Improved Survival Prediction by Combining Radiological Imaging and S-100B Levels Into a Multivariate Model in Metastatic Melanoma Patients Treated With Immune Checkpoint Inhibition

Burgermeister, Simon; Gabryś, Hubert S.; Basler, Lucas; Hogan, Sabrina A.; Pavic, Matea; Bogowicz, Marta; Gómez, Julia María Martínez; Vuong, Diem; Tanadini‐Lang, Stephanie; Forster, R. E.; Huellner, Martin W.; Dummer, Reinhard; Levesque, Mitchell P.; Gückenberger, Matthias

doi:10.3389/fonc.2022.830627

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…In addition, serum S-100B protein can reflect the therapeutic effect of patients and be employed as a prognostic indicator of MM. The elevation of the S-100B protein level in serum of MM cases occurred earlier than the onset of clinical symptoms [ 18 , 19 ]. In the present study, the serum LDH, MIA, and S-100B levels in the influential cohort were reduced compared to the ineffective cohort, and the AUC value of the combined evaluation for clinical effectiveness with serum LDH, MIA, and S-100B (0.839) was higher than that with LDH (0.699) alone ( P < 0.05).…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of Lactate Dehydrogenase, Melanoma Inhibitory Protein, and S-100B Protein in Patients with Malignant Melanoma

Fan

Liu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Objective. This investigation probed the prognostic potential for lactate dehydrogenase (LDH), melanoma inhibitory activity protein (MIA), and S-100B protein in cases of malignant melanoma. Methods. 84 cases were segregated into effective cohort (n = 64) and ineffective cohort (n = 20) depending on clinical efficacy. The cases were followed up for three years and segregated into mortality cohort (n = 29) and survival cohort (n = 55) depending upon 3-year survival. Serum LDH, MIA, and S-100B levels were compared across the effective and ineffective cohorts. Serum LDH, MIA, and S-100B levels in cases of different clinical stages were comparatively analyzed, with correlations of these indicators with the clinical stage being evaluated. ROC evaluated the prognostic potential of serum LDH, MIA, and S-100B. Cases were segregated into the high-level and low-level cohorts according to serum LDH, MIA, and S-100B levels, and the survival rates of cases were compared. Results. The levels of LDH, MIA, and S-100B in the effective cohort were significantly lower than those in the ineffective cohort. The AUC value of the composite indicator of serum LDH, MIA, and S-100B for effectiveness evaluation was (0.839). Serum LDH, MIA, and S-100B levels were positively linked to the clinical stage. AUC value of the composite indicator of serum LDH, MIA, and S-100B for prognosis evaluation prediction (0.942) was elevated compared to LDH (0.632), MIA (0.732), or S-100B (0.828) alone. Survival rate of cases of LDH ≥30.56 mg/L (57.14%, 32/56) was lower than that of cases of LDH <30.56 mg/L (82.14%, 23/28) (log-rank χ2 = 4.672, P < 0.05 ). The survival rate of MIA ≥5.34 ng/mL cases was lower than that of MIA <5.34 ng/mL cases. The survival rate of cases of S-100B ≥ 1.03ug/L was lower than that of S-100B < 1.03ug/L. Conclusion. Serum LDH, MIA, and S-100B protein levels are linked to the clinical stage. The lactate dehydrogenase, melanoma inhibitory protein, and S-100B protein are of good clinical effectiveness and have the prognostic potential for cases of malignant melanoma.

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Section: Discussionmentioning

confidence: 99%

Prognostic Value of Lactate Dehydrogenase, Melanoma Inhibitory Protein, and S-100B Protein in Patients with Malignant Melanoma

Fan

Liu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

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“…Several studies have evaluated the prognostic value of blood and imaging biomarkers, but the prognostic potential of combined imaging and blood markers has not been investigated in metastatic melanoma [11,[25][26][27][28]. We hypothesized that baseline CT-based quantitative radiomics solely and combined with serum LDH can be predictive of responses to immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Tabari

Cox

D'Amore

et al. 2023

Cancers

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Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008–2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76–0.99]) vs. 0.81 (95% CI: [0.65–0.94]) and 0.81 (95% CI: [0.72–0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

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“…Given this, patients have been treated with immune-modulators and ICIs with heterogenous outcomes [8][9][10]. Similar to forecasting the efficacy of a particular chemotherapy for treatment of other cancers, the effectiveness of ICIs in the treatment of melanoma has been difficult to predict for specific patients [11]. Demographic features such as age, sex, BMI, stage and performance status have been investigated.…”

Section: Introductionmentioning

confidence: 99%

Eosinophilia as a Predictive Biomarker for Melanoma Treatment, A Single Center Review

Reuben

2023

BJSTR

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Background: Responses to immune checkpoint inhibitor (ICI) therapy is variable with regards to response type and duration with patients with melanoma. To date an arbitrary treatment duration is applied in absence of clear biomarkers which could otherwise guide therapy. Eosinophilia has recently been investigated to assess its utility as a biomarker with respect to ICI use in this disease.Objective: To compare responses to immunotherapy with developing serologic eosinophilia and relate to patient demographics, stage of disease and response attained.Methods: A single-institution retrospective study of 114 patients with advanced melanoma described by treatment type, disease stage and duration of treatment was performed. Chart review was undertaken and assessment of serologies, time to eosinophilia, if it occurred, correlation with response and stage of disease was investigated. Statistical analysis was performed to assess for significance at p<.05.Results: Of the 114 identified patients, 99 (86.8%) had undergone treatment with ICIs of which 32.3% would also experience eosinophilia. The mean time to eosinophilia was 4.2 months among these patients and the average peak absolute eosinophil count (AEC) was 0.86 K/mm3. In patients who experienced eosinophilia a greater number of complete responses were seen (68.8% vs 59.7%). Eosinophilia was associated with a trend in improved survival (Hazard ratio = 0.38, 95% Confidence Interval 0.74-1.99), but non-statistically significant in this short follow-up period. There were only modest differences in early development (<6 months) versus late development (≥6 months) of eosinophilia and not found to be clearly correlated to the response type. Early development of eosinophilia was seen with ipilimumab. A weak correlation of delayed eosinophilia with advanced age was seen. Conclusion:Eosinophilia development occurred with numerous types of immunotherapies. When seen with ipilimumab this developed earlier with statistical significance. No difference in eosinophilia development correlated with age of patients was seen. A weak statistical correlation with delayed eosinophilia with advanced age was found. ANOVA showed a statistical correlation of eosinophilia with response. A trend to improved O.S. was seen although not statistically significant in the short time of follow-up herein. Eosinophilia may have a contribution to predicting ICI response in this patient population but further study will be required to assess long-term outcomes such as survival.

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Improved Survival Prediction by Combining Radiological Imaging and S-100B Levels Into a Multivariate Model in Metastatic Melanoma Patients Treated With Immune Checkpoint Inhibition

Cited by 4 publications

References 35 publications

Prognostic Value of Lactate Dehydrogenase, Melanoma Inhibitory Protein, and S-100B Protein in Patients with Malignant Melanoma

Prognostic Value of Lactate Dehydrogenase, Melanoma Inhibitory Protein, and S-100B Protein in Patients with Malignant Melanoma

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Eosinophilia as a Predictive Biomarker for Melanoma Treatment, A Single Center Review

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