically available RNA-seq and microRNA-seq TCGA-KIRC datasets (stage I and II only) was used as validation to evaluate the accuracy of the canonical components identified. After matching RNA-seq and microRNA-seq samples and removing multiple samples from the same patient, the validation set had 263 patients total. Of those, 218 were stage I with 16 of those having recurrences, while of the 45 stage II samples 7 recurred. Gene and microRNA weights for the CC score calculated in the discovery set were applied to the validation set. Both datasets were mean normalized to 0 and standard deviation normalized to 1. The four identified CCs with AUC≥0.65 in the discovery set also had an AUC≥0.65 in TCGA-KIRC.</p><p>To determine accuracy of the CCs, the discovery dataset was used to set thresholds in order to stratify TCGA-KIRC into high risk and low risk groups. Thresholds were set using decision trees in the discovery dataset to maximize separation between groups. These thresholds were applied to TCGA-KIRC to separate them; thresholds for high risk were CC9 <0.02, CC20>0.52, CC32>0.23 and CC7 < -0.04. A cox regression was performed with these high and low risk groups in TCGA-KIRC based on their progression free survival for each CC. The recurrence rate in the validation set was ~9%; assuming an equal split between high and low risk groups (13% recurrence and 5%, respectively), an odds ratio of 2.5 has a power of 0.81. All cox regression analyses were found to be significant (p-values CC9 = 6.8E-3, CC20 = 9.4E-4, CC32 = 1.5E-4, and CC7 = 0.021, see <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>) with an odds ratio >2.5 (OR CC32 = 5.7, CC20 = 4.4, CC9 = 3.6, and CC7 = 2.7).</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Kaplan-Meier Curves of the high and low risk groups determined by top four CCs. The 95% confidence interval plotted with each group as the lightly shaded area.</p></caption><graphic xlink:href=\"fonc-10-01383-g0003\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>Here we examined tumors from clinically low risk ccRCC for molecular signatures to understand metastatic potential. Risk stratification using clinicopathological parameters in these patients has limited predictive power. In our dataset, we see no statistical significance between MSKCC and UISS scores in patients who do or do not develop metastatic disease in our cohort. Our low risk ccRCC patient cohort who develop metastasis offers an early time point where molecular signatures can supplement clinical information for better risk stratification. To increase molecular signatures found we chose to use similar numbers of patients who did and did not develop future metastatic disease to have a balanced dataset. We report several multigene and miRNA signatures generated by CCA that show clinical utility in stratifying clinically low risk ccRCC tumors.</p><p>To the best of our knowledge, this is the first report analyzing the combined differential mRNA and miRNA expression between clinically low risk stage I and II ccRCC with vastly different outcomes. Several recent studies have used gene expression analysis to create stratification scoring systems in RCC: Rini et al. found a 16 gene assay to predict recurrence in stage I-III ccRCC using gene expression across almost 1000 samples (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>), Brooks et al. developed ClearCode34 from 72 ccRCC samples (stage I-III) to identify low risk (ccA) and high risk (ccB) groups (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>), Morgan et al. examined 31 cell cycle related genes across 565 patients of clear cell, papillary, or chromophobe RCC to create the R-CCP panel (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). However, they included stage III ccRCC patients and did not use miRNA in conjunction with gene expression. In stage III ccRCC, the tumor is no longer completely isolated to the kidney and has already spread either to the most proximal lymph node, to a major vein or to the tissue surrounding the kidney, which indicates a more aggressive phenotype and increased risk. Therefore, the results found in our discovery population and validation in TCGA-KIRC are a novel and a promising step toward identifying molecular signatures of clinicopathologically defined low risk ccRCC that develop future metastasis.</p><p>In our analysis, several immunoglobulin genes were upregulated in tumors that became metastatic. IgG is overexpressed in ccRCCs in comparison to adjacent normal tissue affecting cell proliferation, migration and invasion (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Immunoglobulin genes have been shown to be active and expressed in many non-B epithelial cancer cells (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). MZB1, which is overexpressed in our metastatic group, has been shown to be necessary for immunoglobulin synthesis (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). It also has an immune regulatory effect that has a survival benefit in other cancers (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>), though in RCC high expression of MZB1 is unfavorable (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Similarly IL1R2, which has higher expression in our metastatic samples, is a mock receptor of IL1R that regulates immune response through competitive inhibition, and has an important role in cancer progression (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Furthermore, our CC7 module is enriched in immune response GO terms. While expression of immune genes is normally associated with immune infiltrating cells, we did not see the future development of metastasis correlate with any specific immune cell type or any cell at all when our bulk RNA-seq data was deconvoluted into cell types via CIBERSORT. These data show that the immune system may play a role in promoting a pro-tumor environment early in patients who develop metastatic disease.</p><p>In addition to the immune module (CC7), other <italic>de novo</italic> mRNA-miRNA modules have functions related to cancer growth. CC9 was enriched in GO terms for kidney development and cell adhesion. Enrichment of kidney epithelium terms directly links this module to ccRCC cells, which predominantly are kidney epithelial cells. One of the two miRNAs passed our adjusted p-value threshold, miR-18a, promotes proliferation and inhibits apoptosis in kidney cancer cell lines and is associated with worse overall survival in RCC (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Furthermore, this particular GO term is related to development from embryos, which implies a more stem-like state compared to a mature kidney, similar to an epithelial-mesenchymal transition. The other microRNA that passed our adjusted p-value threshold, miR-301 which had higher expression in those that developed metastasis, has been shown to be increased in microvesicles released by human renal cancer stem cells to stimulate angiogenesis to prepare the metastatic niche (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). TGFBI is also overexpressed in our metastatic discovery set and has been shown to induce epithelial to mesenchymal transition (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>) as well as being associated with ccRCC tumor progression and poor prognosis (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>).</p><p>One of the hallmarks of ccRCC is the metabolic reprogramming of oxidative-phosphorylation pathways leading to accumulation of lipids and glycogen in the cytoplasm supporting a shift in metabolism known as the Warburg effect (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). Analysis of TCGA-KIRC showed worse survival is associated with upregulation of fatty acid synthesis genes and pentose phosphate pathway genes while better survival was associated with Krebs cycle genes (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). CC20 in our dataset is enriched in cellular respiration and oxidative-phosphorylation terms, and we have shown has predictive value for metastatic potential. One gene related to solute transport that we also see upregulated in the metastatic group is SLC38A5, which alkalinizes tumor cells and promotes growth; it is also a transcriptional target for the oncogene c-Myc (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>).</p><p>Interestingly, there is a gender disparity in the prevalence of RCC, about 2:1 male to female, that is consistent across age, year, and region (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Men are more likely to have a higher grade tumor and are more likely to develop metastases, while women showed a benefit in overall survival (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Some studies have examined mutational differences between men and women in ccRCC, showing that stratification by gender showed BAP1 mutations have a female-specific poorer outcome (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Similarly, Tan and colleagues also showed expression levels of FABP7 and BRN2 had prognostic value in women (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Many of the treatment and stratification options bias toward male because of the increased prevalence, which is also present in our dataset (~84% male). We did have three genes that were differentially expressed in women that were not seen in our overall dataset: CTHRC1, BCL2L14 (overexpressed in metastatic patients) and AGBL4 (underexpressed in metastatic patients). CTHRC1 knockdown has been shown to reduce proliferation and epithelial-to-mesenchymal transition (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>) and increased expression is associated with a poorer prognosis (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>). BCL2L14 is a member of the BCL2 family and AGBL4 is an ATP/GTP binding protein, both without relevance to RCC to the best of our knowledge, but given our results further study may be warranted, particularly in women.</p><p>While our study focuses on early stage ccRCC, we have applied previous molecular panels to our dataset with mixed results, likely because of the equal number of patients who would develop metastases vs. those that did not in a low risk cohort (data not shown). We also applied our CCs to stage III patients in TCGA with two of the CCs (CC32: RNA Binding/Spindle and CC20: Oxphos) having the lowest p-value of ~0.08, implying these are worth further study for metastatic progression across all risk groups. One common limitation regarding genomic-based predictive markers is intratumor heterogeneity. However, CCA takes into account similarly correlated genes and miRNA across samples to make <italic>de novo</italic> modules or pathways that are less susceptible to cellular heterogeneity. Furthermore, bulk deconvolution using cell subset techniques did not show any cell type associated with metastases. Using TCGA-KIRC for validation may have been limited due to the fact that the TCGA-KIRC has local and distant recurrence of disease in their dataset when our cohort consisted only of distant metastases. However, 7% of stage I and 15% of stage II in TCGA-KIRC recurred which is similar to the reported distant metastatic rate, making local recurrences less likely.</p></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>Our results highlight molecular signatures that can risk stratify patients for metastatic potential in clinically low risk ccRCC patients. Our modules provide a potential mechanistic pathway for development of metastases, of which the immune module and immunoglobulin genes are of particular interest. With further validation, the combined mRNA and miRNA modules could be used to improve treatment and survival outcomes for this group of patients.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The datasets generated and analyzed for this study can be found in the Gene Expression Omnibus at identifier GEO: <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE155210\">GSE155210</ext-link>.</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by The Northwell Health System Regional Ethics Committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>ZK, PS, X-HZ, and ATL conceived and designed the study. OY, HK, and AL acquired the data. AS analyzed and interpreted the data. AS, NM, and ATL drafted the manuscript. AS, NM, ZK, LK, SH, MV, X-HZ, and ATL critically revised the manuscript for important intellectual content. AS did the statistical analysis. OY, HK, AL, X-HZ, and ATL provided administrative, technical, and material support. ATL obtained funding and supervised the study. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We are exceptionally grateful to the patients and their families. We also gratefully acknowledge Peter K. Gregersen for his critical reading of the manuscript.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> Funding for this project was done with institutional funds.</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s10\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fonc.2020.01383/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fonc.2020.01383/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.xls\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group 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[ "<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850464</article-id><article-id pub-id-type=\"pmc\">PMC7431519</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01599</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Quantification of Myocardial Dosimetry and Glucose Metabolism Using a 17-Segment Model of the Left Ventricle in Esophageal Cancer Patients Receiving Radiotherapy</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Sha</surname><given-names>Xue</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/949057/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Gong</surname><given-names>Guanzhong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Han</surname><given-names>Chunlei</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Qiu</surname><given-names>Qingtao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/667044/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Yin</surname><given-names>Yong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup></sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/730363/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Turku PET Centre, Turku University Hospital</institution>, <addr-line>Turku</addr-line>, <country>Finland</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Youyong Kong, Southeast University, China</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Xu Zhiyong, Shanghai Jiao Tong University, China; Jiandong Yin, ShengJing Hospital of China Medical University, China</p></fn><corresp id=\"c001\">Correspondence: Yong Yin, <email>[email protected]</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1599</elocation-id><history><date date-type=\"received\"><day>09</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Sha, Gong, Han, Qiu and Yin.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Sha, Gong, Han, Qiu and Yin</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><sec><title>Objective</title><p>Previous studies have shown that increased cardiac uptake of <sup>18</sup>F-fluorodeoxyglucose (FDG) on positron emission tomography (PET) may be an indicator of myocardial injury after radiotherapy (RT). The primary objective of this study was to quantify cardiac subvolume dosimetry and <sup>18</sup>F-FDG uptake on oncologic PET using a 17-segment model of the left ventricle (LV) and to identify dose limits related to changes in cardiac <sup>18</sup>F-FDG uptake after RT.</p></sec><sec><title>Methods</title><p>Twenty-four esophageal cancer (EC) patients who underwent consecutive oncologic <sup>18</sup>F-FDG PET/CT scans at baseline and post-RT were enrolled in this study. The radiation dose and the <sup>18</sup>F-FDG uptake were quantitatively analyzed based on a 17-segment model. The <sup>18</sup>F-FDG uptake and doses to the basal, middle and apical regions, and the changes in the <sup>18</sup>F-FDG uptake for different dose ranges were analyzed.</p></sec><sec><title>Results</title><p>A heterogeneous dose distribution was observed, and the basal region received a higher median mean dose (18.36 Gy) than the middle and apical regions (5.30 and 2.21 Gy, respectively). Segments 1, 2, 3, and 4 received the highest doses, all of which were greater than 10 Gy. Three patterns were observed for the myocardial <sup>18</sup>F-FDG uptake in relation to the radiation dose before and after RT: an increase (5 patients), a decrease (13 patients), and no change (6 patients). In a pairing analysis, the <sup>18</sup>F-FDG uptake after RT decreased by 28.93 and 12.12% in the low-dose segments (0–10 Gy and 10–20 Gy, respectively) and increased by 7.24% in the high-dose segments (20–30 Gy).</p></sec><sec><title>Conclusion</title><p>The RT dose varies substantially within LV segments in patients receiving thoracic EC RT. Increased <sup>18</sup>F-FDG uptake in the myocardium after RT was observed for doses above 20 Gy.</p></sec></abstract><kwd-group><kwd>myocardium</kwd><kwd>radiotherapy</kwd><kwd><sup>18</sup>F-FDG PET</kwd><kwd>17-segment model</kwd><kwd>esophageal cancer</kwd></kwd-group><counts><fig-count count=\"4\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"30\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Esophageal cancer (EC) is the 8th most common cancer and has the 6th highest cancer mortality rate worldwide (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). During the past decade, radiotherapy (RT) has become a primary treatment modality for patients with EC because of its effectiveness and relative safety. However, the heart lies near the middle esophagus and is inevitably irradiated during RT for middle-stage EC patients. Although advances in RT equipment and techniques have prolonged patient survival, delayed latent effects of radiation are currently being encountered in clinical practice (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Previous studies have reported that cardiac toxicity may even diminish the survival gains obtained from anticancer therapy (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Therefore, early observation of changes in cardiac function is extremely important for monitoring and evaluating the occurrence and development of radiation-induced heart disease (RIHD).</p><p>The heart is divided into chambers, arteries and valves, which consist of myocardial, connective, pericardial and vascular tissues. These various cardiac tissues have different radiosensitivities (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Studies have confirmed injuries to cardiac substructures (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>), indicating that various dose constraints might be required (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). The myocardium is a vulnerable heart tissue, but organic injury from RT usually does not appear for several years when the symptoms of the injury have become irreversible, with no effective treatment (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). However, the interval between RT treatment and the detection of RIHD has been reported to range from months to years (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Functional imaging can monitor metabolic changes in myocardial activity before the occurrence of organic injury, and effective intervention measures can be taken at the initial stage of pathological changes (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>Currently, positron emission computed tomography (PET) imaging is considered the “gold standard” for detecting viable myocardium. Oncologic PET is usually performed in a fasting state because postprandial high blood glucose induces insulin secretion, which results in increased <sup>18</sup>F-FDG uptake by muscle and fat and decreased <sup>18</sup>F-FDG uptake by the tumor. In the fasting state, the ischemic myocardium can take up <sup>18</sup>F-FDG, whereas normal myocardium and necrotic myocardium do not take up glucose. Under a glucose load, <sup>18</sup>F-FDG is ingested by both normal and ischemic myocardium and can be used to evaluate the survival state of the myocardium (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Although previous studies have shown that increased cardiac uptake of <sup>18</sup>F-FDG on PET may be an indicator of myocardial injury after RT, only the global left ventricle (LV) was considered, and the radiation dose and <sup>18</sup>F-FDG uptake in specific myocardial segments were not evaluated. Cardiac imaging studies have also increasingly found discrete focal changes in the heart. A better understanding of the effects of the radiation dose requires a more detailed assessment of the radiation dose for cardiac subvolumes.</p><p>This study has two objectives. First, the dosimetry and <sup>18</sup>F-FDG uptake in oncologic PET are quantified using a 17-segment model of the LV proposed by the American Heart Association (AHA). Second, the relationship between the changes in the myocardial <sup>18</sup>F-FDG uptake and the irradiated dose in EC patients who underwent radiotherapy RT is investigated (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Our hypothesis is that the myocardial segment that receives higher doses will show increased <sup>18</sup>F-FDG uptake. The confirmation of this hypothesis can be used as a preliminary basis to accurately evaluate the cardiac dose-response relationship and implement timely treatment measures in the initial stages of pathological changes.</p></sec><sec sec-type=\"materials\|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Patient Selection</title><p>An institutional review board approved a retrospective review of the medical records for this analysis. Twenty-four EC patients who underwent consecutive oncologic <sup>18</sup>F-FDG PET/CT scans at baseline (1–2 weeks before radiotherapy) and post-RT (2–3 months after radiotherapy) were enrolled in the study. The main inclusion criteria were as follows: (1) the heart was covered by the radiation field during the scan and (2) a fasting time >12 h prior to PET was observed. The exclusion criteria were as follows: (1) prior treatment with chemotherapy, (2) history of cardiac disease, congestive heart failure or coronary artery disease, and (3) a fasting blood glucose level higher than 150 mg/dl before the <sup>18</sup>F-FDG injection.</p></sec><sec id=\"S2.SS2\"><title>Radiotherapy Design</title><p>The prescribed dose of the planning target volume (PTV) was 60 Gy, and 95% of the PTV was required to receive the prescribed dose. The gross tumor volume (GTV) consisted of the primary tumor and metastatic regional lymph nodes observed on CT and a whole body PET/CT scan. The clinical target volume (CTV) was formed by the GTV with a 1.0-cm margin in all directions. A 0.5-cm margin for CTV was expanded to delineate the PTV. Treatment was delivered as three-dimensional conformal radiation therapy (3D-CRT) or intensity-modulated radiation therapy (IMRT) at 2.0 Gy per fraction with 6-MV photon beams. The constraints of the organ at risk (OAR) were V<sub>20</sub><sub><italic>Gy</italic></sub> < 30% and V<sub>30</sub><sub><italic>Gy</italic></sub> < 20% for the total lung; a maximum dose <45 Gy for the spinal cord; and V<sub>30</sub><sub><italic>Gy</italic></sub> < 40% and V<sub>40</sub><sub><italic>Gy</italic></sub> < 30% for the heart. <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> demonstrates the PTV and radiation fields in the IMRT plan.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Schematic diagram of the relationship between the heart and PTV. The PTV and heart are depicted in green and color, respectively.</p></caption><graphic xlink:href=\"fonc-10-01599-g001\"/></fig></sec><sec id=\"S2.SS3\"><title><sup>18</sup>F-FDG PET Image Acquisition</title><p>The <sup>18</sup>F-FDG PET/CT images were acquired from a combined PET/CT scanner (Philips Healthcare, Cleveland, OH) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). The median fasting blood glucose level was 5.8 mmol/l (interquartile range: 5.0–6.8). Approximately 1 h after the <sup>18</sup>F-FDG injection, a spiral CT was obtained, followed by a PET emission scan from the distal femur to the top of the skull. The PET emission images were corrected by the measured attenuation and reconstructed using a conventional iterative ordered-subsets expectation maximization algorithm.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Planes of myocardial PET imaging.</p></caption><graphic xlink:href=\"fonc-10-01599-g002\"/></fig></sec><sec id=\"S2.SS4\"><title>Myocardium Delineation</title><p>The LV contouring was performed by a radiologist with 20 years of experience and reviewed by two senior cardiologists. Differences in the results were resolved by consensus. Myocardium delineation was performed by Carimas software (version 2.9)<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup> based on the AHA 17-segment model, which rearranges the myocardium circumferential profile image from the apex to the base into concentric circles from the interior to the exterior, thereby projecting the entire LV myocardial onto a bull’s eye diagram. As shown in <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>, when contouring the LV, the myocardium was automatically divided into basal (segments 1–6), middle (segments 7–12), and apical (segments 13–16) regions.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Left myocardium segmentation <bold>(A)</bold> and polar map <bold>(B)</bold>.</p></caption><graphic xlink:href=\"fonc-10-01599-g003\"/></fig></sec><sec id=\"S2.SS5\"><title>Dosimetry Data Acquisition</title><p>The above mentioned delineation results were used to extract the radiation dose to individual segments from the planning CT images. The individual segments were combined to determine the dose to the basal, middle and apical regions. We classified the myocardium into three groups depending on the radiation dose received: lower than 10, 10–20, and 20–30 Gy. No segment received a dose greater than 20 Gy.</p></sec><sec id=\"S2.SS6\"><title>Myocardial Metabolism Measurement</title><p>The myocardial metabolism level was evaluated from the <sup>18</sup>F-FDG uptake value. The 17-segment model was used to measure the myocardial metabolic parameters, which were then used in turn to carry out a quantitative analysis. For the quantitative analysis, the mean standardized uptake values (SUVs) were obtained from the baseline and the post-RT PET images. Changes in the SUV (ΔSUV) were calculated using the following formula: ΔSUV = (SUV after RT) – (SUV before RT). The SUV ratio (SUVR) was defined as the ΔSUV divided by the baseline SUV. Finally, the change in the <sup>18</sup>F-FDG PET uptake was correlated with the radiation dose for the LV myocardium.</p></sec><sec id=\"S2.SS7\"><title>Statistical Analysis</title><p>The SPSS 22.0 software program (SPSS, Chicago, IL, United States) was used for all the statistical tests, and the quantitative parameters were presented in terms of the mean ± standard deviation (SD). The Wilcoxon signed-rank test was used to compare the differences in different patterns of patient characteristics, and differences in <sup>18</sup>F-FDG uptake values between baseline and post-RT. Only <italic>P</italic>-values < 0.05 were considered statistically significant.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Patients</title><p>Between January 2016 and December 2018, 24 patients (ranging from 51 to 73 years of age, with a mean age of 61 years) with middle thoracic EC who underwent PET scans at baseline and post-RT were retrospectively enrolled in this study. Pathology reports confirmed that all the enrolled patients had esophageal squamous cell carcinoma. The heart volumes ranged from 377.1 to 805.7 with a median of 593.7 cm<sup>3</sup>. None of the patients showed symptomatic cardiac events during the entire planned RT process or for 3 months following RT. No significant differences in the baseline patient characteristics were observed in 3 pattern cohorts, with <italic>P</italic> values ranging from 0.018 to 1.000 (see <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Patient characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Characteristic</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">All patients</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Increased</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No changes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Decreased</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age, years, median (range)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61 (51–73)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63 (54–67)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 (58–63)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62 (51–73)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.886</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.233</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor location</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Middle</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor length, cm, median(range)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.8 (4.0–8.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.9 (4.3–6.9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.7 (5.0–8.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.8 (4.0–7.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.583</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Histology</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Squamous cell carcinoma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tumor staging</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.301</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">I B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.223</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">II</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.135</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">III A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.082</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IIIB</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.018</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IIIC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.135</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IV</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.223</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IV A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.223</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mean heart volume, cm<sup>3</sup>(range)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.067</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">593.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">495.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">628.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">608.2</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(377.1–805.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(470.1–495.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(574.5–682.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(377.1–805.7)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Concurrent chemotherapy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Radiotherapy technique</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.170</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IMRT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3D-CRT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prescribed dose</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No. of fractions</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 Gy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.000</td></tr></tbody></table><table-wrap-foot><attrib><italic>IMRT, intensity-modulated radiation therapy; 3D-CRT, 3-dimensional conformal radiation therapy.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS2\"><title>Doses to the Left Myocardial Segments</title><p>The median maximum, mean, and minimum LV irradiation doses were 18.14 ± 9.08, 6.54 ± 4.07, and 2.59 ± 2.23 Gy, respectively. Valuable data were recorded for discrete areas of the LV. Significant differences were observed in the dose distribution for the LV: the median mean and median maximum doses were both higher in the basal region (9.59 and 16.12 Gy, respectively) than in the middle region (6.54 and 11.53 Gy, respectively) and the apical region (5.09 and 13.17 Gy, respectively). These results were mirrored for the segments of the left myocardium, with the basal segments receiving the highest doses (segments 1, 2, 3, and 4). <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> shows the doses to the individual myocardium segments in the polar map.</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Irradiation dose and FDG uptake of individual segment in the myocardial 17-segment model.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Segment</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean dose, Gy (range)</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">SUV<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SUVR (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Baseline</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Post-RT</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Basal</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.26 (1.89–24.63)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.78 (2.13–6.81)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.82 (1.24–4.59)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–19.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.217</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.25 (1.46–28.27)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.29 (2.65–7.57)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.33 (1.41–6.12)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–18.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.378</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.58 (0.62–24.54)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.28 (2.38–7.70)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.22 (1.12–7.38)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–21.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.304</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.07 (0.45–27.72)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.68 (2.03–6.42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.85 (0.97–6.86)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–20.84</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.398</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.69 (0.53–23.65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.73 (2.10–5.91)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.11 (1.48–8.14)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–17.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.292</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.84 (1.01–15.35)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.38 (2.07–5.80)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.65 (1.26–5.83)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–17.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.156</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Middle</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.64 (1.14–20.79)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.40 (1.84–6.52)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.53 (1.19–4.12)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–20.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.084</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.13 (0.96–25.88)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.67 (1.87–7.05)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.74 (1.26–5.01)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–19.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.090</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.48 (0.45–19.53)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.09 (2.16–7.44)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.97 (1.19–6.86)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–24.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.040</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.44 (0.32–19.68)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.87 (2.16–7.39)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.86 (1.04–7.16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–24.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.045</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.92 (0.36–20.86)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.73 (2.11–6.59)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.94 (1.59–7.32)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–21.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.020</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.85 (0.64–10.89)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.68 (2.18–6.62)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.80 (1.22–5.31)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–22.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.033</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Apical</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.38 (0.61–8.33)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.20 (1.91–5.76)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.35 (1.11–4.53)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–24.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.019</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.16 (0.42–13.51)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.25 (1.85–6.19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.39 (0.89–5.15)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–23.45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.047</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.59 (0.24–20.64)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.20 (2.00–5.80)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.41 (1.02–5.26)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–22.59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.024</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.78 (0.36–9.49)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.11 (1.95–5.73)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.30 (1.11–4.99)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–24.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.005</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.54 (0.49–6.57)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.68 (1.66–5.02)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.96 (0.86–4.13)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–24.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.007</td></tr></tbody></table></table-wrap></sec><sec id=\"S3.SS3\"><title><sup>18</sup>F-FDG Uptake of Left Myocardial Segments</title><p>An analysis of the changes in the <sup>18</sup>F-FDG uptake at two time points showed three patterns for the overall myocardial accumulation of <sup>18</sup>F-FDG uptake related to the radiation dose (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). A subsequent quantitative analysis demonstrated that <sup>18</sup>F-FDG uptake increased in 5 patients (average SUVR: 16.68%), decreased in 13 patients (average SUVR: −41.38%) and did not change significantly in 6 patients (average SUVR: −5.53%). However, directly focusing on specific LV segments showed that the post-RT uptake of the 17 segments tended to decrease relative to the baseline uptake: the post-RT uptake decreased in the basal, middle and apical regions by 19.09 ± 1.77, 22.30 ± 2.01, and 23.99 ± 0.89%, respectively. In agreement with these results, the <sup>18</sup>F-FDG uptake of the segmental myocardium decreased significantly in segments 9–17 in the apical region (<italic>P</italic> < 0.05). <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> provides a detailed summary of baseline and post-RT <sup>18</sup>F-FDG uptake for the baseline and post-RT in the left myocardium segments.</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Baseline and post-RT PET imaging in patients with three myocardial accumulation patterns.</p></caption><graphic xlink:href=\"fonc-10-01599-g004\"/></fig></sec><sec id=\"S3.SS4\"><title>Changes of <sup>18</sup>F-FDG Uptake With Different Dose Ranges</title><p>We directly paired the radiation dose and the <sup>18</sup>F-FDG uptake of the LV segments using the AHA 17-segment model. In our study, the numbers of segments that received doses in the ranges of 0–10, 10–20, and 20–30 Gy were 296, 74, and 38, respectively. <sup>18</sup>F-FDG uptake in the segments receiving 0–10 and 10–20 Gy decreased by 28.93 and 12.12% after RT, respectively. The <sup>18</sup>F-FDG uptake in the segments receiving 20–30 Gy increased by 7.24% after RT.</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>Subacute changes following thoracic RT have been demonstrated in discrete areas of the LV, and guidelines have been recently developed to help determine the dose to subvolumes of the LV (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Therefore, the radiation dose to cardiac subvolumes needs to be more accurately quantified to better understand the effect of the radiation dose. Previous studies have calculated cardiac doses by using modeled patients with recreated 2D treatment fields and by employing set geometric rules to define cardiac subvolumes or by using anatomical landmarks to divide the heart into standard axial imaging planes (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>–<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). The primary strength of the present study is that the dosimetric values and <sup>18</sup>F-FDG uptake were evaluated by performing a segmental analysis and directly pairing the radiation dose and <sup>18</sup>F-FDG uptake in the myocardial segments using the 17-segment AHA model.</p><p>The 17-segment model method proposed by the AHA aims to accurately divide the LV according to the anatomical structure of the heart and is the closest scheme available to that visualized by clinical ultrasound and radionuclide myocardial imaging (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Erven et al. (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>) discussed the feasibility of dividing the LV into 17 segments but did not report the doses for each segment. Tang et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>) used a 17-segment model to show that the dose distribution varied across LV subregions in breast cancer patients. The middle and anterior apical segments (segments 7 and 13) and the LV apical regions (segments 13, 14, 15, 16, and 17) received higher radiation doses than the other segments. The results of the present study also show a heterogeneous dose to the LV for EC patients. The basal region (segments 1, 2, 3, and 4) received a higher radiation dose than the apical and middle regions. Such reporting of specific regional dose delivery provides the most accurate spatial description of delivered radiation doses to the heart, particularly during the middle stages of EC when the LV is very frequently exposed. The use of automatic software sketching has reduced the sketching error between observers. In addition, analyzing the dose value and the <sup>18</sup>F-FDG uptake of the myocardium under the same LV-VOI conditions produced a highly accurate dose-response relationship. This analytical method accurately describes the radiation doses to the myocardium and its subspaces. Further understanding of cardiotoxicity requires the precise matching of the radiation dose to regional imaging defects, which underlines the need for determining the dose distribution in detail (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>).</p><p>Changes in the <sup>18</sup>F-FDG uptake of the segments of the myocardium were also analyzed in the current study. To suppress the physiological myocardial accumulation of <sup>18</sup>F-FDG, we selected patients who fasted for ≥18 h prior to the <sup>18</sup>F-FDG PET scan, both pre- and post-RT. Ishida et al. (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>) reported that the physiological accumulation of <sup>18</sup>F-FDG in the irradiated myocardium was suppressed in <italic>a</italic> ≥ 18-h fasting group compared with <italic>a</italic> < 18-h fasting group. Suppression of physiological myocardial <sup>18</sup>F-FDG accumulation appeared to facilitate the detection of abnormal myocardial <sup>18</sup>F-FDG accumulation. Cardiotoxicity related to external RT is a recognized phenomenon in clinical practice and has traditionally been investigated by radionuclide ventriculography or gated blood pool imaging (popularly known as the multigated acquisition [MUGA] scan) and 2D echocardiography. By comparison, the observation of <sup>18</sup>F-FDG PET/CT in the present study is novel and may have considerable significance if translated into clinical practice and utilized to monitor the effect of RT in a patient-specific manner (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). In this study, three radiation dose patterns were observed for the myocardial accumulation of <sup>18</sup>F-FDG on PET: an increase, a decrease or no change. Although the exact molecular pathway remains to be determined, in-depth research in this field can be applied to the specific diagnosis and management of different patients such that corresponding preventive or therapeutic measures can be provided.</p><p>Last, the changes in <sup>18</sup>F-FDG uptake by myocardial segments were analyzed for different dose ranges. Very few studies have explored this topic to date, and the two existing publications on RT in EC present conflicting results. Jingu et al. (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>) considered focally increased <sup>18</sup>F-FDG uptake in the basal myocardium after RT to be an indicator of radiation-induced cardiac damage, whereas Konski et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>) found no correlation between the percent change in the myocardial SUV and cardiac toxicity. A recent study by Evans et al. (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>) found that in lung cancer patients treated with stereotactic body RT (SBRT), <sup>18</sup>F-FDG uptake increased when the 20 Gy isodose line exceeded 5 cm<sup>3</sup> of the heart. By comparison, the results of the present study show that <sup>18</sup>F-FDG uptake in myocardial segments receiving a low dose (0–20 Gy) decreased after RT, whereas <sup>18</sup>F-FDG uptake in the myocardial segments receiving 20–30 Gy increased after RT. Therefore, minimizing the volumes of myocardium being irradiated by more than 20 Gy can be expected to reduce the incidence of myocardial injury. However, this hypothesis needs to be evaluated in larger scale prospective studies with longer follow-up times.</p><p>The current study has several limitations. First, the 5-year incidence rate of heart disease or pericardial effusion following RT ranges from 11.1 to 13.8% (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>–<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Further follow-up is required to reveal the clinical significance of abnormal myocardial accumulation of <sup>18</sup>F-FDG following RT. Second, the heart is a mobile structure, and its location on 2 PET scans may vary both interfractionally and intrafractionally relative to the planned CT scan, rendering the regional dose distribution corresponding to <sup>18</sup>F-FDG PET a good approximation, at best. Third, strictly enrolling patients who fasted for >18 h before undergoing PET scans resulted in a limited number of patients for this retrospective study. More patients need to be evaluated in future prospective studies.</p><p>Despite these drawbacks, we consider our data on cardiac toxicity during RT to be robust. Currently, we are not suggesting that the study results should be used to modify current treatment modalities. However, we are recommending that efforts should be made to reduce the cardiac dose and irradiated volumes during thoracic RT, which may benefit patients, especially those with favorable prognoses. Very few publications on the subject of this study are currently available. Thus, this investigation is the first of its kind: specific cardiac changes on PET/CT are related to dose information to detect myocardial activity in early stage post-RT. Although none of the investigated patients experienced symptomatic cardiac events between receiving RT and 3 months following RT, we consider a longer follow-up with higher numbers of patients to be essential for assessing the clinical significance of the considered abnormalities. We recommend that considerable effort should be expended to identify means of improving RT techniques to further minimize incidental irradiation of the heart.</p></sec><sec id=\"S5\"><title>Conclusion</title><p>Radiotherapy doses vary substantially within specific LV segments in the setting of thoracic EC RT. Increased <sup>18</sup>F-FDG uptake in the myocardium after RT was observed when receiving a dose higher than 20 Gy. Determining the <sup>18</sup>F-FDG uptake and corresponding RT dose in the LV segments can help to guide focus in the diagnosis of radiation-induced cardiac toxicity.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The retrospective study was reviewed and approved by The Ethics Committee (IRB) at Shandong Cancer Hospital and Institute. After a vote (Total 11, Agree 11, Disagree 0), the IRB agreed that the study followed the guidelines of Good Clinical Practice (GCP) and that the research could be conducted at Shandong Cancer Hospital and Institute (No. 201807013).</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>XS designed the study and wrote the initial draft of the manuscript. GG and CH contributed to the design, analysis and interpretation of the data, and assisted in the preparation of the manuscript. QQ and YY contributed to the data collection and interpretation, and critically reviewed the manuscript. All authors approved the final version of the manuscript and have agreed to be accountable for all aspects of the work and for ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the Key Technology Research and Development Program of Shandong (2018GSF118048 and 2018GSF118006).</p></fn></fn-group><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.turkupetcentre.fi/carimas\">www.turkupetcentre.fi/carimas</ext-link></p></fn></fn-group><ref-list><title>References</title><ref 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