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Article

Primary Tumor Characteristics as Biomarkers of Immunotherapy Response in Advanced Melanoma: A Retrospective Cohort Study

by
Rachel S. Goodman
1,†,
Seungyeon Jung
1,†,
Kylie Fletcher
1,
Hannah Burnette
2,
Ismail Mohyuddin
3,
Rebecca Irlmeier
1,4,
Fei Ye
1,4 and
Douglas B. Johnson
2,*
1
Vanderbilt University School of Medicine, Nashville, TN 37240, USA
2
Department of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, TN 37240, USA
3
Vanderbilt University, Nashville, TN 37240, USA
4
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37240, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2024, 16(13), 2355; https://doi.org/10.3390/cancers16132355
Submission received: 27 May 2024 / Revised: 16 June 2024 / Accepted: 25 June 2024 / Published: 27 June 2024
(This article belongs to the Section Cancer Immunology and Immunotherapy)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

Melanoma survival rates have been vastly improved by the use of immune checkpoint inhibitors. However, some patients do not respond to the treatment or experience progression of disease. Because of this variability of response, markers predicting efficacy of these treatments is of interest. This study aimed to clarify the role of primary tumor characteristics in melanoma treatment and survival. The authors demonstrated that cutaneous melanoma and its subtypes were significantly associated with response, progression-free survival, and overall survival compared with acral or unknown primary melanoma. Other primary features did not demonstrate an association on multivariable analyses. Thus, primary features, other than cutaneous primary, should likely not influence metastatic treatment selection.

Abstract

Identifying patients likely to benefit from immune checkpoint inhibitor (ICI) treatment remains a crucial goal for melanoma. The objective of this study is to assess the association between primary tumor features and immunotherapy response and survival in advanced melanoma patients. In this single-center retrospective cohort study, disease characteristics, response to immunotherapy, PFS, and OS were assessed among melanoma patients (excluding mucosal and uveal primaries) treated with ICI. Among 447 patients, 300 (67.1%) received anti-PD-1 monotherapy and 147 (32.9%) received ipilimumab/nivolumab. A total of 338 (75.6%) had cutaneous melanoma, 29 (6.5%) had acral melanoma, and 80 (17.9%) had melanoma of unknown primary. Ulceration and stage at initial presentation were associated with inferior outcomes on univariate analysis. However, on multivariate analysis, this result was not observed, but cutaneous melanoma and each of its subtypes (superficial spreading, nodular, other, unknown) were positively associated with response, longer PFS, and longer OS. Metastatic stage (M1c, M1d) at presentation (OR = 1.8, p < 0.05) and BRAFV600E mutation status (OR = 1.6, p < 0.001) were associated with shorter PFS. This study is limited by its retrospective and single-center design. Cutaneous melanoma and its subtypes were significantly associated with response, PFS, and OS compared with acral or unknown primary melanoma.

1. Introduction

The advent of immune checkpoint inhibitors (ICI) targeting cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed death 1 (PD-1) has transformed the management of advanced melanoma. Five-year survival has increased from 5% historically to >50% with combination immunotherapy [1,2]. While many patients exhibit durable responses, approximately half experience progression of the disease [3,4,5]. The variability in response remains unclear.
Despite the growing use of ICI across tumor types, there is a lack of well-defined prognostic or predictive markers of ICI efficacy and survival. While efforts have been made to identify factors associated with response (and lack thereof), such as tumor factors, immune microenvironment, and host factors [6,7,8,9,10,11,12], the impact of primary tumor characteristics on therapy response is unclear. One study found that primary melanoma thickness, ulceration, and stage were associated with brain metastases [13]. Thus, the primary tumor characteristics could reflect the underlying disease biology (e.g., hypoxia, immune response, aggressiveness) that impacts other aspects of metastatic behavior including the immunotherapy response. Another study demonstrated that primary melanomas located on sun-exposed skin treated with first-line anti-PD1 therapy had improved progression-free survival (PFS) compared to those on intermittently sun-exposed areas or sun-protected areas [14].
In this single-center retrospective cohort study, we assessed clinical features, primary tumor characteristics, response, and survival outcomes in 447 patients with unresectable or metastatic melanoma treated with ICI.

2. Methods

2.1. Overview

We conducted a single-center retrospective cohort study to assess the characteristics of primary melanomas and ICI outcomes. The study was approved by the Vanderbilt University Medical Center institutional review board. Patients receiving one or more doses of anti-PD-1 monotherapy or combination of ipilimumab and nivolumab were included. Patients with melanoma of mucosal or uveal origin were excluded due to their distinct tumor biology and published lower responses to anti-PD-1 [15,16,17].

2.2. Data Collection

Retrospective clinical data were collected, including date of initial diagnosis, anatomic site of primary, subtype (cutaneous, acral, or unknown primary), histologic subtype (superficial spreading [SSM], nodular, other, unknown), Breslow thickness, ulceration status (0/1), number of mitoses, presence of lymphovascular invasion (LVI), and tumor-infiltrating lymphocytes (TILs). The “other” cutaneous histologic subtype category aggregated multiple subtypes including desmoplastic, spindle cell, lentigo maligna, amelanotic, spitzoid, and balloon cell. Pre-immunotherapy treatment details, BRAF and other mutation status, number of positive sentinel nodes (0, 1–2, 3+), M status, and stage (AJCC 8th edition [18]) after the staging workup were collected. LDH level and stage (AJCC) at the time of immunotherapy initiation were collected. Immunotherapy outcomes were evaluated according to the therapeutic response (RECIST criteria), progression details, and date of last follow-up, which was defined as the date of last survival follow-up or death, whichever occurred later.

2.3. Statistical Analyses

Descriptive statistics were used to summarize patient and treatment characteristics. Univariate analyses assessed the association between each primary tumor characteristic and the response to ICI, progression-free survival (PFS), and overall survival (OS). Categorical variables and continuous variables were analyzed using a chi-squared test and a Wilcoxon rank-sum test, respectively. PFS and OS were tested using log-rank tests. Continuous variables were summarized by mean, median, and interquartile range (IQR). To estimate the independent effect of each clinical variable on the outcome, we conducted multivariable analyses using logistic regression for the response and Cox regression for the PFS and OS. In the Cox models, variables that did not meet the proportional hazard assumption were treated as time-dependent covariates, allowing their effect on the outcome to change over time. The multivariate analyses results were reported as odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CIs). A backwards selection identified the independent variables that were most strongly associated with the outcome to avoid overfitting and improve model interpretation. To assess the impact of treatment, a reduced model without treatment status was compared to the full model using a likelihood ratio test. All of the statistical analyses were conducted using R v4.2.2.

3. Results

3.1. Patient and Primary Tumor Characteristics

A total of 447 patients (137 female [30.6%]; mean [SD] age, 60.9 [14.6] years) were included. Of these, 300 (67.1%) patients received anti-PD-1 monotherapy, and 147 (32.9%) patients received ipilimumab and nivolumab. BRAFV600E mutations were present in 140 (31.3%) of patients, and 177 (39.6%) patients had elevated LDH at the start of treatment.
In total, 338 (75.6%) patients had cutaneous melanoma, 29 (6.5%) had acral melanoma, and 80 (17.9%) had melanoma of unknown primary (Table 1). Among the patients with cutaneous melanoma, 107 (23.9%) had SSM, 76 (17.0%) had nodular, 29 (6.5%) had another subtype, and 126 (28.2%) were unknown (subtype not specified, but cutaneous primary confirmed). “Other” cutaneous subtypes included lentigo maligna (N = 11, 37.9%), desmoplastic (N = 8, 27.6%) and spindle cell (N = 8, 27.6%). At initial presentation, the mean [SD] Breslow thickness was 4.2 mm [5.3], ulceration was present in 137 (30.6%) patients, the mean [SD] mitotic count was 6.5/mm2 [6.7], and LVI and TILs were present in 169 (37.8%) patients and 128 (28.6%) patients, respectively. One-hundred ninety (42.5%) patients had localized disease, 125 (27.9%) had regional disease, and 110 (24.5%) had metastatic disease at presentation.
An objective response rate (ORR; complete or partial response) occurred in 128 (42.7%) patients treated with anti-PD-1 and 69 (46.9%) for those who received ipilimumab and nivolumab (Table 2). The mean PFS [SD] was 19.3 months [26.5] for anti-PD-1, and 20.2 months [25] for the combination therapy. The mean OS [SD] was similarly comparable—31.1 months [30.9] for anti-PD-1 versus 28.7 months [25.1] for the combination therapy.

3.2. Univariate Analysis

3.2.1. Response to ICI

The melanoma site of origin, ulceration, number of positive sentinel nodes, and the stage at presentation showed significant associations with the ORR (Table 3). By site of origin, the ORR was 44.7% (151/338) for cutaneous, 10.3% (3/29) for acral, and 36.7% (29/79) for unknown primary (p < 0.01). The ORRs by subtype were as follows: 39.4% for SSM (43/109), 53% for nodular (41/78), and 55% for other types (16/29). The ORR in patients with ulceration was 35.8% (49/137) and 51.0% (79/155) for those without ulceration. The ORR was 53.2% (82/154) for those with 0 positive sentinel nodes, 28% (27/95) for 1–2 positive sentinel nodes, and 39% (12/31) for 3+ positive sentinel nodes (p < 0.01). For the stage, the ORR was 49.1% (86/175) for patients who initially presented with stage I/II melanoma and 35.3% (96/272) for patients presenting with stage III/IV melanoma (p < 0.01). The Breslow thickness (p = 0.3), mitoses (p = 0.5), LVI (p = 0.5), TILs (p = 1), and M status at presentation (p = 0.56) were not associated with the response.

3.2.2. Progression-Free Survival

Among all patients, the median PFS was 8.2 months (95% CI: 5.8–10.4, N = 445), 3.6 months for patients with acral melanoma (95% CI: 2.7–5.6), 7.6 months for SSM (95% CI: 4.3–16.2), 13.8 months for nodular (95% CI: 8.1–30.3), 34.3 months for other cutaneous subtype (95% CI: 16.02–NA), 7.2 months for unknown cutaneous subtype (95% CI: 3.6–13.1), and 7 months for unknown primary (95% CI: 4.1–11.3) (p < 0.01) (Figure 1A). The patients were categorized into four quartiles based on their Breslow thickness: Q1 (<1.4 mm), Q2 (1.4–2.7 mm), Q3 (2.7–5 mm), and Q4 (>5 mm) (Figure 1B). There was no significant difference in the PFS among the quartiles, with PFSs of 7.2, 9.6, 9.7, and 8.9 months for Q1–4, respectively (p = 0.9). The stage at initial disease presentation was associated with the PFS, with median PFSs of 11.8, 14.0, 5.6, and 7 months for stages I, II, III, and IV, respectively (p = 0.05) (Figure 1C). For the M status, the overall difference in the PFS was not statistically significant (p = 0.3), with median PFSs of 8.6, 18.4, 24.4, 5.3, and 5.7 months for M0, M1a, M1b, M1c, and M1d, respectively. The median PFSs were 5.2 months (95% CI: 3.7–9.4) and 18.4 months (95% CI: 10.0–26.2) in those with and without ulceration, respectively (p < 0.01) (Figure 1D). Other factors associated with PFS (or with nearly significant trends) included the number of positive sentinel nodes (p < 0.01), mitoses (p = 0.07), primary presentation (p = 0.06), and BRAF mutation (p < 0.01), whereas LVI (p = 0.1) was not associated with PFS.

3.2.3. Overall Survival

The median OS for all patients was 28 months (95% CI: 21.9–41.1), with 247 out of 446 patients (55%) having died before last follow-up. As with PFS, the OS was different between those with ulceration (21.9 months, 95% CI: 17.0–39.7) and without ulceration (47.7 months, 95% CI: 29.2–NA) (p = 0.02). Similarly, OS varied by M stage, with median OS values of 35.1, 45.3, not reached, 16.9, and 9.6 months for those with M0, M1a, M1b, M1c, and M1d, respectively (p = 0.04) (Figure 2D). The number of positive sentinel nodes was also associated with OS (p < 0.01).
OS analysis by subtype did not reveal statistically significant differences, with median OS values of 18.6, 27.4, 36.1, 47.7, and 41.1 months for acral, superficial spreading, nodular, other cutaneous, unknown cutaneous subtypes, and unknown subtype, respectively (p = 0.21) (Figure 2A). Moreover, the association between the stage at presentation and OS was not statistically significant (p = 0.10): the median OSs were 49.1, 47.2, 21.0, and 21.9 months for stages I-IV (Figure 2C). Other factors not associated with OS included LVI (p = 0.6), TILs (p = 0.1), Breslow thickness (p = 0.64), and BRAF mutation (p = 0.19) (Figure 2B).

3.3. Multivariable Analysis

3.3.1. Response to ICI

The multivariable analyses with backward selection revealed that gender, TILs, treatment, Breslow thickness, stage at presentation, M status, LVI, age, prior therapy, ulceration, mitoses, and elevated LDH were not significantly associated with the response to ICI (p > 0.05). In the reduced logistical regression model, the superficial spreading subtype (OR = 6.5, p < 0.01), nodular subtype (OR = 10.5, p < 0.01), other cutaneous subtype (OR = 9.0, p < 0.01), unknown cutaneous subtype (OR = 7.4, p < 0.01), and unknown subtype (OR = 7.1, p < 0.01) were associated with the response (reference group: acral). The BRAFV600E mutation (OR = 0.4, p < 0.01) and 1–2 positive sentinel nodes (reference group: 0 positive sentinel nodes; OR = 0.50, p < 0.01) were negatively associated with the response. When treatment was added to the model, it was not a significant predictor of the therapeutic response (OR = 1.1, p = 0.7) and did not significantly affect the model likelihood ratio test or discrimination indexes. We also wondered whether the loss of association of ulceration with response was due to a more aggressive metastatic presentation in patients with ulceration. Ulceration was associated with higher metastatic stage at presentation with a marginally significant p-value (p = 0.06) but not with higher LDH (p = 0.45) (Table 4).

3.3.2. Progression-Free Survival

The backwards selection removed the number of positive sentinel nodes, TILs, Breslow thickness, age, LVI, and treatment from the model (p > 0.05). The reduced cox regression model showed that SSM (OR = 0.6, p < 0.05), nodular subtype (OR = 0.5, p < 0.05), other cutaneous subtype (OR = 0.3, p < 0.001), unknown cutaneous subtype (OR = 0.5, p < 0.05), unknown primary (OR = 0.5, p < 0.05), and male gender (OR = 0.7, p < 0.05) were associated with longer PFS. Metastatic stage (M1c, M1d) at presentation (OR = 1.8, p < 0.05) and BRAFV600E mutation status (OR = 1.6, p < 0.001) were associated with shorter PFS. The presence of ulceration, mitoses, initial presentation stage (II, III, M1a, M1b), elevated LDH, and prior therapy were not associated with PFS.

3.3.3. Overall Survival

The number of positive sentinel lymph nodes, TILs, gender, LVI, Breslow thickness, treatment status, ulceration, mitoses, and M status were removed in the model selection for OS (p > 0.05). SSM (OR = 0.6, p = 0.03), other cutaneous subtype (OR = 0.5, p < 0.05), unknown cutaneous subtype (OR = 0.5, p < 0.01) were associated with longer OS while age (OR = 1, p < 0.001), prior therapy (OR = 1.1, p < 0.05), and BRAFV600E mutation status (OR = 1.4, p < 0.05) were associated with shorter OS. The treatment type did not have an independent effect (p = 0.5).
Table 4. Multivariable analysis: response to anti-PD-1, PFS, and OS.
Table 4. Multivariable analysis: response to anti-PD-1, PFS, and OS.
Response to Anti-PD-1
VariableOdds ratio95% CIp-value
SSM subtype6.501.8–23.450.004
Nodular type10.482.850–38.50<0.001
Other cutaneous type8.972.13–37.710.003
Unknown subtype7.362.06–26.320.002
Unknown primary7.071.90–26.330.004
1–2 positive sentinel nodes0.500.30–0.830.008
3+ positive sentinel nodes0.680.32–1.410.300
BRAF mutation0.380.24–0.610.001
PFS
VariableOdds ratio95% CIp-value
SSM subtype0.580.36–0.940.026
Nodular type0.530.33–0.860.010
Other cutaneous type0.310.15–0.61<0.001
Unknown subtype0.550.34–0.880.013
Unknown primary0.500.28–0.880.016
Ulceration1.240.92–1.660.162
Mitoses0.990.97–1.010.202
Stage II at presentation1.140.73–1.780.561
Stage III at presentation1.420.93–2.160.103
Stage IV M1a/b at presentation0.940.53–1.680.842
Stage IV M1c/d at presentation1.791.07–3.000.027
Male gender0.750.58–0.960.022
Prior treatment1.040.91–1.200.542
LDH > ULN0.920.79–1.060.236
BRAF mutation1.641.27–2.11<0.001
OS
VariableOdds ratio95% CIp-value
SSM subtype0.580.35–0.950.029
Nodular type0.650.39–1.090.105
Other cutaneous type0.460.24–0.900.023
Unknown subtype0.500.31–0.830.008
Unknown primary0.710.42–1.190.196
Age1.011.00–1.02<0.001
Prior treatment1.141.02–1.270.015
LDH > ULN1.040.93–1.160.474
BRAF mutation1.381.04–1.840.028

4. Discussion

This is the first study to comprehensively assess whether primary tumor characteristics and initial presentation impacted the subsequent immunotherapy response in the metastatic setting. Though a more advanced stage of initial presentation, ulceration, and histologic subtype were associated with inferior outcomes in the univariate analyses, they were less or not significant in the multivariable analysis. Instead, cutaneous melanoma and each of its subtypes (superficial spreading, nodular, other, unknown) were significantly associated with improved clinical outcomes. Other features of the primary, including Breslow depth, TILs, mitoses, and LVI, did not impact the immunotherapy response after controlling for multiple variables.
There remains a need to identify the markers of response and survival to better select patients likely to benefit from ICI. The melanoma biology influences the immune response by regulating antigen expression and presentation, thereby playing a critical role in determining the likelihood of an immunotherapy response. The increased tumor cell expression of MHC-II and PD-L1, influenced by tumor infiltrating lymphocytes, has shown promise [19,20,21,22,23], although they remain imperfect [7,24,25]. A high tumor mutational burden (TMB) is a potential marker of response, although it has a limited predictive capacity [26,27]. Similarly, an inflamed tumor microenvironment, as expressed by CD8+ T cells and interferon-γ signatures, has also been correlated with a response to ICI therapies [28,29]. Therapeutic resistance has also been associated with transcriptional programs of mesenchymal differentiation, angiogenesis, and hypoxia, as well as JAK-STAT mutations [30,31]. While these biomarkers have promise, it is possible that the clinical and pathologic features of primary tumors could also have predictive value.
The ulceration and stage of presentation were associated with poorer PFS and OS in the univariate analyses, with similar but more modest effects seen with high mitotic rates, though these associations did not persist in the multivariable models. Thus, it is likely that factors like ulceration and a high mitotic rate are not only associated with higher rates of metastatic progression and death in the pre-ICI era [32] but also with other adverse metastatic features, such as visceral organ involvement, bulky disease, and high LDH, which correlate with ICI failure [33,34]. Indeed, ulceration was associated with higher metastatic stage at presentation with a marginally significant p-value, although not with higher LDH.
The lack of an independent association between most of the primary tumor features and outcomes following ICI therapy for metastatic disease suggests that these features may not have a major predictive role in this setting. However, they add valuable prognostic information following initial diagnosis. Since most of these adverse prognostic factors were identified prior to immunotherapy development, it is noteworthy that they presage not only inferior outcomes in an era lacking effective systemic therapies but also poor responses to active modern treatment options.
All of the cutaneous subtypes were more likely to respond compared with the acral melanoma, though “other” cutaneous subtypes (lentigo maligna, desmoplastic, spindle cell, etc.) had the longest PFS. Specifically, the ORR was greatest for the desmoplastic (N = 8, RR: 75%) and amelanotic subtypes (N = 3, RR: 67%), followed by lentigo maligna (N = 11, RR: 45%) and spindle cell (N = 8, RR: 38%). It is well known that the desmoplastic melanoma has a higher response in part due to its high mutation burden [35]. However, larger studies on less common atypical subtypes (such as amelanotic and lentigo maligna) Nare warranted to assess for other unexpected beneficial associations.
Several known prognostic factors were recapitulated. The metastatic stage (M1c, M1d) was associated with shorter PFS, while age and prior therapy correlated with shorter OS [36,37]. The BRAFV600E mutation negatively impacted ICI response, possibly due to prior BRAF/MEK inhibitors in some patients. The optimal first-line regimen for BRAF-mutant melanomas remains contested, although it should consist of immunotherapy for most patients [38]. Ongoing studies aim to determine combination and sequence strategies (NCT02908672, NCT02967692, NCT02631447).
One prior study assessed primary tumor characteristics as potential biomarkers of immunotherapy response. LVI was significantly associated with an immunotherapy response and prolonged PFS and OS in advanced melanoma patients [39]. Our findings are somewhat in line with this, as LVI was associated with prolonged OS. Notably, our study has a significantly larger cohort and assessed multiple other clinical variables.
The limitations of this study included its retrospective and single-center nature, and relatively small sample size. Prospective, multi-center studies with a larger and more diverse sample size should be conducted to enhance generalizability.
These findings suggest that the clinical and pathological variables of primary tumors are associated with immunotherapy response, PFS, and OS in melanoma, though these do not appear to be independent of the adverse prognostic features of the metastatic presentation. Thus, primary features, other than cutaneous primary, should likely not be integrated in predictive models [40] for metastatic treatment, but may provide additional prognostic data surrounding initial presentation. Future research should aim to replicate and validate these results to help stratify patients by likelihood of response to ICI therapy.

5. Conclusions

Features of the primary tumor were associated with clinical outcomes when assessing as univariate analyses (primarily ulceration and initial stage of presentation). However, these associations were largely not significant after adjusting for known prognostic variables, with the exception of cutaneous histology vs. acral and mucosal primaries. This suggests that adverse prognostic features of initial presentation may correlate with later inferior outcomes with modern therapies, although these variables are captured with known prognostic features at the time of metastatic disease.

Author Contributions

Conceptualization, R.S.G., S.J. and D.B.J.; methodology, R.S.G., S.J., R.I., F.Y. and D.B.J.; software, R.I. and F.Y.; validation, R.S.G., S.J., R.I., F.Y. and D.B.J.; formal analysis, R.S.G., S.J., R.I., F.Y. and D.B.J.; investigation, R.S.G., S.J., K.F., H.B., I.M. and R.I.; resources, R.S.G., S.J., K.F., H.B., I.M., R.I., F.Y. and D.B.J.; data curation, R.S.G., S.J., K.F., H.B., I.M. and R.I.; writing—original draft preparation, R.S.G., S.J. and D.B.J.; writing—review and editing, R.S.G., S.J., K.F., H.B., I.M., R.I., F.Y. and D.B.J.; visualization, R.S.G., S.J., I.M., R.I., F.Y. and D.B.J.; supervision, F.Y. and D.B.J.; project administration, D.B.J.; funding acquisition, D.B.J. All authors have read and agreed to the published version of the manuscript.

Funding

RSG receives funding from the SCRIPS Foundation, Burroughs Wellcome Fund. SJ receives funding from Medical Scholars, Vanderbilt University Medical Center. KF receives funding from Medical Scholars, Vanderbilt University Medical Center. DBJ receives funding from the NCI R01CA227481 Susan and Luke Simons Directorship for Melanoma, the James C. Bradford Melanoma Fund, and the Van Stephenson Melanoma Fund.

Institutional Review Board Statement

Reviewed and approved by Vanderbilt IRB; approval #100178.

Informed Consent Statement

Informed consent was obtained for all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on reasonable request.

Conflicts of Interest

DBJ has served on advisory boards or as a consultant for BMS, Catalyst Biopharma, Iovance, Jansen, Mallinckrodt, Merck, Mosaic ImmunoEngineering, Novartis, Oncosec, Pfizer, Targovax, and Teiko, has received research funding from BMS and Incyte, and has patents pending for the use of MHC-II as a biomarker for immune checkpoint inhibitor response and for abatacept as a treatment for immune-related adverse events.

References

  1. Mangin, M.-A.; Boespflug, A.; Maucort Boulch, D.; Vacheron, C.-H.; Carpentier, I.; Thomas, L.; Dalle, S. Decreased Survival in Patients Treated by Chemotherapy after Targeted Therapy Compared to Immunotherapy in Metastatic Melanoma. Cancer Med. 2021, 10, 3155–3164. [Google Scholar] [CrossRef] [PubMed]
  2. Ernst, M.; Giubellino, A. The Current State of Treatment and Future Directions in Cutaneous Malignant Melanoma. Biomedicines 2022, 10, 822. [Google Scholar] [CrossRef] [PubMed]
  3. Wolchok, J.D.; Chiarion-Sileni, V.; Gonzalez, R.; Grob, J.-J.; Rutkowski, P.; Lao, C.D.; Cowey, C.L.; Schadendorf, D.; Wagstaff, J.; Dummer, R.; et al. Long-Term Outcomes with Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients with Advanced Melanoma. J. Clin. Oncol. 2022, 40, 127–137. [Google Scholar] [CrossRef]
  4. Patrinely, J.R.; Baker, L.X.; Davis, E.J.; Song, H.; Ye, F.; Johnson, D.B. Outcomes after Progression of Disease with Anti-PD-1/PD-L1 Therapy for Patients with Advanced Melanoma. Cancer 2020, 126, 3448–3455. [Google Scholar] [CrossRef] [PubMed]
  5. Wang, D.Y.; Eroglu, Z.; Ozgun, A.; Leger, P.D.; Zhao, S.; Ye, F.; Luke, J.J.; Joseph, R.W.; Haq, R.; Ott, P.A.; et al. Clinical Features of Acquired Resistance to Anti-PD-1 Therapy in Advanced Melanoma. Cancer Immunol. Res. 2017, 5, 357–362. [Google Scholar] [CrossRef] [PubMed]
  6. Bai, R.; Lv, Z.; Xu, D.; Cui, J. Predictive Biomarkers for Cancer Immunotherapy with Immune Checkpoint Inhibitors. Biomark. Res. 2020, 8, 34. [Google Scholar] [CrossRef] [PubMed]
  7. Huang, A.C.; Zappasodi, R. A Decade of Checkpoint Blockade Immunotherapy in Melanoma: Understanding the Molecular Basis for Immune Sensitivity and Resistance. Nat. Immunol. 2022, 23, 660–670. [Google Scholar] [CrossRef] [PubMed]
  8. Augustin, R.C.; Luke, J.J. Rapidly Evolving Pre- and Post-Surgical Systemic Treatment of Melanoma. Am. J. Clin. Dermatol. 2024, 25, 421–434. [Google Scholar] [CrossRef] [PubMed]
  9. Kött, J.; Hoehne, I.L.; Heidrich, I.; Zimmermann, N.; Reese, K.-L.; Zell, T.; Geidel, G.; Rünger, A.; Schneider, S.W.; Pantel, K.; et al. High Serum Levels of CCL20 Are Associated with Recurrence and Unfavorable Overall Survival in Advanced Melanoma Patients Receiving Immunotherapy. Cancers 2024, 16, 1737. [Google Scholar] [CrossRef] [PubMed]
  10. Leek, L.V.M.; Notohardjo, J.C.L.; de Joode, K.; Velker, E.L.; Haanen, J.B.A.G.; Suijkerbuijk, K.P.M.; Aarts, M.J.B.; de Groot, J.W.B.; Kapiteijn, E.; van den Berkmortel, F.W.P.J.; et al. Multi-Omic Analysis Identifies Hypoalbuminemia as Independent Biomarker of Poor Outcome upon PD-1 Blockade in Metastatic Melanoma. Sci. Rep. 2024, 14, 11244. [Google Scholar] [CrossRef] [PubMed]
  11. Brand, C.L.; Hunger, R.E.; Seyed Jafari, S.M. Eosinophilic Granulocytes as a Potential Prognostic Marker for Cancer Progression and Therapeutic Response in Malignant Melanoma. Front. Oncol. 2024, 14, 1366081. [Google Scholar] [CrossRef] [PubMed]
  12. Wu, G.; Chen, B.; Jiang, J.; Chen, Y.; Chen, Y.; Wang, H. Identification of a Pyroptosis-based Model for Predicting Clinical Outcomes from Immunotherapy in Patients with Metastatic Melanoma. Cancer Med. 2022, 12, 4921–4937. [Google Scholar] [CrossRef] [PubMed]
  13. Qian, M.; Ma, M.W.; Fleming, N.H.; Lackaye, D.J.; Hernando, E.; Osman, I.; Shao, Y. Clinicopathological Characteristics at Primary Melanoma Diagnosis as Risk Factors for Brain Metastasis. Melanoma Res. 2013, 23, 461–467. [Google Scholar] [CrossRef] [PubMed]
  14. Russo, D.; Dalle, S.; Dereure, O.; Mortier, L.; Dalac-Rat, S.; Dutriaux, C.; Leccia, M.-T.; Legoupil, D.; Montaudié, H.; Maubec, E.; et al. Differential Gradients of Immunotherapy vs Targeted Therapy Efficacy According to the Sun-Exposure Pattern of the Site of Occurrence of Primary Melanoma: A Multicenter Prospective Cohort Study (MelBase). Front. Oncol. 2023, 13, 1250026. [Google Scholar] [CrossRef] [PubMed]
  15. Najjar, Y.G.; Navrazhina, K.; Ding, F.; Bhatia, R.; Tsai, K.; Abbate, K.; Durden, B.; Eroglu, Z.; Bhatia, S.; Park, S.; et al. Ipilimumab plus Nivolumab for Patients with Metastatic Uveal Melanoma: A Multicenter, Retrospective Study. J. Immunother. Cancer 2020, 8, e000331. [Google Scholar] [CrossRef] [PubMed]
  16. Shoushtari, A.N.; Munhoz, R.R.; Kuk, D.; Ott, P.A.; Johnson, D.B.; Tsai, K.K.; Rapisuwon, S.; Eroglu, Z.; Sullivan, R.J.; Luke, J.J.; et al. The Efficacy of Anti-PD-1 Agents in Acral and Mucosal Melanoma. Cancer 2016, 122, 3354–3362. [Google Scholar] [CrossRef] [PubMed]
  17. Algazi, A.P.; Tsai, K.K.; Shoushtari, A.N.; Munhoz, R.R.; Eroglu, Z.; Piulats, J.M.; Ott, P.A.; Johnson, D.B.; Hwang, J.; Daud, A.I.; et al. Clinical Outcomes in Metastatic Uveal Melanoma Treated with PD-1 and PD-L1 Antibodies. Cancer 2016, 122, 3344–3353. [Google Scholar] [CrossRef] [PubMed]
  18. Keung, E.Z.; Gershenwald, J.E. The Eighth Edition American Joint Committee on Cancer (AJCC) Melanoma Staging System: Implications for Melanoma Treatment and Care. Expert. Rev. Anticancer Ther. 2018, 18, 775–784. [Google Scholar] [CrossRef] [PubMed]
  19. Robert, C.; Long, G.V.; Brady, B.; Dutriaux, C.; Maio, M.; Mortier, L.; Hassel, J.C.; Rutkowski, P.; McNeil, C.; Kalinka-Warzocha, E.; et al. Nivolumab in Previously Untreated Melanoma without BRAF Mutation. N. Engl. J. Med. 2015, 372, 320–330. [Google Scholar] [CrossRef] [PubMed]
  20. Kaunitz, G.J.; Cottrell, T.R.; Lilo, M.; Muthappan, V.; Esandrio, J.; Berry, S.; Xu, H.; Ogurtsova, A.; Anders, R.A.; Fischer, A.H.; et al. Melanoma Subtypes Demonstrate Distinct PD-L1 Expression Profiles. Lab. Investig. 2017, 97, 1063–1071. [Google Scholar] [CrossRef] [PubMed]
  21. Rodig, S.J.; Gusenleitner, D.; Jackson, D.G.; Gjini, E.; Giobbie-Hurder, A.; Jin, C.; Chang, H.; Lovitch, S.B.; Horak, C.; Weber, J.S.; et al. MHC Proteins Confer Differential Sensitivity to CTLA-4 and PD-1 Blockade in Untreated Metastatic Melanoma. Sci. Transl. Med. 2018, 10, eaar3342. [Google Scholar] [CrossRef] [PubMed]
  22. Johnson, D.B.; Bordeaux, J.; Kim, J.Y.; Vaupel, C.; Rimm, D.L.; Ho, T.H.; Joseph, R.W.; Daud, A.I.; Conry, R.M.; Gaughan, E.M.; et al. Quantitative Spatial Profiling of PD-1/PD-L1 Interaction and HLA-DR/IDO-1 Predicts Improved Outcomes of Anti-PD-1 Therapies in Metastatic Melanoma. Clin. Cancer Res. 2018, 24, 5250–5260. [Google Scholar] [CrossRef] [PubMed]
  23. Johnson, D.B.; Estrada, M.V.; Salgado, R.; Sanchez, V.; Doxie, D.B.; Opalenik, S.R.; Vilgelm, A.E.; Feld, E.; Johnson, A.S.; Greenplate, A.R.; et al. Melanoma-Specific MHC-II Expression Represents a Tumour-Autonomous Phenotype and Predicts Response to Anti-PD-1/PD-L1 Therapy. Nat. Commun. 2016, 7, 10582. [Google Scholar] [CrossRef] [PubMed]
  24. Davis, A.A.; Patel, V.G. The Role of PD-L1 Expression as a Predictive Biomarker: An Analysis of All US Food and Drug Administration (FDA) Approvals of Immune Checkpoint Inhibitors. J. Immunother. Cancer 2019, 7, 278. [Google Scholar] [CrossRef] [PubMed]
  25. Lu, S.; Stein, J.E.; Rimm, D.L.; Wang, D.W.; Bell, J.M.; Johnson, D.B.; Sosman, J.A.; Schalper, K.A.; Anders, R.A.; Wang, H.; et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-Analysis. JAMA Oncol. 2019, 5, 1195–1204. [Google Scholar] [CrossRef] [PubMed]
  26. Ning, B.; Liu, Y.; Wang, M.; Li, Y.; Xu, T.; Wei, Y. The Predictive Value of Tumor Mutation Burden on Clinical Efficacy of Immune Checkpoint Inhibitors in Melanoma: A Systematic Review and Meta-Analysis. Front. Pharmacol. 2022, 13, 748674. [Google Scholar] [CrossRef] [PubMed]
  27. Johnson, D.B.; Frampton, G.M.; Rioth, M.J.; Yusko, E.; Xu, Y.; Guo, X.; Ennis, R.C.; Fabrizio, D.; Chalmers, Z.R.; Greenbowe, J.; et al. Targeted Next Generation Sequencing Identifies Markers of Response to PD-1 Blockade. Cancer Immunol. Res. 2016, 4, 959–967. [Google Scholar] [CrossRef]
  28. Tumeh, P.C.; Harview, C.L.; Yearley, J.H.; Shintaku, I.P.; Taylor, E.J.M.; Robert, L.; Chmielowski, B.; Spasic, M.; Henry, G.; Ciobanu, V.; et al. PD-1 Blockade Induces Responses by Inhibiting Adaptive Immune Resistance. Nature 2014, 515, 568–571. [Google Scholar] [CrossRef] [PubMed]
  29. Grasso, C.S.; Tsoi, J.; Onyshchenko, M.; Abril-Rodriguez, G.; Ross-Macdonald, P.; Wind-Rotolo, M.; Champhekar, A.; Medina, E.; Torrejon, D.Y.; Shin, D.S.; et al. Conserved Interferon-γ Signaling Drives Clinical Response to Immune Checkpoint Blockade Therapy in Melanoma. Cancer Cell 2021, 39, 122. [Google Scholar] [CrossRef] [PubMed]
  30. Hugo, W.; Zaretsky, J.M.; Sun, L.; Song, C.; Moreno, B.H.; Hu-Lieskovan, S.; Berent-Maoz, B.; Pang, J.; Chmielowski, B.; Cherry, G.; et al. Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell 2016, 165, 35–44. [Google Scholar] [CrossRef] [PubMed]
  31. Shin, D.S.; Zaretsky, J.M.; Escuin-Ordinas, H.; Garcia-Diaz, A.; Hu-Lieskovan, S.; Kalbasi, A.; Grasso, C.S.; Hugo, W.; Sandoval, S.; Torrejon, D.Y.; et al. Primary Resistance to PD-1 Blockade Mediated by JAK1/2 Mutations. Cancer Discov. 2017, 7, 188–201. [Google Scholar] [CrossRef] [PubMed]
  32. Gershenwald, J.E.; Scolyer, R.A.; Hess, K.R.; Sondak, V.K.; Long, G.V.; Ross, M.I.; Lazar, A.J.; Faries, M.B.; Kirkwood, J.M.; McArthur, G.A.; et al. Melanoma Staging: Evidence-Based Changes in the American Joint Committee on Cancer Eighth Edition Cancer Staging Manual. CA Cancer J. Clin. 2017, 67, 472–492. [Google Scholar] [CrossRef] [PubMed]
  33. Tumeh, P.C.; Hellmann, M.D.; Hamid, O.; Tsai, K.K.; Loo, K.L.; Gubens, M.A.; Rosenblum, M.; Harview, C.L.; Taube, J.M.; Handley, N.; et al. Liver Metastasis and Treatment Outcome with Anti-PD-1 Monoclonal Antibody in Patients with Melanoma and NSCLC. Cancer Immunol. Res. 2017, 5, 417–424. [Google Scholar] [CrossRef] [PubMed]
  34. Joseph, R.W.; Elassaiss-Schaap, J.; Kefford, R.; Hwu, W.-J.; Wolchok, J.D.; Joshua, A.M.; Ribas, A.; Hodi, F.S.; Hamid, O.; Robert, C.; et al. Baseline Tumor Size Is an Independent Prognostic Factor for Overall Survival in Patients with Melanoma Treated with Pembrolizumab. Clin. Cancer Res. 2018, 24, 4960–4967. [Google Scholar] [CrossRef] [PubMed]
  35. Eroglu, Z.; Zaretsky, J.M.; Hu-Lieskovan, S.; Kim, D.W.; Algazi, A.; Johnson, D.B.; Liniker, E.; Kong, B.; Munhoz, R.; Rapisuwon, S.; et al. High Response Rate to PD-1 Blockade in Desmoplastic Melanomas. Nature 2018, 553, 347–350. [Google Scholar] [CrossRef] [PubMed]
  36. Wong, S.K.; Blum, S.M.; Sun, X.; Da Silva, I.P.; Zubiri, L.; Ye, F.; Bai, K.; Zhang, K.; Ugurel, S.; Zimmer, L.; et al. Efficacy and Safety of Immune Checkpoint Inhibitors in Young Adults with Metastatic Melanoma. Eur. J. Cancer 2023, 181, 188–197. [Google Scholar] [CrossRef] [PubMed]
  37. Kugel, C.H.; Douglass, S.M.; Webster, M.R.; Kaur, A.; Liu, Q.; Yin, X.; Weiss, S.A.; Darvishian, F.; Al-Rohil, R.N.; Ndoye, A.; et al. Age Correlates with Response to Anti-PD1, Reflecting Age-Related Differences in Intratumoral Effector and Regulatory T-Cell Populations. Clin. Cancer Res. 2018, 24, 5347–5356. [Google Scholar] [CrossRef] [PubMed]
  38. Atkins, M.B.; Lee, S.J.; Chmielowski, B.; Tarhini, A.A.; Cohen, G.I.; Truong, T.-G.; Moon, H.H.; Davar, D.; O’Rourke, M.; Stephenson, J.J.; et al. Combination Dabrafenib and Trametinib Versus Combination Nivolumab and Ipilimumab for Patients with Advanced BRAF-Mutant Melanoma: The DREAMseq Trial—ECOG-ACRIN EA6134. J. Clin. Oncol. 2023, 41, 186–197. [Google Scholar] [CrossRef]
  39. Loo, K.; Gauvin, G.; Soliman, I.; Renzetti, M.; Deng, M.; Ross, E.; Luo, B.; Wu, H.; Reddy, S.; Olszanski, A.J.; et al. Primary Tumor Characteristics and Next Generation Sequencing Mutations as Biomarkers for Melanoma Immunotherapy Response. Pigment Cell Melanoma Res. 2020, 33, 878–888. [Google Scholar] [CrossRef] [PubMed]
  40. Pires da Silva, I.; Ahmed, T.; McQuade, J.L.; Nebhan, C.A.; Park, J.J.; Versluis, J.M.; Serra-Bellver, P.; Khan, Y.; Slattery, T.; Oberoi, H.K.; et al. Clinical Models to Define Response and Survival with Anti-PD-1 Antibodies Alone or Combined with Ipilimumab in Metastatic Melanoma. J. Clin. Oncol. 2022, 40, 1068–1080. [Google Scholar] [CrossRef] [PubMed]
Cancers 16 02355 g001
Figure 1. Primary tumor characteristics and progression-free survival. Progression-free survival by (A) subtype of melanoma; (B) Breslow thickness; (C) stage at presentation; (D) ulcerated vs. non-ulcerated.
Figure 1. Primary tumor characteristics and progression-free survival. Progression-free survival by (A) subtype of melanoma; (B) Breslow thickness; (C) stage at presentation; (D) ulcerated vs. non-ulcerated.
Cancers 16 02355 g001
Cancers 16 02355 g002
Figure 2. Primary tumor characteristics and overall survival. Overall survival by (A) subtype of melanoma; (B) Breslow thickness; (C) stage at presentation; (D) ulcerated vs. non-ulcerated.
Figure 2. Primary tumor characteristics and overall survival. Overall survival by (A) subtype of melanoma; (B) Breslow thickness; (C) stage at presentation; (D) ulcerated vs. non-ulcerated.
Cancers 16 02355 g002
Table 1. Primary tumor characteristics.
Table 1. Primary tumor characteristics.
PD-1 Monotherapy
(N = 300)		Ipi/Nivo
(N = 147)		Overall
(N = 447)
Subtype
Acral22 (7.3%)7 (4.8%)29 (6.5%)
Cutaneous–superficial spreading68 (22.7%)39 (26.5%)107 (23.9%)
Cutaneous–nodular53 (17.7%)23 (15.6%)76 (17.0%)
Cutaneous–other24 (8.0%)5 (3.4%)29 (6.5%)
Cutaneous–unknown94 (31.3%)32 (21.8%)126 (28.2%)
Unknown primary39 (13.0%)41 (27.9%)80 (17.9%)
Breslow thickness (mm)
Mean (SD)4.1 (4.7)4.3 (6.4)4.2 (5.3)
Median [Q1, Q3]2.8 [1.5, 4.5]2.1 [1.2, 5.8]2.7 [1.4, 5.0]
n/a86 (28.7%)58 (39.5%)144 (32.2%)
Ulceration
096 (32.0%)59 (40.1%)155 (34.7%)
1105 (35.0%)32 (21.8%)137 (30.6%)
n/a99 (33.0%)56 (38.1%)155 (34.7%)
Mitoses (mm2)
Mean (SD)6.9 (7.4)5.4 (4.6)6.5 (6.7)
Median [Q1, Q3]5.0 [2.0, 10.0]4.0 [1.0, 8.0]4.0 [2.0, 10.0]
n/a143 (47.7%)78 (53.1%)221 (49.4%)
Lymphovascular invasion
024 (8.0%)10 (6.8%)34 (7.6%)
1118 (39.3%)51 (34.7%)169 (37.8%)
n/a158 (52.7%)86 (58.5%)244 (54.6%)
Tumor-infiltrating lymphocytes
046 (15.3%)20 (13.6%)66 (14.8%)
189 (29.7%)39 (26.5%)128 (28.6%)
n/a165 (55.0%)88 (59.9%)253 (56.6%)
Number of positive sentinel nodes
098 (32.7%)56 (38.1%)154 (34.5%)
1–276 (25.3%)19 (12.9%)95 (21.3%)
3+21 (7.0%)10 (6.8%)31 (6.9%)
No SLN performed105 (35.0%)62 (42.2%)167 (37.4%)
Stage at presentation
146 (15.3%)32 (21.8%)78 (17.4%)
266 (22.0%)31 (21.1%)97 (21.7%)
3125 (41.6%)40 (27.2%)165 (36.9%)
463 (21.0%)44 (29.9%)107 (23.9%)
M status at presentation
M0237 (79.0%)102 (69.4%)339 (75.8%)
M1a18 (6.0%)8 (5.4%)26 (5.8%)
M1b9 (3.0%)2 (1.4%)11 (2.5%)
M1c26 (8.7%)18 (12.2%)44 (9.8%)
M1d10 (3.3%)17 (11.6%)27 (6.0%)
Primary presentation
Localized124 (41.3%)66 (44.9%)190 (42.5%)
Locoregional13 (4.3%)9 (6.1%)22 (4.9%)
Regional/Stage III99 (33.0%)26 (17.7%)125 (27.9%)
Metastatic64 (21.3%)46 (31.3%)110 (24.6%)
Missing1 (0.3%)1 (0.7%)2 (0.4%)
This table demonstrates the primary tumor characteristics of the study patients.
Table 2. Confounding factors and outcomes of interest.
Table 2. Confounding factors and outcomes of interest.
PD-1 Monotherapy
(N = 300)		Ipi/Nivo
(N = 147)		Overall
(N = 447)
Age
Mean (SD)62.6 (14.2)57.2 (14.9)60.9 (14.6)
Median [Q1, Q3]64.0 [54.0, 73.0]61.0 [47.0, 68.5]63.0 [51.0, 72.0]
Gender
Female94 (31.3%)43 (29.3%)137 (30.6%)
Male206 (68.7%)104 (70.7%)310 (69.4%)
Pre-immunotherapy treatment (metastatic)
No119 (39.7%)87 (59.2%)206 (46.1%)
Yes181 (60.3%)59 (40.1%)240 (53.7%)
Missing0 (0%)1 (0.7%)1 (0.2%)
LDH > ULN
0178 (59.3%)64 (43.5%)242 (54.1%)
1107 (35.7%)70 (47.6%)177 (39.6%)
Missing15 (5.0%)13 (8.8%)28 (6.3%)
BRAF V600E
No215 (71.7%)91 (61.9%)306 (68.5%)
Yes84 (28.0%)56 (38.1%)140 (31.3%)
Missing1 (0.3%)0 (0%)1 (0.2%)
Response to anti-PD-1
PD/SD172 (57.3%)78 (53.1%)250 (55.9%)
CR/PR128 (42.7%)69 (46.9%)197 (44.1%)
Missing with PD/SD3 (1.0%)10 (6.8%)13 (2.9%)
Progressed
093 (31.0%)52 (35.4%)145 (32.4%)
1207 (69.0%)95 (64.6%)302 (67.6%)
PFS (months)
Mean (SD)19.3 (26.5)20.2 (25.0)19.6 (26.0)
Median [Q1, Q3]7.4 [2.6, 23.0]7.2 [2.4, 30.5]7.4 [2.5, 26.2]
Died
0125 (41.7%)75 (51.0%)200 (44.7%)
1175 (58.3%)72 (49.0%)247 (55.3%)
OS (months)
Mean (SD)31.1 (30.9)28.7 (25.1)30.3 (29.1)
Median [Q1, Q3]19.5 [8.3, 48.7]22.2 [7.7, 44.8]19.8 [8.0, 48.2]
This table demonstrates the confounding factors in the study patients and their outcomes of interest.
Table 3. Univariate analysis: response to anti-PD-1, PFS, and OS.
Table 3. Univariate analysis: response to anti-PD-1, PFS, and OS.
Response to Anti-PD-1
Variablep-valueFDR adjusted p-value
Subtype0.0010.003
Breslow thickness (mm)0.3270.545
Ulceration0.0090.023
Mitoses (mm2)0.5270.589
Lymphovascular invasion0.4760.589
Tumor-infiltrating lymphocytes11
Number of positive sentinel nodes00.003
Stage at presentation0.0080.023
M status at presentation0.530.589
Primary presentation0.030.06
BRAF mutation<0.001<0.001
PFS *
Variablep-valueFDR adjusted p-value
Subtype0.0040.012
Breslow thickness (mm)0.8890.889
Ulceration0.0020.01
Mitoses (mm2)0.070.116
Lymphovascular invasion0.0930.133
Tumor-infiltrating lymphocytes0.3160.351
Number of positive sentinel nodes0.0010.008
Stage at presentation0.0460.114
M status at presentation0.310.351
Primary presentation0.0590.116
BRAF mutation<0.001<0.001
OS *
Variablep-valueFDR adjusted p-value
Subtype0.210.262
Breslow thickness (mm)0.6410.641
Ulceration0.0390.118
Mitoses (mm2)0.0590.118
Lymphovascular invasion0.5660.629
Tumor-infiltrating lymphocytes0.0980.141
Number of positive sentinel nodes0.0040.04
Stage at presentation0.0950.141
M status at presentation0.0240.118
Primary presentation0.0590.118
BRAF mutation0.190.257
* For continuous variables, the values are categorized into quantiles for the log-rank test. (p-values and FDR-adjusted p-values for log-rank tests of clinical variables by PFS).
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MDPI and ACS Style

Goodman, R.S.; Jung, S.; Fletcher, K.; Burnette, H.; Mohyuddin, I.; Irlmeier, R.; Ye, F.; Johnson, D.B. Primary Tumor Characteristics as Biomarkers of Immunotherapy Response in Advanced Melanoma: A Retrospective Cohort Study. Cancers 2024, 16, 2355. https://doi.org/10.3390/cancers16132355

AMA Style

Goodman RS, Jung S, Fletcher K, Burnette H, Mohyuddin I, Irlmeier R, Ye F, Johnson DB. Primary Tumor Characteristics as Biomarkers of Immunotherapy Response in Advanced Melanoma: A Retrospective Cohort Study. Cancers. 2024; 16(13):2355. https://doi.org/10.3390/cancers16132355

Chicago/Turabian Style

Goodman, Rachel S., Seungyeon Jung, Kylie Fletcher, Hannah Burnette, Ismail Mohyuddin, Rebecca Irlmeier, Fei Ye, and Douglas B. Johnson. 2024. "Primary Tumor Characteristics as Biomarkers of Immunotherapy Response in Advanced Melanoma: A Retrospective Cohort Study" Cancers 16, no. 13: 2355. https://doi.org/10.3390/cancers16132355

APA Style

Goodman, R. S., Jung, S., Fletcher, K., Burnette, H., Mohyuddin, I., Irlmeier, R., Ye, F., & Johnson, D. B. (2024). Primary Tumor Characteristics as Biomarkers of Immunotherapy Response in Advanced Melanoma: A Retrospective Cohort Study. Cancers, 16(13), 2355. https://doi.org/10.3390/cancers16132355

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