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Omics-based molecular classifications empowering in precision oncology

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Abstract

Background

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors’ molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

Conclusion

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

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Abbreviations

NGS:

Next-generation sequencing

SNV:

Single nucleotide variations

CNV:

Copy number variation

MSI:

Microsatellite instability

CRC:

Colorectal cancer

MMR:

Mismatch repair

NSCLC:

Non-small cell lung cancer

RNA-seq:

RNA sequencing

NRG1:

Neuregulin-1

ncRNAs:

Non-coding RNAs

TCGA:

The Cancer Genome Atlas Program

OC:

Ovarian cancer

HGSOC:

High-grade serous ovarian cancer

TNM:

Tumor-node-metastasis

MS:

Mass spectrometry

OLFM4:

Olfactomedin-4

iTRAQ:

Isobaric tags for relative and absolute quantitation

LCMS:

Liquid chromatography-tandem mass spectrometry

LUAD:

Lung adenocarcinoma

PTM:

Post-translational modification

ENO1:

Alpha-enolase 1

CEA:

Carcinoembryonic antigen

PON1:

Paraoxonase/amylase 1

CTCs:

Circulating tumor cells

ctDNA:

Circulating tumor DNA

EVs:

Extracellular vesicles

EFGR:

Epidermal growth factor receptor

LSLC:

Lung adenocarcinoma stemlike cells

LBCs:

Lung adenocarcinoma bulk cells

TME:

Tumor microenvironment

POLEmut:

POLE ultra-mutated

MMRd:

Mismatch repair defects

NSMP:

No specific molecular profile

RFS:

Recurrence-free survival

HRD:

Homologous recombination defect

PARP:

Poly ADP-ribose polymerase

ICB:

Immune checkpoint blockade

PD-L1:

Programmed death receptor-ligand 1

TMB:

Tumor mutational load

MSI-H:

Microsatellite high instability

TNBC:

Triple-negative breast cancer

LAR:

Luminal androgen receptor

IM:

Immunomodulatory

BLIS:

Basal-like immunosuppression

MES:

Mesenchymal-like

CDK4/6:

Cyclin-dependent kinases4/6

MS:

Mass spectrometry

SOAT1:

Sterol O-acyltransferase 1

EC:

Esophageal cancer

EMT:

Epithelial-mesenchymal transition

DSP:

Digital Spatial Profiling

PDAC:

Pancreatic ductal adenocarcinoma

ALCHEMIST:

Adjuvant Lung Cancer Enrichment Marker Identification and Sequencing Trial

Lung-MAP:

Lung Cancer Master Protocol

I-PREDICT:

Investigation of profile-related evidence determining individualized cancer therapy

DFS:

Disease-free survival rate

AI:

Artificial intelligence

3D:

Three-dimensional

References

  1. L. Zhao, V.H.F. Lee, M.K. Ng, H. Yan, M.F. Bijlsma, Molecular subtyping of cancer: current status and moving toward clinical applications. Brief Bioinform. 20(2), 572–584 (2019)

    Article  CAS  PubMed  Google Scholar 

  2. R.A. Burrell, N. McGranahan, J. Bartek, C. Swanton, The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501(7467), 338–345 (2013)

    Article  CAS  PubMed  Google Scholar 

  3. N. McGranahan, C. Swanton, Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27(1), 15–26 (2015)

    Article  CAS  PubMed  Google Scholar 

  4. J. Souglakos, J. Philips, R. Wang, et al., Prognostic and predictive value of common mutations for treatment response and survival in patients with metastatic colorectal cancer. Br. J. Cancer 101(3), 465–472 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Y. Lei, R. Tang, J. Xu, et al., Applications of single-cell sequencing in cancer research: progress and perspectives. J. Hematol. Oncol. 14(1), 91 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  6. K.A. Hoadley, C. Yau, D.M. Wolf, et al., Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158(4), 929–944 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. L.R. Yates, J. Seoane, C. Le Tourneau, et al., The European Society for Medical Oncology (ESMO) Precision Medicine Glossary. Ann. Oncol. 29(1), 30–35 (2018)

    Article  CAS  PubMed  Google Scholar 

  8. National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease, Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease (2011)

  9. N.A. Brown, K.S.J. Elenitoba-Johnson, Enabling precision oncology through precision diagnostics. Annu. Rev. Pathol. 15, 97–121 (2020)

    Article  CAS  PubMed  Google Scholar 

  10. X.S. Wang, S. Lee, H. Zhang, G. Tang, Y. Wang, An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data. Nat. Commun. 13(1), 2936 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. T. Yap, A. Celentano, C. Seers, M.J. McCullough, C.S. Farah, Molecular diagnostics in oral cancer and oral potentially malignant disorders-a clinician’s guide. J. Oral Pathol. Med. 49(1), 1–8 (2020)

    Article  PubMed  Google Scholar 

  12. A.J. Vargas, C.C. Harris, Biomarker development in the precision medicine era: lung cancer as a case study. Nat. Rev. Cancer 16(8), 525–537 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. V.A. Hristova, D.W. Chan, Cancer biomarker discovery and translation: proteomics and beyond. Expert Rev. Proteomics 16(2), 93–103 (2019)

    Article  CAS  PubMed  Google Scholar 

  14. A. Hackshaw, C.A. Clarke, A.R. Hartman, New genomic technologies for multi-cancer early detection: rethinking the scope of cancer screening. Cancer Cell 40(2), 109–113 (2022)

    Article  CAS  PubMed  Google Scholar 

  15. J. Wang, D.C. Dean, F.J. Hornicek, H. Shi, Z. Duan, RNA sequencing (RNA-Seq) and its application in ovarian cancer. Gynecol. Oncol. 152(1), 194–201 (2019)

    Article  CAS  PubMed  Google Scholar 

  16. M.F. Berger, E.R. Mardis, The emerging clinical relevance of genomics in cancer medicine. Nat. Rev. Clin. Oncol. 15(6), 353–365 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. A.M. Tsimberidou, E. Fountzilas, M. Nikanjam, R. Kurzrock, Review of precision cancer medicine: evolution of the treatment paradigm. Cancer Treat Rev. 86, 102019 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. G. Zhu, L. Pei, H. Xia, Q. Tang, F. Bi, Role of oncogenic KRAS in the prognosis, diagnosis and treatment of colorectal cancer. Mol. Cancer 20(1), 143 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  19. X.B. Wan, A.Q. Wang, J. Cao, et al., Relationships among KRAS mutation status, expression of RAS pathway signaling molecules, and clinicopathological features and prognosis of patients with colorectal cancer. World J. Gastroenterol. 25(7), 808–823 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. F. Zhang, W. Gu, M.E. Hurles, J.R. Lupski, Copy number variation in human health, disease, and evolution. Annu. Rev. Genomics Hum. Genet. 10, 451–481 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. X. Wang, Y. Han, J. Li, et al., Multi-omics analysis of copy number variations of RNA regulatory genes in soft tissue sarcoma. Life Sci. 265, 118734 (2021)

    Article  CAS  PubMed  Google Scholar 

  22. R. Ren, Mechanisms of BCR-ABL in the pathogenesis of chronic myelogenous leukaemia. Nat. Rev. Cancer 5(3), 172–183 (2005)

    Article  CAS  PubMed  Google Scholar 

  23. M. Baretti, D.T. Le, DNA mismatch repair in cancer. Pharmacol. Ther. 189, 45–62 (2018)

    Article  CAS  PubMed  Google Scholar 

  24. R.J. Hause, C.C. Pritchard, J. Shendure, S.J. Salipante, Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 22(11), 1342–1350 (2016)

    Article  CAS  PubMed  Google Scholar 

  25. A. Latham, P. Srinivasan, Y. Kemel, et al., Microsatellite instability is associated with the presence of Lynch syndrome pan-cancer. J. Clin. Oncol. 37(4), 286–295 (2019)

    Article  CAS  PubMed  Google Scholar 

  26. F. Gelsomino, M. Barbolini, A. Spallanzani, G. Pugliese, S. Cascinu, The evolving role of microsatellite instability in colorectal cancer: a review. Cancer Treat Rev. 51, 19–26 (2016)

    Article  CAS  PubMed  Google Scholar 

  27. R. Sugimoto, T. Sugai, W. Habano, et al., Clinicopathological and molecular alterations in early gastric cancers with the microsatellite instability-high phenotype. Int. J. Cancer 138(7), 1689–1697 (2016)

    Article  CAS  PubMed  Google Scholar 

  28. X. Yang, H. Han, D.D. De Carvalho, F.D. Lay, P.A. Jones, G. Liang, Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell 26(4), 577–590 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. H. Guo, S. Zhou, L. Tan, X. Wu, Z. Wu, R. Ran, Clinicopathological significance of WIF1 hypermethylation in NSCLC, a meta-analysis and literature review. Oncotarget 8(2), 2550–2557 (2017)

    Article  PubMed  Google Scholar 

  30. Y. Baba, K. Nosho, K. Shima, et al., Hypomethylation of the IGF2 DMR in colorectal tumors, detected by bisulfite pyrosequencing, is associated with poor prognosis. Gastroenterology 139(6), 1855–1864 (2010)

    Article  CAS  PubMed  Google Scholar 

  31. J. Luo, Y.N. Li, F. Wang, W.M. Zhang, X. Geng, S-adenosylmethionine inhibits the growth of cancer cells by reversing the hypomethylation status of c-myc and H-ras in human gastric cancer and colon cancer. Int. J. Biol. Sci. 6(7), 784–795 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. S.G. Zhao, W.S. Chen, H. Li, et al., The DNA methylation landscape of advanced prostate cancer. Nat. Genet. 52(8), 778–789 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. S.T. Sizemore, G.M. Sizemore, C.N. Booth, et al., Hypomethylation of the MMP7 promoter and increased expression of MMP7 distinguishes the basal-like breast cancer subtype from other triple-negative tumors. Breast Cancer Res. Treat. 146(1), 25–40 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. R. Uchi, Y. Takahashi, A. Niida, et al., Integrated multiregional analysis proposing a new model of colorectal cancer evolution. PLoS Genet. 12(2), e1005778 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  35. W. Xu, J. Seok, M.N. Mindrinos, et al., Human transcriptome array for high-throughput clinical studies. Proc. Natl. Acad. Sci. U. S. A. 108(9), 3707–3712 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. H. Wu, X. Li, H. Li, Gene fusions and chimeric RNAs, and their implications in cancer. Genes Dis. 6(4), 385–390 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. A. Drilon, R. Somwar, B.P. Mangatt, et al., Response to ERBB3-directed targeted therapy in NRG1-rearranged cancers. Cancer Discov. 8(6), 686–695 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. J. Laskin, S.V. Liu, K. Tolba, et al., NRG1 fusion-driven tumors: biology, detection, and the therapeutic role of afatinib and other ErbB-targeting agents. Ann. Oncol. 31(12), 1693–1703 (2020)

    Article  CAS  PubMed  Google Scholar 

  39. X. Zhou, L. Zhan, K. Huang, X. Wang, The functions and clinical significance of circRNAs in hematological malignancies. J. Hematol. Oncol. 13(1), 138 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  40. Y. Liu, Z. Cheng, Y. Pang, et al., Role of microRNAs, circRNAs and long noncoding RNAs in acute myeloid leukemia. J. Hematol. Oncol. 12(1), 51 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  41. Cancer Genome Atlas Research Network, Integrated genomic analyses of ovarian carcinoma. Nature 474(7353), 609–615 (2011)

    Article  Google Scholar 

  42. P. Todeschini, E. Salviato, L. Paracchini, et al., Circulating miRNA landscape identifies miR-1246 as promising diagnostic biomarker in high-grade serous ovarian carcinoma: a validation across two independent cohorts. Cancer Lett. 388, 320–327 (2017)

    Article  CAS  PubMed  Google Scholar 

  43. M. Vitiello, A. Tuccoli, L. Poliseno, Long non-coding RNAs in cancer: implications for personalized therapy. Cell Oncol. 38(1), 17–28 (2015)

    Article  CAS  Google Scholar 

  44. N. Bartonicek, J.L. Maag, M.E. Dinger, Long noncoding RNAs in cancer: mechanisms of action and technological advancements. Mol. Cancer 15(1), 43 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  45. X. Lu, J. Wang, W. Wang, et al., Copy number amplification and SP1-activated lncRNA MELTF-AS1 regulates tumorigenesis by driving phase separation of YBX1 to activate ANXA8 in non-small cell lung cancer. Oncogene 41(23), 3222–3238 (2022)

    Article  CAS  PubMed  Google Scholar 

  46. J. Li, D. Sun, W. Pu, J. Wang, Y. Peng, Circular RNAs in cancer: biogenesis, function, and clinical significance. Trends Cancer 6(4), 319–336 (2020)

    Article  CAS  PubMed  Google Scholar 

  47. F. Long, Z. Lin, L. Li, et al., Comprehensive landscape and future perspectives of circular RNAs in colorectal cancer. Mol. Cancer 20(1), 26 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. C. Shan, Y. Zhang, X. Hao, J. Gao, X. Chen, K. Wang, Biogenesis, functions and clinical significance of circRNAs in gastric cancer. Mol. Cancer 18(1), 136 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  49. I. Valo, P. Raro, A. Boissard, et al., OLFM4 expression in ductal carcinoma in situ and in invasive breast cancer cohorts by a SWATH-based proteomic approach. Proteomics 19(21–22), e1800446 (2019)

    Article  PubMed  Google Scholar 

  50. C.E. Birse, R.J. Lagier, W. FitzHugh, et al., Blood-based lung cancer biomarkers identified through proteomic discovery in cancer tissues, cell lines and conditioned medium. Clin. Proteomics 12(1), 18 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  51. C.H. Hsu, C.W. Hsu, C. Hsueh, et al., Identification and characterization of potential biomarkers by quantitative tissue proteomics of primary lung adenocarcinoma. Mol. Cell. Proteomics 15(7), 2396–2410 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. P. Mertins, J.W. Qiao, J. Patel, et al., Integrated proteomic analysis of post-translational modifications by serial enrichment. Nat. Methods 10(7), 634–637 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Y. Hu, J. Pan, P. Shah, et al., Integrated proteomic and glycoproteomic characterization of human high-grade serous ovarian carcinoma. Cell Rep. 33(3), 108276 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. L. Dai, Y. Qu, J. Li, et al., Serological proteome analysis approach-based identification of ENO1 as a tumor-associated antigen and its autoantibody could enhance the sensitivity of CEA and CYFRA 21-1 in the detection of non-small cell lung cancer. Oncotarget 8(22), 36664–36673 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  55. Y. Jin, Y. Yang, Y. Su, et al., Identification a novel clinical biomarker in early diagnosis of human non-small cell lung cancer. Glycoconjugate J. 36(1), 57–68 (2019)

    Article  CAS  Google Scholar 

  56. J. Yu, X. Zhai, X. Li, et al., Identification of MST1 as a potential early detection biomarker for colorectal cancer through a proteomic approach. Sci. Rep. 7(1), 14265 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  57. S.D. Schully, D.M. Carrick, L.E. Mechanic, et al., Leveraging biospecimen resources for discovery or validation of markers for early cancer detection. J. Natl. Cancer Inst. 107(4), djv012 (2015)

    Google Scholar 

  58. D.F. Ransohoff, Proteomics research to discover markers: what can we learn from Netflix? Clin. Chem. 56(2), 172–176 (2010)

    Article  CAS  PubMed  Google Scholar 

  59. S. Perakis, M.R. Speicher, Emerging concepts in liquid biopsies. BMC Med. 15(1), 75 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  60. G. Siravegna, S. Marsoni, S. Siena, A. Bardelli, Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 14(9), 531–548 (2017)

    Article  CAS  PubMed  Google Scholar 

  61. W. Li, J.B. Liu, L.K. Hou, et al., Liquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring. Mol. Cancer 21(1), 25 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  62. Q. Zhou, Q. Geng, L. Wang, et al., Value of folate receptor-positive circulating tumour cells in the clinical management of indeterminate lung nodules: a non-invasive biomarker for predicting malignancy and tumour invasiveness. EBioMedicine 41, 236–243 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  63. Z. Liu, Y. Han, Q. Dang, et al., Roles of circulating tumor DNA in PD-1/PD-L1 immune checkpoint inhibitors: current evidence and future directions. Int. Immunopharmacol. 111, 109173 (2022)

    Article  CAS  PubMed  Google Scholar 

  64. S.G. Ramanand, Y. Chen, J. Yuan, et al., The landscape of RNA polymerase II-associated chromatin interactions in prostate cancer. J. Clin. Invest. 130(8), 3987–4005 (2020)

    CAS  PubMed  PubMed Central  Google Scholar 

  65. W. Feng, D.C. Dean, F.J. Hornicek, H. Shi, Z. Duan, Exosomes promote pre-metastatic niche formation in ovarian cancer. Mol. Cancer 18(1), 124 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  66. S.A. Melo, L.B. Luecke, C. Kahlert, et al., Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 523(7559), 177–182 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Y. Li, Q. Zheng, C. Bao, et al., Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Cell Res. 25(8), 981–984 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. K.R. Jakobsen, B.S. Paulsen, R. Baek, K. Varming, B.S. Sorensen, M.M. Jorgensen, Exosomal proteins as potential diagnostic markers in advanced non-small cell lung carcinoma. J. Extracell. Vesicles 4, 26659 (2015)

    Article  PubMed  Google Scholar 

  69. H.T. Luo, Y.Y. Zheng, J. Tang, et al., Dissecting the multi-omics atlas of the exosomes released by human lung adenocarcinoma stem-like cells. NPJ Genom. Med. 6(1), 48 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. O. Vaksman, C. Tropé, B. Davidson, R. Reich, Exosome-derived miRNAs and ovarian carcinoma progression. Carcinogenesis 35(9), 2113–2120 (2014)

    Article  CAS  PubMed  Google Scholar 

  71. X. He, X. Liu, F. Zuo, H. Shi, J. Jing, Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Semin. Cancer Biol. 88, 187–200 (2023)

    Article  CAS  PubMed  Google Scholar 

  72. J.D. Cohen, A.A. Javed, C. Thoburn, et al., Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers. Proc. Natl. Acad. Sci. U. S. A. 114(38), 10202–10207 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Y. Wang, C. Zhang, P. Zhang, et al., Serum exosomal microRNAs combined with alpha-fetoprotein as diagnostic markers of hepatocellular carcinoma. Cancer Med. 7(5), 1670–1679 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. X. Gongye, M. Tian, P. Xia, et al., Multi-omics analysis revealed the role of extracellular vesicles in hepatobiliary & pancreatic tumor. J. Control. Release 350, 11–25 (2022)

    Article  CAS  PubMed  Google Scholar 

  75. J.J. Adashek, V. Subbiah, R. Kurzrock, From tissue-agnostic to N-of-one therapies: (R) evolution of the precision paradigm. Trends Cancer 7(1), 15–28 (2021)

    Article  CAS  PubMed  Google Scholar 

  76. R. Dienstmann, L. Vermeulen, J. Guinney, S. Kopetz, S. Tejpar, J. Tabernero, Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat. Rev. Cancer 17(2), 79–92 (2017)

    Article  CAS  PubMed  Google Scholar 

  77. A. Talhouk, M.K. McConechy, S. Leung, et al., Confirmation of ProMisE: a simple, genomics-based clinical classifier for endometrial cancer. Cancer 123(5), 802–813 (2017)

    Article  CAS  PubMed  Google Scholar 

  78. E. Stelloo, R.A. Nout, E.M. Osse, et al., Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer-combined analysis of the PORTEC cohorts. Clin. Cancer Res. 22(16), 4215–4224 (2016)

    Article  CAS  PubMed  Google Scholar 

  79. S. Kommoss, M.K. McConechy, F. Kommoss, et al., Final validation of the ProMisE molecular classifier for endometrial carcinoma in a large population-based case series. Ann. Oncol. 29(5), 1180–1188 (2018)

    Article  CAS  PubMed  Google Scholar 

  80. A. Leon-Castillo, S.M. de Boer, M.E. Powell, et al., Molecular classification of the PORTEC-3 trial for high-risk endometrial cancer: impact on prognosis and benefit from adjuvant therapy. J. Clin. Oncol. 38(29), 3388–3397 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. K. Tamura, K. Hasegawa, N. Katsumata, et al., Efficacy and safety of nivolumab in Japanese patients with uterine cervical cancer, uterine corpus cancer, or soft tissue sarcoma: multicenter, open-label phase 2 trial. Cancer Sci. 110(9), 2894–2904 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. P.A. Konstantinopoulos, W. Luo, J.F. Liu, et al., Phase II study of avelumab in patients with mismatch repair deficient and mismatch repair proficient recurrent/persistent endometrial cancer. J. Clin. Oncol. 37(30), 2786–2794 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Y. Antill, P.S. Kok, K. Robledo, et al., Clinical activity of durvalumab for patients with advanced mismatch repair-deficient and repair-proficient endometrial cancer. A nonrandomized phase 2 clinical trial. J. Immunother. Cancer 9(6), e002255 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  84. A. Oaknin, A.V. Tinker, L. Gilbert, et al., Clinical activity and safety of the anti-programmed death 1 monoclonal antibody dostarlimab for patients with recurrent or advanced mismatch repair-deficient endometrial cancer: a nonrandomized phase 1 clinical trial. JAMA Oncol. 6(11), 1766–1772 (2020)

    Article  PubMed  Google Scholar 

  85. Cancer Genome Atlas Research N, C. Kandoth, N. Schultz, et al., Integrated genomic characterization of endometrial carcinoma. Nature 497(7447), 67–73 (2013)

    Article  Google Scholar 

  86. Y. Wang, W. Luo, Y. Wang, PARP-1 and its associated nucleases in DNA damage response. DNA Repair (Amst) 81, 102651 (2019)

    Article  CAS  PubMed  Google Scholar 

  87. Y. Li, J. Feng, C. Zhao, et al., A new strategy in molecular typing: the accuracy of an NGS panel for the molecular classification of endometrial cancers. Ann. Transl. Med. 10(16), 870 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  88. L.P. Diggs, E.C. Hsueh, Utility of PD-L1 immunohistochemistry assays for predicting PD-1/PD-L1 inhibitor response. Biomark. Res. 5, 12 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  89. R. Mandal, T.A. Chan, Personalized oncology meets immunology: the path toward precision immunotherapy. Cancer Discov. 6(7), 703–713 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. N.A. Rizvi, M.D. Hellmann, A. Snyder, et al., Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348(6230), 124–128 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. T. Shukuya, D.P. Carbone, Predictive markers for the efficacy of anti-PD-1/PD-L1 antibodies in lung cancer. J. Thorac. Oncol. 11(7), 976–988 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  92. J. Rodon, J.C. Soria, R. Berger, et al., Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat. Med. 25(5), 751–758 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. D. Varešlija, N. Priedigkeit, A. Fagan, et al., Transcriptome characterization of matched primary breast and brain metastatic tumors to detect novel actionable targets. J. Natl. Cancer Inst. 113(2), 218 (2021)

    Article  Google Scholar 

  94. Z. Liu, L. Liu, S. Weng, et al., Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer. Nat. Commun. 13(1), 816 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Z. Zhou, Y. Zhang, J. Li, et al., Crosstalk between regulated cell death and immunity in redox dyshomeostasis for pancreatic cancer. Cell. Signal. 109, 110774 (2023)

    Article  CAS  PubMed  Google Scholar 

  96. J.J. Adashek, S. Kato, R. Parulkar, et al., Transcriptomic silencing as a potential mechanism of treatment resistance. JCI Insight 5(11), e134824 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  97. Y.Z. Jiang, D. Ma, C. Suo, et al., Genomic and transcriptomic landscape of triple-negative breast cancers: subtypes and treatment strategies. Cancer Cell 35(3), 428–440 e425 (2019)

    Article  CAS  PubMed  Google Scholar 

  98. A.M. Newman, C.L. Liu, M.R. Green, et al., Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12(5), 453–457 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. M. Angelova, P. Charoentong, H. Hackl, et al., Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol. 16(1), 64 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  100. M.L. Telli, K.M. Timms, J. Reid, et al., Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin. Cancer Res. 22(15), 3764–3773 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. J.M. Balko, L.J. Schwarz, N. Luo, et al., Triple-negative breast cancers with amplification of JAK2 at the 9p24 locus demonstrate JAK2-specific dependence. Sci. Transl. Med. 8(334), 334ra353 (2016)

    Article  Google Scholar 

  102. A. Sonnenblick, S. Brohee, D. Fumagalli, et al., Constitutive phosphorylated STAT3-associated gene signature is predictive for trastuzumab resistance in primary HER2-positive breast cancer. BMC Med. 13, 177 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  103. S. Myhre, O.C. Lingjaerde, B.T. Hennessy, et al., Influence of DNA copy number and mRNA levels on the expression of breast cancer related proteins. Mol. Oncol. 7(3), 704–718 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. E.S. Park, R. Rabinovsky, M. Carey, et al., Integrative analysis of proteomic signatures, mutations, and drug responsiveness in the NCI 60 cancer cell line set. Mol. Cancer Ther. 9(2), 257–267 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. L. Restrepo-Perez, C. Joo, C. Dekker, Paving the way to single-molecule protein sequencing. Nat. Nanotechnol. 13(9), 786–796 (2018)

    Article  CAS  PubMed  Google Scholar 

  106. J.B. Muller, P.E. Geyer, A.R. Colaco, et al., The proteome landscape of the kingdoms of life. Nature 582(7813), 592–596 (2020)

    Article  PubMed  Google Scholar 

  107. A.P. Diz, M. Martinez-Fernandez, E. Rolan-Alvarez, Proteomics in evolutionary ecology: linking the genotype with the phenotype. Mol. Ecol. 21(5), 1060–1080 (2012)

    Article  CAS  PubMed  Google Scholar 

  108. K. Krug, E.J. Jaehnig, S. Satpathy, et al., Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 183(5), 1436–1456 e1431 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. M.A. Gillette, S. Satpathy, S. Cao, et al., Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182(1), 200–225 e235 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. N. Xu, Z. Yao, G. Shang, et al., Integrated proteogenomic characterization of urothelial carcinoma of the bladder. J. Hematol. Oncol. 15(1), 76 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Y. Jiang, A. Sun, Y. Zhao, et al., Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma. Nature 567(7747), 257–261 (2019)

    Article  CAS  PubMed  Google Scholar 

  112. Cancer Genome Atlas Research Network, Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169(7), 1327–1341 e1323 (2017)

    Article  Google Scholar 

  113. A.X. Zhu, R.S. Finn, J. Edeline, et al., Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol. 19(7), 940–952 (2018)

    Article  PubMed  Google Scholar 

  114. W. Liu, L. Xie, Y.H. He, et al., Large-scale and high-resolution mass spectrometry-based proteomics profiling defines molecular subtypes of esophageal cancer for therapeutic targeting. Nat. Commun. 12(1), 4961 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. A.S. Nam, R. Chaligne, D.A. Landau, Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat. Rev. Genet. 22(1), 3–18 (2021)

    Article  CAS  PubMed  Google Scholar 

  116. D. Lee, Y. Park, S. Kim, Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform. 22(3), bbaa188 (2021)

    Article  PubMed  Google Scholar 

  117. C. Guo, Z. Liu, Y. Yu, et al., Integrated analysis of multi-omics alteration, immune profile, and pharmacological landscape of pyroptosis-derived lncRNA pairs in gastric cancer. Front. Cell Dev. Biol. 10, 816153 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  118. D.R. Mani, K. Krug, B. Zhang, et al., Cancer proteogenomics: current impact and future prospects. Nat. Rev. Cancer 22(5), 298–313 (2022)

    Article  CAS  PubMed  Google Scholar 

  119. N. Lal, B.S. White, G. Goussous, et al., KRAS mutation and consensus molecular subtypes 2 and 3 are independently associated with reduced immune infiltration and reactivity in colorectal cancer. Clin. Cancer Res. 24(1), 224–233 (2018)

    Article  CAS  PubMed  Google Scholar 

  120. W. Liao, M.J. Overman, A.T. Boutin, et al., KRAS-IRF2 axis drives immune suppression and immune therapy resistance in colorectal cancer. Cancer Cell. 35(4), 559–572 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. S. Hamarsheh, O. Gross, T. Brummer, R. Zeiser, Immune modulatory effects of oncogenic KRAS in cancer. Nat. Commun. 11(1), 5439 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. W. Chong, X. Zhu, H. Ren, et al., Integrated multi-omics characterization of KRAS mutant colorectal cancer. Theranostics 12(11), 5138–5154 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Z. Liu, Y. Liu, L. Qian, et al., A proteomic and phosphoproteomic landscape of KRAS mutant cancers identifies combination therapies. Mol. Cell 81(19), 4076–4090 (2021)

    Article  CAS  PubMed  Google Scholar 

  124. D.K. Brubaker, J.A. Paulo, S. Sheth, et al., Proteogenomic network analysis of context-specific KRAS signaling in mouse-to-human cross-species translation. Cell Syst. 9(3), 258–270 e256 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. L. Cao, C. Huang, D. Cui Zhou, et al., Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 184(19), 5031–5052 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. I. Sangrador, X. Molero, F. Campbell, et al., Zeb1 in stromal myofibroblasts promotes Kras-driven development of pancreatic cancer. Cancer Res. 78(10), 2624–2637 (2018)

    Article  CAS  PubMed  Google Scholar 

  127. D.H. Peng, S.T. Kundu, J.J. Fradette, et al., ZEB1 suppression sensitizes KRAS mutant cancers to MEK inhibition by an IL17RD-dependent mechanism. Sci. Transl. Med. 11(483), eaaq1238 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  128. Y. Adachi, K. Ito, Y. Hayashi, et al., Epithelial-to-mesenchymal transition is a cause of both intrinsic and acquired resistance to KRAS G12C inhibitor in KRAS G12C-mutant non-small cell lung cancer. Clin. Cancer Res. 26(22), 5962–5973 (2020)

    Article  CAS  PubMed  Google Scholar 

  129. A. Kazi, L. Chen, S. Xiang, et al., Global phosphoproteomics reveal CDK suppression as a vulnerability to KRas addiction in pancreatic cancer. Clin. Cancer Res. 27(14), 4012–4024 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Y. Wu, Y. Cheng, X. Wang, J. Fan, Q. Gao, Spatial omics: navigating to the golden era of cancer research. Clin. Transl. Med. 12(1), e696 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  131. H. Mi, S. Sivagnanam, C.B. Betts, et al., Quantitative spatial profiling of immune populations in pancreatic ductal adenocarcinoma reveals tumor microenvironment heterogeneity and prognostic biomarkers. Cancer Res. 82(23), 4359–4372 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. K.L. McNamara, J.L. Caswell-Jin, R. Joshi, et al., Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response. Nat. Cancer 2(4), 400–413 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. W.L. Hwang, K.A. Jagadeesh, J.A. Guo, et al., Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. Nat. Genet. 54(8), 1178–1191 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. H. Mi, W.J. Ho, M. Yarchoan, A.S. Popel, Multi-scale spatial analysis of the tumor microenvironment reveals features of cabozantinib and nivolumab efficacy in hepatocellular carcinoma. Front. Immunol. 13, 892250 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. E. Fountzilas, A.M. Tsimberidou, H.H. Vo, R. Kurzrock, Clinical trial design in the era of precision medicine. Genome Med. 14(1), 101 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  136. A. Marusyk, M. Janiszewska, K. Polyak, Intratumor heterogeneity: the Rosetta stone of therapy resistance. Cancer Cell 37(4), 471–484 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. S. Kato, K.H. Kim, H.J. Lim, et al., Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-one strategy. Nat. Commun. 11(1), 4965 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. J.K. Sicklick, S. Kato, R. Okamura, et al., Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat. Med. 25(5), 744–750 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. A. Drilon, T.W. Laetsch, S. Kummar, et al., Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. N. Engl. J. Med. 378(8), 731–739 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. R.C. Doebele, A. Drilon, L. Paz-Ares, et al., Entrectinib in patients with advanced or metastatic NTRK fusion-positive solid tumours: integrated analysis of three phase 1-2 trials. Lancet Oncol. 21(2), 271–282 (2020)

    Article  CAS  PubMed  Google Scholar 

  141. N.C. Turner, B. Kingston, L.S. Kilburn, et al., Circulating tumour DNA analysis to direct therapy in advanced breast cancer (plasmaMATCH): a multicentre, multicohort, phase 2a, platform trial. Lancet Oncol. 21(10), 1296–1308 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. I-SPY2 Trial Consortium, D. Yee, A.M. DeMichele, et al., Association of event-free and distant recurrence-free survival with individual-level pathologic complete response in neoadjuvant treatment of stages 2 and 3 breast cancer: three-year follow-up analysis for the I-SPY2 adaptively randomized clinical trial. JAMA Oncol. 6(9), 1355–1362 (2020)

    Article  Google Scholar 

  143. R. Govindan, S.J. Mandrekar, D.E. Gerber, et al., ALCHEMIST trials: a golden opportunity to transform outcomes in early-stage non-small cell lung cancer. Clin. Cancer Res. 21(24), 5439–5444 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. M.W. Redman, V.A. Papadimitrakopoulou, K. Minichiello, et al., Biomarker-driven therapies for previously treated squamous non-small-cell lung cancer (Lung-MAP SWOG S1400): a biomarker-driven master protocol. Lancet Oncol. 21(12), 1589–1601 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. E.R. Ahn, M. Rothe, P.K. Mangat, et al., Pertuzumab plus trastuzumab in patients with endometrial cancer with ERBB2/3 amplification, overexpression, or mutation: results from the TAPUR study. JCO Precis. Oncol. 7, e2200609 (2023)

    Article  PubMed  Google Scholar 

  146. N.S. Azad, R.J. Gray, M.J. Overman, et al., Nivolumab is effective in mismatch repair-deficient noncolorectal cancers: results from arm Z1D-A subprotocol of the NCI-MATCH (EAY131) study. J. Clin. Oncol. 38(3), 214–222 (2020)

    Article  CAS  PubMed  Google Scholar 

  147. A.M. Tsimberidou, D.S. Hong, S. Fu, et al., Precision medicine: preliminary results from the initiative for molecular profiling and advanced cancer therapy 2 (IMPACT2) study. NPJ Precis. Oncol. 5(1), 21 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. C. Le Tourneau, J.P. Delord, A. Goncalves, et al., Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 16(13), 1324–1334 (2015)

    Article  CAS  PubMed  Google Scholar 

  149. J.K. Sicklick, S. Kato, R. Okamura, et al., Molecular profiling of advanced malignancies guides first-line N-of-1 treatments in the I-PREDICT treatment-naive study. Genome Med. 13(1), 155 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. J.J.H. Park, G. Hsu, E.G. Siden, K. Thorlund, E.J. Mills, An overview of precision oncology basket and umbrella trials for clinicians. CA Cancer J. Clin. 70(2), 125–137 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  151. A.J. Redig, P.A. Janne, Basket trials and the evolution of clinical trial design in an era of genomic medicine. J. Clin. Oncol. 33(9), 975–977 (2015)

    Article  CAS  PubMed  Google Scholar 

  152. K.T. Flaherty, R. Gray, A. Chen, et al., The molecular analysis for therapy choice (NCI-MATCH) trial: lessons for genomic trial design. J. Natl. Cancer Inst. 112(10), 1021–1029 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  153. H.J. Johansson, F. Socciarelli, N.M. Vacanti, et al., Breast cancer quantitative proteome and proteogenomic landscape. Nat. Commun. 10(1), 1600 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  154. B. Soldevilla, C. Carretero-Puche, G. Gomez-Lopez, et al., The correlation between immune subtypes and consensus molecular subtypes in colorectal cancer identifies novel tumour microenvironment profiles, with prognostic and therapeutic implications. Eur. J. Cancer 123, 118–129 (2019)

    Article  CAS  PubMed  Google Scholar 

  155. A. Prat, E. Pineda, B. Adamo, et al., Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast 24(Suppl 2), S26–35 (2015)

    Article  PubMed  Google Scholar 

  156. F. Ades, D. Zardavas, I. Bozovic-Spasojevic, et al., Luminal B breast cancer: molecular characterization, clinical management, and future perspectives. J. Clin. Oncol. 32(25), 2794–2803 (2014)

    Article  PubMed  Google Scholar 

  157. P. Eroles, A. Bosch, J.A. Perez-Fidalgo, A. Lluch, Molecular biology in breast cancer: intrinsic subtypes and signaling pathways. Cancer Treat Rev. 38(6), 698–707 (2012)

    Article  CAS  PubMed  Google Scholar 

  158. C. Marchio, L. Annaratone, A. Marques, L. Casorzo, E. Berrino, A. Sapino, Evolving concepts in HER2 evaluation in breast cancer: heterogeneity, HER2-low carcinomas and beyond. Semin. Cancer Biol. 72, 123–135 (2021)

    Article  CAS  PubMed  Google Scholar 

  159. J. Guinney, R. Dienstmann, X. Wang, et al., The consensus molecular subtypes of colorectal cancer. Nat. Med. 21(11), 1350–1356 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. A. Esteva, A. Robicquet, B. Ramsundar, et al., A guide to deep learning in healthcare. Nature Med. 25(1), 24–29 (2019)

    Article  CAS  PubMed  Google Scholar 

  161. B.H. Kann, A. Hosny, H. Aerts, Artificial intelligence for clinical oncology. Cancer Cell 39(7), 916–927 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. K. Bera, K.A. Schalper, D.L. Rimm, V. Velcheti, A. Madabhushi, Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16(11), 703–715 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  163. A. Hosny, C. Parmar, J. Quackenbush, L.H. Schwartz, H. Aerts, Artificial intelligence in radiology. Nat. Rev. Cancer 18(8), 500–510 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. B. Bhinder, C. Gilvary, N.S. Madhukar, O. Elemento, Artificial intelligence in cancer research and precision medicine. Cancer Discov. 11(4), 900–915 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. H. Yang, L. Gan, R. Chen, D. Li, J. Zhang, Z. Wang, From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach. Brief Bioinform. 24(1), bbac528 (2023)

    Article  PubMed  Google Scholar 

  166. E. Durinikova, K. Buzo, S. Arena, Preclinical models as patients’ avatars for precision medicine in colorectal cancer: past and future challenges. J. Exp. Clin. Cancer Res. 40(1), 185 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  167. M. Jung, S. Ghamrawi, E.Y. Du, J.J. Gooding, M. Kavallaris, Advances in 3D bioprinting for cancer biology and precision medicine: from matrix design to application. Adv. Healthc. Mater. 11(24), e2200690 (2022)

    Article  PubMed  Google Scholar 

  168. A.T. Byrne, D.G. Alferez, F. Amant, et al., Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat. Rev. Cancer 17(4), 254–268 (2017)

    Article  CAS  PubMed  Google Scholar 

  169. H. Xu, X. Lyu, M. Yi, W. Zhao, Y. Song, K. Wu, Organoid technology and applications in cancer research. J. Hematol. Oncol. 11(1), 116 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  170. H. Xu, D. Jiao, A. Liu, K. Wu, Tumor organoids: applications in cancer modeling and potentials in precision medicine. J. Hematol. Oncol. 15(1), 58 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  171. J. Pape, M. Emberton, U. Cheema, 3D cancer models: the need for a complex stroma, compartmentalization and stiffness. Front. Bioeng. Biotechnol. 9, 660502 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  172. A.M.K. Law, L. Rodriguez de la Fuente, T.J. Grundy, G. Fang, F. Valdes-Mora, D. Gallego-Ortega, Advancements in 3D cell culture systems for personalizing anti-cancer therapies. Front. Oncol. 11, 782766 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. S. El Harane, B. Zidi, N. El Harane, K.H. Krause, T. Matthes, O. Preynat-Seauve, Cancer spheroids and organoids as novel tools for research and therapy: state of the art and challenges to guide precision medicine. Cells 12(7), 1001 (2023)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This project was funded by the Science and Technology Department of Henan (221100310100).

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Author notes

  1. Zhaokai Zhou and Ting Lin contributed equally to this work and shared the first authorship.

Authors and Affiliations

  1. Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China

    Zhaokai Zhou, Ting Lin, Aoyang Zhou, Yuyuan Zhang, Siyuan Weng, Xinwei Han & Zaoqu Liu

  2. Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China

    Zhaokai Zhou

  3. Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China

    Shuang Chen & Haijiao Zou

  4. Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Ge Zhang

  5. Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China

    Yudi Xu

  6. Interventional Institute of Zhengzhou University, Zhengzhou, Henan, 450052, China

    Xinwei Han & Zaoqu Liu

  7. Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan, 450052, China

    Xinwei Han & Zaoqu Liu

  8. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Zaoqu Liu

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Contributions

ZKZ, ZQL, and XWH provided direction and guidance throughout the preparation of this manuscript. ZKZ and TL wrote and edited the manuscript. ZKZ reviewed and made significant revisions to the manuscript. SC, GZ, HJZ, YDX, AYZ, YYZ and SYW, collected and prepared the related papers. All authors read and approved the final manuscript.

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Correspondence to Xinwei Han or Zaoqu Liu.

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Zhou, Z., Lin, T., Chen, S. et al. Omics-based molecular classifications empowering in precision oncology. Cell Oncol. (2024). https://doi.org/10.1007/s13402-023-00912-8

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