Dual Checkpoint Blockade in Merkel Cell Carcinoma: Lessons from CheckMate 358 and the Questions That Remain
2025
Merkel Cell Carcinoma
Ipilimumab
Nivolumab
A multidisciplinary summary and perspective on the CheckMate 358 trial in advanced Merkel cell carcinoma, exploring the role of dual checkpoint blockade, challenges of trial interpretation, real-world practice patterns, and evolving views on therapeutic intent and long-term outcomes.
Keywords
Merkel Cell Carcinoma, Nivolumab, Ipilimumab
THIS IS A DRAFT
Featured Article
| Bhatia, S. et al. Nivolumab With or Without Ipilimumab in Patients With Recurrent or Metastatic Merkel Cell Carcinoma: A Nonrandomized, Open-Label, International, Multicenter Phase I/II Study. Journal of Clinical Oncology 43, 1137–1147 (2025).1 |
Introduction
On April 10, 2025, the Society of Cutaneous Oncology (SoCO) Journal Club gathered to review a recent study published in the Journal of Clinical Oncology: “Nivolumab With or Without Ipilimumab in Patients With Recurrent or Metastatic Merkel Cell Carcinoma: A Nonrandomized, Open-Label, International, Multicenter Phase I/II Study1.” The discussion drew an interdisciplinary audience of clinicians and researchers representing expertise across medical oncology, dermatologic oncology, surgical oncology, and translational immunotherapy (Figure 1).
For the first time, the session also welcomed participation from clinical scientists, pharmacists, and team leads from Bristol Myers Squibb’s Cutaneous Oncology division—adding a valuable industry perspective to the conversation. Attendees from academic and clinical institutions included representatives from Massachusetts General Hospital, University of Washington, Moffitt Cancer Center, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Northwestern University, and the National Institutes of Health. Their experience managing Merkel Cell Carcinoma (MCC) varied (Figure 2), which reflects responses from academic participants only.
Figure 2. Merkel Cell Carcinoma Experience
Figure 2: Survey responses regarding attendees’ experience managing Merkel Cell Carcinoma. The percentage of total respondents is displayed to the right of each bar.
This perspective summarizes key themes and insights raised during the session, reflecting on the study’s implications for frontline treatment decision-making in advanced MCC. Views expressed are those of the authors and do not necessarily reflect official positions of the Society of Cutaneous Oncology or participating institutions.
Background
Immune checkpoint inhibition has dramatically reshaped the therapeutic landscape for advanced MCC. Since 2016—following the demonstration of clinical benefit in studies by Nghiem et al.2 and Kaufman et al.3—PD-1/PD-L1 monotherapy has become the standard first-line approach, achieving response rates between 30% and 60% with durable remissions in a subset of patients.
Despite these advances, most patients either do not experience an initial response or eventually relapse4, and no standard second-line therapy has been established5–7. In this context, efforts to augment immune responses beyond PD-1 blockade have gained increasing interest.
Dual immune checkpoint blockade with anti–PD-1 and anti–CTLA-4 agents has demonstrated synergistic efficacy in melanoma8 and other malignancies9, and represents a logical extension in MCC—particularly given the high mutational burden and viral antigens often present in this disease10–13,Wong2015a?,Feng2008a?. A prospective study published in The Lancet in 2022 evaluated nivolumab plus ipilimumab (NIVO + IPI) with or without stereotactic body radiation therapy (SBRT) in both ICI-naive and ICI-experienced patients with MCC14. Among 24 ICI-naive patients, the combination of nivolumab (NIVO) (240 mg every 2 weeks) and ipilimumab (IPI) (1 mg/kg every 6 weeks) yielded a notable 100% objective response rate (ORR), with durable responses in both irradiated and non-irradiated lesions However, response rates were substantially lower in the post-PD-1 population, and the addition of SBRT did not appear to enhance systemic efficacy.
Although early-phase studies have demonstrated activity in both first- and second-line settings, enthusiasm for dual checkpoint blockade in MCC remains limited by sparse prospective data, toxicity concerns, and uncertainty around patient selection. In the pre–Journal Club survey, 81% of participants reported having recommended ipilimumab for a patient with MCC (Figure 3), and only a minority felt confident in articulating a preferred dual immune checkpoint inhibitor (ICI) regimen (Figures 4-5).
Figure 3. Experience Recommending Dual Checkpoint Blockade in MCC
Figure 3. Pre–Journal Club survey responses from clinicians with experience managing MCC, indicating whether they had ever recommended combination nivolumab plus ipilimumab. Responses reflect self-reported practice patterns prior to the Journal Club session. Percentages are shown to the right of each bar.
Figures 4-5. Preferred Dosing Strategies for NIVO + IPI in MCC
Figure 4. Preferred Dosing Strategy for First-Line NIVO + IPI
Figure 4: Among clinicians who had used dual checkpoint blockade in MCC, responses regarding preferred dosing regimens in the first-line setting. Multiple regimens are currently in use, and no consensus exists. The percentage of total respondents is displayed to the right of each bar.
Figure 5. Dosing Preferences for NIVO + IPI After Prior Anti–PD-1 Therapy
Figure 5: Survey responses describing dosing strategies for Nivolumab plus Ipilimumab in the second-line or post–PD-1 setting. Choices reflect clinical variability and uncertainty in optimal re-challenge protocols. The percentage of total respondents is displayed to the right of each bar.
These results highlight a critical knowledge and practice gap—one that CheckMate 358 seeks to help address.
Study Design
CheckMate 358 is a multicenter, open-label, multicohort phase I/II trial designed to evaluate NIVO-based immunotherapy across a range of virus-associated cancers. In addition to exploring neoadjuvant approaches in earlier-stage disease15, the trial included disease-specific cohorts in the advanced/metastatic setting, including for Merkel cell carcinoma. The results presented by Bhatia and colleagues focus on the recurrent/metastatic MCC cohort, which assessed NIVO with or without IPI in patients who were ICI–naive.
Patients were enrolled sequentially into two cohorts: the NIVO monotherapy cohort between October 2015 and January 2016, and the combination NIVO + IPI cohort between July 2016 and October 2018. This nonrandomized, sequential design resulted in a longer follow-up period and more favorable baseline characteristics in the monotherapy cohort, complicating direct cross-arm comparisons.
Patients in the monotherapy cohort received NIVO at a dose of 240 mg every two weeks. In the combination cohort, patients were treated with NIVO at 3 mg/kg every two weeks plus IPI at 1 mg/kg every six weeks. The primary endpoint was ORR by Response Evaluation Criteria in Solid Tumors v1.1, as assessed by local investigators. Secondary endpoints included duration of response (DoR), progression-free survival (PFS), overall survival (OS), and safety outcomes.
This analysis represents the largest prospective study to date examining dual checkpoint blockade in advanced MCC and provides important context for ongoing clinical decision-making around sequencing and combination strategies in ICI-naive patients.
Main Findings
In the CheckMate 358 MCC cohort, 68 ICI–naïve patients were treated with either NIVO monotherapy (n = 25) or combination NIVO + IPI (n = 43). The ORR was 60% (95% condifence intervals (CI), 38.7–78.9) with NIVO and 58% (95% CI, 42.1–73) with NIVO + IPI. Complete responses occurred in 32% of patients receiving NIVO and 19% with NIVO + IPI. Responses were durable in both groups, with median DoR of 60.6 months (95% CI, 16.7–NA) and 25.9 months (95% CI, 10.4–NA), respectively.
While response rates were similar, outcomes for the NIVO monotherapy group appeared more favorable. Median PFS was 21.3 months (95% CI, 9.2–62.5) with NIVO, compared to 8.4 months (95% CI, 3.7–24.3) with NIVO + IPI. Median OS was 80.7 months (95% CI, 23.3–NA) for NIVO and 29.8 months (95% CI, 8.5–48.3) with the combination.
Safety data revealed a higher incidence of grade 3–4 treatment-related adverse events with NIVO + IPI (47%) compared to NIVO monotherapy (28%). Immune-related toxicities led to treatment discontinuation in 26% of patients on combination therapy and 20% in the monotherapy group. One treatment-related death occurred in each arm.
In this sequential, non-comparative analysis, dual checkpoint blockade demonstrated activity but did not show evidence of improved efficacy over PD-1 monotherapy, while toxicity appeared higher with the combination. Smaller prior studies have suggested high activity of NIVO + IPI in MCC; however, differences in trial design, patient selection, and baseline characteristics limit definitive comparisons. Nonetheless, CheckMate 358 remains the largest prospective evaluation of combination immunotherapy in ICI-naïve advanced MCC to date.
Discussion
Why This Study Matters
CheckMate 358 represents an important addition to a field where prospective data remain limited. Although immune checkpoint inhibitors have transformed the treatment landscape for advanced MCC, most clinical insights have come from single-arm studies of anti–PD-1 or anti–PD-L1 monotherapy. Prospective evaluations of dual checkpoint blockade—particularly NIVO + IPI—have been sparse, despite the regimen’s established role in melanoma and prior exploration in other virally mediated and immunogenic cancers16.
This study includes the largest prospectively enrolled cohort of patients with advanced, treatment-naïve MCC treated with NIVO + IPI (n=33). While CheckMate 358 was not powered or designed for formal head-to-head comparison between treatment arms, its multi-arm structure, prospective design, and transparent reporting provide meaningful insight into the potential role of dual immune checkpoint blockade in this setting.
This need for prospective clarity is further underscored by findings from the pre–Journal Club survey, which revealed considerable heterogeneity in how experts approach treatment for advanced MCC. Despite belonging to a focused community of Merkel cell carcinoma specialists, respondents offered widely differing recommendations when presented with hypothetical clinical scenarios (Figure 6).
Figure 6. Variability in Treatment Preferences Among MCC Providers
A. Treatment Preferences: Low Tumor Burden MCC
Figure 6A – Treatment Preferences: Low Tumor Burden MCC.
Pre–Journal Club survey responses to a case describing a 58-year-old man (58M) with Eastern Cooperative Oncology Group performance status of 0 (ECOG0), no past medical history (PMH), and Merkel cell carcinoma metastatic to regional lymph nodes and a solitary adrenal lesion. Of 33 total respondents, 21 identified as clinicians and selected a management approach; 12 responses were excluded from the plot (“Not Applicable Clinician” (n=3), “I Am Not A Clinician” (n=5), or “Not Answered” (n=4)). Among clinicians, there was notable variation in treatment preferences, ranging from PD-1 monotherapy to dual checkpoint blockade, reflecting real-world uncertainty in managing lower tumor burden disease.
B. Survey Responses for High Tumor Burden MCC
Figure 6B – Treatment Preferences: High Tumor Burden MCC.
Pre–Journal Club survey responses to a case describing a 58-year-old man (58M) with Eastern Cooperative Oncology Group performance status of 0 (ECOG0), no past medical history (PMH), and metastatic Merkel cell carcinoma involving regional lymph nodes and more than 20 liver metastases (mets). Of 33 total respondents, 21 identified as clinicians and selected a management approach; 12 responses were excluded from the plot (“Not Applicable Clinician” (n=3), “I Am Not A Clinician” (n=5), or “Not Answered” (n=4)). Compared to the lower burden case, a greater proportion of clinicians favored dual checkpoint blockade, reflecting a perception that intensified immunotherapy may be warranted in patients with extensive disease.
The Backstory Behind the Paper
Although enrollment for the MCC cohort of CheckMate 358 concluded several years ago, the data had not yet been published, and even study investigators were not aware of the final results. Interest in the dataset was renewed following the 2nd International Merkel Cell Carcinoma Symposium in Seattle in 2022, where the Moffitt-led study of NIVO + IPI—with or without SBRT—was presented. The impressive response rates reported, particularly in the immunotherapy-only arm, prompted investigators from CheckMate 358 to advocate for publication of their own findings. While the outcomes differed, there was consensus that sharing both experiences would meaningfully contribute to the evidence base and help guide treatment decisions in a rare and challenging disease.
The revival of interest in CheckMate 358 coincided with a broader recognition of how much real-world practice patterns had evolved—and diversified—since the trial was originally conducted. As the pre–Journal Club survey highlighted, treatment preferences for advanced MCC have grown increasingly heterogeneous, reflecting both expanding therapeutic options and enduring gaps in the evidence base.
Contextualizing Results Across Trials
The differing outcomes observed in the Moffitt study and CheckMate 358 merit careful consideration but should be interpreted with caution. As is common in rare diseases, cross-trial comparisons are inherently limited by differences in study design, patient populations, and temporal or geographic factors. While the higher response rate reported in the Moffitt cohort may suggest enhanced activity of NIVO + IPI in that setting, direct comparisons are difficult to justify. Understanding these differences requires close attention to trial structure, timing, and patient selection—factors that likely contributed to the observed variation in outcomes.
The Moffitt study employed a randomized design; however, randomization addressed the addition of SBRT, not the choice of systemic therapy—all patients received NIVO + IPI. In contrast, CheckMate 358 was a non-randomized, multi-arm, non-comparative trial. Notably, the absence of a NIVO monotherapy arm in the Moffitt study renders any comparison with other datasets unanchored, limiting the ability to contextualize the observed efficacy of dual checkpoint blockade against a shared referencea. As a result, it remains uncertain whether the observed outcomes reflect therapeutic synergy, patient selection, or random variability.
In comparative effectiveness research, an “anchored” analysis uses a common comparator (anchor) across studies to support indirect comparisons17. In the absence of a shared reference arm—as in the Moffitt study—any comparison is considered “unanchored” and is more susceptible to bias and confounding.
Although both trials evaluated NIVO plus IPI in ICI-naïve patients, their dosing regimens differed slightly. The Moffitt study used fixed-dose nivolumab (240 mg every two weeks) with ipilimumab (1 mg/kg every six weeks), while CheckMate 358 employed weight-based nivolumab (3 mg/kg every two weeks) alongside the same ipilimumab schedule. While these variations in NIVO dosing are unlikely to explain the differences in efficacy or toxicity, they cannot be entirely dismissed.
More plausibly, several sources of bias may help account for the divergent outcomes. Notably, the NIVO + IPI arm of CheckMate 358 opened after PD-1 monotherapy had become standard frontline therapy, likely influencing enrollment patterns. Patients for whom PD-1 monotherapy was deemed appropriate may have been steered toward routine care rather than trial participation—potentially enriching the combination arm with individuals who had more advanced disease or fewer treatment options.
This hypothesis is supported by notable imbalances in baseline characteristics: the combination cohort had higher rates of adverse prognostic features, including ECOG performance status of 1 (63% vs. 40%), stage IV disease (93% vs. 80%), virus-negative MCC (42% vs. 28%), and a larger median tumor burden (72 mm vs. 55.5 mm). At the same time, a greater proportion of patients in the monotherapy group had received prior chemotherapy (40% vs. 23%), likely reflecting earlier enrollment before anti–PD-1 agents were broadly accessible. However, this latter factor would be expected to bias outcomes in favor of the combination cohort, as prior chemotherapy has been associated with reduced response to subsequent immunotherapy18. These competing biases—some favoring one arm, some favoring the other—illustrate the internal contradictions within CheckMate 358 and complicate efforts to draw definitive conclusions from its results.
Such internal dynamics may help explain why the combination arm did not appear to outperform monotherapy in this study—a finding that could otherwise seem discordant with the Moffitt study or broader clinical experience, where PD-1 monotherapy has yielded response rates closer to 50%, and dual checkpoint blockade has demonstrated response rates up to 100% in select cohorts. Importantly, these complexities should not be conflated with cross-trial comparison; rather, they reflect how evolving treatment standards and trial timing can shape study populations in ways that meaningfully influence observed outcomes.
Another perspective is to view this as an illustration of the “law of small numbers”b. The 100% response rate in the Moffitt cohort—13 out of 13 patients treated with immunotherapy alone—is striking (100% ORR with 95% CI [75.3%-100%]) but statistically fragile. A single non-response would have dropped the rate to 92.3%, and confidence intervals around such estimates remain wide (95% CI [64%-99.8%]). In contrast, CheckMate 358 enrolled a larger NIVO + IPI cohort (43 patients), yielding a more moderate response rate of 58%, but with narrower uncertainty bounds (95% CI [42.1%-73%]).
One way to integrate these findings is through a Bayesian lens—a statistical framework that formally combines prior knowledge with new evidence. Rather than interpreting the studies in opposition, we might view them as complementary: the Moffitt data serve as a prior belief about the activity of dual checkpoint blockade in MCC, and the CheckMate 358 results provide new evidence that updates that belief. This yields a posterior distribution—a probabilistic view of the full range of likely true response rates, not just a single point estimate, with uncertainty explicitly quantified.
Importantly, Bayesian updating is agnostic to the order in which data are incorporated: mathematically, the synthesis is the same regardless of sequence. But from a real-world perspective, chronology still shapes perception. Although the Moffitt results were published earlier (2022), they reflect data accrued between March 2017 and December 2021. In contrast, enrollment for the CheckMate 358 combination cohort occurred even earlier (July 2016 to October 2018), but the updated MCC-specific results were published only recently. Thus, while the reporting sequence suggests one narrative, the actual timing of data collection tells another. Bayesian thinking allows us to integrate both, balancing early enthusiasm with empirical grounding.
The accompanying figure (Figure 7) illustrates this concept. An initially optimistic belief—based on the Moffitt study’s 100% response rate—is tempered by more moderate data from CheckMate 358. The result is a more calibrated, evidence-weighted estimate of efficacy that captures both early optimism and subsequent trial findings.
Figure 7 - Bayesian synthesis of ORR for NIVO + IPI in MCC
A. Bayesian synthesis of ORR for NIVO + IPI in MCC
Show Code Used For Bayesian Synthesis
# Create a theta sequence
theta <- seq(0, 1, length.out = 1000)
# Define each distribution
df <- tibble(
theta = theta,
`Prior (Moffitt IST)` = dbeta(theta, 14, 1),
`Likelihood (CheckMate 358)` = dbeta(theta, 21, 12), # using only first line (21/33)
`Posterior (Combined Data)` = dbeta(theta, 14+21, 1+12) # => dbeta(theta, 35, 13)
) %>%
pivot_longer(cols = -theta, names_to = "Distribution", values_to = "Density") %>%
mutate(Distribution = factor(Distribution, levels = c(
"Prior (Moffitt IST)",
"Likelihood (CheckMate 358)",
"Posterior (Combined Data)"
)))
df <- df %>%
mutate(theta = theta * 100)
# Posterior: Prior from 13/13 → Beta(14 + 21, 1 + 12) = Beta(35, 13)
alpha_post_13 <- 14 + 21
beta_post_13 <- 1 + 12
posterior_mean_13 <- alpha_post_13 / (alpha_post_13 + beta_post_13)
posterior_mode <- (alpha_post_13 - 1) / (alpha_post_13 + beta_post_13 - 2)
# Empirical mode from the posterior distribution
posterior_df <- df %>% filter(Distribution == "Posterior (Combined Data)")
empirical_mode_pct <- posterior_df$theta[which.max(posterior_df$Density)]
posterior_ci_13 <- qbeta(c(0.025, 0.975), alpha_post_13, beta_post_13)
posterior_ci_13_lower <- round(posterior_ci_13[1],3)
posterior_ci_13_upper <- round(posterior_ci_13[2],3)
# Rescale posterior means for plotting in %
posterior_mean_13_pct <- posterior_mean_13 * 100
# Rescale 95% credible intervals
posterior_ci_13_pct <- posterior_ci_13 * 100
# Plot
p <- ggplot(
df,
aes(x = theta, y = Density, color = Distribution)) +
geom_line(size = 1.2) +
labs(
title = "Bayesian Synthesis of Evidence Across Two Studies",
subtitle = "Updating prior belief from one trial with new data from another",
x = "True Response Rate (%)",
y = "Density (Higher = More Likely True Response Rate)"
) +
scale_color_manual(values = c(
"Prior (Moffitt IST)" = "#1f77b4",
"Likelihood (CheckMate 358)" = "#ff7f0e",
"Posterior (Combined Data)" = "#2ca02c"
)) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(
hjust = 0.5, face = "bold", size = 16,
margin = margin(b = 5)
),
plot.subtitle = element_text(
hjust = 0.5, face = "plain", size = 14,
margin = margin(b = 10)
),
legend.title = element_blank(),
legend.position = c(0.02, 0.98), # Top-left inside the plot
legend.justification = c(0, 1), # Anchor legend box top-left
legend.background = element_rect(fill = "white", color = "gray80"),
legend.box.background = element_rect(color = "gray80"),
legend.box.margin = margin(6, 6, 6, 6)
) +
coord_cartesian(ylim = c(0, 15)) +
geom_vline(xintercept = posterior_mean_13 * 100, linetype = "dashed", color = "#2ca02c") +
geom_vline(xintercept = empirical_mode_pct, linetype = "dotted", color = "#2ca02c") +
scale_x_continuous(
breaks = seq(0, 100, by = 10),
labels = scales::label_percent(scale = 1)
) +
annotate(
"text",
x = posterior_mean_13 * 100 - 1, y = 12,
label = paste0("Mean = ", round(posterior_mean_13 * 100, 1), "%"),
color = "#2ca02c", hjust = 1, size = 3.5) +
annotate(
"text",
x = empirical_mode_pct + 1, y = 11,
label = paste0("Mode = ", round(empirical_mode_pct, 1), "%"),
hjust = 0, color = "#2ca02c", size = 3.5) +
geom_ribbon(data = df %>%
filter(Distribution == "Posterior (Combined Data)",
theta >= posterior_ci_13_pct[1],
theta <= posterior_ci_13_pct[2]),
aes(ymin = 0, ymax = Density),
fill = "#2ca02c", alpha = 0.1, inherit.aes = TRUE)
p
Figure 7A: Bayesian synthesis of objective response rate for NIVO + IPI in MCC.
This panel illustrates how Bayesian methodology can integrate evidence across studies to yield a more calibrated estimate of treatment efficacy. The prior belief, shown in blue, is based on the Moffitt IST cohort, where all 13 patients treated with NIVO + IPI responded to therapy. This is modeled using a Beta(14,1) distribution, representing belief about the likely range of true response rates, shaped by 13 observed successes and a small amount of prior uncertainty. The likelihood curve, shown in orange, is derived from the CheckMate 358 trial, where 21 of 33 patients responded in the first-line setting, modeled as a Beta(21,12) distribution. The green curve represents the resulting posterior distribution, which integrates these two sources of evidence.
The posterior has a mean of 72.9% and a mode of 73.9%. The mean reflects the average expectation across the entire distribution—the “center of mass” of the uncertainty—while the mode identifies the most probable point estimate, corresponding to the peak of the distribution. In most cases, for Beta distributions such as this one, the mean and mode are close, but they can diverge slightly when the distribution is skewed. Both metrics provide complementary insights: the mean offers a balanced summary of all possibilities, while the mode highlights the single most likely true response rate based on available data.
The 95% credible interval, ranging from 59.7% to 84.4%, captures the range within which the true response rate lies with 95% probability according to the posterior. The y-axis represents density—the relative plausibility of different response rates rather than absolute probability—and taller regions correspond to more likely values. To aid interpretability, the x-axis is rescaled from 0 to 100%.
The Beta distribution is commonly used in Bayesian analysis of proportions (such as objective response rates) because it flexibly models outcomes bounded between 0 and 1 and can be easily updated with new binomial data. Thus, each curve in this figure provides a visual summary of belief, evidence integration, and uncertainty in clinical trial interpretation.
B. Bayesian updating of ORR in the ±SBRT track
Show Code Used For Bayesian Synthesis
# Step 1: Clean base theta
base_theta <- seq(0, 1, length.out = 1000)
# Step 2: Define the dataset using base_theta
df_b <- tibble(
theta = rep(base_theta, times = 3),
Distribution = rep(c(
"Prior: Moffitt ±SBRT (24/24)",
"Likelihood: CheckMate 358 (21/33)",
"Posterior: Moffitt ±SBRT → CheckMate 358"
), each = length(base_theta)),
Density = c(
dbeta(base_theta, 25, 1), # Moffitt prior (24/24)
dbeta(base_theta, 21, 12), # Likelihood (Checkmate)
dbeta(base_theta, 46, 13) # Moffitt ±SBRT → CheckMate
)
) %>%
mutate(
LineType = case_when(
str_detect(Distribution, "^Flat Prior|^Prior") ~ "Prior",
str_detect(Distribution, "^Likelihood") ~ "Likelihood",
str_detect(Distribution, "^Posterior") ~ "Posterior",
TRUE ~ "Other"
)
)
df_b <- df_b %>%
mutate(Distribution = factor(Distribution, levels = c(
"Prior: Moffitt ±SBRT (24/24)",
"Likelihood: CheckMate 358 (21/33)",
"Posterior: Moffitt ±SBRT → CheckMate 358"
)))
df_b <- df_b %>%
mutate(theta = theta * 100)
alpha_post_25 <- 46
beta_post_25 <- 13
# Posterior 2: Moffitt ±SBRT → CheckMate = Beta(46, 13)
posterior_mean_moffitt_b <- 46 / (46 + 13)
posterior_mode <- (alpha_post_25 - 1) / (alpha_post_25 + beta_post_25 - 2)
# Empirical mode from the posterior distribution
posterior_df <- df_b %>% filter(Distribution == "Posterior: Moffitt ±SBRT → CheckMate 358")
empirical_mode_pct <- posterior_df$theta[which.max(posterior_df$Density)]
posterior_ci_moffitt_b <- qbeta(c(0.025, 0.975), 46, 13)
posterior_mean_moffitt_b_pct <- posterior_mean_moffitt_b * 100
posterior_ci_moffitt_b_pct <- posterior_ci_moffitt_b * 100
# Step 3: Plot
ggplot(df_b, aes(x = theta, y = Density, color = Distribution)) +
geom_line(size = 1.2) +
labs(
title = "Bayesian Updating: ±SBRT Cohort",
subtitle = "Impact of prior selection on posterior estimate using CheckMate 358",
x = "True Response Rate (%)",
y = "Density (Higher = More Likely True Response Rate)"
) +
scale_color_manual(values = c(
"Prior: Moffitt ±SBRT (24/24)" = "#1f77b4",
"Likelihood: CheckMate 358 (21/33)" = "#ff7f0e",
"Posterior: Moffitt ±SBRT → CheckMate 358" = "#2ca02c"
)) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 15),
plot.subtitle = element_text(hjust = 0.5, size = 13),
legend.title = element_blank(),
legend.text = element_text(size = 9),
legend.position = c(0.02, 0.98),
legend.justification = c(0, 1),
legend.background = element_rect(fill = "white", color = "gray80"),
legend.box.background = element_rect(color = "gray80"),
legend.box.margin = margin(4, 4, 4, 4),
legend.box = "vertical"
)+
coord_cartesian(ylim = c(0, 15)) +
scale_x_continuous(
breaks = seq(0, 100, by = 10),
labels = scales::label_percent(scale = 1)
) +
geom_vline(
xintercept = posterior_mean_moffitt_b * 100, linetype = "dashed", color = "#2ca02c"
) +
geom_vline(xintercept = empirical_mode_pct, linetype = "dotted", color = "#2ca02c") +
annotate(
"text",
x = posterior_mean_moffitt_b * 100 -17, y = 12,
label = paste0("Mean = ", round(posterior_mean_moffitt_b * 100, 1), "%"),
color = "#2ca02c", hjust = 0, size = 3.5) +
annotate(
"text",
x = empirical_mode_pct + 1, y = 11,
label = paste0("Mode = ", round(empirical_mode_pct, 1), "%"),
hjust = 0, color = "#2ca02c", size = 3.5) +
geom_ribbon(data = df_b %>%
filter(Distribution == "Posterior: Moffitt ±SBRT → CheckMate 358",
theta >= posterior_ci_moffitt_b_pct[1],
theta <= posterior_ci_moffitt_b_pct[2]),
aes(ymin = 0, ymax = Density),
fill = "#2ca02c", alpha = 0.1, inherit.aes = TRUE)
Figure 7B: Bayesian updating of objective response rate in the ±SBRT cohort.
This panel demonstrates how prior assumptions influence posterior interpretation, using Bayesian methods to update expectations of treatment efficacy for NIVO + IPI in MCC. The blue curve represents the prior belief based on the full Moffitt ±SBRT cohort, in which all 24 ICI-naïve patients treated with dual checkpoint blockade responded. This is modeled as a Beta(25,1) distribution—an optimistic view shaped by 24 observed responses plus a small amount of prior uncertainty.
The orange curve reflects the likelihood from the CheckMate 358 trial, where 21 of 33 patients responded in the first-line setting, modeled as a Beta(21,12) distribution. The green curve represents the resulting posterior distribution—our updated belief after combining the prior with the new evidence. The posterior has a mean of 78% and a mode of 79%. The mean represents the average expectation across all possible response rates, while the mode reflects the most likely single value (i.e., the peak of the distribution). These values are often similar but not identical when the distribution is skewed, as in this case. Showing both helps communicate both the center and the shape of our belief distribution.
As in the previous panel, the y-axis represents density—that is, the relative plausibility of different response rates based on the evidence. Taller regions indicate more likely values, and the shaded area shows the most credible 95% range for the true ORR. To enhance interpretability, the x-axis is scaled from 0–100% rather than the usual 0–1 probability scale.
The Beta distribution is a common and intuitive tool in Bayesian statistics, particularly for binary outcomes like treatment response. It is defined by two parameters—α (successes) and β (failures)—which are easily updated with binomial data. In rare cancers such as MCC, where randomized data are limited, this method allows investigators to transparently combine prior experience with new results to form calibrated, quantitative expectations for treatment efficacy.
Balancing Rigor and Reality
The contrasting outcomes of the Moffitt and CheckMate 358 studies naturally raise the question of whether a well-powered comparative trial—NIVO versus NIVO + IPI—might help clarify the clinical role of dual checkpoint blockade in MCC. In their published manuscript, the CheckMate 358 investigators advocate for such a trial, highlighting it as a necessary next step to better contextualize their findings. Notably, the presence of multiple, and at times opposing, biases within CheckMate 358 itself—such as more advanced disease in the combination cohort but more prior chemotherapy in the monotherapy group—further underscores the limitations of non-randomized comparisons and the need for a formal head-to-head trial. Yet even this relatively straightforward comparison remains aspirational in the context of a rare cancer. Both trials were supported by the pharmaceutical sponsor, and such backing may become less feasible as these agents approach the end of their patent protection.
A more expansive proposal also emerged during the Journal Club discussion: a trial comparing NIVO, NIVO + IPI, and a triplet regimen of nivolumab/relatlimab/ipilimumab, modeled on RELATIVITY-048. While the scientific rationale is strong, the feasibility of such a study is less certain. MCC disproportionately affects older adults, and the toxicity burden of multi-agent immunotherapy may limit the viability or desirability of these approaches in real-world settings.
There remains a persistent tension between the desire to rigorously interrogate clinical questions and the realities of conducting such studies. Even with a strong hypothesis, the logistical challenges of enrolling older patients—many with high comorbidity burdens or elevated frailty indices—often constrain what is realistically achievable. Taken together—whether viewed independently or synthesized through a Bayesian lens—the Moffitt and CheckMate 358 studies may stand as the only prospective data available to guide the use of dual checkpoint blockade in MCC for the foreseeable future.
Bridging the Gap Between Evidence and Practice
While the feasibility of conducting a well-powered comparative trial remains uncertain, it is clear from the pre–Journal Club survey that many clinicians are already using NIVO + IPI in practice—particularly in patients with high tumor burden or PD-1–refractory disease. Still, significant heterogeneity persists, and several respondents identified tangible barriers to broader adoption of dual checkpoint blockade (Figure 8).
Figure 8. Reported Barriers to Using Nivolumab + Ipilimumab in MCC
Figure 8 – Reported Barriers to Using Nivolumab + Ipilimumab in MCC.
Pre–Journal Club survey responses to a question asking clinicians to identify barriers to using combination NIVO + IPI in MCC. Responses reflect only those from clinicians who reported experience managing MCC and selected at least one barrier (n = 20). The most frequently cited concerns included treatment-related toxicity (70%) and a lack of high-level data specific to MCC (45%). Insurance or Medicare denial was also reported by 15% of clinicians. These findings underscore the real-world implementation challenges that persist despite growing clinical interest in dual checkpoint blockade.
Insurance coverage, in particular, emerged as a nuanced barrier. While only 15% of MCC-managing clinicians explicitly selected insurance or Medicare denial as a barrier in the initial survey question (Figure 8), a separate item explored this issue more directly. Among 19 eligible respondents, only one reported a confirmed denial of NIVO + IPI, yet nearly half were unsure whether a denial had occurred (Figure 9). These findings suggest that the problem may lie less in outright rejection and more in the lack of transparency or communication around payor decisions—leaving many clinicians uncertain about coverage outcomes even when they initiate the request.
Figure 9. Reported Insurance Denial of NIVO + IPI
Figure 9 – Reported Insurance Denial of Nivolumab + Ipilimumab in MCC. Pre–Journal Club survey responses from clinicians managing MCC, indicating whether they had recommended NIVO + IPI for a patient and encountered ultimate denial by a payor (e.g., even after a peer-to-peer appeal). Among 19 eligible clinicians, only 5% reported a confirmed denial, while 47% were unsure. These findings highlight uncertainty around coverage outcomes and the opacity of payer decision-making processes.
One key tool in overcoming these barriers is the inclusion of therapies in the National Comprehensive Cancer Network (NCCN) guidelines, which serve as a critical resource for guiding evidence-informed clinical practice and can also help support coverage decisions in the absence of formal FDA approval. Despite this, a notable subset of clinicians remain unaware that NIVO + IPI is already referenced in the NCCN guidelines for MCC as “Useful in Certain Circumstances”—a designation that has been in place for several guideline cycles (Figure 10)20.
Figure 10. Provider Awareness of NCCN Guidelines Referencing NIVO + IPI
Figure 10 – Awareness of NCCN Guideline Support for NIVO + IPI in MCC.
Pre–Journal Club survey responses from clinicians who reported managing MCC, addressing awareness of National Comprehensive Cancer Network guideline inclusion of NIVO + IPI. As of 2025, the NCCN lists this regimen under “Useful in Certain Circumstances.” Among 20 eligible respondents, 80% were aware of the guideline inclusion, while 20% were not. These findings highlight a persistent gap in awareness, even within a highly specialized community, and underscore the importance of guideline visibility for ensuring access and facilitating insurance coverage for regimens not formally FDA-approved in this setting.
This knowledge gap may partly explain variable access to and confidence in using dual checkpoint blockade. Beyond guidelines, many clinicians also believe that product labeling could help solidify the place of NIVO + IPI in MCC treatment algorithms—yet the path to regulatory endorsement remains complex (Figure 11).
Figure 11. Perceived Usefulness of Product Labeling for NIVO + IPI
Figure 11. Perspectives on Adding MCC to the Product Label for Ipilimumab.
Pre–Journal Club survey responses from clinicians who manage Merkel cell carcinoma, addressing whether the inclusion of MCC in the ipilimumab product label would be helpful. Among 20 eligible respondents, 85% indicated that such labeling would be helpful, while 15% did not. These findings reflect strong clinician interest in formal labeling, which could facilitate treatment access, clarify regulatory standing, and reduce payer-related friction—particularly given the existing NCCN guideline support for the regimen.
However, others expressed reservations—particularly in the first-line setting—where demonstrating the individual contribution of each drug component remains a regulatory expectation. Many clinicians felt that current data do not definitively justify labeling in the frontline setting, in large part due to the absence of a direct head-to-head comparison between NIVO and NIVO + IPI. Without such comparative evidence, enthusiasm for broad labeling in treatment-naive patients remains tempered.
In contrast, there was more support for labeling in the PD-1–refractory setting. In this context—where no standard of care is firmly established and where clinicians face greater uncertainty—many respondents found the existing body of evidence more compelling. This includes retrospective institutional experiences6,21–27 and the Moffitt study cohort that received NIVO + IPI in the post–PD-1 setting14 (Table 2).
These sources have not only contributed to greater confidence in the rationale for using dual checkpoint blockade after PD-1 monotherapy failure, but have also informed ongoing discussions around supplemental indications for product labeling. The aggregate response rate of 31%—with a lower bound 95% confidence interval excluding 20% (Figure 12)—approaches a threshold historically used to justify accelerated approval for serious illnesses lacking an established standard of care28.
Figure 12. NIVO + IPI Response in MCC After Anti–PD-1: Observed and Modeled Estimates
Figure 12A. Observed Objective Response Rates for Ipi/Nivo in the Post–PD-1 Setting
Subgroup-level response data from published retrospective and prospective studies, with 95% confidence intervals.
Figure 12A. Best overall response from the various studies of ipilimumab with or without anti-PD-1 in the post PD-1 setting are displayed here with corresponding 95% confidence intervals. References: LoPiccolo et al.21, Shalhout et al.6, Glutsch et al.24, Kim et al.14, Khaddour et al.25, Ferdinandus et al.26 and Leven et al.27. Of note, two patients in LoPiccolo et al.21 were treated with monotherapy ipilimumab and were not included in the aggregate N (both were non-responders). Data reproduced with permission from Miller DM, JoCO 20247.
Figure 12B. Bayesian Posterior Distribution of ORR for NIVO + IPI After Anti–PD-1
Estimated probability distribution of the true ORR based on aggregate data (21/67 responders), assuming a flat prior.
Show Code Used For Bayesian Posterior Distribution of ORR for NIVO + IPI
library(tidyverse)
# Step 1: Define the theta sequence (true response rates from 0 to 1)
theta_raw <- seq(0, 1, length.out = 1000)
# Step 2: Define prior and posterior
prior_alpha <- 1
prior_beta <- 1
# Data: 19 responders out of 67
data_alpha <- 21
data_beta <- 67 - 21
# Posterior distribution: Beta(22, 47)
posterior_alpha <- prior_alpha + data_alpha
posterior_beta <- prior_beta + data_beta
# Posterior mean and 95% credible interval
posterior_mean <- posterior_alpha / (posterior_alpha + posterior_beta)
posterior_ci <- qbeta(c(0.025, 0.975), posterior_alpha, posterior_beta)
# Step 3: Create data frame for plotting
df <- tibble(
theta_raw = theta_raw,
theta = theta_raw * 100, # percent scale for x-axis
Density = dbeta(theta_raw, posterior_alpha, posterior_beta)
)
# Define the 95% credible interval again (on a 0–100 scale)
ci_lower <- round(posterior_ci[1] * 100, 1)
ci_upper <- round(posterior_ci[2] * 100, 1)
# Step 4: Plot
bayesian_orr_post_pd1 <- ggplot(df, aes(x = theta, y = Density)) +
geom_line(size = 1.2, color = "darkgreen") +
geom_vline(
xintercept = posterior_mean * 100,
linetype = "dashed", color = "darkgreen") +
annotate(
"text",
x = posterior_mean * 100 + 1,
y = max(df$Density) * 1.01,
label = paste0("Mean = ", round(posterior_mean * 100, 1), "%"), hjust = 0, size = 4) +
geom_ribbon(
data = df %>%
filter(theta >= posterior_ci[1]*100, theta <= posterior_ci[2]*100),
aes(ymin = 0, ymax = Density),
fill = "darkgreen", alpha = 0.1) +
labs(
title = "Bayesian Estimate of ORR for NIVO + IPI Post–PD-1",
subtitle = "Posterior distribution using a flat prior and aggregate data (21/67)",
x = "Objective Response Rate (%)",
y = "Density"
) +
scale_x_continuous(breaks = seq(0, 100, 10)) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
plot.subtitle = element_text(face = "bold", hjust = 0.5),
legend.position = "none") +
# Add annotation for the CI range
# Annotate lower bound (left side)
annotate(
"text",
x = ci_lower,
y = max(df$Density) * 0.1,
label = paste0(ci_lower, "%"),
color = "darkgreen",
size = 4,
hjust = 0
) +
# Annotate upper bound (right side)
annotate(
"text",
x = ci_upper,
y = max(df$Density) * 0.1,
label = paste0(ci_upper, "%"),
color = "darkgreen",
size = 4,
hjust = 1
)
bayesian_orr_post_pd1
Figure 12B. Posterior distribution of the objective response rate (ORR) for ipilimumab plus nivolumab in the post–PD-1 setting based on 21 responders out of 67 patients across aggregated studies. A flat prior (Beta(1,1)) was used to reflect minimal prior assumptions. The resulting posterior has a mean of approximately 31.9%, with a 95% credible interval from 21.5% to 43.3% (shaded in green). This approach offers a probabilistic interpretation of response likelihood, complementing the frequentist confidence intervals shown in Figure 12A.
These tensions—between evidentiary rigor and practical need—are reflected in the split response shown below (Figure 13).
Figure 13. Do You Believe Labeling Is Justified?
Figure 13. Clinician Perspectives on Justification for IPI Labeling in MCC.
Pre–Journal Club survey responses from clinicians managing Merkel cell carcinoma, indicating the clinical context in which they believe current data support formal labeling of NIVO + IPI. Among 20 respondents, half (50%) felt the regimen is justified in the anti–PD-1–refractory setting, while 15% supported labeling in both the first- and second-line settings. Another 15% felt the data do not yet support labeling, and 20% were unsure. Notably, none selected the first-line setting alone, underscoring concerns about the strength of evidence for initial therapy and the importance of component attribution in regulatory decisions..
Together, these results emphasize two priorities: clinician awareness of current recommendations and the ongoing need to strengthen the evidence base to support broader clinical and regulatory recognition. The gap between evidence and practice reflects not only uncertainty around treatment choice, but also underlying questions about therapeutic intent—questions that emerged throughout the Journal Club discussion.
Intent of Therapy: Curative or Palliative?
Therapeutic intent—whether systemic treatment for advanced MCC should be framed as palliative or potentially curative—emerged as a point of reflection during the Journal Club discussion. While advanced MCC has historically been regarded as an incurable disease, the growing use of immune checkpoint inhibitors has challenged that assumption, prompting some to reconsider the goals of therapy.
In these conversations, the role of cytotoxic chemotherapy also came under discussion—not as a curative approach in itself, but as a potential tool for cytoreduction in patients with life-threatening or bulky disease. While some felt that chemotherapy may be necessary in select cases—particularly those with imminent organ compromise—others cautioned that it carries the risk of immunosuppression and could potentially diminish the efficacy of subsequent immunotherapy. Further study is needed to clarify how best to sequence or combine these approaches to maximize benefit without compromising long-term outcomes.
The conversation also revealed differences in how clinicians communicate with patients. Some argued that invoking the possibility of cure—even cautiously—is appropriate, especially given the durable responses increasingly documented with checkpoint inhibitors in MCC4. Others expressed concern that the word “curative” may foster unrealistic expectations, suggesting instead that therapy should be presented as palliative in intent, while acknowledging the potential for long-term benefit in a subset of patients.
There was broad agreement, however, that durable response is a meaningful and achievable goal in MCC, and that terminology should be chosen carefully to balance hope with realism. Whether framed as “disease control,” “durable remission,” or “functional cure,” the language clinicians use reflects both the evolving therapeutic landscape and the human dimensions of oncologic care. Some pointed to long-term follow-up data from melanoma—such as the 10-year results from CheckMate 067—as evidence that durable remission can, in some cases, be tantamount to cure8. Yet others noted that without similar long-term data in MCC, it is understandable that many providers remain hesitant to use curative language when counseling patients. In rare diseases, where sample sizes are small and recurrence patterns remain incompletely defined, the ability to track long-term outcomes becomes especially important—not only for regulatory recognition, but for shaping how clinicians discuss prognosis and therapeutic goals at the bedside.
Notably, across cancer types, there are only a few settings where randomized data have demonstrated that adding IPI to NIVO clearly improves outcomes. In advanced melanoma8 and mismatch repair–deficient colorectal cancer29, this combination appears to increase the chance of survival for a subset of patients. Yet in other contexts, such as resected melanoma30 or cervical cancer31, adding ipilimumab has not provided clear benefit and has introduced greater toxicity. Where MCC fits in this spectrum remains unknown. The data from CheckMate 358—despite being the largest prospective experience to date—are inconclusive, and the possibility that dual checkpoint blockade could increase the chance of cure for some MCC patients remains an open and important question.
There remains a pressing need within the Merkel cell carcinoma community to systematically track and publish long-term survival outcomes, whether as extended follow-up from previously reported trials or as retrospective analyses of institutional experience. These efforts are critical to refining our understanding of durable benefit, informing the role of ipilimumab, and guiding how we speak with patients about prognosis, treatment goals, and the true possibilities of modern immunotherapy.
Conclusion
The CheckMate 358 trial remains one of the only prospective studies to examine dual checkpoint blockade in advanced Merkel cell carcinoma. While it did not show a clear advantage for NIVO + IPI over NIVO alone, it provides a foundation for further investigation. The Journal Club discussion highlighted the complexity of this question, shaped by statistical outcomes, clinical context, shifting treatment standards, and real-world experience.
Key uncertainties remain. Whether the addition of ipilimumab meaningfully increases the likelihood of long-term remission—or even cure—for a subset of patients with MCC is still an open question. The contrast between trials like CheckMate 067 and CheckMate 915 in melanoma illustrates that ipilimumab’s value is not uniform across settings, and that its role in MCC must be defined through disease-specific data. Until then, clinicians are left navigating a space where enthusiasm, caution, and experience must coexist.
Addressing these questions will require continued data generation and collaboration across the clinical and research community. Long-term follow-up, retrospective analyses, and prospective efforts to capture real-world outcomes will all play a role. As treatment options evolve and expectations for evidence grow, continued work is needed to clarify which therapies offer the greatest and most durable benefit to patients with MCC.
Materials and Methods
This Perspectives on the Science piece was published using Quarto®. The figures depicting the survey data were created using R (version 4.0.0) and the tidyverse suite of packages, including ggplot2. The image on the “Perspectives on the Science” page was created by the authors (DMM) using the rosemary package. GPT-4, a language model developed by OpenAI, was employed in the drafting and editing of this manuscript. GPT-4 provided assistance in manuscript structuring, and generation of content, ensuring a comprehensive and cohesive presentation of the research and discussion points32.
Two separate surveys were conducted using REDCap®. The pre-Journal Club survey was distributed to all Society of Cutaneous Oncology members, with 33 respondents. The post-Journal Club survey was only administered to attendees who remained through the session, yielding 21 respondents. This difference in total responses accounts for variations in figures.
A Bayesian analysis was conducted to illustrate the influence of prior assumptions on posterior estimates of objective response rates (ORR) for NIVO + IPI in MCC. Two separate analyses were performed. First, Bayesian synthesis was applied to studies in the first-line setting, using Beta distributions to encode prior beliefs from the Moffitt IST cohort (e.g., 13/13 or 24/24 responders), updated using the binomial likelihood of observed outcomes from the CheckMate 358 trial (21/33 responders). Second, a Bayesian estimation of ORR was conducted for the post–PD-1 setting, based on aggregate data from retrospective and early-phase studies reporting 19 responses in 61 evaluable patients. A flat prior (Beta(1,1)) was used, and the posterior was calculated as Beta(20, 43). Posterior distributions for both analyses were visualized using dbeta() over a rescaled x-axis (0–100%) to enhance clinical interpretability. The posterior mean and 95% credible intervals were reported, and shaded ribbons were used to highlight uncertainty in the estimated ORR.
Bibliography
Appendix
Abbreviations, Acknowledgments, Disclosures, Disclaimer, License, Publication Stage
Abbreviations
CI: confidence interval, DoR: duration of response, ECOG: Eastern Cooperative Oncology Group, FDA: U.S. Food and Drug Administration, ICI: immune checkpoint inhibitor, IPI: ipilimumab, MCC: Merkel cell carcinoma, MCPyV: Merkel cell polyomavirus, MDC: multidisciplinary care, mets: metastases, NCCN: National Comprehensive Cancer Network, NED: no evidence of disease, NIVO: nivolumab, ORR: objective response rate, OS: overall survival, PD-1: programmed death-1, PFS: progression-free survival, PMH: past medical history, SBRT: stereotactic body radiation therapy, SOCO: Society of Cutaneous Oncology
Acknowledgments
The authors would like to thank Suzanne Topalian for her contributions to the Journal Club discussion, which helped inform several points in this Perspectives piece.
We also wish to acknowledge the following individuals from Bristol Myers Squibb for their participation in the Journal Club discussion and for sharing helpful insights regarding the development and interpretation of CheckMate 358: Leon Sakkal, Abraham Selvan, Divya Patel, Omid Najmi, Christopher Lao, and Gregory Norigian.
Disclosures
Conflict of Interests
Dr. Miller has received honoraria for serving as a consultant or participation on advisory boards for Almirall, Bristol Myers Squibb, Merck, EMD Serono, Regeneron, Sanofi Genzyme, Pfizer, Castle Biosciences, and Checkpoint Therapeutics. He has stock options from Checkpoint Therapeutics and Avstera Therapeutics. He has received institutional research funding from Regeneron, Kartos Therapeutics. NeoImmune Tech, Inc, Project Data Sphere, ECOG-ACRIN and the American Skin Association. Dr. Chandra is a Steering Committee Member for Bristol Myers Squibb, and has been an advisory board member for Merck, Novartis, Pfizer, Regeneron, Replimune, and Immunocore. Dr. Nghiem report compensation/support from UpToDate (honoraria), Almirall (advisory role), Incyte (institutional research funding), and has a patent pending for high-affinity T-cell receptors that target the Merkel polyomavirus, Patent filed: “Merkel cell polyomavirus T antigen-specific TCRs and uses thereof” (institution)
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Citation
BibTeX citation:
@article{miller,
author = {Miller, David M. and Chandra, Sunandana and Brownell, Isaac
and Nghiem, Paul T.},
publisher = {Society of Cutaneous Oncology},
title = {Dual {Checkpoint} {Blockade} in {MCC:} {Lessons} from
{CheckMate} 358 and the {Questions} {That} {Remain}},
journal = {Journal of Cutaneous Oncology},
volume = {3},
number = {1},
url = {https://journalofcutaneousoncology.io/perspectives/Vol_3_Issue_1/Nivo_Plus_Ipi_in_MCC/},
doi = {10.59449/joco.2025.05.01},
issn = {2837-1933},
langid = {en}
}
For attribution, please cite this work as:
Miller, D. M., Chandra, S., Brownell, I. &
Nghiem, P. T. Dual
Checkpoint Blockade in MCC: Lessons from CheckMate 358 and the Questions
That Remain. Journal of Cutaneous Oncology
3,.