Abstract
BACKGROUND
Advances in deep sequencing technology have uncovered a widespread, pro-tumorigenic role of G protein α subunits, particularly those encoded by GNAS/GNAQ/GNA11 (GNA*), in a diverse collection of malignancies. The objectives of this study were to: (1) determine the GNA* aberration status among a cohort of 1348 cancer patients, and (2) examine tumor mutational burden, overall survival rates, and treatment outcomes in GNA* positive versus wild-type patients.
METHODS
For each patient, clinical and genomic data were collected from medical records. Next generation sequencing was performed for each patient (182 to 236 genes).
RESULTS
GNA* aberrations were identified in a subset of patients with 8 of 12 cancer types examined, with a significant association with appendiceal cancer and ocular melanoma (both p < 0.0001, multivariate analysis); overall, 4.1% of our cancer population was affected. GNA* abnormalities were associated with higher numbers of co-alterations in univariate (but not multivariate analysis) and were most commonly accompanied by AURKA, CBL and LYN co-alterations (all p < 0.0001, multivariate analysis). GNA* alterations correlated with a trend to lower median overall survival (p = 0.085). Median tumor mutational burden was 4 mutations/megabase in both GNA*-altered and GNA*-wild-type tumors. For our limited sample of GNA*-positive patients, longer survival did not correlate with any specific treatment regimens.
CONCLUSIONS
In our sample, GNAS, GNAQ, and GNA11 were widely altered across cancer types and are often accompanied by specific genomic abnormalities in AURKA, CBL and LYN. Targeting GNA* alterations may therefore require drugs that address the GNA* signal and important co-alterations.
Keywords: G-protein, mutation burden, deep sequencing, GNAS, GNAQ, GNA11
CONDENSED ABSTRACT
GNAS, GNAQ, and GNA11 (GNA*) are ubiquitously altered across many diverse cancer types. Future development of novel therapies targeting these GNA* aberrations and important co-alterations is needed.
INTRODUCTION
Over the past decade, aberrations in genes coding for G-protein activating subunits have been increasingly implicated in tumorigenesis.1 G-protein activating subunits, such as those encoded by the genes GNAS, GNAQ, GNA11 and GNA12, bind to G protein-coupled receptors (GPCRs) and play central roles in cellular signaling transduction.2,3 G-proteins and GPCRs are ubiquitously expressed and are critical throughout transcription, cell division, motility, and secretion. Specifically, the GNAS locus encodes the alpha subunit of the stimulatory G protein (Gαs), which activates a cAMP dependent pathway to regulate the actions of hormones, neurotransmitters, and other paracrine/autocrine signaling cascades.4 In addition to the recognized role of GNAS aberrations in hereditary endocrinopathies including pseudohypoparathyroidism and Albright syndrome,4–6 the role of GNA aberrations as driver mutations in diverse tumors has become clearer.1,7
The advent of next-generation sequencing has unearthed diverse genomic aberrations that are associated with different cancer types.8–12 Specifically, recent deep sequencing analysis has revealed that 4.4% of tumors carry GNAS aberrations.1 Several studies have shown that GNAS aberrations span a wide variety of endocrine tumors, including those originating from the pituitary (28%), pancreas (12%), thyroid (5%), and parathyroid (3%), ovary (3%), endometrium (2%).1 However, more recent reports have demonstrated that the pro-tumorigenic effect of these mutations is not limited to endocrine lesions; GNAS aberrations are also harbored by intramuscular myxomas, fibrous dysplasias, colorectal cancers, appendiceal mucinous tumors, hepatocellular carcinoma, and both mucinous and non-mucinous lung adenocarcinomas.13–17 One potential mechanism by which gain-of-function mutations in GNAS can lead to carcinogenesis is the regulation of inflammatory mediators such as cyclooxygenase-2 (COX-2) derived prostaglandins by Gαs.15 It is possible that these mutations can lead to an autonomous pro-inflammatory state, which promotes tumorigenesis. This is particularly likely in the setting of colon cancer, which has been previously linked to COX-2 overexpression.18–20
Likewise, GNAQ/GNA11 (Gαq) mutations are harbored by 5.6% of tumors. For example, activating mutations of GNAQ/GNA11 are present in 66% and 6% of melanomas arising in the eye and skin, respectively.20 Moreover, GNAQ/GNA11 has been implicated as the driver oncogene in uveal melanoma.21 Other studies have found GNAQ mutations in 83% of blue nevi and 59% of tumors arising from the meninges.22,23 Despite the presence of GNAQ family alterations in benign lesions such as blue nevi, these alterations are believed to drive tumorigenesis, a phenomenon noted with other driver oncogenes as well.24,25
Advances in deep sequencing technology have unearthed a previously unappreciated widespread, pro-tumorigenic role of G protein α subunits, particularly those encoded by GNAS and GNAQ/GNA11, in a diverse collection of malignancies.1 The aim of this study was to determine the GNAS/GNAQ/GNA11 aberration status among a cohort of 1348 cancer patients, and to compare mutational burden, co-alteration status, overall survival rates, and treatment outcomes between GNA* positive versus GNA* wild-type patients.
METHODS
Patients:
A total of 1348 patients with genomic data were included. For each patient, a variety of clinical and demographic data was obtained, as well as a variety of molecular information from their tumor samples, including gene, type of aberration (amplification, frameshift, missense/nonsense mutation, etc.), and whether the variant was of known or unknown significance. All data was derived from medical records as part of the UC San Diego Profile Related Evidence Determining Individualized Cancer Therapy (PREDICT) study (NCT02478931), which was performed in accordance with UCSD IRB guidelines.
Molecular Data:
Next generation sequencing (NGS) for each patient was performed by Foundation Medicine (FoundationOne; http://www.foundationone.com). For the included patients, the utilized gene panels varied from 182 to 236, but all panels included GNAS, GNAQ and GNA11. The test is a clinical-grade, clinical laboratory improvement Amendments (CLIA) approved NGS test. Typical median depth of coverage is greater than 500X. This test can detect base substitutions, insertions and deletions (indels), copy number alterations (CNAs) and rearrangements using a routine tissue sample (including core or fine needle biopsies).
Tumor mutational burden (TMB) was estimated for each patient using a validated algorithm: the number of somatic mutations detected on NGS (interrogating 1.2 mb of the genome) are quantified, and that value is then extrapolated to the entire exome.26,27 Alterations likely or known to be bona fide oncogenic drivers and germline polymorphisms are excluded. TMB was measured in total mutations per megabase (mb).26 Variants of unknown significance (VUS) were excluded from all analyses except for TMB.
Statistical Analysis:
All statistical processing was performed using the R Statistical Programming Language (21).28 For comparisons between continuous values, the Wilcoxon rank-sum test was used; for comparing frequencies, the two-sided Fisher’s exact test was used. Haldane’s correction was applied whenever count data equaled zero.29 Statistical significance was taken to occur at the alpha = 0.05 level.
Outcomes:
Overall survival was calculated by the method of Kaplan and Meier, starting from the date of diagnosis. Data were censored at the date of last follow up for patients who were still alive. Differences between survival curves were assessed using the log-rank test as well as the Cox proportional hazards method.
RESULTS
Patient Characteristics:
Thirteen hundred and forty-eight patients were analyzed, 630 of whom (46.7%) were male. The median age of the patients was 56.1 years (range 1.0 to 95.1 years). The most common diagnoses were brain (167/1348, 12.4%), breast (155/1348, 11.5%), lung (143/1348, 10.6%) and gastrointestinal cancers excluding appendiceal and colorectal (134/1348, 9.9%). Of all 1348 patients, 55 had GNAS, GNAQ or GNA11 aberrations (4.1%). There was no association between age, gender, or ethnicity and GNA* aberrations. Overall, in univariate analysis, GNA* aberrations correlated with peritoneal metastases (OR = 2.8, 1.2 to 6.0, p = 0.012) and both appendiceal (OR = 9.9, 4.7 to 20.8, p < 0.0001) and ocular melanoma diagnoses (OR = 75.0, 7.6 to 729.0, p = 0.0003). These observations are in line with previous results.21,22,30,31 Additionally, we found GNA* aberrations to be associated with fewer brain tumor diagnoses (OR = 0.06 with Haldane’s correction, 0.004 to 1.0, p = 0.0012). These results are summarized in Table 1. Analyzing pathologic and tissue molecular characteristics, we found GNAS/GNAQ/GNA11 aberrations to be significantly associated with adenocarcinoma histology (OR = 2.0, 1.0 to 3.8, p = 0.037), as summarized in Table 2.
Table 1.
Demographic and clinical characteristics of patients by GNAS/GNAQ/GNA11 aberration* status (n = 1348).
| Characteristic | All patients (n = 1348) | GNA† wild type (n = 1293) | GNA† aberrant (n = 55) | P- value** | Odds ratio (95% CI) |
|---|---|---|---|---|---|
| Age at diagnosis (years; median, range) | 56.1 (1.0, 95.1) | 56.1 (1.0, 95.1) | 54.3 (24.30, 86.8) | 0.32 | |
| Male | 630 (46.7%) | 606 (46.9%) | 24 (43.6%) | 0.68 | |
| Ethnicity | |||||
| Asian | 122 (9.1%) | 119 (9.2%) | 3 (5.5%) | 0.47 | |
| African American | 45 (3.3%) | 41 (3.2%) | 4 (7.3%) | 0.11 | |
| Hispanic | 76 (5.6%) | 73 (5.6%) | 3 (5.5%) | 0.99 | |
| Caucasian | 930 (69.0%) | 892 (69.0%) | 38 (69.1%) | 0.99 | |
| Overall Survival from Diagnosis (months; KM median, 95% CI) | 34.8 (31.4 to 39.1) | 35.2 (31.6 to 39.4) | 26.3 (18.8 to 50.1) | 0.085‡ | |
| Best Recorded PFS (months; KM median, 95% CI) | 7.1 (6.4 to 7.8) | 7 (6.4 to 7.7) | 10 (3.0 to NA) | 0.48‡ | |
| Location of First Metastasis | |||||
| Liver | 252 (18.7%) | 244 (18.9%) | 8 (14.5%) | 0.48 | |
| Lungs | 225 (16.7%) | 216 (16.7%) | 9 (16.4%) | 0.99 | |
| Bone | 191 (14.2%) | 183 (14.2%) | 8 (14.5%) | 0.85 | |
| CNS | 99 (7.3%) | 94 (7.3%) | 5 (9.1%) | 0.59 | |
| Peritoneum | 94 (7.0%) | 85 (6.6%) | 9 (16.4%) | 0.012 | 2.8 (1.2 to 6.0) |
| Tumor Diagnosis | |||||
| Brain | 167 (12.4%) | 167 (12.9%) | 0 (0.0%) | 0.0012 | 0.06 (0.004 to 1.0) |
| Breast | 155 (11.5%) | 146 (11.3%) | 9 (16.4%) | 0.28 | |
| Lung | 143 (10.6%) | 137 (10.6%) | 6 (10.9%) | 0.83 | |
| Gastrointestinal (excluding appendiceal and colorectal) | 134 (9.9%) | 130 (10.1%) | 4 (7.3%) | 0.65 | |
| Hematologic | 126 (9.3%) | 121 (9.4%) | 5 (9.1%) | 0.99 | |
| Colorectal | 115 (8.5%) | 108 (8.4% | 7 (12.7%) | 0.22 | |
| Melanoma (excluding ocular) | 61 (4.5%) | 57 (4.4%) | 4 (7.3%) | 0.31 | |
| Head and neck | 51 (3.8%) | 50 (3.9%) | 1 (1.8%) | 0.72 | |
| Ovarian | 44 (3.3%) | 43 (3.3%) | 1 (1.8%) | 0.99 | |
| Appendiceal | 43 (3.2%) | 32 (2.5%) | 11 (20.0%) | 3.9–7 | 9.9 (4.7 to 20.8) |
| Bladder | 37 (2.7%) | 36 (2.8%) | 1 (1.8%) | 0.99 | |
| Lymphatic | 30 (2.2%) | 30 (2.3%) | 0 (0.0%) | 0.63 | |
| Thyroid | 29 (2.2%) | 29 (2.2%) | 0 (0.0%) | 0.63 | |
| Sarcoma | 26 (1.9%) | 25 (1.9%) | 1 (1.8%) | 0.99 | |
| Skin cancer, non-melanoma | 18 (1.3%) | 18 (1.4%) | 0 (0.0%) | 0.99 | |
| Renal | 16 (1.2%) | 16 (1.2%) | 0 (0.0%) | 0.99 | |
| Prostate | 8 (0.6%) | 8 (0.6%) | 0 (0.0%) | 0.99 | |
| Ocular melanoma | 4 (0.3%) | 1 (0.08%) | 3 (5.5%) | 0.0003 | 75.0 (7.6 to 729.0) |
| Cervical | 4 (0.3%) | 4 (0.3%) | 0 (0.0%) | 0.99 | |
| Other diagnosis | 137 (10.2%) | 134 (10.4%) | 3 (5.5%) | 0.36 |
Aberrations refer to characterized alterations. “GNA†” refers to GNAS, GNAQ or GNA11 aberrations.
For comparing continuous values, the Wilcoxon rank-sum test was used; for comparing frequencies, the two-sided Fisher’s exact test was used. Bolded text indicates statistical significance at the alpha = 0.05 level.
Haldane’s correction was used to estimate the odds ratio, adding 0.5 to all cells in contingency table if any cell count was equal to zero.
Log-rank test.
Abbreviations: CI = confidence interval; CNS = central nervous system; KM = Kaplan-Meier; PFS = progression-free survival.
Table 2.
Pathologic and tissue molecular characteristics of patient tumors by GNAS/GNAQ/GNA11 aberration status (n = 1348).*
| Characteristic | All patients (n = 1348) | GNA† wild type (n = 1293) | GNA† aberrant (n = 55) | P-value ** | Odds ratio (95% CI) |
|---|---|---|---|---|---|
| Histology | |||||
| Adenocarcinoma | 217 (16.1%) | 202 (15.6%) | 15 (27.3%) | 0.037 | 2.0 (1.0 to 3.8) |
| Squamous | 76 (5.6%) | 75 (5.8%) | 1 (1.8%) | 0.36 | |
| Metastatic | 1046/1258 (83.1%) | 1003/1210 (82.9%) | 43/47 (91.5%) | 0.16 | |
| Number of aberrations (including VUSs) (median, IQR) | 10 (4 to 15) | 9 (4 to 14) | 17 (12 to 26) | <2.2–16 | Not applicable |
| Number of characterized aberrations (excludes VUSs) (median, IQR) | 3 (2 to 6) | 3 (2 to 5) | 6 (3 to 9) | 2.50–6 | Not applicable |
| Number of characterized genomic co-alterations (excludes VUSs) (median, IQR) (GNA† alterations excluded from the counts) | 3 (2 to 6) | 3 (2 to 5) | 5 (2 to 8) | 0.0028 | Not applicable |
| Tumor mutational burden (TMB) (median, IQR) (includes VUSs) | 4 (2 to 6.75) | 4 (2 to 6) | 4 (1.75 to 7) | 0.83 | |
| Tumor mutational burden (TMB): Count of “High” (>20 mutations/mb) | 73 (6.9%) (73/1062) |
71 (7.0%) (71/1010) |
2 (3.8%) (2/52) |
0.57 | |
| Additional alterations | |||||
| TP53 | 543 (40.3%) | 526 (40.7%) | 17 (30.9%) | 0.16 | |
| KRAS | 202 (15.0%) | 185 (14.3%) | 17 (30.9%) | 0.0029 | 2.7 (1.4 to 5.0) |
| MYC | 119 (8.8%) | 108 (8.4%) | 11 (20.0%) | 0.0067 | 2.8 (1.2 to 5.6) |
| AURKA | 20 (1.5%) | 13 (1.0%) | 7 (12.7%) | 6.58–6 | 14.3 (4.6 to 40.6) |
| CDKN2A | 210 (15.6%) | 204 (15.8%) | 6 (10.9%) | 0.45 | |
| ARID1A | 68 (5.0%) | 63 (4.9%) | 5 (9.1%) | 0.19 | |
| SMAD4 | 59 (4.4%) | 54 (4.2%) | 5 (9.1%) | 0.088 | |
| EGFR | 115 (8.5%) | 111 (8.9%) | 4 (7.3%) | 0.99 | |
| APC | 106 (7.9%) | 102 (7.9%) | 4 (7.3%) | 0.99 | |
| CDKN2A/B | 117 (8.7%) | 113 (8.7%) | 4 (7.3%) | 0.99 | |
| CCND1 | 64 (4.7%) | 60 (4.6%) | 4 (7.3%) | 0.33 | |
| FGF3 | 53 (3.9%) | 49 (3.8%) | 4 (7.3%) | 0.27 | |
| FGF19 | 53 (3.9%) | 49 (3.8%) | 4 (7.3%) | 0.27 | |
| FGF4 | 52 (3.9%) | 48 (3.7%) | 4 (7.3%) | 0.16 | |
| MCL1 | 40 (3.0%) | 36 (2.8%) | 4 (7.3%) | 0.076 | |
| SRC | 9 (0.67%) | 5 (0.39%) | 4 (7.3%) | 0.00027 | 20.1 (3.9 to 96.1) |
| TOP1 | 8 (0.59%) | 4 (0.31%) | 4 (7.3%) | 0.00015 | 25.1 (4.5 to 138.5) |
| PIK3CA | 138 (10.2%) | 135 (10.4%) | 3 (5.5%) | 0.36 | |
| NF1 | 77 (5.7%) | 74 (5.7%) | 3 (5.5%) | 0.99 | |
| ERBB2 | 59 (4.4%) | 56 (4.3%) | 3 (5.5%) | 0.73 | |
| TERT | 88 (6.5%) | 85 (6.6%) | 3 (5.5%) | 0.99 | |
| BRCA2 | 48 (3.6%) | 45(3.5%) | 3 (5.5%) | 0.44 | |
| PDGFRA | 29 (2.2%) | 26 (2.0%) | 3 (5.5%) | 0.11 | |
| DNMT3A | 36 (2.7%) | 33 (2.6%) | 3 (5.5%) | 0.18 | |
| CCNE1 | 25 (1.9%) | 22 (1.7%) | 3 (5.5%) | 0.078 | |
| AKT2 | 16 (1.2%) | 13 (1.0%) | 3 (5.5%) | 0.025 | 5.7 (1.0 to 21.5) |
| CBL | 8 (0.59%) | 5 (0.39%) | 3 (5.5%) | 0.0031 | 14.8 (2.2 to 78.2) |
| LYN | 4 (0.30%) | 1 (0.078%) | 3 (5.5%) | 0.00025 | 73.4 (5.8 to 3802.0) |
| ZNF703 | 24 (1.8%) | 21 (1.6%) | 3 (5.5%) | 0.071 | |
| PTEN | 100 (7.4%) | 98 (7.6%) | 2 (3.6%) | 0.43 | |
| MET | 38 (2.8%) | 36 (2.8%) | 2 (3.6%) | 0.67 | |
| MLL2 | 50 (3.7%) | 48 (3.7%) | 2 (3.6%) | 0.99 | |
| BRAF | 89 (6.6%) | 88 (6.8%) | 1 (1.8%) | 0.26 | |
| NOTCH1 | 51 (3.8%) | 50 (3.9%) | 1 (1.8%) | 0.72 | |
| RB1 | 59 (4.4%) | 58 (4.5%) | 1 (1.8%) | 0.51 |
Unless otherwise stated, aberrations refer to characterized alterations. “GNA†” refers to GNAS, GNAQ or GNA11 aberrations.
For comparing continuous values, the Wilcoxon rank-sum test was used; for comparing frequencies, the two-sided Fisher’s exact test was used. Bolded text indicates statistical significance at the alpha = 0.05 level.
Abbreviations: CI = confidence interval; VUS = variant of unknown significance.
Molecular landscape of patients with GNA* alterations:
Patients with GNA* aberrant tumors also had an increased number of aberrations overall, an association which remained after excluding VUSs and excluding GNA* aberrations from the total count (p = 0.0028 for this final comparison). In univariate analysis of the co-aberration network, of the 35 most frequent mutations, a total of 8 aberrations were significantly associated with GNA* aberrations at the alpha = 0.05 level: KRAS, MYC, AURKA, SRC, TOP1, AKT2, CBL and LYN. These results are summarized in Table 2.
A total of 271 independent aberrations that occurred in two or more patients were identified from the data. The frequency of the aberrations followed a power law distribution with scaling parameter alpha = 1.55 (1.51 to 1.59). For each unique aberration, we tested the hypothesis whether there were more co-alterations overall in patients with that aberration than in patients without that aberration. We excluded variants of unknown significance and we did not count the index aberration in the total number. We found that a total of 105/271 (38.7%) are significant at p ≤ 0.05; using the Bonferroni correction32 for multiple comparisons, a total of 31/271 (11.4%) are significant at p ≤ 0.00018.
Additionally, we checked to see whether frequency of an aberration was related to odds of being significant, with correction for multiple comparisons. A logistic regression model was run with (significant) ~ (frequency), yielding an odds ratio of 1.013 (1.005 to 1.022, p = 0.0014). Thus, increased frequency of an aberration was associated with a greater tendency for that aberration to be associated with a higher number of co-alterations. Rare aberrations therefore do not seem uniquely associated with higher total number of co-alterations.
Multivariate Analysis of Covariates Associated with GNA* Alterations
Characteristics independently associated with GNA* alterations:
To test the stability of these associations, we ran a multivariable logistic regression model of GNAS/GNAQ/GNA11 aberration status versus all features that were found in univariate analysis to be associated with GNA* at p ≤ 0.1. From the results above, this included 18 variables: adenocarcinoma histology, peritoneal metastasis, brain cancer diagnosis, ocular melanoma diagnosis, appendiceal cancer diagnosis, number of characterized genomic co-alterations, and aberrations in KRAS, MYC, SMAD4, AURKA, MCL1, SRC, TOP1, CCNE1, AKT2, CBL, LYN and ZNF703. Of these features, ocular melanoma diagnosis, appendiceal histology, and KRAS, AURKA, SRC, CBL and LYN aberrations were significantly associated with GNA* aberrations at the alpha = 0.05 level as shown in Table 3.
Table 3.
| GNA* mutant odds ratio (95% CI) | p-value | Significant at p < 0.00028** | |
|---|---|---|---|
| Significant: | |||
| Ocular Melanoma | 2.09 (1.77 to 2.48) | <2.2–16 | Yes |
| Appendiceal cancer | 1.23 (1.17 to 1.30) | 9.26–14 | Yes |
| KRAS | 1.03 (1.00 to 1.06) | 0.036 | |
| AURKA | 1.73 (1.58 to 1.90) | <2.2–16 | Yes |
| SRC | 1.23 (1.08 to 1.40) | 0.0016 | |
| CBL | 1.34 (1.19 to 1.51) | 2.10–6 | Yes |
| LYN | 1.54 (1.30 to 1.84) | 1.36–6 | Yes |
| Not significant: | |||
| Adenocarcinoma | 1.01 (0.99 to 1.02) | 0.29 | |
| Peritoneal metastasis | 1.03 (0.99 to 1.07) | 0.11 | |
| Brain diagnosis | 0.99 (0.96 to 1.02) | 0.45 | |
| Total Co-alterations | 1.00 (0.99 to 1.01) | 0.35 | |
| MYC | 1.02 (0.99 to 1.05) | 0.27 | |
| SMAD4 | 1.01 (0.96 to 1.06) | 0.72 | |
| MCL1 | 1.04 (0.98 to 1.10) | 0.18 | |
| TOP1 | 0.92 (0.79 to 1.07) | 0.28 | |
| CCNE1 | 1.02 (0.94 to 1.09) | 0.67 | |
| AKT2 | 1.04 (0.95 to 1.14) | 0.41 | |
| ZNF703 | 0.98 (0.91 to 1.05) | 0.52 | |
All the following variables were found to be associated (at p < 0.1) with GNAS in a univariate model (Fisher’s exact test) (from Tables 1 and 2) were included in a multivariate logistic regression model: adenocarcinoma histology, peritoneal metastasis, brain cancer diagnosis, ocular melanoma diagnosis, appendiceal cancer diagnosis, number of characterized genomic co-alterations, KRAS, MYC, SMAD4, AURKA, MCL1, SRC, TOP1, CCNE1, AKT2, CBL, LYN, ZNF703. In the multivariate model, results were considered significant if p < 0.05.
As all 18 factors with p-value of 0.1 or less in univariate were included, the Bonferroni correction was used to account for multiple comparisons. A significant p-value after Bonferroni correction was 0.0028 or less.
Abbreviations: CI = confidence interval.
At the Bonferroni corrected p-value cutoff of 0.05/18 = 0.0028, KRAS is no longer significantly associated with GNA*, but the other features (ocular melanoma, appendiceal cancer, AURKA, SRC, CBL and LYN) remain significantly associated, in alignment with previous reports.13,26,30,33,34 The diagnosis of ocular melanoma is particularly strongly associated (OR = 2.09, p < 0.0001) which is consistent with previous literature findings.21,22,31
Treatment regimens and GNA* alterations:
Using the longest measured (best) progression free survival as an outcome, we ran a Cox proportional hazards model of PFS versus treatment for various treatment regimens, including VEGF inhibitors, taxanes, capecitabine/gemcitabine, platins and hormonal agents. Given the very small numbers of patients with GNA* aberrations who also had a best PFS measured for these regimens (all n ≤ 11), no therapy was found to be significantly associated with PFS. These results are summarized in Supplemental Table 1.
We also examined whether any patients had exceptional responses after therapy for metastatic disease. Of the 43 patients with GNA* alterations who developed metastatic disease, PFS data on systemic treatment was available for 24 of them. Of these 24 patients, four had a PFS of over 1.5 years (16.7%); they were treated with the following regimens: tamoxifen, exemestane, letrozole (all breast cancer), and CyBorD (cyclophosphamide, bortezomib, dexamethasone) (multiple myeloma). These drugs are not implicated in the GNA* pathway and it is likely that the responses are unrelated.
Figure 1 shows the overall aberrational landscape for the 55 GNAS/GNAQ/GNA11 aberrant patients, including associated tumor diagnoses and the top 40 most frequent genomic co-alterations. ZNF217, AURKA, TP53, KRAS, MYC, SMAD4 and cyclin aberrations were frequently found, but as found with the multivariate model above only AURKA, SRC, CBL and LYN remained uniquely and significantly associated with GNA* aberrations in our patient population. The AURKA association is both frequent (17/55, 31.0%) and significantly associated with GNA* aberrations.
Figure 1.
Oncoprint figure showing aberration distribution for the 55 patients with GNA* alterations. Each column represents one patient. Only the 40 most frequent genomic co-alterations were included.
GNA* status and survival:
Figure 2 shows the Kaplan-Meier curve summarizing the relationship between overall survival (defined as interval from diagnosis to death or last follow-up date, with appropriate censoring) and GNA* status. Although individuals with GNA* aberrant tumors tend to have a lower overall survival, (median OS 26.3 vs. 35.2 months), the difference is not statistically significant (log-rank p = 0.085).
Figure 2.
Kaplan-Meier curve showing overall survival from diagnosis for patients with “GNA*” (GNAS/GNAQ/GNA11) aberrant vs. GNA* wild type tumors.
DISCUSSION
Aberrations in genes coding for G-protein activating subunits have been increasingly implicated in the tumorigenesis of a variety of cancers, including those of endocrine, gastrointestinal, cutaneous, and ocular origin. The advent of deep sequencing technology has elucidated the pro-tumorigenic role of various GNA* (GNAS, GNAQ, GNA11) aberrations in a diverse array of malignancies. Thus, the aim of the present study was to determine the spectrum of GNAS/GNAQ/GNA11 aberrations across a cohort of 1348 cancer patients, and to compare mutational burden, overall survival rates, and treatment outcomes between GNA* positive versus GNA* wild-type patients.
Consistent with previous reports, our multivariate analysis highlights a significant association between GNA* aberrations and appendiceal cancer (OR = 2.09, p < 0.0001). A possible explanation for this correlation is that mutant GNAS may disrupt the cAMP-dependent signaling pathway that induces mucin gene expression, which is the hallmark of these tumors.13 Likewise, we also demonstrated a significant association between positive GNA* aberration status and ocular melanoma diagnoses (OR = 2.09, p < 0.0001). One likely explanation for this association is the implication of GNAQ in endothelin signaling, which is central to melanocyte survival during early development.22,35
We found that the higher the frequency of an alteration, the more likely it was to be accompanied by co-alterations. Even so, GNA* aberrations, which were found in 4.1% of our population, frequently co-occurred with higher numbers of co-alterations as well as with several specific aberrations including KRAS, AURKA, SRC, CBL and LYN, which also affect critical oncogenic pathways. These co-alterations have important therapeutic implications, as they may guide the development of novel drug combinations that dually target these genetic pathways.
Although individuals with GNA*-aberrant tumors had lower overall survival rates than patients with wild-type tumors (median survival of 26.3 vs. 35.2 months from diagnosis), this difference showed only a trend toward statistical significance (log-rank p = 0.085). The calculations may have been limited by the small number of GNA*-positive relative to GNA*-negative patients. (n = 55 vs. n = 1293). Thus, future studies with a larger sample size of patients with GNA* aberrations may provide more insight into the prognostic implications of these mutations. We also did not find a relationship between PFS and GNA* status for a variety of treatment regimens, including VEGF inhibitors, taxanes, capecitabine/gemcitabine, platins and hormonal agents, but small samples size precluded robust statistical analysis.
There are several limitations to the study, including its retrospective nature and the fact that germline controls were not used to assess the molecular characteristics. However, in regard to the latter, the NGS assay is nonetheless well validated and now approved by the Food and Drug Administration. Another limitation of the study was that the proportion of different histologies tested was influenced by referral patterns to our institution and the adoption of genomic technology by specific physicians. For instance, brain tumors were tested in disproportionately high numbers, because of the number of patients seen and because our neuro-oncologists were early adopters of molecular testing. Finally, it might be of interest to know if GNA* abnormalities are early or late events. Future studies might consider evaluating paired primary and metastatic tumors.
In conclusion, GNA* aberrations were found across a range of malignancies, with ocular melanoma and appendiceal cancer having the strongest associations. These diseases are rare and have a dearth of effective therapies and clinical trials. Co-alterations in AURKA, SRC, CBL and LYN genes were often found in the presence of GNA* mutations. Development of drugs to target GNA* abnormalities and the alterations that frequently occur with them is needed.
Supplementary Material
Acknowledgments
Funding
Funded in part by the Joan and Irwin Jacobs fund and by National Cancer Institute grant P30 CA016672 (RK)
Footnotes
Competing interests
Dr. Kurzrock has research funding from Genentech, Merck Serono, Incyte, Pfizer, Sequenom, Foundation Medicine, and Guardant Health, as well as consultant fees from XBiotech, Loxo and Actuate Therapeutics, speaker fees from Roche and an ownership interest in Curematch, Inc.
Dr. Goodman receives speaker fees from Seattle Genetics.
Dr. Murugesan and Dr. Frampton are paid employees of Foundation Medicine, Inc., Cambridge, MA, United States of America.
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