AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Cancer Biology & Medicine Article
PDF (4.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Original Article | Open Access

Artificial intelligence-based comprehensive analysis of immune-stemness-tumor budding profile to predict survival of patients with pancreatic adenocarcinoma

Tianxing Zhou1,*, Quan Man1,2,*, Xueyang Li1,3, Yongjie Xie1, Xupeng Hou1,3, Hailong Wang4, Jingrui Yan1, Xueqing Wei5, Weiwei Bai1, Ziyun Liu1,3, Jing Liu1,3 ( ), Jihui Hao1 ( )
Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
Department of Hepatopancreatobiliary Surgery, Tongliao City Hospital, Tongliao 028000, China
Department of Breast Oncoplastic Surgery
Department of Cancer Cell Biology
Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin 300060, China

*These authors contributed equally to this work.

Show Author Information

Abstract

Objective

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy. CD8+ T cells, cancer stem cells (CSCs), and tumor budding (TB) have been significantly correlated with the outcome of patients with PDAC, but the correlations have been independently reported. In addition, no integrated immune-CSC-TB profile for predicting survival in patients with PDAC has been established.

Methods

Multiplexed immunofluorescence and artificial intelligence (AI)-based comprehensive analyses were used for quantification and spatial distribution analysis of CD8+ T cells, CD133+ CSCs, and TB. In vivo humanized patient-derived xenograft (PDX) models were established. Nomogram analysis, calibration curve, time-dependent receiver operating characteristic curve, and decision curve analyses were performed using R software.

Results

The established ‘anti-/pro-tumor’ models showed that the CD8+ T cell/TB, CD8+ T cell/CD133+ CSC, TB-adjacent CD8+ T cell, and CD133+ CSC-adjacent CD8+ T cell indices were positively associated with survival of patients with PDAC. These findings were validated using PDX-transplanted humanized mouse models. An integrated nomogram-based immune-CSC-TB profile that included the CD8+ T cell/TB and CD8+ T cell/CD133+ CSC indices was established and shown to be superior to the tumor-node-metastasis stage model in predicting survival of patients with PDAC.

Conclusions

‘Anti-/pro-tumor’ models and the spatial relationship among CD8+ T cells, CSCs, and TB within the tumor microenvironment were investigated. Novel strategies to predict the prognosis of patients with PDAC were established using AI-based comprehensive analysis and machine learning workflow. The nomogram-based immune-CSC-TB profile can provide accurate prognosis prediction for patients with PDAC.

Keywords

Artificial intelligencenomogramCSCsPDACCD8tumor budding

Electronic Supplementary Material

Download File(s)
cbm-20-3-196_ESM1.pdf (1.6 MB)
cbm-20-3-196_ESM2.pdf (364.9 KB)
cbm-20-3-196_ESM3.pdf (500.9 KB)

References

1

Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021; 71: 7-33.
Crossref Google Scholar
2

Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014; 74: 2913-21.
Crossref Google Scholar
3

Tempero MA, Malafa MP, Al-Hawary M, Behrman SW, Benson AB, Cardin DB, et al. Pancreatic adenocarcinoma, version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2021; 19: 439-57.
Crossref Google Scholar
4

Sun H, Zhang D, Huang C, Guo Y, Yang Z, Yao N, et al. Hypoxic microenvironment induced spatial transcriptome changes in pancreatic cancer. Cancer Biol Med. 2021; 18: 616-30.
Crossref Google Scholar
5

Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018; 24: 541-50.
Crossref Google Scholar
6

Liu Q, Luo Q, Ju Y, Song G. Role of the mechanical microenvironment in cancer development and progression. Cancer Biol Med. 2020; 17: 282-92.
Crossref Google Scholar
7

Chen P, Hsu WH, Han J, Xia J, DePinho RA. Cancer stemness meets immunity: from mechanism to therapy. Cell Rep. 2021; 34: 108597.
Crossref Google Scholar
8

Di Caro G, Cortese N, Castino GF, Grizzi F, Gavazzi F, Ridolfi C, et al. Dual prognostic significance of tumour-associated macrophages in human pancreatic adenocarcinoma treated or untreated with chemotherapy. Gut. 2016; 65: 1710-20.
Crossref Google Scholar
9

Ma Y, Hwang RF, Logsdon CD, Ullrich SE. Dynamic mast cellstromal cell interactions promote growth of pancreatic cancer. Cancer Res. 2013; 73: 3927-37.
Crossref Google Scholar
10

McAndrews KM, Chen Y, Darpolor JK, Zheng X, Yang S, Carstens JL, et al. Identification of functional heterogeneity of carcinomaassociated fibroblasts with distinct IL6-mediated therapy resistance in pancreatic cancer. Cancer Discov. 2022; 12: 1580-97.
Crossref Google Scholar
11

Farchoukh L, Hartman DJ, Ma C, Celebrezze J, Medich D, Bahary N, et al. Intratumoral budding and automated CD8-positive T-cell density in pretreatment biopsies can predict response to neoadjuvant therapy in rectal adenocarcinoma. Mod Pathol. 2021; 34: 171-83.
Crossref Google Scholar
12

Wang Y, Fang T, Huang L, Wang H, Zhang L, Wang Z, et al. Neutrophils infiltrating pancreatic ductal adenocarcinoma indicate higher malignancy and worse prognosis. Biochem Biophys Res Commun. 2018; 501: 313-9.
Crossref Google Scholar
13

Lianyuan T, Gang L, Ming T, Dianrong X, Chunhui Y, Zhaolai M, et al. Tumor associated neutrophils promote the metastasis of pancreatic ductal adenocarcinoma. Cancer Biol Ther. 2020; 21: 937-45.
Crossref Google Scholar
14

Liu J, Jiang W, Zhao K, Wang H, Zhou T, Bai W, et al. Tumoral EHF predicts the efficacy of anti-PD1 therapy in pancreatic ductal adenocarcinoma. J Exp Med. 2019; 216: 656-73.
Crossref Google Scholar
15

Wörmann SM, Diakopoulos KN, Lesina M, Algül H. The immune network in pancreatic cancer development and progression. Oncogene. 2014; 33: 2956-67.
Crossref Google Scholar
16

Carstens JL, Correa de Sampaio P, Yang D, Barua S, Wang H, Rao A, et al. Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer. Nat Commun. 2017; 8: 15095.
Crossref Google Scholar
17

Korkaya H, Wicha MS. Cancer stem cells: nature versus nurture. Nat Cell Biol. 2010; 12: 419-21.
Crossref Google Scholar
18

Bayik D, Lathia JD. Cancer stem cell-immune cell crosstalk in tumour progression. Nat Rev Cancer. 2021; 21: 526-36.
Crossref Google Scholar
19

Dianat-Moghadam H, Mahari A, Salahlou R, Khalili M, Azizi M, Sadeghzadeh H. Immune evader cancer stem cells direct the perspective approaches to cancer immunotherapy. Stem Cell Res Ther. 2022; 13: 150.
Crossref Google Scholar
20

Lugli A, Zlobec I, Berger MD, Kirsch R, Nagtegaal ID. Tumour budding in solid cancers. Nat Rev Clin Oncol. 2021; 18: 101-15.
Crossref Google Scholar
21

Karamitopoulou E, Zlobec I, Born D, Kondi-Pafiti A, Lykoudis P, Mellou A, et al. Tumour budding is a strong and independent prognostic factor in pancreatic cancer. Eur J Cancer. 2013; 49: 1032-9.
Crossref Google Scholar
22

Nearchou IP, Lillard K, Gavriel CG, Ueno H, Harrison DJ, Caie PD. Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer. Cancer Immunol Res. 2019; 7: 609-20.
Crossref Google Scholar
23

Ong CW, Kim LG, Kong HH, Low LY, Iacopetta B, Soong R, et al. CD133 expression predicts for non-response to chemotherapy in colorectal cancer. Mod Pathol. 2010; 23: 450-7.
Crossref Google Scholar
24

Duan J, Xie Y, Qu L, Wang L, Zhou S, Wang Y, et al. A nomogrambased immunoprofile predicts overall survival for previously untreated patients with esophageal squamous cell carcinoma after esophagectomy. J Immunother Cancer. 2018; 6: 100.
Crossref Google Scholar
25

Zhou T, Liu J, Xie Y, Yuan S, Guo Y, Bai W, et al. ESE3/EHF, a promising target of rosiglitazone, suppresses pancreatic cancer stemness by downregulating CXCR4. Gut. 2022; 71: 357-71.
Crossref Google Scholar
26

Nguyen PHD, Wasser M, Tan CT, Lim CJ, Lai HLH, Seow JJW, et al. Trajectory of immune evasion and cancer progression in hepatocellular carcinoma. Nat Commun. 2022; 13: 1441.
Crossref Google Scholar
27

Lee CJ, Dosch J, Simeone DM. Pancreatic cancer stem cells. J Clin Oncol. 2008; 26: 2806-12.
Crossref Google Scholar
28

Miranda A, Hamilton PT, Zhang AW, Pattnaik S, Becht E, Mezheyeuski A, et al. Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proc Natl Acad Sci U S A. 2019; 116: 9020-9.
Crossref Google Scholar
29

Zlobec I, Lugli A, Baker K, Roth S, Minoo P, Hayashi S, et al. Role of APAF-1, E-cadherin and peritumoral lymphocytic infiltration in tumour budding in colorectal cancer. J Pathol. 2007; 212: 260-8.
Crossref Google Scholar
30

Lugli A, Karamitopoulou E, Panayiotides I, Karakitsos P, Rallis G, Peros G, et al. CD8+ lymphocytes/tumour-budding index: an independent prognostic factor representing a ‘pro-/anti-tumour’ approach to tumour host interaction in colorectal cancer. Br J Cancer. 2009; 101: 1382-92.
Crossref Google Scholar
31

Lang-Schwarz C, Melcher B, Haumaier F, Lang-Schwarz K, Rupprecht T, Vieth M, et al. Budding and tumor-infiltrating lymphocytes - combination of both parameters predicts survival in colorectal can cer and leads to new prognostic subgroups. Hum Pathol. 2018; 79: 160-7.
Crossref Google Scholar
32

Koelzer VH, Herrmann P, Zlobec I, Karamitopoulou E, Lugli A, Stein U. Heterogeneity analysis of metastasis associated in colon cancer 1 (MACC1) for survival prognosis of colorectal cancer patients: a retrospective cohort study. BMC Cancer. 2015; 15: 160.
Crossref Google Scholar
33

Ramgolam K, Lauriol J, Lalou C, Lauden L, Michel L, de la Grange P, et al. Melanoma spheroids grown under neural crest cell conditions are highly plastic migratory/invasive tumor cells endowed with immunomodulator function. PLoS One. 2011; 6: e18784.
Crossref Google Scholar
34

Tsuchiya H, Shiota G. Immune evasion by cancer stem cells. Regen Ther. 2021; 17: 20-33.
Crossref Google Scholar
35

Hiraoka N, Ino Y, Yamazaki-Itoh R, et al. Intratumoral tertiary lymphoid organ is a favourable prognosticator in patients with pancreatic cancer. Br J Cancer. 2015; 112: 1782-90.
Crossref Google Scholar
36

Zhang M, He Y, Sun X, Li Q, Wang W, Zhao A, et al. A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients. J Ovarian Res. 2014; 7: 19.
Crossref Google Scholar
37

Pointer DT, Jr., Roife D, Powers BD, Murimwa G, Elessawy S, Thompson ZJ, et al. Neutrophil to lymphocyte ratio, not platelet to lymphocyte or lymphocyte to monocyte ratio, is predictive of patient survival after resection of early-stage pancreatic ductal adenocarcinoma. BMC Cancer. 2020; 20: 750.
Crossref Google Scholar
38

Iorio V, Rosati A, D’Auria R, De Marco M, Marzullo L, Basile A, et al. Combined effect of anti-BAG3 and anti-PD-1 treatment on macrophage infiltrate, CD8(+) T cell number and tumour growth in pancreatic cancer. Gut. 2018; 67: 780-2.
Crossref Google Scholar
39

Teng MW, Ngiow SF, Ribas A, Smyth MJ. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res. 2015; 75: 2139-45.
Crossref Google Scholar
40

Beatty GL, Li Y, Long KB. Cancer immunotherapy: activating innate and adaptive immunity through CD40 agonists. Expert Rev Anticancer Ther. 2017; 17: 175-86.
Crossref Google Scholar
41

Bockorny B, Semenisty V, Macarulla T, Borazanci E, Wolpin BM, Stemmer SM, et al. BL-8040, a CXCR4 antagonist, in combination with pembrolizumab and chemotherapy for pancreatic cancer: the COMBAT trial. Nat Med. 2020; 26: 878-85.
Crossref Google Scholar
42

Van Cutsem E, Tempero MA, Sigal D, Oh DY, Fazio N, Macarulla T, et al. Randomized phase Ⅲ trial of pegvorhyaluronidase alfa with nab-paclitaxel plus gemcitabine for patients with hyaluronan-high metastatic pancreatic adenocarcinoma. J Clin Oncol. 2020; 38: 3185-3194.
Crossref Google Scholar
43

Komar HM, Serpa G, Kerscher C, Schwoegl E, Mace TA, Jin M, et al. Inhibition of Jak/STAT signaling reduces the activation of pancreatic stellate cells in vitro and limits caerulein-induced chronic pancreatitis in vivo. Sci Rep. 2017; 7: 1787.
Crossref Google Scholar
44

Noel M, O’Reilly EM, Wolpin BM, Ryan DP, Bullock AJ, Britten CD, et al. Phase 1b study of a small molecule antagonist of human chemokine (C-C motif) receptor 2 (PF-04136309) in combination with nab-paclitaxel/gemcitabine in first-line treatment of metastatic pancreatic ductal adenocarcinoma. Invest New Drugs. 2020; 38: 800-11.
Crossref Google Scholar
45

Zhu Y, Knolhoff BL, Meyer MA, Nywening TM, West BL, Luo J, et al. CSF1/CSF1R blockade reprograms tumor-infiltrating macrophages and improves response to T-cell checkpoint immunotherapy in pancreatic cancer models. Cancer Res. 2014; 74: 5057-69.
Crossref Google Scholar
Cancer Biology & Medicine
Volume 20 Issue 3,
March 2023
Pages 196-217
DOI: 10.20892/j.issn.2095-3941.2022.0569
Cite this article:
Zhou T, Man Q, Li X, et al. Artificial intelligence-based comprehensive analysis of immune-stemness-tumor budding profile to predict survival of patients with pancreatic adenocarcinoma. Cancer Biology & Medicine, 2023, 20(3): 196-217. https://doi.org/10.20892/j.issn.2095-3941.2022.0569

131

Views

4

Downloads

3

Crossref

6

Web of Science

5

Scopus

Altmetrics
Received: 17 September 2022
Accepted: 04 January 2023
Published: 24 March 2023
©2023 Cancer Biology & Medicine.

Creative Commons Attribution-NonCommercial 4.0 International License
Return