Candidate Biomarkers for Specific Intraoperative Near-Infrared Imaging of Soft Tissue Sarcomas: A Systematic Review
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Search Strategy
2.2. Eligibility Criteria
2.3. Data Extraction
2.4. Biomarker Selection Scoring System
- Sample size. The number of samples indicate how much evidence is acquired.
- Percentage of biomarker-positive STS samples. This is calculated based on the amount of STS samples that positively showed presence of the biomarker in each included article, independent of the percentage of positive tumor cells within each sample. Immunohistochemistry was used to assess the percentage of positive STS in tissue samples.
- Pattern of expression. Ideally, the target is expressed diffusely by all tumor cells (particularly at the tumor border) to guide surgical resection. The pattern of expression is defined as diffuse when expression is randomly spread throughout the tumor sample and focal when expression is located in a specific region of the tumor sample. When different samples show variable expression patterns (diffuse and focal), the expression pattern for the whole cohort is defined as heterogeneous. No distinction was made based on exact location of expression within tumor samples. While this review included studies evaluating tissue samples and tissue microarrays, data regarding the pattern of expression was extracted from studies including tissue samples.
- Internalization. This is important because internalization after binding of the tracer creates a long-lasting signal for tumor-specific imaging.
- Previously imaged. If there is prove that imaging is possible, it has more potential to be translated to the clinics. The distinction between imaging with or without NIRF is important for its applicability in NIRF imaging. This criterium was tumor type independent.
3. Results
3.1. Study Selection
3.2. Candidate Biomarkers
3.2.1. TEM1
3.2.2. VEGFR
3.2.3. EGFR
3.2.4. IGF-1R
3.2.5. PDGFR
3.2.6. CD40
3.3. Potential NIRF Imaging Tracers Safety Profile
4. Discussion
4.1. Research Aim
4.2. Comparing the Selected Biomarkers
4.3. MFS, USTS and SS
4.4. Comparison of Potential NIRF Imaging Tracers
4.5. Strengths and Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Search Strategy
Appendix B. Search Previously Imaged and Search Internalization
Appendix C. STS Subtypes Examined for Each of the Top 7 Biomarkers
Biomarker | STS Subtypes |
---|---|
TEM1 | Angiosarcoma, desmoplastic small round cell, epithelioid haemangioendothelioma, epithelioid sarcoma, fibrosarcoma, inflammatory myofibroblastic sarcoma, kaposi sarcoma, LMS, liposarcoma, MPNST, malignant solitary fibrous tumor, myxofibrosarcoma, RMS, spindle cell sarcoma NOS, synovial sarcoma, USTS, and uterine sarcoma |
VEGFR-1 | Alveolar STS, angiosarcoma, endometrial stromal sarcoma, Kaposi sarcoma, LMS, liposarcoma, MPNST, malignant solitary fibrous tumor, myxofibrosarcoma, myxoid liposarcoma, pulmonary artery sarcoma, RMS, sarcoma NOS, synovial sarcoma, and USTS |
VEGFR-2 | Alveolar STS, angiosarcoma, endometrial stromal sarcoma, epithelioid hemangioendotheliomas, fibrosarcoma, LMS, liposarcoma, MPNST, malignant solitary fibrous tumor, myxofibrosarcoma, pulmonary artery sarcoma, RMS, sarcoma NOS, synovial sarcoma, and USTS |
EGFR | Acral myxoinflammatory fibroblastic sarcoma, alveolar soft part sarcoma, atypical fibroxanthoma, desmoplastic tumor, endometrial stromal sarcoma, epithelioid sarcoma, fibromatosis, fibromyxoid sarcoma, fibrosarcoma, follicular dendritic cell sarcoma, intimal sarcoma, liposarcoma, LMS, MPNST, myofibroblastic sarcoma, myoxyoinflammatory fibroblastic sarcoma, myxofibrosarcoma, myxoid lipsarcoma, myxoid sarcoma, pleomorphic dermal sarcoma, RMS, sarcoma NOS, synovial sarcoma, endifferentiated endometrial sarcoma, USTS, and undifferentiated stromal sarcoma |
IGF-1R | Alveolar STS, angiosarcoma, desmoplastic tumor, fibrosarcoma, LMS, liposarcoma, MPNST, mesenchyoma, myxofibrosarcoma, RMS, sarcoma NOS, spindle cell sarcoma, synovial sarcoma, and USTS |
PDGFRα | Alveolar soft part sarcoma, Angiosarcoma, dermatofibrosarcoma protuberans, endometrial stromal sarcoma, fibromyxoid sarcoma, fibrosarcoma, liposarcoma, LMS, MPNST, myofibroblastic sarcoma, myoxyoinflammatory fibroblastic sarcoma, myxofibrosarcoma, myxoid liposarcoma, pulmonary artery sarcoma, RMS, sarcoma NOS, solitary fibrous tumor, synovial sarcoma, undifferentiated endometrial sarcoma, undifferentiated uterine sarcoma, undifferentiated stromal sarcoma, and USTS |
CD40 | Kaposi sarcoma, liposarcoma, LMS, MPNST, RMS, and USTS |
Appendix D. Toxicity of Clinically Available Monoclonal Antibodies in Patients with STS
Clinical Trial | Phase | Tumor Type | Evaluable for Toxicity | Median a,* Age (Years) | Treatment | Most Common Adverse Events | Percentage Patients with ≥3 Adverse Events (vs. Placebo) | Most Common Grade ≥ 3 Adverse Events |
---|---|---|---|---|---|---|---|---|
TEM1 (Ontuxizumab) | ||||||||
Jones et al., 2019 | 2 | STS | 209 | 55 | Ontuxizumab 8mg/kg + G/D vs. placebo + G/D b | Fatigue (74% vs. 66%), anemia (61% vs. 60%), nausea (56% vs. 52%), diarrhea (44% vs. 36%), and peripheral edema (42% vs. 45%) | Not reported | Pyrexia (4% vs. 0%) and anemia (1% vs. 3%) c |
IGF-1R (Teprotomumab, Figitumumab and Cixutumumab) | ||||||||
Pappo et al., 2014 | 2 | STS + osteosarcoma | 163 | 31 | Teprotumumab 9 mg/kg; 1 dose per week | Fatigue (20.2%), nausea (14.1%), hyperglycemia (9.2%), and muscle spasms (8.6%) | 10.4% | Hyperglycemia (2.5%), dehydration (1.8%), fatigue (1.8%), and hyponatremia (1.2%) |
Olmos et al., 2010 | 1 | STS + Ewing sarcoma + myxoid chondrosarcoma | 29 | 30 | Figitumumab 20 mg/kg; 1 dose per 3–4 weeks | Hyperglycemia (17%), skin reactions (rash, urticaria, infection, eczema) (13.8%), increased GGT (10.3%), headache (10.3%), and fatigue (10.3%) | 17.2% | Vomiting (3.4%), back pain (3.4%), DVT (3.4%), increased uric acid concentration (3.4%), and increased AST, ALT or GGT (3.4%) |
Wagner et al., 2015 | 2 | STS + Ewing sarcoma + osteosarcoma | 44 | 14–181 | Cixutumumab 6 mg/kg and Temsirolimus 8 mg/m2; 1 dose per week | Mucositis, electrolyte disturbances and myelosuppression | Not reported | Neutropenia (13.6%), thrombocytopenia (11.4%), hypokalemia (11.4%), oral mucositis (9.1%), and hypophosphatemia (9.1%) |
Schöffski et al., 2013 | 2 | STS + Ewing family of tumors | 113 | 27.5–33.1 | Cixutumumab 10 mg/kg; 1 dose per 2 weeks | Nausea (26.1%), fatigue (23.4%), diarrhea (22.5%), hyperglycemia (19.8%), and anorexia (17.1%) | Not reported | Hyperglycemia (5.4%), pain (5.4%), thrombocytopenia (4.5%), asthenia (4.5%), and anemia (3.6%) |
Schwartz et al., 2013 | 2 | STS + sarcoma of bone | 174 | Mean: 48.1 | Cixutumumab 6 mg/kg and Temsirolimus 25 mg; 1 dose per week | Oral mucositis (71.3%), hypercalcemia (68.4%), fatigue (65.5%), thrombocytopenia (63.8%), and anemia (62.6%) | Not reported | Anemia (9%), hyperglycemia (10%), hypophosphatemia (9%), lymphopenia (14%), oral mucositis (11%), and thrombocytopenia (11%) |
PDGFR (Olaratumab) | ||||||||
Tap et al., 2020 | 3 | STS | 506 | Mean: 56.9 | Olaratumab 15 mg/kg + Doxorubicin 75 mg/m2 vs. Placebo + Doxorubicin 75 mg/m2 d | Nausea (59.5% vs. 66.7%), neutropenia (55.3% vs. 57.8%), fatigue (54.1% vs. 59%), alopecia (43.6% vs. 49.8%), and anemia (42.8% vs. 45.4%) | Not reported | Neutropenia (46.3% vs. 49%), leukopenia (23.3% vs. 23.7%), febrile neutropenia (17.5% vs. 16.5%), anemia (13.6% vs. 12.4%), and thrombocytopenia (9.3% vs. 8.4%) |
Yonemori et al., 2018 | 1 | STS | 19 | 41.5–52 a | Olaratumab 15 mg/kg + Doxorubicin 25-75 mg/m2 e | ALT increased (52.6%), neutrophil count decreased (52.6%), WBC count decreased (47.4%), anemia (36.8%), and GGT increased (31.6%) | 57.9% | Decreased neutrophil count (42.1%), decreased WBC count (42.1%), increased ALT (15.8%), anemia (10.5%), and febrile neutropenia (10.5%) |
Tap et al., 2016 | 2 | STS | 133 | 58.5 | Olaratumab 15 mg/kg + Doxorubicin 75 mg/m2 vs. Doxorubicin f | Nausea (73.4% vs. 52.3%), fatigue (68.8% vs. 69.2%), neutropenia (57.8% vs. 35.4%), mucositis (53.1% vs. 35.4%), and alopecia (51.6% vs. 40%) | 67% vs. 55% | Neutropenia (53.2% vs. 32.3%), leukopenia (36% vs. 16.9%), febrile neutropenia (12.5% vs. 13.8%), anemia (12.5% vs. 9.2%), and fatigue (9.4% vs. 3.1%) |
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Score | 0 | 1 | 2 |
---|---|---|---|
Sample size | 0–100 | 101–500 | >500 |
Percentage of positive STS samples | 0–33% | 33–67% | >67% |
Pattern of expression * | Focal | Heterogeneous | Diffuse |
Internalization | not described | Yes | |
Previously imaged | not described | Yes, but not with NIRF imaging | Yes, NIRF imaging |
Biomarker | Therapeutic Antibody | N | % Positive STS (Mean% + Range) | Pattern of Expression | Internalization | Previously Imaged | Score | Literature |
---|---|---|---|---|---|---|---|---|
Tumor endothelial marker 1 (TEM1/ endosialin/ CD248) | Ontuxizumab (MORAb-004) | 768 | 77% (55–100) | Diffuse | Yes, [25] | NIRF imaging [26] | 9 | [26,27,28,29] |
Vascular endothelial growth factor receptor-1 (VEGFR-1) | Aflibercept Bevacizumab | 477 | 76% (22–100) | Diffuse | Yes, [30] | NIRF imaging [30,31] | 8 | [32,33,34,35,36,37,38,39] |
Epidermal growth factor receptor (EGFR) | Cetuximab | 1918 | 53% (0–100) | Diffuse | Yes, [40] | NIRF imaging [41] | 8 | [27,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76] |
Vascular endothelial growth factor receptor-2 (VEGFR-2) | Aflibercept Bevacizumab Ramucirumab | 449 | 71% (11–100) | Diffuse | Yes, [77] | NIRF imaging [78] | 7 | [33,34,35,36,38,39,79,80,81] |
Insulin-like growth factor 1 receptor (IGF-1R) | Ganitumab (AMG 479) Teprotumumab Cixutumumab Figitumumab | 507 | 63% (25–100) | Diffuse | Yes, [82] | NIRF imaging [83] | 7 | [63,64,82,84,85,86,87,88,89] |
Platelet derived growth factor receptor α (PDGFRα) | Olaratumab | 1536 | 64% (0–100) | Diffuse | Yes, [84] | NIRF imaging [85] | 7 | [27,34,36,38,42,43,44,45,46,47,48,49,51,82,86,87,88,89,90,91,92] |
Cluster of differentiation 40 (CD40) | APX005M | 153 | 62% (17–86) | Diffuse | Yes, [93] | NIRF imaging [94] | 7 | [95,96,97,98] |
Programmed death-ligand 1 (PD-L1/CD 274/B7-H1) | Atezolizumab Avelumab Durvalumab Envafolimab | 1492 | 31% (0–76) | Heterogeneous (focal and diffuse) | Yes, [99] | NIRF imaging [100] | 6 | [101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118] |
Leucine-rich repeat containing 15 (LRRC15) | ABBV-085 | 635 | 40% | Diffuse | Not described | Not described | 4 | [102] |
Receptor tyrosine kinase-like orphan receptor 2 (ROR2) | CAB-ROR2-ADC | 237 | 72% | Not described | Not described | Not described | 3 | [119] |
Cytotoxic T-Lymphocyte-associated protein 4 (CTLA-4/CD152) | Ipilimumab Tremelimumab | 10 | 30% | Not described | Yes, [120] | Not with NIRF imaging [120] | 2 | [59] |
Biomarker | N | Positive Tumors Mean% (Range) | Expression Pattern | Present after RTx | Literature |
---|---|---|---|---|---|
Myxofibrosarcoma | |||||
TEM1 | 34 | 100 (100) | Diffuse | Yes, [27] | [27] |
EGFR | 97 | 38 (0–89) | Heterogeneous | Yes, [27] | [26,53,65] |
PDGFRα | 34 | 77 (77) | Not described | Yes, [27] | [27] |
Undifferentiated soft tissue sarcoma | |||||
TEM1 | 128 | 81 (73–89) | Diffuse | N.D. | [28,29] |
VEGFR-1 | 81 | 68 (68) | Not described | N.D. | [36] |
EGFR | 287 | 62 (5–95) | Heterogeneous | N.D. | [50,57,65,70] |
VEGFR-2 | 81 | 6 (6) | Not described | N.D. | [36] |
IGF-1R | 120 | 25 (25) | Not described | N.D. | [90] |
PDGFRα | 432 | 79 (63–99) | Diffuse | N.D. | [35,50,126] |
Synovial sarcoma | |||||
TEM1 | 70 | 71 (62–80) | Heterogeneous | N.D. | [28,29] |
VEGFR-1 | 27 | 70 (70) | Not described | N.D. | [27] |
EGFR | 160 | 86 (71–100) | Heterogeneous | Yes, [127] | [52,58,66,69,70,71] |
VEGFR-2 | 27 | 4 (4) | Not described | N.D. | [27] |
IGF-1R | 195 | 57 (35–80) | Not described | N.D. | [81,82,128,129] |
PDGFRα | 136 | 69 (44–84) | Not described | N.D. | [35,81,88,91] |
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Rijs, Z.; Shifai, A.N.; Bosma, S.E.; Kuppen, P.J.K.; Vahrmeijer, A.L.; Keereweer, S.; Bovée, J.V.M.G.; van de Sande, M.A.J.; Sier, C.F.M.; van Driel, P.B.A.A. Candidate Biomarkers for Specific Intraoperative Near-Infrared Imaging of Soft Tissue Sarcomas: A Systematic Review. Cancers 2021, 13, 557. https://doi.org/10.3390/cancers13030557
Rijs Z, Shifai AN, Bosma SE, Kuppen PJK, Vahrmeijer AL, Keereweer S, Bovée JVMG, van de Sande MAJ, Sier CFM, van Driel PBAA. Candidate Biomarkers for Specific Intraoperative Near-Infrared Imaging of Soft Tissue Sarcomas: A Systematic Review. Cancers. 2021; 13(3):557. https://doi.org/10.3390/cancers13030557
Chicago/Turabian StyleRijs, Zeger, A. Naweed Shifai, Sarah E. Bosma, Peter J. K. Kuppen, Alexander L. Vahrmeijer, Stijn Keereweer, Judith V. M. G. Bovée, Michiel A. J. van de Sande, Cornelis F. M. Sier, and Pieter B. A. A. van Driel. 2021. "Candidate Biomarkers for Specific Intraoperative Near-Infrared Imaging of Soft Tissue Sarcomas: A Systematic Review" Cancers 13, no. 3: 557. https://doi.org/10.3390/cancers13030557
APA StyleRijs, Z., Shifai, A. N., Bosma, S. E., Kuppen, P. J. K., Vahrmeijer, A. L., Keereweer, S., Bovée, J. V. M. G., van de Sande, M. A. J., Sier, C. F. M., & van Driel, P. B. A. A. (2021). Candidate Biomarkers for Specific Intraoperative Near-Infrared Imaging of Soft Tissue Sarcomas: A Systematic Review. Cancers, 13(3), 557. https://doi.org/10.3390/cancers13030557