Developing a Nomogram for Preoperative Prediction of Cervical Cancer Lymph Node Metastasis by Multiplex Immunofluorescence

doi:10.21203/rs.3.rs-2092953/v1

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Research Article

Developing a Nomogram for Preoperative Prediction of Cervical Cancer Lymph Node Metastasis by Multiplex Immunofluorescence

https://doi.org/10.21203/rs.3.rs-2092953/v1

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Journal Publication

published 30 May, 2023

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Background

Most traditional procedures can destroy tissue natural structure, and the information on spatial distribution and temporal distribution of immune milieu in situ would be lost. We aimed to explore the potential mechanism of pelvic lymph node (pLN) metastasis of cervical cancer (CC) by multiplex immunofluorescence (mIF) and construct a nomogram for preoperative prediction of pLN metastasis in patients with CC.

Methods

Patients (180 IB1-IIA2 CC patients of 2009 FIGO (International Federation of Gynecology and Obstetrics)) were divided into two groups based on pLN status. Tissue microarray (TMA) was prepared and tumor-infiltrating immune markers were assessed by mIF. Multivariable logistic regression analysis and nomogram were used to develop the predicting model.

Results

Multivariable logistic regression analysis constructs a predictive model and the area under the curve (AUC) can reach 0.843. By internal validation with the remaining 40 percent of cases, a new ROC curve has emerged and the AUC reached 0.888.

Conclusions

This study presents an immune nomogram, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CC.

Cervical cancer

preoperative individualized prediction

multiplex immunofluorescence

nomogram

internally validation

Cervical cancer (CC) is one of the leading malignant tumors in the United States [1, 2]. Lymph node (LN) metastasis has been considered a vital factor in the development of CC [3–6]. Patients with this stage IB1-IIA2 CC (2009 International Federation of Gynecology and Obstetrics (FIGO) stage) will undergo pelvic LN (pLN) resection [7] and cause a variety of complications, including bleeding, nerve injury, lower pelvic lymphocele, and extremity lymphedema [8, 9] [10, 11]. Thus, it will be particularly important to further study the mechanism of CC pLN metastasis. Furthermore, preoperative assessment of pLN status in CC patients is therefore critical for clinical decision-making.

Increasing evidence has demonstrated that pLN metastasis is a complex process involving tumor immune milieu [12–15]. However, molecular mechanisms behind metastasis processes remain obscure. Traditional procedures have been used to elucidate the mechanism by regularly exploring molecular pathways [14, 16, 17]. However, due to the tissue-destructive nature of most of these methods, the spatial distribution and temporal distribution of the immune milieu in situ will not be preserved [18]. Although morphological examination including conventional immunohistochemistry (IHC) or immunofluorescence (IF) can reveal some information, its effect is extremely limited because of high inter-observer variability and the capacity to label only one marker per tissue section [19].

Multiplex immunohistochemistry/immunofluorescence (m-IHC/IF) has emerged and provides high-throughput multiplex staining and further standardized quantitative analysis for highly efficient, reproducible, and cost-effective tissue studies [19–21]. It can show up to seven targets simultaneously on a single slide. Afterward, HALO (Indica Labs, Albuquerque, USA), an image analysis system not only can be used for quantitative tissue analysis but also can reveal the spatial location of each target [22–26].

In the current study, we sought to comprehensively compare the quantification of immune markers and their spatial orientation interrelation between CC in situ tissue with positive pLN and negative pLN. Based on differential expression of immune-related indicators, a clinical prediction model was constructed for individual preoperative prediction of pLN metastasis and an internal validation was assessed. In the future, we can predict pLN metastasis by cervical biopsy, thus avoiding its dissection and improving patients’ quality of life.

Microarray Dataset Collection and Data Process

To systematically analyze the differential immune makeup between cervical cancer patients with positive LN and negative LN, the cancer genome atlas (TCGA), a comprehensive database for inferring the relative abundance of diverse cell infiltrates was conducted.

Patient Cohort

All procedures were ethically approved by the institutional Ethics Review Committee of Fudan University Shanghai Cancer Center (FDCC). Appropriate written informed consent was obtained from all patients before sample collection. All experimental methods comply with relevant regulations and ethical principles.

A retrospective cohort study was conducted in the Department of Gynecology Oncology, FDCC, which included 180 patients with the 2009 FIGO stage IB1-IIA2 who underwent radical abdominal hysterectomy with or without bilateral salpingo-oophorectomy and pelvic ± para-aortic lymphadenectomy from 2009 to 2012. All the enrolled patients had undergone standard pelvic lymphadenectomy by an experienced gynecological oncologist. All the microscopic slides were reviewed by the same professional gynecologic pathologist and were reconfirmed by another experienced gynecologic pathologist. A total of patients (90 with positive pLN and 90 with negative pLN) were assayed and their tissue specimens and clinical records were retrospectively studied.

Making Tissue Microarray (TMA)

The 180 CC tissues (in situ) with pLN positive metastasis or negative metastasis were prepared into TMA as previously described [31, 32].

M-IF Staining Protocol

Opal 7-colour kit (NEL811001KT, PerkinElmer) was used for mIF. TMAs were dewaxed and rehydrated. In the first step, the antigen was retrieved at 125 ℃ for 3 min and then cooled to room temperature (RT). Washed with TBST three times for 5min, incubated in H₂O₂ for 10 min. Repeated washed and blocked with blocking buffer. The primary antibody, PDL-1 (ab237726, Abcam, 1:500, dye 480) was incubated at RT for 30min. Slides were washed and an HRP-conjugated secondary antibody was incubated at RT for 10min. TSA dye (1:100) was applied for 10min after washes. The procedures were repeated six times using the following antibodies, CD3 (ab16669, Abcam, 1:200, dye 690; used as T lymphocyte cell marker [33]), CD8 (ab93278, Abcam, 1:100, dye 570; used as cytotoxic T cell marker [34]), CD56 (ab75813, Abcam, 1:500, dye 620; used as NK cell marker [35]), CD68 (ab213363, 1:1000, Abcam, dye 780; used as pan-macrophage marker [36]), programmed death-1 (PD-1) (ab237728, Abcam, 1:300, dye 520), programmed death ligand-1 (PD-L1) (ab237726, 1:500, dye 480) [37]. Secondary antibodies anti-mouse (NEF822001EA, PerkinElmer) or anti-rabbit (NEF812001EA, PerkinElmer) were used at a 1:1000 dilution.

Constructing a Diagnostic Prediction Model

By comparing the difference in quantification and spatial distribution of immune markers, we can construct a diagnostic prediction model by randomly selecting sixty percent of the cases [38, 39].

Validation of the Prediction Nomogram

Internal validation was performed using the rest of the 40 percent data set.

Statistical Analysis

Statistical analysis was performed with Graphpad Prism 8 and R language. Unsupervised hierarchical clustering was conducted to define the immune subtypes by different markers. Comparisons between two conditions were based on a two-sided Student’s test. *P༜0.05 were judged to be statistically significant.

TCGA of Cervical Squamous Cell Carcinoma (CESC)

Our TCGA data have systematically analyzed the differential gene makeup in the two groups. The expression profiles of fragments per kilobase of transcript per million fragments mapped (FPKM) based on TCGA Cervical Squamous Cell Carcinoma (CESC) were used to analyze the expression differences of non-metastasis and metastasis in CC patients. The differential screening criteria were set at |log2(FC)|>1, *P༜0.05, and the results showed that forty-two genes were upregulated and sixty-seven genes were downregulated (Fig. 1A and Fig. B). Further data mining revealed that LN metastasis of CESC may be related to immune infiltration (Fig. 1C).

Patient Characteristics

The study cohort contained 180 cases of CC with high-quality TMA. The median age of the patients was 46 years (range, 23–71 years). The histological diagnosis was all squamous cell carcinoma and based on the FIGO 2009 guidelines, nineteen (21.1%) subjects were stage IB1, twenty (22.2%) subjects were stage IB2, twenty-nine (32.3%) subjects were stage IIA1, and twenty-two (24.4%) subjects were stage IIA2. The median follow-up time was 61.05 months (range, 7.93–78.50 months) while thirteen (14.4%) subjects relapsed and seven (7.8%) subjects died.

Immune Infiltrates in CC

By mIF and HALO system was further applied to scan the relative ratio of each target on each tissue on the TMA [27, 28]. The results can be graphically presented in heatmaps and their color can be digitized (Fig. 2).

To better understand the complex immune characteristics in CC, we quantified the immune stains by the HALO system. These six immunologic markers revealed distinct positive staining ratios in 180 samples. We observed CD8 marker (19.87% ± 3.47% vs 29.65% ± 3.31%; P༜0.05), CD68 + macrophages (1.23% ± 0.38% vs 0.35% ± 0.94%; P༜0.05), PD-1 (39.23% ± 3.37% vs 19.06% ± 2.43%; P༜0.0001), PD-L1 (51.41% ± 3.47% vs 11.62% ± 1.67%; P༜0.0001) in CC with positive pLN and negative pLN respectively (Fig. 3).

In a word, our results demonstrated that the positive proportion of CD68, PD-1 and PD-L1 immune cell infiltration were significantly up-regulated with pLN metastasis, while CD8 was significantly decreased, CD3 and CD56 did not change.

Distinct Spatial Distribution of Immune Infiltration

The HALO can not only quantify the number of immune cells on a panel but also locate their position, and measure their spatial distance [20, 29, 30]. In this way, we can further find the relative number and location of immune cells in situ tissue of CC after pLN metastasis more visually and thereby draw the relationship of immune cells which would promote LN metastasis.

The average distance (um) of CD8 to CD56 was 8.33 ± 1.26% in the pLN-negative group, 3.86 ± 1.08% in the pLN-positive group (pLN-negative versus pLN-positive, P < 0.05) (Fig. 4A). The average distance (um) of CD8 to CD68 was 75.08 ± 21.91% in the pLN-negative group, 5.56 ± 1.62% in the pLN-positive group (pLN-negative versus pLN-positive, P < 0.01) (Fig. 4B). The average distance (um) of CD8 to PD-1 was 4.12 ± 0.76% in the pLN-negative group, 1.30 ± 0.08% in the pLN-positive group (pLN-negative versus pLN-positive, P < 0.001) (Fig. 4C). The average distance (um) of CD8 to PD-L1 was 5.58 ± 0.81% in the pLN-negative group, 1.50 ± 0.16% in the pLN-positive group (pLN-negative versus pLN-positive, P < 0.0001) (Fig. 4D). In these data, CD8 + T cells were significantly close to NK cells, macrophages and tumor cells with pLN metastasis.

The average distance (um) of CD56 to PD-1 was 2.11 ± 0.24% in the pLN-negative group, 0.91 ± 0.09% in the pLN-positive group (pLN-negative versus pLN-positive, P < 0.0001) (Fig. 4E). The average distance (um) of CD56 to PD-L1 was 2.57 ± 0.32% in the pLN-negative group, 1.26 ± 0.18% in the pLN-positive group (pLN-negative versus pLN-positive, P < 0.0001) (Fig. 4F). Thus, NK cells were significantly close to tumor cells with pLN metastasis.

The average distance (um) of PD-1 to PD-L1 was 2.09 ± 0.10% in the pLN-negative group, 1.74 ± 0.09% in the pLN-positive group (pLN-negative versus pLN-positive, P < 0.05) (Fig. 4G).

Development of an Individualized Prediction Model

Multivariable logistic regression analysis began with the following immune variables: CD3, CD8, CD56, CD68, PD-1, PD-L1, the distance between CD8 and CD56, CD8 and CD68, CD8 and PD-1, CD8 and PD-L1, CD56 and PD-1, CD56 and PD-L1, PD-1 and PDL-1. The immune signature was applied to develop a diagnostic model for pLN metastasis by using the two groups. A ROC curve is made for each meaningful marker and every cutoff value has emerged. Thereafter, these cutoff values are divided into two groups. At this point, we can make a diagnostic ROC curve and the AUC reaches 0.843 (Fig. 5A). The model that incorporated these above independent predictors was developed and presented as the nomogram (Fig. 5B).

3.6 Internally Validation of the Prediction Model. With the nomogram, we then tested the remaining 40 percent of cases and obtained a ROC curve with an AUC reaching 0.888. (Fig. 5C).

Patients with the 2009 FIGO stage of IB1-IIA2 CC will undergo pLN resection and impair the patient’s quality of life [8, 40]. Beginning with this study, our TCGA data originating from bioinformatics analysis demonstrated that LN metastasis of CESC may be related to immune infiltration. Results stressed that the major immune infiltration cell types were natural killer (NK) cells, macrophages, and T cells.

In addition, traditional procedures have been used to elucidate the mechanism by regularly exploring such molecular pathways which would destroy the spatial structure[41, 42]. By mIF and HALO system, their spatial orientation interrelation of immune cells and immune markers would be preserved [19, 43]. Our results further revealed that immune infiltration, including CD68, PD-1, and PD-L1 were significantly up-regulated and CD8 was significantly down-regulated with pLN metastasis of CC. This can be explained by macrophages and the immune molecule PD-1, PD-L1 may promote pLN metastasis of CC.

Thirdly, by spatial proximity analysis, the average distance (um) between CD8 and CD56, CD8 and CD68, CD8 and PD-1, CD8 and PD-L1, CD56 and PD-1, CD56 and PD-L1 were significantly closer with pLN metastasis. These data have demonstrated that CD8 + T cells, NK cells, and macrophages may be involved in pLN metastasis of CC.

Lastly, based on randomly selected sixty percent of patients, a diagnostic prediction model was established and the AUC can reach 0.843. The nomogram incorporates five items of PD-1, PD-L1, the average distance of CD56 to PD-1, the average distance of CD56 to PD-L1, and the average distance of PD-1 to PD-L1. By internal validation with the remaining 40 percent of cases, a new ROC curve has emerged and the AUC reached 0.888. Our nomogram can serve as an effective preoperative predictive tool to assess pLN status in CC patients.

The cervical biopsy is an essential step before the diagnosis of CC. In the process, we can take part of cancer tissue through the cervical biopsy for mIF detection. HALO system is further applied in quantitative and spatial analysis. By the diagnostic prediction model, we can predict pLN metastasis preoperatively, thus avoiding unnecessary routine pLN dissection. In this way, we can avoid additional surgical trauma and possible complications for patients.

The limitations of our study include external validation for the model. External validation is needed to acquire high-level evidence for clinical application.

In conclusion, we construct an immune nomogram that incorporates five items of PD-1, PD-L1, the average distance of CD56 to PD-1, the average distance of CD56 to PD-L1, and the average distance of PD-1 to PD-L1, which can be conveniently used to facilitate the preoperative individualized prediction of pLN metastasis in patients with CC.

Ethics approval and consent to participate

All study surgical procedures and experiment protocols have been approved by the Ethics Committee of Experimental Research, Shanghai Medical College, Fudan University. All participants have been informed the potential risks and benefits and each patient has signed the informed consent form. All experimental methods comply with relevant regulations and ethical principles.

Consent for publication

Not applicable.

Availability of data and materials

The data that support the results of this study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no conflict of interest.

Funding

This study was supported by the National Natural Science Foundation of China (numbers 81972431, 82002747); Shanghai Sailing Program (number 20YF1408000); and Shanghai Anticancer Association EYAS PROJECT (number SACA-CY19A07).

Author’s contributions

JC W, QH G, and J Z: Conceptualization, Data curation, Investigation, Writing - original draft. Y W: Conceptualization, Data curation, Investigation, Writing - original draft, Formal analysis, Methodology, Resources, Software, Validation, Visualization. SY L: Conceptualization, Project administration, Supervision, Writing - original draft. SM W: Conceptualization, Funding acquisition, Project administration, Supervision, Writing - original draft, Writing - review & editing. XZ J, XH W: Conceptualization, Funding acquisition, Project administration, Supervision, Writing - original draft, Writing - review & editing.

Acknowledgements

Not applicable

Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Stat 2021 CA Cancer J Clin. 2021;71:7–33. 10.3322/caac.21654.
Waggoner SE. Cervical cancer. Lancet. 2003;361:2217–25. 10.1016/S0140-6736(03)13778-6.
Gien LT, Covens A. Lymph node assessment in cervical cancer: prognostic and therapeutic implications. J Surg Oncol. 2009;99:242–7. 10.1002/jso.21199.
Cibula D, McCluggage WG. Sentinel lymph node (SLN) concept in cervical cancer: Current limitations and unanswered questions. Gynecol Oncol. 2019;152:202–7. 10.1016/j.ygyno.2018.10.007.
Wu S, Zheng J, Li Y, Yu H, Shi S, Xie W, Liu H, Su Y, Huang J, Lin T. A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer, Clin Cancer Res 23 (2017) 6904–11. 10.1158/1078 – 0432.CCR-17-1510.
Li K, Sun H, Guo Q. Combinative evaluation of primary tumor and lymph nodes in predicting pelvic lymphatic metastasis in early-stage cervical cancer: A multiparametric PET-CT study. Eur J Radiol. 2019;113:153–7. 10.1016/j.ejrad.2019.02.013.
Guo Q, Zhu J, Wu Y, Wen H, Xia L, Yu M, Wang S, Ju X, Wu X. Comparison of different lymph node staging systems in patients with node-positive cervical squamous cell carcinoma following radical surgery. J Cancer. 2020;11:7339–47. 10.7150/jca.48085.
Lea JS, Lin KY. Cervical cancer. Obstet Gynecol Clin North Am. 2012;39:233–53. 10.1016/j.ogc.2012.02.008.
Undurraga M, Loubeyre P, Dubuisson JB, Schneider D, Petignat P. Early-stage cervical cancer: is surgery better than radiotherapy? Expert Rev Anticancer Ther. 2010;10:451–60. 10.1586/era.09.192.
Newman ML, Brennan M, Passik S. Lymphedema complicated by pain and psychological distress: a case with complex treatment needs. J Pain Symptom Manage. 1996;12:376–9. 10.1016/s0885-3924(96)00187-x.
Hareyama H, Hada K, Goto K, Watanabe S, Hakoyama M, Oku K, Hayakashi Y, Hirayama E, Okuyama K. Prevalence, classification, and risk factors for postoperative lower extremity lymphedema in women with gynecologic malignancies: a retrospective study. Int J Gynecol Cancer. 2015;25:751–7. 10.1097/IGC.0000000000000405.
Fridman WH, Galon J, Pages F, Tartour E, Sautes-Fridman C, Kroemer G. Prognostic and predictive impact of intra- and peritumoral immune infiltrates. Cancer Res. 2011;71:5601–5. 10.1158/0008-5472.CAN-11-1316.
Whiteside TL. Immune cells in the tumor microenvironment. Mechanisms responsible for functional and signaling defects. Adv Exp Med Biol. 1998;451:167–71.
Karaman S, Detmar M. Mechanisms of lymphatic metastasis. J Clin Invest. 2014;124:922–8. 10.1172/JCI71606.
Balsat C, Blacher S, Herfs M, Van de Velde M, Signolle N, Sauthier P, Pottier C, Gofflot S, De Cuypere M, Delvenne P, Goffin F, Noel A, Kridelka F. A specific immune and lymphatic profile characterizes the pre-metastatic state of the sentinel lymph node in patients with early cervical cancer. Oncoimmunology. 2017;6:e1265718. 10.1080/2162402X.2016.1265718.
Kawada K, Taketo MM. Significance and mechanism of lymph node metastasis in cancer progression. Cancer Res. 2011;71:1214–8. 10.1158/0008-5472.CAN-10-3277.
Pouliot F, Johnson M, Wu L. Non-invasive molecular imaging of prostate cancer lymph node metastasis. Trends Mol Med. 2009;15:254–62. 10.1016/j.molmed.2009.04.004.
Huang YK, Wang M, Sun Y, Di Costanzo N, Mitchell C, Achuthan A, Hamilton JA, Busuttil RA, Boussioutas A. Macrophage spatial heterogeneity in gastric cancer defined by multiplex immunohistochemistry. Nat Commun. 2019;10:3928. 10.1038/s41467-019-11788-4.
Tan WCC, Nerurkar SN, Cai HY, Ng HHM, Wu D, Wee YTF, Lim JCT, Yeong J, Lim TKH. Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Commun (Lond). 2020;40:135–53. 10.1002/cac2.12023.
Nearchou IP, Gwyther BM, Georgiakakis ECT, Gavriel CG, Lillard K, Kajiwara Y, Ueno H, Harrison DJ, Caie PD. Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients. NPJ Digit Med. 2020;3:71. 10.1038/s41746-020-0275-x.
Zou J, Qin W, Yang L, Wang L, Wang Y, Shen J, Xiong W, Yu S, Song S, Ajani JA, Lin SY, Mills GB, Yuan X, Chen J, Peng G. Genetic alterations and expression characteristics of ARID1A impact tumor immune contexture and survival in early-onset gastric cancer. Am J Cancer Res. 2020;10:3947–72.
Lykkegaard Andersen N, Brugmann A, Lelkaitis G, Nielsen S, Friis Lippert M, Vyberg M. Virtual Double Staining: A Digital Approach to Immunohistochemical Quantification of Estrogen Receptor Protein in Breast Carcinoma Specimens. Appl Immunohistochem Mol Morphol. 2018;26:620–6. 10.1097/PAI.0000000000000502.
Toki MI, Merritt CR, Wong PF, Smithy JW, Kluger HM, Syrigos KN, Ong GT, Warren SE, Beechem JM, Rimm DL. High-Plex Predictive Marker Discovery for Melanoma Immunotherapy-Treated Patients Using Digital Spatial Profiling, Clin Cancer Res 25 (2019) 5503–12. 10.1158/1078 – 0432.CCR-19-0104.
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74. 10.1016/j.cell.2011.02.013.
Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100:57–70. 10.1016/s0092-8674(00)81683-9.
Hainaut P, Plymoth A. Targeting the hallmarks of cancer: towards a rational approach to next-generation cancer therapy. Curr Opin Oncol. 2013;25:50–1. 10.1097/CCO.0b013e32835b651e.
Shiraishi T, Shinto E, Nearchou IP, Tsuda H, Kajiwara Y, Einama T, Caie PD, Kishi Y, Ueno H. Prognostic significance of mesothelin expression in colorectal cancer disclosed by area-specific four-point tissue microarrays. Virchows Arch. 2020;477:409–20. 10.1007/s00428-020-02775-y.
Cloughesy TF, Mochizuki AY, Orpilla JR, Hugo W, Lee AH, Davidson TB, Wang AC, Ellingson BM, Rytlewski JA, Sanders CM, Kawaguchi ES, Du L, Li G, Yong WH, Gaffey SC, Cohen AL, Mellinghoff IK, Lee EQ, Reardon DA, O'Brien BJ, Butowski NA, Nghiemphu PL, Clarke JL, Arrillaga-Romany IC, Colman H, Kaley TJ, de Groot JF, Liau LM, Wen PY. R.M. Prins, Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. Nat Med. 2019;25:477–86. 10.1038/s41591-018-0337-7.
Giraldo NA, Nguyen P, Engle EL, Kaunitz GJ, Cottrell TR, Berry S, Green B, Soni A, Cuda JD, Stein JE, Sunshine JC, Succaria F, Xu H, Ogurtsova A, Danilova L, Church CD, Miller NJ, Fling S, Lundgren L, Ramchurren N, Yearley JH, Lipson EJ, Cheever M, Anders RA, Nghiem PT, Topalian SL, Taube JM. Multidimensional, quantitative assessment of PD-1/PD-L1 expression in patients with Merkel cell carcinoma and association with response to pembrolizumab. J Immunother Cancer. 2018;6:99. 10.1186/s40425-018-0404-0.
Koelzer VH, Sirinukunwattana K, Rittscher J, Mertz KD. Precision immunoprofiling by image analysis and artificial intelligence. Virchows Arch. 2019;474:511–22. 10.1007/s00428-018-2485-z.
Glinsmann-Gibson B, Wisner L, Stanton M, Larsen B, Rimsza L, Maguire A. Recommendations for Tissue Microarray Construction and Quality Assurance. Appl Immunohistochem Mol Morphol. 2020;28:325–30. 10.1097/PAI.0000000000000739.
Wang Z, Liu Y, Lu L, Yang L, Yin S, Wang Y, Qi Z, Meng J, Zang R, Yang G. Fibrillin-1, induced by Aurora-A but inhibited by BRCA2, promotes ovarian cancer metastasis. Oncotarget. 2015;6:6670–83. 10.18632/oncotarget.3118.
Brandt D, Hedrich CM. TCRalphabeta(+)CD3(+)CD4(-)CD8(-) (double negative) T cells in autoimmunity. Autoimmun Rev. 2018;17:422–30. 10.1016/j.autrev.2018.02.001.
van der Leun AM, Thommen DS, Schumacher TN. CD8(+) T cell states in human cancer: insights from single-cell analysis. Nat Rev Cancer. 2020;20:218–32. 10.1038/s41568-019-0235-4.
Michel T, Poli A, Cuapio A, Briquemont B, Iserentant G, Ollert M, Zimmer J. Human CD56bright NK Cells: An Update. J Immunol. 2016;196:2923–31. 10.4049/jimmunol.1502570.
da Silva RP, Gordon S. Phagocytosis stimulates alternative glycosylation of macrosialin (mouse CD68), a macrophage-specific endosomal protein. Biochem J. 1999;338(Pt 3):687–94.
Sharpe AH, Pauken KE. The diverse functions of the PD1 inhibitory pathway. Nat Rev Immunol. 2018;18:153–67. 10.1038/nri.2017.108.
Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, Ma ZL, Liu ZY. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016;34:2157–64. 10.1200/JCO.2015.65.9128.
Zhang JX, Song W, Chen ZH, Wei JH, Liao YJ, Lei J, Hu M, Chen GZ, Liao B, Lu J, Zhao HW, Chen W, He YL, Wang HY, Xie D, Luo JH. Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol. 2013;14:1295–306. 10.1016/S1470-2045(13)70491-1.
Luhrs O, Ekdahl L, Geppert B, Lonnerfors C, Persson J. Resection of the upper paracervical lymphovascular tissue should be an integral part of a pelvic sentinel lymph node algorithm in early stage cervical cancer. Gynecol Oncol. 2021;163:289–93. 10.1016/j.ygyno.2021.08.031.
Wu H, Song S, Yan A, Guo X, Chang L, Xu L, Hu L, Kuang M, Liu B, He D, Zhao R, Wang L, Wu X, Gu J, Ruan Y. RACK1 promotes the invasive activities and lymph node metastasis of cervical cancer via galectin-1. Cancer Lett. 2020;469:287–300. 10.1016/j.canlet.2019.11.002.
Shang C, Wang W, Liao Y, Chen Y, Liu T, Du Q, Huang J, Liang Y, Liu J, Zhao Y, Guo L, Hu Z, Yao S. LNMICC Promotes Nodal Metastasis of Cervical Cancer by Reprogramming Fatty Acid Metabolism. Cancer Res. 2018;78:877–90. 10.1158/0008-5472.CAN-17-2356.
Carbone A, Gloghini A, Pruneri G, Dolcetti R. Optimizing checkpoint inhibitors therapy for relapsed or progressive classic Hodgkin lymphoma by multiplex immunohistochemistry of the tumor microenvironment. Cancer Med. 2019;8:3012–6. 10.1002/cam4.2168.

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Developing a Nomogram for Preoperative Prediction of Cervical Cancer Lymph Node Metastasis by Multiplex Immunofluorescence

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Journal Publication

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Abstract

Background

Methods

Results

Conclusions

Figures

Background

Materials And Methods

Microarray Dataset Collection and Data Process

Patient Cohort

Making Tissue Microarray (TMA)

M-IF Staining Protocol

Constructing a Diagnostic Prediction Model

Validation of the Prediction Nomogram

Statistical Analysis

Results

TCGA of Cervical Squamous Cell Carcinoma (CESC)

Patient Characteristics

Immune Infiltrates in CC

Distinct Spatial Distribution of Immune Infiltration

Development of an Individualized Prediction Model

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

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