Assessment of clinical outcomes with immune checkpoint inhibitor therapy in melanoma patients with CDKN2A and TP53 pathogenic mutations.

BACKGROUND: CDKN2A and TP53 mutations are recurrent events in melanoma, occurring in 13.3% and 15.1% of cases respectively and are associated with poorer outcomes. It is unclear what effect CDKN2A and TP53 mutations have on the clinical outcomes of patients treated with checkpoint inhibitors.
METHODS: All patients with cutaneous melanoma or melanoma of unknown primary who received checkpoint inhibitor therapy and underwent genomic profiling with the 50-gene Mayo Clinic solid tumor targeted cancer gene panel were included. Patients were stratified according to the presence or absence of mutations in BRAF, NRAS, CDKN2A, and TP53. Patients without mutations in any of these genes were termed quadruple wild type (QuadWT). Clinical outcomes including median time to progression (TTP), median overall survival (OS), 6-month and 12-month OS, 6-month and 12-month without progression, ORR and disease control rate (DCR) were analyzed according to the mutational status of CDKN2A, TP53 and QuadWT.
RESULTS: A total of 102 patients were included in this study of which 14 had mutations of CDKN2A (CDKN2Amut), 21 had TP53 mutations (TP53mut), and 12 were QuadWT. TP53mut, CDKN2Amut and QuadWT mutational status did not impact clinical outcomes including median TTP, median OS, 6-month and 12-month OS, 6-month and 12-month without progression, ORR and DCR. There was a trend towards improved median TTP and DCR in CDKN2Amut cohort and a trend towards worsened median TTP in the QuadWT cohort.
CONCLUSION: Cell cycle regulators such as TP53 and CDKN2A do not appear to significantly alter clinical outcomes when immune checkpoint inhibitors are used.

Introduction

Activating mutations of BRAF and NRAS are the most common mutations observed in melanoma. They are present in approximately 51–63% and 26–28% respectively of the molecular mutations in melanomas [1,2]. Accordingly, the prognostic implications of these mutations are well characterized [3]. Both BRAF mutations (BRAF) and NRAS mutations (NRAS) have been shown to be early events that occur in benign and pre-invasive lesions and are not sufficient to induce carcinogenesis [4,5]. Rather, an accumulation of additional pathogenic mutations is required for pre-malignant BRAF or NRAS lesions to progress to invasive melanoma [4].

Mitogenic driver mutations such as BRAF and NRAS induce senescence in premalignant disease and require secondary mutations in cell cycle control genes to convert BRAF and NRAS aberrations into oncogenes [4,6-9]. Loss of function mutations of CDKN2A and TP53 are two significant genomic alterations that allow oncogene driven melanocytes to overcome senescence and evade apoptosis [10,11]. Given the importance of TP53 and CDKN2A mutations in the pathogenesis of invasive melanoma it is understandable that both of these mutations are common mutations in melanoma. According to The Cancer Genome Atlas (TCGA) data genomic alterations of TP53 and CDKN2A are found in 15.1% and 13.3% of melanomas respectively [2], and are are frequently co-mutated with BRAF and NRAS mutations. When CDKN2A mutations are present they are found to be co-mutated with BRAF, NRAS and non-NRAS/BRAF mutations at rates of 33.3% - 67.4%, 23.9% - 40.7% and 8.7% - 29.9% respectively [2,12]. Similarly TP53 is co-mutated with BRAF, NRAS and non-BRAF/NRAS mutations with frequencies of 33.1% - 73.9%, 17.4% - 35.7% and 8.7% - 32.2% respectively. CDKN2A and TP53 mutations were present together in 5.5% - 8.3% of cases. BRAF, NRAS, CDKN2A and TP53 mutations were absent in 8.3% - 32.2% of cases.

Historically both TP53 and CDKN2A mutations are associated with a poor prognosis in melanoma patients. Several studies have shown that patients with TP53 or CDKN2A mutations have a shorter expected survival [13-15]. This occurs, at least in part, because TP53 and CDKN2A mutated tumors are more resistant to chemotherapy [13]. Several preclinical studies have demonstrated that TP53 and CDKN2A mutations lead to a loss of normal cell cycle regulation which in turn causes malignant cells to develop chemoresistance [16,17]. This paradigm of poor outcomes and chemoresistance is pervasive and has been demonstrated in multiple other malignancies. This is further supported by the observation that the use of cyclin-dependent kinase (CDK) inhibitors can enhance responsiveness to chemotherapy in tumors with loss of P16INK4a [CDKN2A loss of function mutation] [18,19]. However, it is not clear that this paradigm remains true in melanoma with the era of checkpoint inhibitors.

Neither BRAF nor NRAS mutations are thought to directly impact the efficacy of immunotherapy; however, previous studies have demonstrated nuances in response rates with checkpoint inhibitors according to genotypes. Douglas et al were the first to report the influence of NRAS on immunotherapy outcomes and concluded that individuals harboring NRAS had improved response rates, clinical benefit and progression free survival [20]. However, this study included all subtypes of melanoma and also only contained a small cohort of patients who received programmed death-1 (PD-1) inhibitors. Kim et al subsequently published a study assessing the effects of TP53 and non-V600 BRAF mutations (BRAF) on clinical outcomes of cutaneous melanomas [21]. Neither TP53 nor BRAF mutations were associated with overall survival (OS) with ipilumumab treatment. There is a paucity of literature discussing the clinical outcomes of patients with CDKN2A and TP53 mutations since the introduction of immune checkpoint inhibitors. Herein we report the effect of TP53 and CDKN2A mutations on the response to immune checkpoint inhibitors, including PD1 inhibitors, in patients with advanced cutaneous melanoma and melanoma of unknown primary.

Results

Mutational status and patient characteristics

A total of 207 melanoma patients had genomic profiling using our in house 50 gene panel, of which 102 patients met the inclusion criteria for this analysis (Fig 1). Genomic profiling was performed between March 1, 2014 and October 1, 2016. Clinical data were collected between January 1, 1990 and April 7, 2017. Of the 102 patients evaluated 14 (13.7%) patients were identified to have CDKN2A, 21 (20.6%) had TP53, and 12 (11.8%) were Quad; the genotypes of CDKN2A, TP53 and Quad patients are displayed in S1 Fig. The patient characteristics for this cohort of patients are summarized in Table 1.

Fig 1

Flow diagram of patient selection.

Table 1

Patient characteristics stratified by TP53 and CDKN2A mutations.

	TP53mut	TP53WT	P Value	CDKN2Amut	CDKN2AWT	P Value	QuadWT	Not Quad WT	P Value
	(N = 21)	(N = 81)		(N = 14)	(N = 88)		(N = 12)	(N = 90)
Age at diagnosis			0.83 a			0.50 a			0.15a
Median	60.7	62.3		63.9	61.2		71.9	60.8
Range	(32.4–82.2)	(22.8–91.0)		(31.6–88.5)	(22.8–91.0)		(41.3–77.3)	(22.8–91.0)
Gender			0.74 b			0.87 b			0.67b
Male	14 (66.7%)	57 (70.4%)		10 (71.4%)	61 (69.3%)		9 (75.0%)	62 (68.9%)
Female	7 (33.3%)	24 (29.6%)		4 (28.6%)	27 (30.7%)		3 (25.0%)	28 (31.1%)
Ethnicity			0.05 b			0.69 b			0.71b
Caucasian	20 (95.2%)	81 (100.0%)		14 (100.0%)	87 (98.9%)		12 (100.0%)	89 (98.9%)
Hispanic	1 (4.8%)	0 (0%)		0 (0%)	1 (1.1%)		0 (0%)	1 (1.1%)
Sites of Disease
CNS	3 (16.7%)	20 (26.7%)	0.38 b	3 (23.1%)	20 (25.0%)	0.88 b	4 (36.4%)	19 (23.2%)	0.34b
Liver	5 (27.8%)	19 (25.3%)	0.83 b	5 (38.5%)	19 (23.8%)	0.26 b	5 (45.5%)	19 (23.2%)	0.11b
Lung	10 (55.6%)	37 (49.3%)	0.64 b	8 (61.5%)	39 (48.8%)	0.39 b	9 (81.8%)	38 (46.3%)	0.03b
Adrenal	1 (5.6%)	6 (8.0%)	0.72 b	2 (15.4%)	5 (6.3%)	0.25 b	1 (9.1%)	6 (7.3%)	0.83b
Bone	4 (22.2%)	16 (21.3%)	0.93 b	0 (0%)	20 (25.0%)	0.04 b	2 (18.2%)	18 (22.0%)	0.78b
Skin	2 (11.1%)	16 (21.3%)	0.32 b	0 (0%)	18 (22.5%)	0.06 b	1 (9.1%)	17 (20.7%)	0.36b
Lymph Node	8 (44.4%)	33 (44.0%)	0.97 b	7 (53.8%)	34 (42.5%)	0.44 b	6 (54.5%)	35 (42.7%)	0.46b
Other	4 (25.0%)	20 (29.0%)	0.75 b	6 (50.0%)	18 (24.7%)	0.07 b	1 (10.0%)	23 (30.7%)	0.17b
Melanoma Subtype			0.10 b			0.80 b			0.41b
Cutaneous	15 (71.4%)	70 (86.4%)		12 (85.7%)	73 (83.0%)		11 (91.7%)	74 (82.2%)
Unknown Primary	6 (28.6%)	11 (13.6%)		2 (14.3%)	15 (17.0%)		1 (8.3%)	16 (17.8%)
Metastases			0.32 b			0.81 b			0.95b
Yes	18 (85.7%)	75 (92.6%)		13 (92.9%)	80 (90.9%)		11 (91.7%)	82 (91.1%)
No	3 (14.3%)	6 (7.4%)		1 (7.1%)	8 (9.1%)		1 (8.3%)	8 (8.9%)
Name of Therapy			0.14 b			0.68 b			0.03b
Pembrolizumab	7 (33.3%)	46 (56.8%)		8 (57.1%)	45 (51.1%)		7 (58.3%)	46 (51.1%)
Nivolumab	1 (4.8%)	5 (6.2%)		0 (0%)	6 (6.8%)		3 (25.0%)	3 (3.3%)
Ipilimumab	11 (52.4%)	23 (28.4%)		5 (35.7%)	29 (33.0%)		2 (16.7%)	32 (35.6%)
Nivolumab/Ipilimumab	2 (9.5%)	3 (3.7%)		0 (0%)	5 (5.7%)		0 (0%)	5 (5.6%)
Other therapy	0 (0%)	4 (4.9%)		1 (7.1%)	3 (3.4%)		0 (0%)	4 (4.4%)
Other Therapy Name			-			-			-
Ipilimumab/Dabrafenib	0 (0%)	1 (25.0%)		0 (0.0%)	1 (33.3%)		0 (0%)	1 (25.0%)
Ipilimumab/Dacarbazine	0 (0%)	1 (25.0%)		1 (100.0%)	0 (0%)		0 (0%)	1 (25.0%)
Pembrolizumab/Indoximod	0 (0%)	2 (50.0%)		0 (0.0%)	2 (66.7%)		0 (0%)	2 (50.0%)
Lines of Therapy			0.84 b			0.43 b			0.87b
1	18 (85.7%)	68 (84.0%)		12 (85.7%)	74 (84.1%)		11 (91.7%)	75 (83.3%)
2	3 (14.3%)	10 (12.3%)		1 (7.1%)	12 (13.6%)		1 (8.3%)	12 (13.3%)
3	0 (0%)	1 (1.2%)		0 (0%)	1 (1.1%)		0 (0%)	1 (1.1%)
4	0 (0%)	2 (2.5%)		1 (7.1%)	1 (1.1%)		0 (0%)	2 (2.2%)
LDH elevated c			0.45 b			0.06 b			0.09b
Yes	2 (10.5%)	16 (21.6%)		1 (8.3%)	17 (21.0%)		4 (33.3%)	14 (17.3%)
No	13 (68.4%)	40 (54.1%)		5 (41.7%)	48 (59.3%)		8 (66.7%)	45 (55.6%)
Not tested	4 (21.1%)	18 (24.3%)		6 (50.0%)	16 (19.8%)		0 (0%)	22 (27.2%)
Number of metastatic sites			0.79 b			0.38 a			0.35
Median Number of Sites	1.0	2.0	0.48a	2.0	1.5		2.0	2.0	0.32a
Range	0–5.0	0–5.0		0–5.0	0–5.0		0–4.0	0–5.0
Response Rate (ORR) d			0.30 b			0.54 b			0.73b
ORR	9 (47.4%)	23 (34.3%)		5 (45.5%)	27 (36.0%)		5 (41.7%)	27 (36.5%)
Disease Control Rate (DCR) d			0.58 b			0.15 b			0.86b
DCR	11 (57.9%)	34 (50.7%)		8 (72.7%)	37 (49.3%)		6 (50.0%)	39 (52.7%)
Duration of Immunotherapy(months) e			0.87 a			0.50 a			0.54a
Median	2	3		4.0	3.0		3.0	2.0
Range	1.0–9.0	0–13.0		0–9.0	0–13.0		1.0–9.0	0–13.0

a Wilcoxon rank-sum test;

b Chi square test;

c 9 subjects missing LDH data;

d 16 subjects missing response data;

e 24 subjects with incomplete duration data;

Overall response rate (ORR) = complete response + partial response; disease control rate (DCR) = complete response + partial response + stable disease; TP53mut: TP53 pathogenic mutation; TP53WT: TP53 wild type; CDKN2Amut: CDKN2A pathogenic mutation; CDKN2AWT: CDKN2A wild type; QuadWT: Quadruple wild type; TTP: Time to progression

Of the 102 patients included in the analysis 93 patients had metastatic disease. Metastases were present in 92.9% of CDKN2A, 85.7% of TP53 and 91.7% of Quad patients. The presence of these mutations did not affect the sites of disease with exception of a lack of bone metastases in CDKN2A patients and increased lung metastases in the Quad cohort. Cutaneous melanoma was by far the most common subtype of melanoma, while melanoma of unknown primary was far less common with the latter comprising 14.3%, 28.6% and 8.3% in CDKN2A, TP53 and Quad patients, respectively. In the CDKN2A and Quad cohorts PD-1 inhibitors were the most commonly used agents representing 57.1% and 83.3% of immunotherapies respectively, while in the TP53 cohort the most common immunotherapy was ipilumumab (CTLA-4 inhibitor) with 52.4% of patients receiving the CTLA-4 inhibitor. Combination immunotherapies were more commonly used in the TP53 cohort as compared to the CDKN2A or Quad cohorts. The demographics of the entire cohort were predominantly Caucasian and male.

Time to progression outcomes

There were no statistically significant differences in TTP identified between the various mutational cohorts (Fig 2). The median TTP for CDKN2A and CDKN2A were 14.0 months (95% CI: 3.0 months–NE) and 6.0 months (95% CI: 3.0–9.0 months) respectively. The median TTP for TP53 and TP53 were 8.0 (95% CI: 3.0 months–NE) and 6.0 months (95% CI: 3.0–13.0 months) respectively. Those with Quad had a TTP of 3.5 months (95% CI: 2.0 months–NE) versus 6.0 months (95% CI: 4.0–14.0 months) in those that did not have quadruple wild type. All trends were preserved for TTP in the CDKN2A, TP53 and Quad cohorts at 6 and 12-month intervals (Table 2). The proportion of patients without progression at 12-months for CDKN2A and CDKN2A patients were 60.0% (95% CI: 28.5–81.2%) and 38.3% (95% CI: 27.4–49.0%) respectively. For TP53 and TP53 the percentage of patients without progression at 12-months were 44.4% (95% CI: 22.5–64.4%) and 40.7% (95% CI: 29.2–51.9%) respectively. The percentage of patients with Quad mutational status without progression at 12-months were 33.3% (95% CI: 10.3–58.8%) versus 42.3% (95% CI: 31.2–53.1%) in those without Quad status. Fig 2 displays TTP graphs for CDKN2A, TP53 and Quad cohorts.

Fig 2

Time-to-progression by mutation status.

Table 2

Time to progression by mutation.

Mutation	Event/Total	Median Months (95% CI)KM	w/o progression (%) at 6-Months	w/o progression (%) at 12-Months
			(95% CI)KM	(95% CI)KM
TP53mut	12/21	8.0 (3.0-NE)	55.0 (31.3–73.5%)	44.4 (22.5–64.4%)
TP53WT	48/81	6.0 (3.0–13.0)	44.2 (32.6–55.2%)	40.7 (29.2–51.9%)
CDKN2Amut	7/14	14.0 (3.0-NE)	68.6 (35.9–87.0%)	60.0 (28.5–81.2%)
CDKN2AWT	53/88	6.0 (3.0–9.0)	43.2 (32.2–53.7%)	38.3 (27.4–49.0%)
QuadWT	8/12	3.5 (2.0-NE)	33.3 (10.3–58.8%)	33.3 (10.3–58.8%)
Otherwise	52/90	6.0 (4.0–14.0)	48.5 (37.3–58.8%)	42.3 (31.2–53.1%)
Overall TTP:	60/102	6.0 (4.0–13.0)	46.6 (36.2–56.3%)	41.2 (30.9–51.2%)

CI: Confidence interval; KM: Kaplan-Meier estimate; NE: Not estimable; TP53mut: TP53 pathogenic mutation; TP53WT: TP53 wild type; CDKN2Amut: CDKN2A pathogenic mutation; CDKN2AWT: CDKN2A wild type; QuadWT: Quadruple wild type; w/o: Without

Overall survival outcomes

There were no statistically significant differences in OS between the various mutational cohorts (Fig 3). The median OS for CDKN2A and CDKN2A patients were 41.0 months (95% CI: 17.0–76.0 months) and 57.0 months (95% CI: 26.0 months–NE) respectively. For those with TP53 and TP53 mutational status the median OS were NE (95% CI: 21.0 months–NE) and 57.0 months (95% CI: 41.0 months–NE) respectively. The median OS for Quad cohort was NE (95% CI: 7.0 months–NE) and those without Quad had a median OS of 57.0 months (95% CI: 41.0 months–NE). The proportion of patients alive at 12 months with CDKN2A and CDKN2A mutational status were 100% (95% CI: 100.0–100.0%) and 74.5% (95% CI: 61.8–83.5%) respectively. The percentage of TP53 and TP53 patients alive at 12 months were 87.7% (95% CI: 58.8–96.8%) and 75.4% (95% CI: 62.2–84.6%) respectively. The proportion of patients with 12-month OS in the Quad cohort was 70.1% (95% CI: 32.3–89.5%) versus those without Quad was 79.5% (95% CI: 67.6% - 87.4%). The OS outcomes are also shown in Table 3.

Fig 3

Overall survival by mutation status.

Table 3

Overall survival from metastatic diagnosis by mutation.

Mutation	Event/Total	Median Months (95% CI)KM	Survival (%) at 6-Months	Survival (%) at 12-Months
			(95% CI)KM	(95% CI)KM
TP53mut	5/18	NE (21.0-NE)	94.4 (66.6–99.2%)	87.7 (58.8–96.8%)
TP53WT	21/75	57.0 (41.0-NE)	90.0 (80.2–95.1%)	75.4 (62.2–84.6%)
CDKN2Amut	3/13	41.0 (17.0–76.0)	100.0 (100.0–100.0%)	100.0 (100.0–100.0%)
CDKN2AWT	23/80	57.0 (26.0-NE)	89.5 (80.1–94.6%)	74.5 (61.8–83.5%)
QuadWT	4/11	NE (7.0-NE)	90.9 (50.8–98.7%)	70.1 (32.3–89.5%)
Otherwise	22/82	57.0 (41.0-NE)	90.9 (81.9–95.6%)	79.5 (67.6–87.4%)
Overall Survival:	26/93	57.0 (41.0-NE)	91.0 (82.7–95.4%)	78.2 (67.0–85.9%)

CI: Confidence interval; KM: Kaplan-Meier estimate; NE: Not estimable; TP53mut: TP53 pathogenic mutation; TP53WT: TP53 wild type; CDKN2Amut: CDKN2A pathogenic mutation; CDKN2AWT: CDKN2A wild type; QuadWT: Quadruple wild type

Response to immunotherapy

There was no statistically significant difference in overall response rate (ORR) or disease control rate (DCR) between the different mutational cohorts as shown in Table 1. The ORR for CDKN2A and CDKN2A patients were 45.5% and 36.0% respectively (p-value = 0.54), while the DCR for CDKN2A and CDKN2A were 72.7% and 49.3% respectively (p-value = 0.15). The ORR for TP53 and TP53 patients were 47.4% and 34.3% respectively (p-value = 0.30), while the DCR for TP53 and TP53 were 57.9% and 50.7% respectively (p-value = 0.58). The ORR for Quad and non-Quad patients were 41.7% and 36.5% respectively (p-value = 0.73). The DCR for Quad patients was 50.0% versus those without quadruple wild type who had a DCR of 52.7% (p-value = 0.86).

Discussion

Despite the negative prognostic significance typically ascribed to loss of TP53 in malignancies, the data from this study demonstrates no adverse prognostic or predictive significance for mutations of TP53 or CDKN2A in melanoma patients treated with immune checkpoint inhibitor therapy. The lack of deleterious effect from mutations in genes controlling cell cycle regulators is further supported by consistency across mutational cohorts in regards to TTP, 12-month OS, DCR and ORR. While not statistically significant, CDKN2A patients appeared to have a trend towards improved DCR and TTP. However, a larger cohort would be needed to investigate whether clinical outcomes are truly enhanced in patients with CDKN2A.

The devaluation of cell cycle regulators with immune checkpoint inhibitor therapy is likely explained by the mechanism in which cytotoxic T cells induce cell death. Chemotherapy primarily induces apoptosis in malignant cells via cellular stress and the intrinsic caspase pathway. For instance, many chemotherapy treatments will induce DNA damage, which will in turn signal cell cycle regulators such as TP53 and CDKN2A to activate the intrinsic caspase pathway to induce apoptosis [22-24]. The lethality of immune checkpoint inhibitors is derived primarily from the activation of cytotoxic T cells, which induce apoptosis through granzyme [25]. Granzyme is a serine protease that enters the cytoplasm via perforin and directly activates the caspase pathway independent of cell cycle regulators and induces apoptosis. Additionally, activation of the adaptive immune system will also initiate the extrinsic caspase pathway via death ligands such as tumor necrosis factor (TNF) super family and FasL. Therefore, the cytotoxic effects activated by the adaptive immune system do not appear to be driven by the internal machinery of the cell cycle and its regulators. Rather, T cell recognition of tumor cells via tumor epitopes and immune activating markers that initiates the introduction of granzyme are the more relevant drivers for checkpoint inhibitors. The clinical findings from this small retrospective study support the preclinical rationale that immune checkpoints are not adversely affected by the absence of cell cycle regulators. In addition, these findings are further supported by a previous study that did not show an adverse impact of TP53 mutations on clinical outcomes when patients were treated with ipilimumab [21].

It is more difficult to interpret the findings of the Quad cohort given that this cohort includes a diverse collection of mutations. A number of pathogenic mutations were identified in the Quad group including: KIT (n = 3), BRAF (n = 2), APC (n = 1), CTNNB1 (n = 1), HRAS (n = 1) and STK11 (n = 1). Similar to the CDKN2A and TP53 cohorts there was no statistically significant difference in clinical outcomes observed in this study. However, there was a trend towards worsened median TTP in this patient cohort (3.5 months vs 6.0 months). However, other clinical outcomes including ORR, DCR and 12-month OS were similar between Quad cohort and the non-Quad cohorts. Given the small size of this cohort (n = 12) and heterogeneous genotype of this cohort conclusions cannot be drawn.

The small size of the study cohort and the retrospective nature of this study are limitations of this exploratory study. Because of the limited sample size the effect of co-mutations on clinical outcomes could not be analyzed with this cohort of patients. Additionally there was heterogeneous use of PD-1 inhibitors and CTLA-4 inhibitors between mutational cohorts. For instance, the TP53 cohort and non-Quad cohort both had a higher proportion of ipilimumab use. Given that PD-1 inhibitors are known to have higher response rates and improved clinical outcomes compared to CTLA-4 inhibitors this may have underestimated the benefit of checkpoint inhibitors in these cohorts. However, despite the difference in treatment modalities there appeared to be consistency across clinical outcomes with similar OS, TTP, ORR and DCR results. Additionally, the TP53 and non-Quad cohorts did not appear to fare any worse despite the higher utilization of ipilimumab.

This exploratory study suggests that immune checkpoint inhibitors are able to function at least as well in the presence of CDKN2A or TP53 pathogenic mutations. The lack of clear driver mutations such as CDKN2A, TP53, NRAS or BRAF mutations (Quad) also did not seem to significantly impact clinical outcomes in this cohort of patients. While the role for CDKN2A and TP53 are integral to oncogenesis of melanoma and escape from senescence these mutations do not appear to have a significant deleterious effect on prognosis when immune checkpoint inhibitors are used for therapy.

Given the increasing frequency with which large gene mutation panels are being ordered by practicing clinicians, it is necessary to analyze the significance of common mutations in a given cancer type in order to both focus the clinician on relevant findings, and help them ignore irrelevant ones. Researchers with access to large databases of clinical and genomic findings should systematically analyze the association between common genetic events and clinical outcomes. As in this case, such retrospective studies are exploratory and can help guide larger prospective studies.

Methods

Study population/study design

This is a retrospective study which was approved by Mayo Clinic IRB(16–005168). No consent was needed as information was obtain anonymously. This study was conducted in accordance with principles for human experimentation as defined in the Declaration of Helsinki and International Conference on Harmonization Good Clinical Practice guidelines. No participating physicians have conflicts of interest to declare. The Mayo Clinic IRB waived the requirement for informed consent since the data was analyzed anonymously. Patients were identified from all three Mayo Clinic campuses (Minnesota, Arizona and Florida). Patients with a diagnosis of metastatic or unresectable cutaneous melanoma or melanoma of unknown primary whose tumors were analyzed with our 50 gene Solid Tumor Targeted Cancer Gene Panel were included. Patients who received an immune checkpoint inhibitor at any point during their treatment course were included. However, data associated with the first immunotherapy regimen and overall patient outcomes were evaluated for this analysis. Response to targeted therapy, chemotherapy and subsequent lines of immunotherapy treatments were collected but are not the focus of this study. This study allowed for treatment with cytotoxic T-lymphocyte associated protein 4 (CTLA-4) inhibitors, PD-1 inhibitors, or combinations that included either a PD-1 inhibitor or CTLA-4 inhibitor.

The objective of this study is to investigate the impact of the presence of CDKN2A mutations (CDKN2A), TP53 mutations (TP53) and quadruple wild type (Quad) mutational status on clinical outcomes in patients who received immune checkpoint inhibitors. Patients who did not carry TP53, CDKN2A, NRAS or BRAF mutations were termed Quad. The primary endpoint measured was median time-to-progression (TTP) with secondary endpoints including the percentage of participants without progression at 6 and 12 months, median overall survival (OS), OS at 6 and 12 months, disease control rate (DCR) and overall response rate (ORR) to immunotherapy. Response rates were assessed using available CT or MRI imaging and their associated reports. Calculations were based on the best overall response using the immune related response criteria (irRC) and were categorized as complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD). Pathologic tumor characteristics, patient demographic and clinical details were also collected by chart review.

Genomic profiling

The Solid Tumor Targeted Cancer Gene Panel is a 50 gene panel that evaluated the following genes: ABL1, AKT1, ALK, APC, ATM, BRAF, CDH11, CKDN2A, CSF1R, CTNNB1, EGFR, ERBB2, ERBB4, EZH2, FBXW7, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, KRAS, MET, MLH1, MPL, NOTCH1, NPM1, NRAS, PDGFRA, PIK3CA, PTEN, PTPN11, RB1, RET, SMAD4, SMARCB1, SMO, SRC, STK11, TP53 and VHL. This is a laboratory-developed test using Research Use Only reagents. Extracted DNA from the clinical specimen is fragmented, adapter ligated, and a sequence library of fragments is prepared using a custom capture hybridization method. Individual patient samples are indexed for identification and the library is sequenced on an Illumina platform. Sequence data are processed through the Mayo Clinic Clinical Genome Sequencing Lab bioinformatics pipeline and a variant call file is generated for final analysis and reporting(Unpublished Mayo method). This testing is clinically available through Mayo Clinic.

Statistics

Patient characteristics were compared between mutation statuses (TP53 versus TP53 wild type [TP53], CDKN2A versus CDKN2A wild type [CDKN2A] and Quad versus non-Quad). Wilcoxon rank-sum compared non-normally distributed continuous data and chi-square tests compared categorical data. Nonparametric survival analysis was used to model TTP and OS. TTP was defined as the time from first line immunotherapy date until date of progression. A patient’s progression time was censored if they received subsequent treatment, were lost to follow-up, or death occurred before known progression. OS was defined as the time from metastatic diagnosis date until date of death. Survival time was censored when patients were lost to follow-up. Kaplan-Meier (KM) method was used to estimate event rates, median time and 95% confidence intervals. Median TTP and OS estimates were not estimable (NE) where rates were greater than 50% at the last time point in the cohort. Log-rank test was used to compare TTP and OS event rates between mutation statuses. P values ≤ .05 were considered statistically significant. Analyses were performed in SAS Statistical Software 9.4 (SAS Institute, Cary, NC).

Genotypes CDKN2A, TP53 and quadruple wild type cohorts.

(DOCX)

Click here for additional data file.

Transfer Alert

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29 Oct 2019

PONE-D-19-27499

Assessment of Clinical Outcomes with Immune Checkpoint Inhibitor Therapy in Melanoma Patients with CDKN2A and TP53 pathogenic mutations

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: No

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: DeLeon and colleagues have undertaken a retrospective single centre review of the prognostic and predictive impact of CDKN2A and p53 mutations on TTP/OS and response in immunotherapy treated patients.

The analysis suggests no impact on of these mutations.

I have a number of suggestions which the authors may consider

1) Given the impact of clinicopathological factors on outcome (eg Liver mets, LDH, primary histology) these associations with mutation and outcome should be considered

2) Combining single agent ipilimumab with anti-PD1 based therapy (and even targeted therapy) creates significant heterogeneity. Suggest PD1 based vs ipi alone

Reviewer #2: The manuscript by DeLeon et al addresses an important clinical question as to whether somatic mutations in cell cycle regulators (TP53 and CDKN2A) are predictive of outcomes in melanoma patients treated with checkpoint inhibitor immunotherapy. Although the results show no significant associations, the findings are useful for clinicians in that mutations in either TP53 or CDKN2A need not guide checkpoint inhibitor treatment decisions.

The authors acknowledge that the sample size (n=102 patients) is a major limiting factor that likely influences the study outcomes, however, this will hopefully prompt other melanoma researchers to investigate using larger datasets that will settle the debate.

It would also be interesting in future to delineate between types of checkpoint inhibitors (antiPD-1, antiCTLA-4 or combination) as well as mutation type (likely gain of function vs. loss of function) not possible in this study with low samples sizes for each category.

Minor revisions:

• The word “somatic” mutations should be used in the title and abstract to distinguish from germline mutations which are common in CDKN2A in melanoma.

• When you quote the mutation frequencies as a range it would be clearer to mention you’ve looked at both the Hodis and TCGA datasets.

• “CDKN2A and TP53 mutations were present together” would be better written as “co-occur.”

• “BRAF, NRAS, CDKN2A and TP53 mutations were absent” would be better written as QuadWT (BRAF, NRAS, CDKN2A and TP53) accounted for 8.3%-32.2% of cases.

• “demonstrated in multiple other malignancies” needs a reference.

• “ipilumumab” misspelled and first time appears should mention it targets CTLA-4.

• Consistency in PD1 or PD-1.

• NE should be defined the first time it appears in the text.

• Will the full mutation datasets be made available i.e. the specific single nucleotide variants, is it in the coding or non-coding region? synonymous or non-synonymous? Missense or nonsense etc. How do you know it’s “pathogenic?”

• Why is NF1 not included in the panel? This gene is altered in up to 20% of melanoma cases and BRAF/NRAS/NF1 triple wild-type patients are regarded as a difficult category to treat.

• Why not show your BRAF and NRAS data? You mention in the introduction that the literature is nuanced regarding mutational status vs clinical outcomes for checkpoint inhibitors in melanoma. You have solid numbers in these categories – would your data not help resolve this?

**********

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Reviewer #1: Yes: Matteo Carlino

Reviewer #2: Yes: Jessamy Tiffen

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17 Feb 2020

We thank you and the reviewers for the thorough evaluation of our manuscript and the thoughtful comments. In response to the reviewers’ comments, we are submitting a revised manuscript entitled “Assessment of Clinical Outcomes with Immune Checkpoint Inhibitor Therapy in Melanoma Patients with Somatic CDKN2A and TP53 pathogenic mutations.” We have made appropriate edits to the manuscript, which are highlighted, and address each comment point by point as below in bold.

Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Response: Once the manuscript has been accepted for publication, we will provide data requested. We will include the data spread sheets and statistics. If there is specific data that is required, I will attempt to obtain but I have included what was used for this project.

Reviewer #1: DeLeon and colleagues have undertaken a retrospective single centre review of the prognostic and predictive impact of CDKN2A and p53 mutations on TTP/OS and response in immunotherapy treated patients.

The analysis suggests no impact on of these mutations.

I have a number of suggestions which the authors may consider

1) Given the impact of clinicopathological factors on outcome (eg Liver mets, LDH, primary histology) these associations with mutation and outcome should be considered

Response: Thank you very much for your overall favorable impression of our efforts to investigate treatments and outcomes in this rare disease, and for your comments. We agree that these factors do impact the outcomes of patients with this disease. Table 1. represents the patient's baseline characteristics which did include liver mets and LDH. In our data set these clinicopathological factors did not meet statistical significance. They did not play a role in outcomes of our patients. This is likely the result of our study being under powered to determine each factors significance. If we examine a larger population, it would be interesting to see if these play a role. Unfortunately, given the size of our study I'm unable to include more data related to these factors.

2) Combining single agent ipilimumab with anti-PD1 based therapy (and even targeted therapy) creates significant heterogeneity. Suggest PD1 based vs ipi alone

Response: Agreed, adding more agents does create more heterogeneity within treatment paradigms in leads a question of which agent is the most impact full. In our study we attempted to treat patients with standard of care or standard practice within our center. Table 1. Shows that there is no statistical significance and/or difference between the use of these agents in the setting of these mutations. Again, this is likely from our limitation of sample size and power. If this study could be completed on a larger scale there could potentially be a statistical difference identified. At this time given her sample size I'm unable to further expound on this data.

Reviewer #2: The manuscript by DeLeon et al addresses an important clinical question as to whether somatic mutations in cell cycle regulators (TP53 and CDKN2A) are predictive of outcomes in melanoma patients treated with checkpoint inhibitor immunotherapy. Although the results show no significant associations, the findings are useful for clinicians in that mutations in either TP53 or CDKN2A need not guide checkpoint inhibitor treatment decisions.

The authors acknowledge that the sample size (n=102 patients) is a major limiting factor that likely influences the study outcomes, however, this will hopefully prompt other melanoma researchers to investigate using larger datasets that will settle the debate.

It would also be interesting in future to delineate between types of checkpoint inhibitors (antiPD-1, antiCTLA-4 or combination) as well as mutation type (likely gain of function vs. loss of function) not possible in this study with low samples sizes for each category.

Minor revisions:

• The word “somatic” mutations should be used in the title and abstract to distinguish from germline mutations which are common in CDKN2A in melanoma.

Response: Thank you very much for your overall favorable impression of our efforts to investigate treatments and outcomes in this disease, and for your comments. We agree this is an important term to delineate so we added and addressed this.

• When you quote the mutation frequencies as a range it would be clearer to mention you’ve looked at both the Hodis and TCGA datasets.

Response: We attempted to make this clear for better understanding for the readers. We edited and addressed this.

• “CDKN2A and TP53 mutations were present together” would be better written as “co-occur.”

Response: Edited and added.

• “BRAF, NRAS, CDKN2A and TP53 mutations were absent” would be better written as QuadWT (BRAF, NRAS, CDKN2A and TP53) accounted for 8.3%-32.2% of cases.

Response: Edited and added.

• “demonstrated in multiple other malignancies” needs a reference.

Response: Added a reference.

• “ipilumumab” misspelled and first time appears should mention it targets CTLA-4.

Response: Edited and corrected.

• Consistency in PD1 or PD-1.

Response: Corrected.

• NE should be defined the first time it appears in the text.

Response: This was a formatting technicality. We have now corrected it.

• Will the full mutation datasets be made available i.e. the specific single nucleotide variants, is it in the coding or non-coding region? synonymous or non-synonymous? Missense or nonsense etc. How do you know it’s “pathogenic?”

Response: If the publication is accepted we do have variants included in our data sets such as: “TP53:c.722C>T:p.S241F(Ser241Phe):MUT” and “CDKN2A 21971164 A > G (benign)/ CDKN2A 21971186 G > A (PATHOGENIC)/CDKN2A 21971141 C > G ("unknown" in workbench)

CDKN2A 21971164 A > G (benign).” We will include that will inform our readers if the variant is pathogenic according to our NGS.

• Why is NF1 not included in the panel? This gene is altered in up to 20% of melanoma cases and BRAF/NRAS/NF1 triple wild-type patients are regarded as a difficult category to treat.

Response: This is a wonderful question. We used a 50 gene panel with pre-specified genes which were determined by Mayo Clinic Lab. The panel is not specifically for melanoma but for solid tumors and unfortunately the 50 gene panel did not include NF1. Mayo clinic laboratory created the gene panel and I do not have access to the data on why they chose the specific genes to be included on that panel. It is a wonderful thought and should be evaluated further in a future study.

• Why not show your BRAF and NRAS data? You mention in the introduction that the literature is nuanced regarding mutational status vs clinical outcomes for checkpoint inhibitors in melanoma. You have solid numbers in these categories – would your data not help resolve this?

Response: This is a challenging question. We wanted to make the focus of our study on CDKN2A and p53 specifically to answer the question if these mutations play a role in response to therapy. We felt that if we included BRAF it would take away from our focus in this manuscript. Our data sets will include the BRAF and NRAS mutations. In a future study looking less specifically at CDKN2A and p53 we may include our data.

Submitted filename: Response to Reviewers2.docx

Click here for additional data file.

27 Feb 2020

Assessment of Clinical Outcomes with Immune Checkpoint Inhibitor Therapy in Melanoma Patients with CDKN2A and TP53 pathogenic mutations

PONE-D-19-27499R1

Dear Dr. Almquist,

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Nikolas K. Haass, MD/PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comfortable with responses to queries raised at initial review ………...…...…………………......……...………....…………...…..

Reviewer #2: My concerns have been adequately addressed however I look forward to seeing full datasets deposited to a public repository upon acceptance, in keeping with the PLOS Data policy.

**********

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Reviewer #1: No

Reviewer #2: Yes: Jessamy Tiffen

9 Mar 2020

PONE-D-19-27499R1

Assessment of Clinical Outcomes with Immune Checkpoint Inhibitor Therapy in Melanoma Patients with CDKN2A and TP53 pathogenic mutations

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