Defining Melanoma Immune Biomarkers—Desert, Excluded, and Inflamed Subtypes—Using a Gene Expression Classifier Reflecting Intratumoral Immune Response and Stromal Patterns
Abstract
:1. Introduction
2. Materials and Methods
2.1. Processing of TCGA Dataset and Gene List
2.2. In Silico Immune Subtyping
2.3. Differential Gene Expression Analysis and Building Subtyping Classifier
2.4. Melanoma Patient Cohort
2.5. Histopathological Assessment
2.6. qPCR for Gene Expression Analysis in Real-World Patient Cohort
2.7. Statistical Analysis
3. Results
3.1. Immune Subtyping of the TCGA Cohort
3.2. Clinical Features of the Immune Subtypes in the TCGA Cohort
3.3. Building the Immune Subtyping Classifier
3.4. Validation of the Immune Subtyping Classifier in a Real-World Patient Cohort
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature | Desert | Excluded | Inflamed | p-Value |
---|---|---|---|---|
N | 95 | 75 | 176 | |
Age, median (range) | 58 (15–86) | 62 (20–90) | 56 (18–90) | 0.031 |
BMI, median (range) | 26.1 (17.8–42.5) | 27.3 (17.6–55.5) | 28.7 (18.3–49.1) | 0.045 |
TMB mut/Mb, median (range) | 12.3 (0.6–129.9) | 16.4 (0.3–102.2) | 25.2 (0.2–1060.3) | 0.403 |
Stage | 0.003 | |||
I | 8 (9%) | 10 (14%) | 40 (25%) | |
II | 41 (47%) | 34 (48%) | 46 (29%) | |
III | 33 (38%) | 23 (32%) | 68 (42%) | |
IV | 5 (6%) | 4 (6%) | 6 (4%) | |
28 undocumented | ||||
Clark level | 0.402 | |||
1 | 2 (3%) | 1 (2%) | 2 (2%) | |
2 | 2 (3%) | 1 (2%) | 11 (10%) | |
3 | 9 (13%) | 13 (22%) | 36 (31%) | |
4 | 44 (62%) | 35 (60%) | 52 (45%) | |
5 | 14 (20%) | 8 (14%) | 14 (12%) | |
102 undocumented | ||||
Breslow in mm, median (range) | 5.8 (0.0–75.0) | 7.3 (0.5–29.0) | 4.2 (0.0–74.0) | 0.032 |
BRAF status | <0.001 | |||
wild type | 34 (72%) | 14 (30%) | 56 (47%) | |
mutated | 13 (28%) | 32 (70%) | 63 (53%) | |
134 undocumented | ||||
NRAS status | 0.049 | |||
wild type | 25 (53%) | 33 (72%) | 87 (72%) | |
mutated | 22 (47%) | 13 (28%) | 22 (28%) | |
134 undocumented | ||||
KIT status | 0.010 | |||
wild type | 42 (89%) | 39 (85%) | 116 (98%) | |
mutated | 5 (11%) | 7 (15%) | 3 (2%) | |
134 undocumented |
Group | mOS, Months (95% CI) | Estimated Survival, % (95% CI) | Univariate Analysis | Multivariate Analysis * | |||
---|---|---|---|---|---|---|---|
HR (95% CI) | padj | HR (95% CI) | padj | ||||
OVERALL SURVIVAL | |||||||
3 years | 5 years | ||||||
INF | 162.0 (72.8–133.5) | 82.7 (77.4–90.0) | 73.4 (66.7–81.9) | 1.00 (ref.) | 1.00 (ref.) | ||
EXC | 61.2 (37.1–85.4) | 72.4 (62.5–85.7) | 50.1 (37.2–64.7) | 1.81 (1.20–2.72) | 0.004 | 1.62 (1.03–2.57) | 0.036 |
DES | 55.6 (23.1–87.9) | 57.2 (46.5–69.7) | 48.2 (36.7–60.8) | 2.38 (1.60–3.41) | 0.002 | 1.57 (0.97–2.55) | 0.056 |
PROGRESSION-FREE SURVIVAL | |||||||
3 years | 5 years | ||||||
INF | 65.9 (47.6–84.3) | 72.2 (64.0–78.8) | 51.5 (42.2–60.0) | 1.00 (ref.) | 1.00 (ref.) | ||
EXC | 47.1 (32.8–61.5) | 58.8 (44.4–60.6) | 37.2 (23.5–50.9) | 1.19 (0.81–1.72) | 0.012 | 1.56 (1.02–2.38) | 0.041 |
DES | 35.0 (8.8–61.2) | 46.4 (32.5–59.2) | 37.2 (23.9–50.5) | 1.68 (1.18–2.39) | 0.377 | 1.13 (0.72–1.78) | 0.586 |
Feature | Desert | Excluded | Inflamed | p-Value |
---|---|---|---|---|
N | 38 | 29 | 29 | |
Age, median (range) | 72 (36–88) | 65 (32–92) | 69 (30–88) | 0.456 |
Stage | 0.985 | |||
I | 2 (5%) | 1 (3%) | 2 (7%) | |
II | 19 (50%) | 17 (59%) | 14 (48%) | |
III | 16 (42%) | 10 (35%) | 12 (41%) | |
IV | 1 (3%) | 1 (3%) | 1 (3%) | |
28 undocumented | ||||
Clark level | 0.378 | |||
3 | 9 (24%) | 13 (46%) | 11 (41%) | |
4 | 22 (60%) | 14 (50%) | 14 (52%) | |
5 | 6 (16%) | 1 (4%) | 2 (7%) | |
4 undocumented | ||||
Breslow depth | 0.300 | |||
1–4 mm | 15 (41%) | 13 (50%) | 17 (63%) | |
>4 mm | 22 (59%) | 13 (50%) | 10 (37%) | |
6 undocumented | ||||
BRAF status | 0.067 | |||
wild-type | 28 (74%) | 14 (48%) | 15 (52%) | |
mutated | 10 (26%) | 15 (52%) | 14 (48%) |
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Mlynska, A.; Gibavičienė, J.; Kutanovaitė, O.; Senkus, L.; Mažeikaitė, J.; Kerševičiūtė, I.; Maskoliūnaitė, V.; Rupeikaitė, N.; Sabaliauskaitė, R.; Gaiževska, J.; et al. Defining Melanoma Immune Biomarkers—Desert, Excluded, and Inflamed Subtypes—Using a Gene Expression Classifier Reflecting Intratumoral Immune Response and Stromal Patterns. Biomolecules 2024, 14, 171. https://doi.org/10.3390/biom14020171
Mlynska A, Gibavičienė J, Kutanovaitė O, Senkus L, Mažeikaitė J, Kerševičiūtė I, Maskoliūnaitė V, Rupeikaitė N, Sabaliauskaitė R, Gaiževska J, et al. Defining Melanoma Immune Biomarkers—Desert, Excluded, and Inflamed Subtypes—Using a Gene Expression Classifier Reflecting Intratumoral Immune Response and Stromal Patterns. Biomolecules. 2024; 14(2):171. https://doi.org/10.3390/biom14020171
Chicago/Turabian StyleMlynska, Agata, Jolita Gibavičienė, Otilija Kutanovaitė, Linas Senkus, Julija Mažeikaitė, Ieva Kerševičiūtė, Vygantė Maskoliūnaitė, Neda Rupeikaitė, Rasa Sabaliauskaitė, Justina Gaiževska, and et al. 2024. "Defining Melanoma Immune Biomarkers—Desert, Excluded, and Inflamed Subtypes—Using a Gene Expression Classifier Reflecting Intratumoral Immune Response and Stromal Patterns" Biomolecules 14, no. 2: 171. https://doi.org/10.3390/biom14020171