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Research ArticleExperimental Studies

Chronological Change in EPHA2 Protein Expression Is Associated With Recurrence of Bladder Cancer

MITSUYUKI KOIZUMI, SHINYA SATO, MITSUYO YOSHIHARA, YOSHIYASU NAKAMURA, HIDEYUKI TERAO, YOICHIRO OKUBO, KOTA WASHIMI, EMI YOSHIOKA, TOMOYUKI YOKOSE, TAKESHI KISHIDA, NAOHIKO KOSHIKAWA and YOHEI MIYAGI
Anticancer Research December 2022, 42 (12) 5783-5794; DOI: https://doi.org/10.21873/anticanres.16085
MITSUYUKI KOIZUMI
1Department of Urology, Kanagawa Cancer Center, Yokohama, Japan;
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SHINYA SATO
2Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
3Department of Pathology, Kanagawa Cancer Center, Yokohama, Japan;
4Morphological Information Analysis Laboratory, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
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  • For correspondence: ssato53@gancen.asahi.yokohama.jp
MITSUYO YOSHIHARA
2Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
4Morphological Information Analysis Laboratory, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
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YOSHIYASU NAKAMURA
2Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
4Morphological Information Analysis Laboratory, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
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HIDEYUKI TERAO
1Department of Urology, Kanagawa Cancer Center, Yokohama, Japan;
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YOICHIRO OKUBO
3Department of Pathology, Kanagawa Cancer Center, Yokohama, Japan;
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KOTA WASHIMI
3Department of Pathology, Kanagawa Cancer Center, Yokohama, Japan;
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EMI YOSHIOKA
3Department of Pathology, Kanagawa Cancer Center, Yokohama, Japan;
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TOMOYUKI YOKOSE
3Department of Pathology, Kanagawa Cancer Center, Yokohama, Japan;
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TAKESHI KISHIDA
1Department of Urology, Kanagawa Cancer Center, Yokohama, Japan;
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NAOHIKO KOSHIKAWA
5Division of Cancer Cell Research, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
6Division of Cancer Cell Research, Institute of Medical Science, University of Tokyo, Tokyo, Japan;
7Department of Life Science and Technology, Tokyo Institute of Technology, Yokohama, Japan
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YOHEI MIYAGI
2Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan;
3Department of Pathology, Kanagawa Cancer Center, Yokohama, Japan;
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Abstract

Background/Aim: Bladder cancer is the most common urinary tract cancer. Patients diagnosed with advanced T-stage/muscle-invasive bladder cancer through transurethral resection of bladder tumors (TURBT) are treated with total radical cystectomy; however, there is a high chance of recurrence. Nevertheless, markers for predicting this recurrence are not currently available. Here, we evaluated the chronological change of ephrin type-A receptor 2 (EPHA2) expression, a molecule known for its role in cell adhesion, to predict bladder cancer recurrence after cystectomy, using TURBT and cystectomy specimens. Materials and Methods: An immunostaining evaluation method that combines whole-slide images and image analysis software was developed to quantify and evaluate stainability objectively. We assessed the correlation between EPHA2 expression and bladder cancer recurrence using this novel immunostaining method and chronological changes in target protein expression in TURBT and radical cystectomy samples. Results: In TURBT specimens, the number of cases with a high N-terminal/C-terminal EPHA2 ratio in the group with recurrence was significantly higher than in the non-recurrent group (p=0.019). The number of cases with a high level of C-terminal EPHA2 positivity in the radical cystectomy specimen when compared to the TURBT specimen obtained from the same patient was significantly higher in the recurrent group than in the non-recurrent group (p=0.0034). Conclusion: EPHA2 appears to be a promising marker for bladder tumor recurrence after cystectomy and its evaluation may enable the selection of appropriate cases for adjuvant therapy among patients undergoing radical cystectomy. Further studies, including mass-scale analysis, are required to confirm these results.

Key Words:
  • Bladder cancer
  • EPHA2
  • cancer recurrence
  • biomarker
  • adipocytes
  • machine learning

Bladder cancer is one of the major urological cancers and is the most common urinary tract cancer (1). Based on its prevalence, it is ranked among the top 10 cancers worldwide (2). The transurethral resection of bladder tumours (TURBT) is a conventional bladder cancer therapy. Following TURBT, the pathological T-stage is diagnosed, and additional treatment is required for advanced cases. For patients with advanced T-stage or muscle-invasive bladder cancer, total radical cystectomy is the standard treatment (3). However, recurrence is not rare after radical cystectomy (4, 5); the local recurrence rate is almost 15% within 2 years (6, 7), and the distant recurrence rate is almost 50% (8, 9). Adjuvant therapies can thus be crucial for patients who have undergone radical cystectomy. However, markers for predicting cancer recurrence after radical cystectomy are currently unavailable. The identification of such markers is critical to ensuring that adjuvant therapy is provided appropriately to patients at the right time.

In this study, we aimed to evaluate the potential of ephrin type-A receptor 2 (EPHA2) as a candidate marker for predicting recurrence. EPHA2 is a member of the Eph receptor tyrosine kinase family and is present on the cell membrane (10, 11). Interestingly, EPHA2 regulates tumor-suppressive and pro-oncogenic functions, dependent on ephrin A1 ligand stimulation (12). The binding of ephrin A1 to EPHA2 induces the autophosphorylation of tyrosine residues in the cytoplasmic tail of EPHA2 (C-terminal EPHA2, EPHA2-C). This inhibits the AKT serine/threonine kinase 1 (AKT) signaling pathway and suppresses tumor growth. Cleavage of the N-terminal EPHA2 (EPHA2-N) by a membrane type 1 matrix metalloproteinase activates the AKT signaling pathway, resulting in the phosphorylation of the cytoplasmic serine residue of EPHA2 (13, 14). Ligand-independent activation of EPHA2 and the stimulation of the ErbB receptor trigger actin rearrangement, which regulates cellular motility, adhesion, and infiltration (13, 15-19). In cancer cells, the expression of EPHA2-N is lower than in normal cells, and this is correlated with prognosis and malignancy (13, 14). Consequently, we hypothesized that EPHA2 might be used to predict the recurrence of advanced bladder cancer following radical cystectomy.

Immunohistochemistry (IHC) is used to evaluate protein expression in tissues. Immunostaining is microscopically evaluated by pathologists; however, its reproducibility is variable (20, 21). In this study, we established an objective immunostaining evaluation method and screened for EPHA2 expression in bladder cancer tissues. This method integrates whole-slide imaging (WSI) with image analysis software to enable the objective quantification of the stainability of tumor tissues. We analyzed whether EPHA2 expression correlates with the recurrence of bladder cancer by combining our novel immunostaining evaluation method with the detection of chronological changes in target protein expression in TURBT and radical cystectomy samples from the same patients.

Materials and Methods

Case selection. This study was conducted according to the guidelines of the Declaration of Helsinki with the approval of the Institutional Review Board of the Kanagawa Cancer Center (approval number: 2018Eki52, May 2018). Informed consent was obtained from all participants. Twenty patients with bladder cancer who underwent both TURBT and total radical cystectomy at the Kanagawa Cancer Centre in Yokohama, Kanagawa, Japan between May 2012, and February 2019, and who provided comprehensive consent, were recruited for this study. Detailed information of the cases is provided in Table I.

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Table I.

Clinical information of the patients with bladder cancer included in this study.

IHC examination. TURBT and radical cystectomy samples of the participants were fixed in 10% neutral buffered formalin. The samples were processed, paraffin-embedded, and cut into 5-μm thick sections for IHC examination. After antigen retrieval using a commercially available solution (415211; Nichirei Biosciences Inc., Tokyo, Japan) or protease (P8038, Sigma–Aldrich, St. Louis, MO, USA), slides were incubated with antibodies against EPHA2-C (sc-398832, Santa Cruz Biotechnology, Santa Cruz, CA, USA) or EPHA2-N [developed in our laboratory (22)] diluted to 1:100 in a blocking solution (SignalStain; Cell Signaling Technology, Danvers, MA, USA) for 1 h at room temperature. After washing with phosphate-buffered saline, the slides were incubated with ready-to-use biotinylated species-specific secondary antibodies (724132 and 724142; Nichirei Biosciences Inc.), washed again, and then incubated with 0.5 mg/ml 3,3′-diaminobenzidine (Nichirei Biosciences Inc.) for visualization.

Digital image-based staining quantification. The stained slides were scanned using an Aperio SC2 scanner (Leica Biosystems, Wetzlar, Germany) and imaged using an Aperio eSlide Manager (Leica Biosystems). The staining of cancer cells in WSI was quantitatively evaluated using the image analysis software, QuPath (23) and Fiji (24). Following conversion to a digital image, snapshots of a randomly selected tumour area were recorded at 10× magnification using QuPath. At least 10 snapshots were obtained for every slide. The stromal area was removed manually, and the positively stained area in the snapshots was automatically calculated using Fiji with the help of a plugin (IHC Profiler) (25, 26). The stained area was divided by the total area of the tumor to accurately calculate staining positivity.

Machine learning-based clustering of stained images. All stained images obtained from QuPath were stored in one folder. Unsupervised clustering was performed using the k-mean algorithm in the Google collaborator environment. Python programming language, version 3.7.12, was used to build the unsupervised clustering architecture.

Statistical analysis. For experimental data analysis, GraphPad Prism 9 software (GraphPad Software, San Diego, CA, USA) was used. Student’s unpaired t-test was used to quantify the statistical significance. Statistical significance was set at p<0.05. Pearson’s chi-squared test or one-sided Fisher’s exact test was used to determine correlations between clinical information and incidence of recurrence.

Results

Digital evaluation of IHC specimens and confirmation of N- and C-terminal EPHA2 expression. To precisely evaluate the EPHA2-positive area in the TURBT and radical cystectomy specimens, digital evaluations of the IHC specimens were performed using WSI and an image analyzer (Figure 1). The positively stained cancer area was evaluated and did not include any of the stromal area. The area positively stained for EPHA2-N was found to be significantly lower than that for EPHA2-C (Figure 2).

Figure 1.
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Figure 1.

Procedure used to determine the positively stained area. Step 1: The slides are scanned to prepare whole-slide images (WSI). Step 2: Snapshots are taken from the WSI using QuPath software. Step 3: The stromal area is removed using Fiji software. Step 4: The positively stained area is calculated only in the tumoral region using Fiji software. Step 5: Staining positivity is calculated by dividing the positively stained area by the whole tumoral area. Non-rec: Non-recurrent cases; Rec: recurrent cases; IHC: immunohistochemistry.

Figure 2.
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Figure 2.

Schematic showing the ephrin type-A receptor 2 (EPHA2) signaling pathway and staining pattern of C-terminal (EPHA2-C) and N-terminal (EPHA2-N) EPHA2. A: Schematic of the EPHA2-mediated regulatory mechanism in tumor progression. B: Representative immunohistochemistry (IHC) images of EPHA2-C and -N expression in normal urothelial epithelium (left) and invasive urothelial carcinoma (right). C: Graphs showing average staining positivity for EPHA2-C (left) and -N (right) in the whole cohort (N=20), as well as in cases at transurethral resection of bladder tumor (TURBT) and radical cystectomy. An unpaired t-test was used for comparison. **Significantly different at p<0.01. Scale bar: 100 μm. AKT: AKT serine/threonine kinase 1; DOCK-4: dedicator of cytokinesis 4; ELMO: engulfment and cell motility Protein; MT1-MMP: membrane type 1 matrix metalloproteinase; RAC-1: rac family small GTPase 1; RhoG: Ras homolog gene family, member G; RSK: ribosomal S6 kinase.

EPHA2 expression in TURBT and cystectomy samples of recurrent and non-recurrent cases. To predict the risk of recurrence at the time of TURBT, the positivity for EPHA2-C and -N staining was evaluated in the TURBT specimens and compared between the recurrent and non-recurrent cases. There was no significant difference in the areas positively stained for EPHA2-C and -N between the two groups (Figure 3). The ratio of EPHA2-N/EPHA2-C expression was also evaluated. The cases exhibiting a ratio >33% were classified as cases with a high N/C ratio based on general evaluation of IHC results (27). Interestingly, the number of cases with a high N/C ratio in the group with recurrence was significantly higher than in the non-recurrent group (Figure 4, p=0.029). In addition, EPHA2-C and -N positivity and the EPHA2-N/C ratio were also evaluated in the radical cystectomy specimens but no significant differences between the recurrent and non-recurrent cases were observed (Figure 5).

Figure 3.
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Figure 3.

Staining positivity of ephrin type-A receptor 2 (EPHA2) in specimens from transurethral resection of bladder tumor (TURBT) in non-recurrent (Non-Rec) and recurrent (Rec) cases. Comparison of the staining positivity of N-terminal (EPHA2-N) (A) and C-terminal (EPHA2-C) (B) EPHA2. C: Ratio of the staining positivity of EPHA2-N to that of EPHA2-C. An unpaired t-test was used for the comparison.

Figure 4.
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Figure 4.

Comparison of the staining positivity for C- and N-terminal ephrin type-A receptor 2 (EPHA2-C and EPHA2-N) between non-recurrent (Non-Rec) and recurrent (Rec) cases in transurethral resection of bladder tumor (TURBT) specimens. Scale bar: 50 μm. One-sided Fisher’s exact test was used for the statistical analysis.

Figure 5.
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Figure 5.

Staining positivity of ephrin type-A receptor 2 (EPHA2) in radical cystectomy specimens in non-recurrent (Non-Rec, n=10) and recurrent cases (Rec, n=10). Comparison of the staining positivity of N-terminal (EPHA2-N) (A) and C-terminal (EPHA2-C) (B) EPHA2. C: Ratio of the staining positivity of EPHA2-N to that of EPHA2-C. D-F: Chronological change in the staining positivity of ephrin type-A receptor 2 (EPHA2) using the ratio in specimens at radical cystectomy to that in transurethral resection of bladder tumor (TURBT) in non-recurrent (Non-Rec, n=10) and recurrent cases (Rec, n=10). D: Comparison of the ratio of EPHA2-N positivity in cystectomy specimens to that in TURBT specimens of the same cases. E: Comparison of the ratio of EPHA2-C positivity in cystectomy specimens to that in TURBT specimens of the same cases. F: Comparison of the EPHA2-N/C ratio in cystectomy specimens to that in TURBT specimens of the same cases. An unpaired t-test was performed for statistical analysis.

Chronological changes in EPHA2 expression in TURBT and radical cystectomy samples between recurrent and non-recurrent cases. The chronological changes in the ratio of the N- and C-terminal EPHA2 protein expression between the TURBT and radical cystectomy were assessed. EPHA2 staining was compared using the ratio of EPHA2-C and EPHA2-N expression in the radical cystectomy specimen to those in TURBT specimen from the same patients (Figure 5). The cases for which the EPHA2-C ratio was >2 were regarded as those with a high EPHA2-C ratio based on other studies (28, 29). The number of cases with a high EPHA2-C ratio in the group with recurrence was significantly higher than in the non-recurrent group (Figure 6, p=0.0054). We also performed additional statistical analysis using combined data from TURBT and cystectomy specimens (Figure 7), but no significant differences were observed.

Figure 6.
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Figure 6.

Chronological changes in the staining positivity of C- (EPHA-C) EPHA2 in transurethral resections of bladder tumor (TURBT) and radical cystectomy specimens in cases that are non-recurrent (Non-Rec) and recurrent (Rec). Scale bar: 50 μm. One-sided Fisher’s exact test was used for the statistical analysis. Ratio of the staining positivity of EPHA2-C (EPHA2-C ratio: staining positivity of EPHA2-C in cystectomy specimens per staining positivity of EPHA2-C in TURBT specimens of the same cases) between non-rec and rec cases were divided into two groups (EPHA2-C ratio ≥2 or EPHA2-C ratio <2).

Figure 7.
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Figure 7.

A-C: Comparison of EPHA2 staining intensity between non-recurrent and recurrent cases (n=10 in each group) in the whole cohort of patients who underwent transurethral resection of bladder tumor (TURBT) and those who underwent cystectomy. D-F: Comparison of EPHA2 staining intensity between patients who underwent transurethral resection of bladder tumor (TURBT) and those who underwent cystectomy (n=20 in each group). An unpaired t-test was performed for statistical analysis.

Machine learning-based comparison of the EPHA2 staining pattern between recurrent and non-recurrent cases. Machine learning-based assessments of the EPHA2 expression patterns in the recurrent and non-recurrent cases were conducted. Based on the staining patterns, the unsupervised clustering analysis using the k-mean approach allowed the cases to be divided into two groups. However, the distribution in each cluster was not significantly different between the recurrent and non-recurrent cases (Figure 8, p=0.1175).

Figure 8.
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Figure 8.

Machine learning-based clustering of EPHA2-N-stained transurethral resection of bladder tumors (TURBT) and Cystectomy specimens between non-recurrent and recurrent cases. Binarized EPHA2-N-stained images of TURBT and radical cystectomy specimens (2-5 images at 10× magnification in one specimen, with a total of 162 images from 40 specimens) were clustered using the K-means algorithm (p=0.1175).

Discussion

The results of the novel, combined, digitalized image analysis conducted in this study indicate that EPHA2 is a promising marker for identifying bladder cancer recurrence. Specifically, we clearly showed the ratio of EPHA2-C expression in the radical cystectomy to that in TURBT specimens significantly increased in the recurrent cases, and the statistical results showed an advantage or at least noninferiority against other recent recurrence prediction markers even our small sample size (30-32). Therefore, assessing a chronological increase of EPHA2-C expression using TURBT and radical cystectomy specimens in same patients might be useful for predicting tumor recurrence after radical cystectomy.

In this study, we established a comprehensive IHC image analysis method to evaluate the positively stained area. We removed the stromal area and specifically selected the tumor area, binarized the images, and assessed the positively stained area using the Fiji plugin tool to achieve objective evaluation. Previous analysis approaches using IHC staining positivity also used image analysis software, such as Fiji; however, they did not segregate the tumor area from the stromal area and thus, were not accurate (33, 34). In contrast, the method developed in this study specifically evaluates only the positively stained tumor area, which is representative of the actual protein expression in the tumor. Hence, owing to the potential diagnostic feature of this method, it is appropriate for the evaluation of disease predictive markers. Furthermore, this novel method is also suitable for assessing the efficacy of molecular targeted therapies based on protein expression levels.

The development of tumor malignancy can be observed by evaluating tumor-associated chronological changes in target molecule expression using pathological specimens acquired at different time points (35). Following chronological changes is important for identifying more aggressive cases. Therefore, we used both TURBT and radical cystectomy specimens to detect changes in EPHA2 expression from the time of TURBT to the time of radical cystectomy. The chronological changes in EPHA2-C expression might be promising as a marker of recurrence following radical cystectomy. Moreover, if the risk of recurrence after cystectomy is detected at the time of TURBT, physicians can administer intense neoadjuvant therapies and ensure that patients receive more frequent surveillance. Therefore, the combination of EPHA2-N and - C IHC results is promising for patients with an advanced T-stage (pT2 or more) after TURBT.

EPHA2 is already well-known as a prognostic marker for several tumor types. Peroxisome proliferator-activated receptor-gamma, one of the key molecules involved in adipocyte differentiation/obesity, also induces EPHA2 expression (36). Obesity significantly increases the rate of bladder cancer recurrence in patients who also smoke (37). Although there was no significant difference of body mass index between recurrent and non-recurrent cases in our study (data not shown), obesity-induced aggressiveness of bladder cancer might be related to EPHA2 expression.

We performed machine learning-based analysis for creating a simpler system for assessing the risk of recurrence. We tried to make a system for judging recurrence risk using clustering analysis of images based on the hypothesis that this would be able to divide patients into recurrent cases and non-recurrent cases. We performed clustering analysis using images of EPHA2-C in all cases; however, it was not possible to divide images by recurrent and non-recurrent cases. Therefore, we did not perform additional analysis; we will try these analyses again using a relevant number of cases in the future.

Although this study provides a novel tool with diagnostic potential, it does have certain limitations. Firstly, it included only a small number of cases, as the Kanagawa Cancer Centre is a specific cancer center and therefore most patients had already undergone TURBT at other hospitals. Therefore, the formalin-fixed, paraffin-embedded TURBT samples were not available from patients that only underwent radical cystectomy at our center. Complicated patient backgrounds were another limitation, as 40% of patients underwent neoadjuvant therapy, and this treatment-related modification may have affected the results of the staining of the cystectomy specimen. However, the effects of this limitation were minimized, as the same percentage of patients in the recurrent and non-recurrent groups were administered neoadjuvant therapy (Table I). Finally, the interval from TURBT to radical cystectomy was different in recurrent and non-recurrent cases (Table I). To overcome these limitations, evaluation of more cases in the future will be necessary via a multi-institute collaborative and prospective study.

To conclude, we conducted a detailed analysis of EPHA2 expression by assessing it as a marker of bladder cancer recurrence in patients after cystectomy. The results showed that EPHA2 might be a useful marker for tumor recurrence when employed to assess chronological expression changes between TURBT and cystectomy. However, further studies, including a large-scale analysis, are required to verify these claims. Additionally, there are promising agents currently available for use in adjuvant therapies, such as anti-programmed cell death protein 1 (38, 39), which are more advanced than conventional chemotherapies (40, 41). In this context, EPHA2 evaluations in the future might enable the selection of appropriate cases for adjuvant therapy from patients undergoing a radical cystectomy.

Acknowledgements

The Authors would like to thank Kumiko Ohrui and Chie Morohashi for helping us with specimen preparation and data acquisition. We would also like to thank Koji Yamamoto for the support with statistical analysis. Graphical abstract and part of Figure 1 were created with Biorender.com. We would like to thank Editage for English language editing. We would also like to express our gratitude towards Platform of Supporting Cohort Study and Biospecimen Analysis (CoBiA) for their support during the study.

This work was supported by the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI; grant number JP20K09422 to Shinya Sato and grant number JP17H06329 to Naohiko Koshikawa), Takeda Science Foundation (Shinya Sato), and Foundation for Promotion of cancer Research (Shinya Sato).

Footnotes

  • Authors’ Contributions

    Conceptualization, S.S. and N.K.; methodology, S.S.; software, S.S.; validation, S.S. and M.K.; data analysis, M.K. and S.S.; investigation, M.K. and S.S.; resources, M.K., H.T, K.W., Y.O., E.Y., S.S., T.Y., M.K., and T.K.; data curation, M.K.; immunohistochemistry, Y.N.; sectioning and preparing slides, M.Y.; writing—original draft preparation, S.S. and M.K.; writing—review and editing, S.S.; visualization, M.K. and S.S.; supervision, Y.M, N.K and T.K.; project administration, S.S.; funding acquisition, S.S. All Authors have read and agreed to the published version of the article.

  • Conflicts of Interest

    The Authors declare no conflicts of interest.

  • Received October 7, 2022.
  • Revision received October 14, 2022.
  • Accepted October 24, 2022.
  • Copyright © 2022 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

References

  1. ↵
    1. Saginala K,
    2. Barsouk A,
    3. Aluru JS,
    4. Rawla P,
    5. Padala SA and
    6. Barsouk A
    : Epidemiology of bladder cancer. Med Sci (Basel) 8(1): 15, 2020. PMID: 32183076. DOI: 10.3390/medsci8010015
    OpenUrlCrossRefPubMed
  2. ↵
    1. Richters A,
    2. Aben KKH and
    3. Kiemeney LALM
    : The global burden of urinary bladder cancer: an update. World J Urol 38(8): 1895-1904, 2020. PMID: 31676912. DOI: 10.1007/s00345-019-02984-4
    OpenUrlCrossRefPubMed
  3. ↵
    1. Gore JL,
    2. Litwin MS,
    3. Lai J,
    4. Yano EM,
    5. Madison R,
    6. Setodji C,
    7. Adams JL,
    8. Saigal CS and Urologic Diseases in America Project
    : Use of radical cystectomy for patients with invasive bladder cancer. J Natl Cancer Inst 102(11): 802-811, 2010. PMID: 20400716. DOI: 10.1093/jnci/djq121
    OpenUrlCrossRefPubMed
  4. ↵
    1. Hussain SA,
    2. Lester JF,
    3. Jackson R,
    4. Gornall M,
    5. Qureshi M,
    6. Elliott A,
    7. Crabb SJ,
    8. Huddart RA,
    9. Vasudev N,
    10. Birtle AJ,
    11. Worlding J,
    12. James ND,
    13. Parikh O,
    14. Vilarino-Varela M,
    15. Alonzi R,
    16. Linch MD,
    17. Riaz IB,
    18. Catto JWF,
    19. Powles T and
    20. Jones RJ
    : Addition of nintedanib or placebo to neoadjuvant gemcitabine and cisplatin in locally advanced muscle-invasive bladder cancer (NEOBLADE): a double-blind, randomised, phase 2 trial. Lancet Oncol 23(5): 650-658, 2022. PMID: 35421369. DOI: 10.1016/S1470-2045(22)00158-9
    OpenUrlCrossRefPubMed
  5. ↵
    1. Omura M,
    2. Kikuchi E,
    3. Shigeta K,
    4. Ogihara K,
    5. Hakozaki K,
    6. Hara S,
    7. Shirotake S,
    8. Ide H,
    9. Yoshimine S,
    10. Ohigashi T,
    11. Mizuno R and
    12. Oya M
    : Potential therapeutic effects of adjuvant chemotherapy after neoadjuvant chemotherapy for locally advanced muscle-invasive bladder cancer. Jpn J Clin Oncol 52(4): 388-396, 2022. PMID: 35106598. DOI: 10.1093/jjco/hyab210
    OpenUrlCrossRefPubMed
  6. ↵
    1. Cornu JN,
    2. Neuzillet Y,
    3. Hervé JM,
    4. Yonneau L,
    5. Botto H and
    6. Lebret T
    : Patterns of local recurrence after radical cystectomy in a contemporary series of patients with muscle-invasive bladder cancer. World J Urol 30(6): 821-826, 2012. PMID: 22940772. DOI: 10.1007/s00345-012-0936-8
    OpenUrlCrossRefPubMed
  7. ↵
    1. Novotny V,
    2. Froehner M,
    3. May M,
    4. Protzel C,
    5. Hergenröther K,
    6. Rink M,
    7. Chun FK,
    8. Fisch M,
    9. Roghmann F,
    10. Palisaar RJ,
    11. Noldus J,
    12. Gierth M,
    13. Fritsche HM,
    14. Burger M,
    15. Sikic D,
    16. Keck B,
    17. Wullich B,
    18. Nuhn P,
    19. Buchner A,
    20. Stief CG,
    21. Vallo S,
    22. Bartsch G,
    23. Haferkamp A,
    24. Bastian PJ,
    25. Hakenberg OW,
    26. Propping S and
    27. Aziz A
    : Risk stratification for locoregional recurrence after radical cystectomy for urothelial carcinoma of the bladder. World J Urol 33(11): 1753-1761, 2015. PMID: 25663359. DOI: 10.1007/s00345-015-1502-y
    OpenUrlCrossRefPubMed
  8. ↵
    1. Daneshmand S
    : Surveillance guidelines based on recurrence patterns after radical cystectomy for bladder cancer: the Canadian Bladder Cancer Network experience. BJU Int 110(9): 1324, 2012. PMID: 22500693. DOI: 10.1111/j.1464-410X.2012.11158.x
    OpenUrlCrossRefPubMed
  9. ↵
    1. Huguet J
    : Follow-up after radical cystectomy based on patterns of tumour recurrence and its risk factors. Actas Urol Esp 37(6): 376-382, 2013. PMID: 23611464. DOI: 10.1016/j.acuro.2013.01.005
    OpenUrlCrossRefPubMed
  10. ↵
    1. Lindberg RA and
    2. Hunter T
    : cDNA cloning and characterization of eck, an epithelial cell receptor protein-tyrosine kinase in the eph/elk family of protein kinases. Mol Cell Biol 10(12): 6316-6324, 1990. PMID: 2174105. DOI: 10.1128/mcb.10.12.6316-6324.1990
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Dodelet VC and
    2. Pasquale EB
    : Eph receptors and ephrin ligands: embryogenesis to tumorigenesis. Oncogene 19(49): 5614-5619, 2000. PMID: 11114742. DOI: 10.1038/sj.onc.1203856
    OpenUrlCrossRefPubMed
  12. ↵
    1. Ireton RC and
    2. Chen J
    : EphA2 receptor tyrosine kinase as a promising target for cancer therapeutics. Curr Cancer Drug Targets 5(3): 149-157, 2005. PMID: 15892616. DOI: 10.2174/1568009053765780
    OpenUrlCrossRefPubMed
  13. ↵
    1. Koshikawa N,
    2. Hoshino D,
    3. Taniguchi H,
    4. Minegishi T,
    5. Tomari T,
    6. Nam SO,
    7. Aoki M,
    8. Sueta T,
    9. Nakagawa T,
    10. Miyamoto S,
    11. Nabeshima K,
    12. Weaver AM and
    13. Seiki M
    : Proteolysis of EphA2 converts it from a tumor suppressor to an oncoprotein. Cancer Res 75(16): 3327-3339, 2015. PMID: 26130649. DOI: 10.1158/0008-5472.CAN-14-2798
    OpenUrlAbstract/FREE Full Text
  14. ↵
    1. Takahashi Y,
    2. Hamasaki M,
    3. Aoki M,
    4. Koga K,
    5. Koshikawa N,
    6. Miyamoto S and
    7. Nabeshima K
    : Activated EphA2 processing by MT1-MMP is involved in malignant transformation of ovarian tumours in vivo. Anticancer Res 38(7): 4257-4266, 2018. PMID: 29970559. DOI: 10.21873/anticanres.12722
    OpenUrlAbstract/FREE Full Text
  15. ↵
    1. Heim K,
    2. Müller-Holzner E,
    3. Pinzger G,
    4. Holböck E and
    5. Wartusch B
    : Cervical intraepithelial glandular neoplasia (adenocarcinoma in situ) of the cervix. Gynakol Rundsch 29(Suppl 2): 407-409, 1989. PMID: 2613064.
    OpenUrlPubMed
    1. Tatsukawa R,
    2. Koga K,
    3. Aoki M,
    4. Koshikawa N,
    5. Imafuku S,
    6. Nakayama J and
    7. Nabeshima K
    : Immunohistochemical demonstration of EphA2 processing by MT1-MMP in invasive cutaneous squamous cell carcinoma. Virchows Arch 469(1): 25-34, 2016. PMID: 27056569. DOI: 10.1007/s00428-016-1934-9
    OpenUrlCrossRefPubMed
    1. Kikuchi K,
    2. Kozuka-Hata H,
    3. Oyama M,
    4. Seiki M and
    5. Koshikawa N
    : Identification of proteolytic cleavage sites of EphA2 by membrane type 1 matrix metalloproteinase on the surface of cancer cells. Methods Mol Biol 1731: 29-37, 2018. PMID: 29318540. DOI: 10.1007/978-1-4939-7595-2_3
    OpenUrlCrossRefPubMed
    1. Wilson K,
    2. Shiuan E and
    3. Brantley-Sieders DM
    : Oncogenic functions and therapeutic targeting of EphA2 in cancer. Oncogene 40(14): 2483-2495, 2021. PMID: 33686241. DOI: 10.1038/s41388-021-01714-8
    OpenUrlCrossRefPubMed
  16. ↵
    1. Kurose H,
    2. Ueda K,
    3. Kondo R,
    4. Ogasawara S,
    5. Kusano H,
    6. Sanada S,
    7. Naito Y,
    8. Nakiri M,
    9. Nishihara K,
    10. Kakuma T,
    11. Akiba J,
    12. Igawa T and
    13. Yano H
    : Elevated expression of EPHA2 is associated with poor prognosis after radical prostatectomy in prostate cancer. Anticancer Res 39(11): 6249-6257, 2019. PMID: 31704854. DOI: 10.21873/anticanres.13834
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Ghofrani M,
    2. Tapia B and
    3. Tavassoli FA
    : Discrepancies in the diagnosis of intraductal proliferative lesions of the breast and its management implications: results of a multinational survey. Virchows Arch 449(6): 609-616, 2006. PMID: 17058097. DOI: 10.1007/s00428-006-0245-y
    OpenUrlCrossRefPubMed
  18. ↵
    1. Sato S,
    2. Maki S,
    3. Yamanaka T,
    4. Hoshino D,
    5. Ota Y,
    6. Yoshioka E,
    7. Kawachi K,
    8. Washimi K,
    9. Suzuki M,
    10. Ohkubo Y,
    11. Yokose T,
    12. Yamashita T,
    13. Ohtori S and
    14. Miyagi Y
    : Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues. Breast Cancer Res Treat 188(3): 649-659, 2021. PMID: 33934277. DOI: 10.1007/s10549-021-06243-2
    OpenUrlCrossRefPubMed
  19. ↵
    1. Koshikawa N,
    2. Minegishi T,
    3. Kiyokawa H and
    4. Seiki M
    : Specific detection of soluble EphA2 fragments in blood as a new biomarker for pancreatic cancer. Cell Death Dis 8(10): e3134, 2017. PMID: 29072678. DOI: 10.1038/cddis.2017.545
    OpenUrlCrossRefPubMed
  20. ↵
    1. Bankhead P,
    2. Loughrey MB,
    3. Fernández JA,
    4. Dombrowski Y,
    5. McArt DG,
    6. Dunne PD,
    7. McQuaid S,
    8. Gray RT,
    9. Murray LJ,
    10. Coleman HG,
    11. James JA,
    12. Salto-Tellez M and
    13. Hamilton PW
    : QuPath: Open source software for digital pathology image analysis. Sci Rep 7(1): 16878, 2017. PMID: 29203879. DOI: 10.1038/s41598-017-17204-5
    OpenUrlCrossRefPubMed
  21. ↵
    1. Schindelin J,
    2. Arganda-Carreras I,
    3. Frise E,
    4. Kaynig V,
    5. Longair M,
    6. Pietzsch T,
    7. Preibisch S,
    8. Rueden C,
    9. Saalfeld S,
    10. Schmid B,
    11. Tinevez JY,
    12. White DJ,
    13. Hartenstein V,
    14. Eliceiri K,
    15. Tomancak P and
    16. Cardona A
    : Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7): 676-682, 2012. PMID: 22743772. DOI: 10.1038/nmeth.2019
    OpenUrlCrossRefPubMed
  22. ↵
    1. Shu J,
    2. Fu H,
    3. Qiu G,
    4. Kaye P and
    5. Ilyas M
    : Segmenting overlapping cell nuclei in digital histopathology images. Annu Int Conf IEEE Eng Med Biol Soc 2013: 5445-5448, 2013. PMID: 24110968. DOI: 10.1109/EMBC.2013.6610781
    OpenUrlCrossRefPubMed
  23. ↵
    1. Varghese F,
    2. Bukhari AB,
    3. Malhotra R and
    4. De A
    : IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PLoS One 9(5): e96801, 2014. PMID: 24802416. DOI: 10.1371/journal.pone.0096801
    OpenUrlCrossRefPubMed
  24. ↵
    1. Allred DC,
    2. Harvey JM,
    3. Berardo M and
    4. Clark GM
    : Prognostic and predictive factors in breast cancer by immunohistochemical analysis. Mod Pathol 11(2): 155-168, 1998. PMID: 9504686.
    OpenUrlPubMed
  25. ↵
    1. Enomoto Y,
    2. Yamashita S,
    3. Yoshinaga Y,
    4. Fukami Y,
    5. Miyahara S,
    6. Nabeshima K and
    7. Iwasaki A
    : Downregulation of DYRK2 can be a predictor of recurrence in early stage breast cancer. Tumour Biol 35(11): 11021-11025, 2014. PMID: 25095982. DOI: 10.1007/s13277-014-2413-z
    OpenUrlCrossRefPubMed
  26. ↵
    1. Kriegsmann K,
    2. Zgorzelski C,
    3. Muley T,
    4. Christopoulos P,
    5. von Winterfeld M,
    6. Herpel E,
    7. Goeppert B,
    8. Mechtersheimer G,
    9. Sinn P,
    10. Stenzinger A,
    11. Schirmacher P,
    12. Winter H,
    13. Eichinger M,
    14. Warth A and
    15. Kriegsmann M
    : Immunohistological expression of oestrogen receptor, progesterone receptor, mammaglobin, human epidermal growth factor receptor 2 and GATA-binding protein 3 in non-small-cell lung cancer. Histopathology 77(6): 900-914, 2020. PMID: 32634256. DOI: 10.1111/his.14203
    OpenUrlCrossRefPubMed
  27. ↵
    1. Herzberg H,
    2. Lifshitz K,
    3. Golan S,
    4. Baniel J,
    5. Malshy K,
    6. Hoffman A,
    7. Amiel GE,
    8. Zreik R,
    9. Freifeld Y,
    10. Dekel Y,
    11. Lasmanovich R,
    12. Lazarovich A,
    13. Rosenzweig B,
    14. Dotan Z,
    15. Yossepowitch O and
    16. Mano R
    : Association between early change in neutrophil-to-lymphocyte ratio after radical cystectomy and treatment outcomes. BJU Int 130(4): 470-477, 2022. PMID: 35476895. DOI: 10.1111/bju.15757
    OpenUrlCrossRefPubMed
    1. Laukhtina E,
    2. Schuettfort VM,
    3. D’Andrea D,
    4. Pradere B,
    5. Mori K,
    6. Quhal F,
    7. Sari Motlagh R,
    8. Mostafaei H,
    9. Katayama S,
    10. Grossmann NC,
    11. Rajwa P,
    12. Zeinler F,
    13. Abufaraj M,
    14. Moschini M,
    15. Zimmermann K,
    16. Karakiewicz PI,
    17. Fajkovic H,
    18. Scherr D,
    19. Compérat E,
    20. Nyirady P,
    21. Rink M,
    22. Enikeev D and
    23. Shariat SF
    : Preoperative plasma level of endoglin as a predictor for disease outcomes after radical cystectomy for nonmetastatic urothelial carcinoma of the bladder. Mol Carcinog 61(1): 5-18, 2022. PMID: 34587660. DOI: 10.1002/mc.23355
    OpenUrlCrossRefPubMed
  28. ↵
    1. Liu Z,
    2. Zhang X,
    3. Wu B,
    4. Zhao Y and
    5. Bai S
    : Development and validation of a model for predicting urethral recurrence in male patients with muscular invasive bladder cancer after radical cystectomy combined with urinary diversion. Cancer Manag Res 12: 7649-7657, 2020. PMID: 32922074. DOI: 10.2147/CMAR.S261809
    OpenUrlCrossRefPubMed
  29. ↵
    1. Kawano S,
    2. Kojima M,
    3. Higuchi Y,
    4. Sugimoto M,
    5. Ikeda K,
    6. Sakuyama N,
    7. Takahashi S,
    8. Hayashi R,
    9. Ochiai A and
    10. Saito N
    : Assessment of elasticity of colorectal cancer tissue, clinical utility, pathological and phenotypical relevance. Cancer Sci 106(9): 1232-1239, 2015. PMID: 26083008. DOI: 10.1111/cas.12720
    OpenUrlCrossRefPubMed
  30. ↵
    1. Alves AF,
    2. Baldissera VD,
    3. Chiela ECF,
    4. Cerski CTS,
    5. Fontes PRO,
    6. Fernandes MDC,
    7. Porawski M and
    8. Giovenardi M
    : Altered expression of COX-2 and TNF-α in patients with hepatocellular carcinoma. Rev Esp Enferm Dig 111(5): 364-370, 2019. PMID: 30810331. DOI: 10.17235/reed.2019.5898/2018
    OpenUrlCrossRefPubMed
  31. ↵
    1. Satoh Y,
    2. Ishikawa Y,
    3. Nakagawa K,
    4. Hirano T and
    5. Tsuchiya E
    : A follow-up study of progression from dysplasia to squamous cell carcinoma with immunohistochemical examination of p53 protein overexpression in the bronchi of ex-chromate workers. Br J Cancer 75(5): 678-683, 1997. PMID: 9043024. DOI: 10.1038/bjc.1997.121
    OpenUrlCrossRefPubMed
  32. ↵
    1. Jiao X,
    2. Tian L,
    3. Zhang Z,
    4. Balcerek J,
    5. Kossenkov AV,
    6. Casimiro MC,
    7. Wang C,
    8. Liu Y,
    9. Ertel A,
    10. Soccio RE,
    11. Chen ER,
    12. Liu Q,
    13. Ashton AW,
    14. Tong W and
    15. Pestell RG
    : Pparγ1 facilitates ErbB2-mammary adenocarcinoma in mice. Cancers (Basel) 13(9): 2171, 2021. PMID: 33946495. DOI: 10.3390/cancers13092171
    OpenUrlCrossRefPubMed
  33. ↵
    1. Wyszynski A,
    2. Tanyos SA,
    3. Rees JR,
    4. Marsit CJ,
    5. Kelsey KT,
    6. Schned AR,
    7. Pendleton EM,
    8. Celaya MO,
    9. Zens MS,
    10. Karagas MR and
    11. Andrew AS
    : Body mass and smoking are modifiable risk factors for recurrent bladder cancer. Cancer 120(3): 408-414, 2014. PMID: 24122218. DOI: 10.1002/cncr.28394
    OpenUrlCrossRefPubMed
  34. ↵
    1. Jue JS,
    2. Koru-Sengul T,
    3. Miao F,
    4. Kroeger ZA,
    5. Moore KJ,
    6. Alameddine M,
    7. Punnen S,
    8. Parekh DJ,
    9. Ritch CR and
    10. Gonzalgo ML
    : Timing of adjuvant chemotherapy and overall survival following radical cystectomy. Urol Oncol 38(3): 75.e15-75.e22, 2020. PMID: 31864939. DOI: 10.1016/j.urolonc.2019.11.001
    OpenUrlCrossRefPubMed
  35. ↵
    1. Bajorin DF,
    2. Witjes JA,
    3. Gschwend JE,
    4. Schenker M,
    5. Valderrama BP,
    6. Tomita Y,
    7. Bamias A,
    8. Lebret T,
    9. Shariat SF,
    10. Park SH,
    11. Ye D,
    12. Agerbaek M,
    13. Enting D,
    14. McDermott R,
    15. Gajate P,
    16. Peer A,
    17. Milowsky MI,
    18. Nosov A,
    19. Neif Antonio J Jr.,
    20. Tupikowski K,
    21. Toms L,
    22. Fischer BS,
    23. Qureshi A,
    24. Collette S,
    25. Unsal-Kacmaz K,
    26. Broughton E,
    27. Zardavas D,
    28. Koon HB and
    29. Galsky MD
    : Adjuvant nivolumab versus placebo in muscle-invasive urothelial carcinoma. N Engl J Med 384(22): 2102-2114, 2021. PMID: 34077643. DOI: 10.1056/NEJMoa2034442
    OpenUrlCrossRefPubMed
  36. ↵
    1. Sternberg CN,
    2. Skoneczna I,
    3. Kerst JM,
    4. Albers P,
    5. Fossa SD,
    6. Agerbaek M,
    7. Dumez H,
    8. de Santis M,
    9. Théodore C,
    10. Leahy MG,
    11. Chester JD,
    12. Verbaeys A,
    13. Daugaard G,
    14. Wood L,
    15. Witjes JA,
    16. de Wit R,
    17. Geoffrois L,
    18. Sengelov L,
    19. Thalmann G,
    20. Charpentier D,
    21. Rolland F,
    22. Mignot L,
    23. Sundar S,
    24. Symonds P,
    25. Graham J,
    26. Joly F,
    27. Marreaud S,
    28. Collette L,
    29. Sylvester R, European Organisation for Research and Treatment of Cancer Genito-Urinary Cancers Group, Groupe d’Etude des Tumeurs Urogénitales, National Cancer Research Institute Bladder Cancer Study Group, National Cancer Institute of Canada Clinical Trials Group and German Association of Urologic Oncology
    : Immediate versus deferred chemotherapy after radical cystectomy in patients with pT3-pT4 or N+ M0 urothelial carcinoma of the bladder (EORTC 30994): an intergroup, open-label, randomised phase 3 trial. Lancet Oncol 16(1): 76-86, 2015. PMID: 25498218. DOI: 10.1016/S1470-2045(14)71160-X
    OpenUrlCrossRefPubMed
  37. ↵
    1. Galsky MD,
    2. Stensland KD,
    3. Moshier E,
    4. Sfakianos JP,
    5. McBride RB,
    6. Tsao CK,
    7. Casey M,
    8. Boffetta P,
    9. Oh WK,
    10. Mazumdar M and
    11. Wisnivesky JP
    : Effectiveness of adjuvant chemotherapy for locally advanced bladder cancer. J Clin Oncol 34(8): 825-832, 2016. PMID: 26786930. DOI: 10.1200/JCO.2015.64.1076
    OpenUrlAbstract/FREE Full Text
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Chronological Change in EPHA2 Protein Expression Is Associated With Recurrence of Bladder Cancer
MITSUYUKI KOIZUMI, SHINYA SATO, MITSUYO YOSHIHARA, YOSHIYASU NAKAMURA, HIDEYUKI TERAO, YOICHIRO OKUBO, KOTA WASHIMI, EMI YOSHIOKA, TOMOYUKI YOKOSE, TAKESHI KISHIDA, NAOHIKO KOSHIKAWA, YOHEI MIYAGI
Anticancer Research Dec 2022, 42 (12) 5783-5794; DOI: 10.21873/anticanres.16085

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Chronological Change in EPHA2 Protein Expression Is Associated With Recurrence of Bladder Cancer
MITSUYUKI KOIZUMI, SHINYA SATO, MITSUYO YOSHIHARA, YOSHIYASU NAKAMURA, HIDEYUKI TERAO, YOICHIRO OKUBO, KOTA WASHIMI, EMI YOSHIOKA, TOMOYUKI YOKOSE, TAKESHI KISHIDA, NAOHIKO KOSHIKAWA, YOHEI MIYAGI
Anticancer Research Dec 2022, 42 (12) 5783-5794; DOI: 10.21873/anticanres.16085
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Keywords

  • bladder cancer
  • EphA2
  • cancer recurrence
  • biomarker
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  • Machine learning
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