Submitted:
22 February 2024
Posted:
28 February 2024
You are already at the latest version
Abstract
Keywords:
Introduction
- -
- Pre-existing bone lesions: Osteosarcoma can develop following pre-existing lesions to the bones, such as trauma or bone pathologies.
- -
- Hereditary factors: In some cases, osteosarcoma may be associated with rare hereditary conditions that increase the risk of developing this type of tumor, such as hereditary retinoblastoma. Indeed, mutations in this gene have been commonly associated with hereditary osteosarcoma. The RB1 protein plays a crucial role in cell cycle control, and its dysfunction can lead to uncontrolled cell proliferation, a common trait in tumors [6]. For example, Li-Fraumeni syndrome is linked to mutations in the TP53 gene, which is involved in tumor suppression. People with this syndrome have a higher risk of developing several types of tumors, including osteosarcoma [7]. Other genetic conditions such as Rothmund-Thomson syndrome, Bloom syndrome, and Werner syndrome can increase the risk of developing osteosarcoma [8].
- -
- Radiation Exposure: Ionizing exposure to high doses of radiation may increase the risk of developing osteosarcoma.
- -
- Rapid growth and development: Because osteosarcoma often affects growing young people, it is thought that rapid bone growth and development may play a role in its formation.
- -
- -
- Tumor grade: Classifying the tumor based on the degree of aggressiveness can provide information on the growth rate and potential of the cancer to spread.
- -
- Extension of the tumor: The size of the tumor and whether it has spread to surrounding tissues can influence treatment and prognosis.
- -
- Metastasis: The presence or absence of metastases, particularly in the lungs, is an important prognostic factor for osteosarcoma. Indeed, in patients with metastatic osteosarcoma treated with neoadjuvant therapy, the “Responder” status shows improved survival (82% at 5-years) compared to “Non-Responder” (70% at 5-years) [13,14].
- -
- Response to neoadjuvant chemotherapy: The response of the tumor to chemotherapy administered before surgery can be a prognostic indicator. A good response may indicate a better prognosis.
- -
- Age: The age of the patient at the time of diagnosis can influence the prognosis. For example, younger patients tend to respond better to treatment.
- -
- Tumor location: The specific location of the tumor within the bone may have prognostic implications [15].
- -
- Clinical data analysis: analyzing large datasets of clinical information from osteosarcoma patients to identify patterns or correlations between demographic factors, treatment protocols, and patient outcomes. This could involve retrospective studies or meta-analyses of existing clinical data [26].
- -
- Radiological and imaging markers: using advanced imaging techniques like MRI, PET-CT scans, or other imaging modalities to identify specific radiological markers associated with tumor aggressiveness, response to treatment, or recurrence. Changes in tumor characteristics visible on imaging might provide insights into prognosis [27].
- -
- Immunohistochemistry studies: examining tissue samples from osteosarcoma patients to identify specific protein markers or antigen expressions associated with disease behavior or response to treatment. Immunohistochemistry studies can reveal valuable information about the tumor immune microenvironment, and it has become a recent research hot spot providing valuable insight into tumor heterogeneity that could influence disease progression [28].
- -
- Molecular biomarkers: investigating specific genetic mutations or molecular markers associated with osteosarcoma progression, response to treatment, or recurrence. This also involves analyzing gene expression profiles, identifying oncogenes or tumor suppressor genes, or exploring epigenetic modifications [25,29]. Also, searching for circulating biomarkers in blood, urine, or other bodily fluids that can indicate disease progression, treatment response, or recurrence. This involves analyzing proteins, circulating tumor cells, circulating tumor DNA (ctDNA), or microRNAs [30].
- -
- Drug sensitivity and resistance studies: investigating factors that contribute to drug resistance or sensitivity in osteosarcoma treatments. Understanding why certain tumors respond differently to therapies can lead to the identification of predictive markers [31].
- -
- Multi-Omics approaches: integrating data from genomics, proteomics, metabolomics, and other omics fields to comprehensively understand the complex molecular landscape of osteosarcoma. This holistic approach might unveil novel markers or pathways relevant to prognosis and treatment response [32].
- -
- Machine learning and artificial intelligence: employing computational methods to analyze complex datasets and identify potential prognostic or predictive markers. Machine learning algorithms can help in discovering patterns and associations that might not be immediately apparent through traditional analysis methods [33].
Relevant Sections
Discussion
Conclusions and Future Directions
References
- Raymond, A.K.; Ayala, A.G.; Knuutila, S. Conventional osteosarcoma. In World Health Organization Classification of Tumors Pathology and Genetics of Tumors of Soft Tissue and Bone; Fletcher, C.D.M., Unni, K.K., Mertens, F., Eds.; IARC Press: Lyon, France, 2002; pp. 264–270. [Google Scholar]
- Damron, T.A.; Ward, W.G.; Stewart, A. Osteosarcoma chondrosarcoma and Ewing’s sarcoma: National Cancer Data Base report. Clin. Orthop. Relat. Res. 2007, 459, 40–47. [Google Scholar] [CrossRef]
- Jiang, W.G.; Sanders, A.J.; Katoh, M.; Ungefroren, H.; Gieseler, F.; Prince, M.; Thompson, S.K.; Zollo, M.; Spano, D.; Dhawan, P.; et al. Tissue invasion and metastasis: Molecular, biological and clinical perspectives. Semin. Cancer Biol. 2015, 35, S244–S275. [Google Scholar] [CrossRef]
- Durfee, R.A.; Mohammed, M.; Luu, H.H. Review of Osteosarcoma and Current Management. Rheumatol. Ther. 2016, 3, 221–243. [Google Scholar] [CrossRef]
- Morrowa, J.J.; Khanna, C. Osteosarcoma Genetics and Epigenetics: Emerging Biology and Candidate Therapies. Crit. Rev. Oncog. 2015, 20, 173–197. [Google Scholar] [CrossRef]
- Yun, J.; Li, Y.; Xu, C.T.; Pan, B.R. Epidemiology and Rb1 gene of retinoblastoma. Int. J. Ophthalmol. 2011, 4, 103–109. [Google Scholar]
- Rocca, V.; Blandino, G.; D’Antona, L.; Iuliano, R.; Di Agostino, S. Li–Fraumeni Syndrome: Mutation of TP53 Is a Biomarker of Hereditary Predisposition to Tumor: New Insights and Advances in the Treatment. Cancers 2022, 14, 3664. [Google Scholar] [CrossRef]
- Hameed, M.; Mandelker, D. Tumor Syndromes Predisposing to Osteosarcoma. Adv. Anat. Pathol. 2018, 25, 217–222. [Google Scholar] [CrossRef]
- Marina, N.M.; Smeland, S.; Bielack, S.S.; Bernstein, M.; Jovic, G.; Krailo, M.D.; Hook, J.M.; Arndt, C.; van den Berg, H.; Brennan, B.; et al. Comparison of MAPIE versus MAP in patients with a poor response to preoperative chemotherapy for newly diagnosed high-grade osteosarcoma (EURAMOS-1): An open-label, international, randomized controlled trial. Lancet Oncol. 2016, 17, 1396–1408. [Google Scholar] [CrossRef]
- Baldini, N. Multidrug resistance a multiplex phenomenon. Nat. Med. 1997, 3, 1380–1385. [Google Scholar] [CrossRef]
- Dorfman, H.D.; Czerniak, B. Bone Tumors; Mosby: St Louis, MO, USA, 1998. [Google Scholar]
- Whelan, J.S.; Bielack, S.S.; Marina, N.; Smeland, S.; Jovic, G.; Hook, J.M.; Krailo, M.; Anninga, J.; Butterfass-Bahloul, T.; Bohling, T.; et al. EURAMOS-1, an international randomised study for osteosarcoma: Results from pre-randomisation treatment. Ann. Oncol. 2015, 26, 407–414. [Google Scholar] [CrossRef]
- Ren, L.; Mendoza, A.; Zhu, J.; Briggs, J.W.; Halsey, C.; Hong, E.S.; Burkett, S.S.; Morrow, J.; Lizardo, M.M.; Osborne, T.; et al. Characterization of the metastatic phenotype of a panel of established osteosarcoma cells. Oncotarget 2015, 6, 29469–29481. [Google Scholar] [CrossRef]
- Wittig, J.C.; Bickels, J.; Priebat, D.; Jelinek, J.; KellarGraney, K.; Shmookler, B.; Malawer, M.M. Osteosarcoma: A multidisciplinary approach to diagnosis and treatment. Am. Fam. Physician. 2002, 65, 1123–1132. [Google Scholar]
- Xina, S.; Weia, G. Prognostic factors in osteosarcoma: A study level meta-analysis and systematic review of current practice. J. Bone Oncol. 2020, 21, 100281. [Google Scholar] [CrossRef]
- Perry, J.A.; Kiezun, A.; Tonzi, P.; Van Allen, E.M.; Carter, S.L.; Baca, S.C.; Cowley, G.S.; Bhatt, A.S.; Rheinbay, E.; Pedamallu, C.S.; et al. Complementary genomic approaches highlight the PI3K/ mTOR pathway as a common vulnerability in osteosarcoma. Proc. Natl. Acad. Sci. USA 2014, 111, E5564–E5573. [Google Scholar] [CrossRef]
- Rickel, K.; Fang, F.; Tao, J. Molecular genetics of osteosarcoma. Bone 2017, 102, 69–79. [Google Scholar] [CrossRef]
- Fang, F.; VanCleave, A.; Helmuth, R.; Torres, H.; Rickel, K.; Wollenzien, H.; Sun, H.; Zeng, E.; Zhao, J.; Tao, J. Targeting the Wnt/β-catenin pathway in human osteosarcoma cells. Oncotarget 2018, 9, 36780–36792. [Google Scholar] [CrossRef]
- Liu, H.; Nazmun, N.; Hassan, S.; Liu, X.; Yang, J. BRAF mutation and its inhibitors in sarcoma treatment. Cancer Med. 2020, 9, 4881–4896. [Google Scholar] [CrossRef]
- Martin, J.W.; Squire, J.A.; Zielenska, M. The Genetics of Osteosarcoma. Sarcoma 2012, 2012, 627254. [Google Scholar] [CrossRef]
- Ji, Z.; Shen, J.; Lan, Y.; Yi, Q.; Liu, H. Targeting signaling pathways in osteosarcoma: Mechanisms and clinical studies. MedComm 2023, 4, e308. [Google Scholar] [CrossRef]
- Gianferante, D.M.; Mirabello, L.; Savage, S.A. Germline and somatic genetics of osteosarcoma—Connecting aetiology, biology and therapy. Nat. Rev. Endocrinol. 2017, 13, 480–491. [Google Scholar] [CrossRef]
- Martin, J.W.; Squire, J.A.; Zielenska, M. The genetics of osteosarcoma. Sarcoma 2012, 2012, 627254. [Google Scholar] [CrossRef]
- Batanian, J.R.; Cavalli, L.R.; Aldosari, N.M.; Ma, E.; SoteloAvila, C.; Ramos, M.B.; Rone, J.D.; Thorpe, C.M.; Haddad, B.R. Evaluation of pediatric osteosarcomas by classic cytogenetic and CGH analyses. Mol. Pathol. 2002, 55, 389–393. [Google Scholar] [CrossRef]
- Zamborsky, R.; Kokavec, M.; Harsanyi, S.; Danisovic, L. Identification of Prognostic and Predictive Osteosarcoma Biomarkers. Med. Sci. 2019, 7, 28. [Google Scholar] [CrossRef]
- Li, W.; Jin, G.; Wu, H.; Wu, R.; Xu, C.; Wang, B.; Liu, Q.; Hu, Z.; Wang, H.; Dong, S.; et al. Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: A large cohort data study. Front. Oncol. 2022, 12, 945362. [Google Scholar] [CrossRef]
- Kubo, T.; Furuta, T.; Johan, M.P.; Ochi, M.; Adachi, N. Value of diffusion-weighted imaging for evaluating chemotherapy response in osteosarcoma: A meta-analysis. Mol. Clin. Oncol. 2017, 7, 88–92. [Google Scholar] [CrossRef]
- Zhu, T.; Han, J.; Yang, L.; Cai, Z.; Sun, W.; Hua, Y.; Xu, J. Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications. Front. Immunol. Immune. 2022, 13, 907550. [Google Scholar] [CrossRef]
- Sun, J.; Xu, H.; Qi, M.; Zhang, C.; Shi, J. Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray. Mol. Med. Rep. 2019, 20, 3075–3084. [Google Scholar] [CrossRef]
- Raimondi, L.; De Luca, A.; Costa, V.; Amodio, N.; Carina, V.; Bellavia, D.; Tassone, P.; Pagani, S.; Fini, M.; Alessandro, R.; et al. Circulating biomarkers in osteosarcoma: New translational tools for diagnosis and treatment. Oncotarget 2017, 8, 100831–100851. [Google Scholar] [CrossRef]
- Hattinger, C.M.; Patrizio, M.P.; Fantoni, L.; Casotti, C.; Riganti, C.; Serra, M. Drug Resistance in Osteosarcoma: Emerging Biomarkers, Therapeutic Targets and Treatment Strategies. Cancers 2021, 13, 2878. [Google Scholar] [CrossRef]
- Tang, S.; Roberts, R.D.; Cheng, L.; Li, L. Osteosarcoma Multi-Omics Landscape and Subtypes. Cancers 2023, 15, 4970. [Google Scholar] [CrossRef]
- Vezakis, I.A.; Lambrou, G.I.; Matsopoulos, G.K. Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach. Cancers 2023, 15, 2290. [Google Scholar] [CrossRef]
- Tirtei, E.; Campello, A.; Asaftei, S.D.; Mareschi, K.; Cereda, M.; Fagioli, F.; Santucci, A. Precision Medicine in Osteosarcoma: MATCH Trial and Beyond. Cells 2021, 10, 281. [Google Scholar] [CrossRef]
- Morganti, S.; Tarantino, P.; Ferraro, E.; D’Amico, P.; Duso, B.A.; Curigliano, G. Next Generation Sequencing (NGS): A Revolutionary Technology in Pharmacogenomics and Personalized Medicine in Cancer. Adv. Exp. Med. Biol. 2019, 1168, 9–30. [Google Scholar]
- Chiappetta, C.; Mancini, M.; Lessi, F.; Aretini, P.; De Gregorio, V.; Puggioni, C.; Carletti, R.; Petrozza, V.; Civita, P.; Franceschi, S.; et al. Whole-exome analysis in osteosarcoma to identify a personalized therapy. Oncotarget 2017, 8, 80416–80428. [Google Scholar] [CrossRef]
- PosthumaDeBoer, J.; Witlox, M.A.; Kaspers, G.J.; van Royen, B.J. Molecular alterations as target for therapy in metastatic osteosarcoma: A review of literature. Clin. Exp. Metastasis 2011, 28, 493–503. [Google Scholar] [CrossRef]
- Assi, A.; Farhat, M.; Hachem, M.C.R.; Zalaquett, Z.; Aoun, M.; Daher, M.; Sebaaly, A.; Kourie, H.R. Tyrosine kinase inhibitors in osteosarcoma: Adapting treatment strategies. J. Bone Oncol. 2023, 43, 100511. [Google Scholar] [CrossRef]
- Chen, C.; Shi, Q.; Xu, J.; Ren, T.; Huang, Y.; Guo, W. Current progress and open challenges for applying tyrosine kinase inhibitors in osteosarcoma. Cell Death Discov. 2022, 8, 488. [Google Scholar] [CrossRef]
- Teleanu, R.I.; Chircov, C.; Grumezescu, A.M.; Teleanu, D.N. Tumor Angiogenesis and Anti-Angiogenic Strategies for Cancer Treatment. J. Clin. Med. 2020, 9, 84. [Google Scholar] [CrossRef]
- Corre, I.; Verrecchia, F.; Crenn, V.; Redini, F.; Trichet, V. The Osteosarcoma Microenvironment: A Complex but Targetable Ecosystem. Cells. 2020, 9, 976. [Google Scholar] [CrossRef]
- Xie, L.; Ji, T.; Guo, W. Anti-angiogenesis target therapy for advanced osteosarcoma. Oncol. Rep. 2017, 38, 625–636. [Google Scholar] [CrossRef]
- Ding, L.; Congwei, L.; Bei, Q.; Tao, Y.; Ruiguo, W.; Heze, Y.; Bo, D.; Zhihong, L. mTOR: An attractive therapeutic target for osteosarcoma? Oncotarget 2016, 7, 50805–50813. [Google Scholar] [CrossRef]
- Chamcheu, J.C.; Roy, T.; Uddin, M.B.; Banang-Mbeumi, S.; Chamcheu, R.C.N.; Walker, A.L.; Liu, Y.Y.; Huang, S. Role and Therapeutic Targeting of the PI3K/Akt/mTOR Signaling Pathway in Skin Cancer: A Review of Current Status and Future Trends on Natural and Synthetic Agents Therapy. Cells 2019, 8, 803. [Google Scholar] [CrossRef]
- Rathore, R.; Van Tine, B.A. Pathogenesis and Current Treatment of Osteosarcoma: Perspectives for Future Therapies. J. Clin. Med. 2021, 10, 1182. [Google Scholar] [CrossRef]
- Yahiro, K.; Matsumoto, Y. Immunotherapy for osteosarcoma. Hum. Vaccin. Immunother. 2021, 17, 1294–1295. [Google Scholar] [CrossRef]
- Zhang, Z.; Tan, X.; Jiang, Z.; Wang, H.; Yuan, H. Immune checkpoint inhibitors in osteosarcoma: A hopeful and challenging future. Front. Pharmacol. 2022, 13, 1031527. [Google Scholar] [CrossRef]
- Supra, R.; Agrawal, D.K. Immunotherapeutic Strategies in the Management of Osteosarcoma. J. Orthop. Sports Med. 2023, 5, 32–40. [Google Scholar] [CrossRef]
- Köksal, H.; Müller, E.; Inderberg, E.M.; Bruland, O.; Wälchli, S. Treating osteosarcoma with CAR T cells. Scand. J. Immunol. 2019, 89, e12741. [Google Scholar] [CrossRef]
- Reed, D.E.; Shokat, K.M. Targeting osteosarcoma. Proc. Natl. Acad. Sci. USA 2014, 111, 18100–18101. [Google Scholar] [CrossRef]
- Shyr, D.; Liu, Q. Next generation sequencing in cancer research and clinical application. Biol. Proced. Online 2013, 15, 4. [Google Scholar] [CrossRef]
- Batanian, J.R.; Cavalli, L.R.; Aldosari, N.M.; Ma, E.; SoteloAvila, C.; Ramos, M.B.; Rone, J.D.; Thorpe, C.M.; Haddad, B.R. Evaluation of pediatric osteosarcomas by classic cytogenetic and CGH analyses. Mol. Pathol. 2002, 55, 389–393. [Google Scholar] [CrossRef]
- Chiappetta, C.; Puggioni, C.; Carletti, R.; Petrozza, V.; Della Rocca, C.; Di Cristofano, C. The nuclear-cytoplasmic trafficking of a chromatin-modifying and remodelling protein (KMT2C), in osteosarcoma. Oncotarget 2018, 9, 30624–30634. [Google Scholar] [CrossRef]
- Shilatifard, A. The COMPASS family of histone H3K4 methylases: Mechanisms of regulation in development and disease pathogenesis. Annu. Rev. Biochem. 2012, 81, 65–95. [Google Scholar] [CrossRef] [PubMed]
- Herz, H.M. Enhancer deregulation in cancer and other diseases. Bioessays 2016, 38, 1003–1015. [Google Scholar] [CrossRef]
- Gaeta, R.; Morelli, M.; Lessi, F.; Mazzanti, C.M.; Menicagli, M.; Capanna, R.; Andreani, L.; Coccoli, L.; Aretini, P.; Franchi, A. Identification of New Potential Prognostic and Predictive Markers in High-Grade Osteosarcoma Using Whole Exome Sequencing. Int. J. Mol. Sci. 2023, 24, 10086. [Google Scholar] [CrossRef]
- Xie, X.; Bian, Y.; Li, H.; Yin, J.; Tian, L.; Jiang, R.; Zeng, Z.; Shi, X.; Lei, Z.; Hou, C.; et al. A Comprehensive Understanding of the Genomic Bone Tumor Landscape: A Multicenter Prospective Study. Front. Oncol. 2022, 8, 835004. [Google Scholar] [CrossRef] [PubMed]
- Vyse, S.; Thway, K.; Huang, P.H.; Jones, R.L. Next-generation sequencing for the management of sarcomas with no known driver mutations. Curr. Opin. Oncol. 2021, 33, 315–322. [Google Scholar] [CrossRef]
- Chudasama, P.; Mughal, S.S.; Sanders, M.A.; Hübschmann, D.; Chung, I.; Deeg, K.I.; Wong, S.H.; Rabe, S.; Hlevnjak, M.; Zapatka, M.; et al. Integrative genomic and transcriptomic analysis of leiomyosarcoma. Nat. Commun. 2018, 9, 1–15. [Google Scholar] [CrossRef]
- Kovac, M.; Blattmann, C.; Ribi, S.; Smida, J.; Mueller, N.S.; Engert, F.; Castro-Giner, F.; Weischenfeldt, J.; Kovacova, M.; Krieg, A.; et al. Exome sequencing of osteosarcoma reveals mutation signatures reminiscent of BRCA deficiency. Nat. Commun. 2015, 6, 8940. [Google Scholar] [CrossRef]
- Xie, L.; Yang, Y.; Guo, W.; Che, D.; Xu, J.; Sun, X.; Liu, K.; Ren, T.; Liu, X.; Yang, Y.; et al. The Clinical Implications of Tumor Mutational Burden in Osteosarcoma. Front. Oncol. 2021, 7, 595527. [Google Scholar] [CrossRef] [PubMed]
- Guimarães, G.M.; Tesser-Gambaa, F.; Petrilli, A.S.; Donato-Macedoa, C.R.P.; Alves, M.T.S.; de Limaa, F.T.; Garcia-Filhoa, R.J.; Oliveiraa, R.; Toledoa, S.R.C. Molecular profiling of osteosarcoma in children and adolescents from different age groups using a next-generation sequencing panel. Cancer Genet. 2021, 258–259, 85–92. [Google Scholar] [CrossRef] [PubMed]
- Ferreira Pires, S.; Sobral de Barros, J.; Souza da Costa, S.; Bandeira do Carmo, G.; de Oliveira Scliar, M.; van Helvoort Lengert, A.; Boldrini, E.; Regini Morini da Silva, S.; Onofre Vidal, D.; Maschietto, M.; et al. Analysis of the Mutational Landscape of Osteosarcomas Identifies Genes Related to Metastasis and Prognosis and Disrupted Biological Pathways of Immune Response and Bone Development. Int. J. Mol. Sci. 2023, 24, 10463. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
