JAMA Oncology | Original Investigation Value of Molecular Classification for Prognostic Assessment of Adrenocortical Carcinoma
Guillaume Assié, MD, PhD; Anne Jouinot, MD; Martin Fassnacht, MD, PhD; Rossella Libé, MD; Simon Garinet, PharmD; Louis Jacob, BS; Nadim Hamzaoui, MD; Mario Neou, MS; Julien Sakat, MD; Bruno de La Villéon, MD; Karine Perlemoine, BS; Bruno Ragazzon, PhD; Mathilde Sibony, MD; Frédérique Tissier, MD; Sébastien Gaujoux, MD, PHD; Bertrand Dousset, MD, PhD; Silviu Sbiera, MD, PhD; Cristina L. Ronchi, MD, PhD; Matthias Kroiss, MD, PhD; Esther Korpershoek, PhD; Ronald De Krijger, MD, PHD; Jens Waldmann, MD, PhD; Marcus Quinkler, MD, PHD; Magalie Haissaguerre, MD, PhD; Antoine Tabarin, MD, PhD; Olivier Chabre, MD, PhD; Michaela Luconi, MD, PhD; Massimo Mannelli, MD, PhD; Lionel Groussin, MD, PhD; Xavier Bertagna, MD, PhD; Eric Baudin, MD, PhD; Laurence Amar, MD, PhD; Joel Coste, MD, PhD; Felix Beuschlein, MD, PHD; Jérôme Bertherat, MD, PhD
IMPORTANCE The risk stratification of adrenocortical carcinoma (ACC) based on tumor proliferation index and stage is limited. Adjuvant therapy after surgery is recommended for most patients. Pan-genomic studies have identified distinct molecular groups closely associated with outcome.
OBJECTIVE To compare the molecular classification for prognostic assessment of ACC with other known prognostic factors.
DESIGN, SETTING, AND PARTICIPANTS In this retrospective biomarker analysis, ACC tumor samples from 368 patients who had undergone surgical tumor removal were collected from March 1, 2005, to September 30, 2015 (144 in the training cohort and 224 in the validation cohort) at 21 referral centers with a median follow-up of 35 months (interquartile range, 18-74 months). Data were analyzed from March 2016 to March 2018.
EXPOSURES Meta-analysis of pan-genomic studies (transcriptome, methylome, chromosome alteration, and mutational profiles) was performed on the training cohort. Targeted biomarker analysis, including targeted gene expression (BUB1B and PINK1), targeted methylation (PAX5, GSTP1, PYCARD, and PAX6), and targeted next-generation sequencing, was performed on the training and validation cohorts.
MAIN OUTCOMES AND MEASURES Disease-free survival. Cox proportional hazards regression and C indexes were used to assess the prognostic value of each model.
RESULTS Of the 368 patients (mean [SD] age, 49 [16] years), 144 were in the training cohort (100 [69.4%] female) and 224 were in the validation cohort (142 [63.4%] female). In the training cohort, pan-genomic measures classified ACC into 3 molecular groups (A1, A2, and A3-B), with 5-year survival of 9% for group A1, 45% for group A2, and 82% for group A3-B (log-rank P < . 001). Molecular class was an independent prognostic factor of recurrence in stage I to III ACC after complete surgery (hazard ratio, 55.91; 95% CI, 8.55-365.40; P < . 001). The combination of European Network for the Study of Adrenal Tumors (ENSAT) stage, tumor proliferation index, and molecular class provided the most discriminant prognostic model (C index, 0.88). In the validation cohort, the molecular classification, determined by targeted biomarker measures, was confirmed as an independent prognostic factor of recurrence (hazard ratio, 5.96 [95% CI, 1.81-19.58], P = . 003 for the targeted classifier combining expression, methylation, and chromosome alterations; and 2.61 [95% CI, 1.31-5.19], P = . 006 for the targeted classifier combining methylation, chromosome alterations, and mutational profile). The prognostic value of the molecular markers was limited for patients with stage IV ACC.
CONCLUSIONS AND RELEVANCE The findings suggest that in localized ACC, targeted classifiers may be used as independent markers of recurrence. The determination of molecular class may improve individual prognostic assessment and thus may spare unnecessary adjuvant treatment.
JAMA Oncol. 2019;5(10):1440-1447. doi:10.1001/jamaoncol.2019.1558 Published online July 11, 2019.
+ Supplemental content
Author Affiliations: Author affiliations are listed at the end of this article.
Corresponding Author: Jérôme Bertherat, MD, PhD, INSERM U1016, CNRS UMR8104, Paris Descartes University, 24 Rue du Faubourg St Jacques, Paris 75014, France (jerome.bertherat@aphp.fr).
T he prognosis of adrenocortical carcinoma (ACC) varies widely, ranging from tumors curable by complete sur- gery to those that are unresectable and fast growing with a high potential for metastatic spread. Current prognos- tic factors include mainly tumor stage, best assessed by the European Network for the Study of Adrenal Tumors (ENSAT) staging system,1 and tumor proliferation index, best mea- sured by mitotic count2,3 or Ki67 immunostaining.4 Other prognostic factors commonly reported include cortisol secretion and age.5-7 Despite such prognostic factors, it is currently not possible to predict whether a patient is cured after complete surgery. Therefore, adjuvant treatment with mitotane is commonly prescribed,8,9 although most patients may not benefit from it.
Integrated pan-genomic studies10,11 have been per- formed in ACC, showing the convergence of genomic altera- tions into distinct molecular subtypes. These molecular sub- groups are associated with clinically relevant differences in outcome. In particular, 1 patient subgroup is associated with a poor outcome, which has been characterized by a specific transcriptomic signature12-14 called C1A, a methylome signa- ture of hypermethylation on CpG islands15 called CIMP, a high number of chromosome alterations called noisy, and an accu- mulation of mutations among a limited number of recurrent driver genes. Conversely, another patient subgroup has been associated with a better outcome, characterized by a C1B tran- scriptomic signature.
In the current study, our aim was to compare molecular classification with other known factors used for prognostic assessment of ACC. In addition, given the limited use of pan- genomic measures in clinical oncogenetics departments, we proposed targeted molecular classifiers that reflect the ACC genomic subtypes.
Methods
Patients
In this prognostic study, 2 cohorts of patients with ACC were included: a training and a validation cohort. The training co- hort consisted of 144 patients (eTable 1 in the Supplement) pre- viously included in the ENSAT or The Cancer Genome Atlas (TCGA) study.10,11 The validation cohort consisted of 224 pa- tients (eTable 2 in the Supplement) from 21 centers in the ENSAT network in France, Germany, the Netherlands, and Italy. The study was performed from March 1, 2005, to September 30, 2015, with a median follow-up of 35 months (interquar- tile range, 18-74 months). (eMethods in the Supplement). Data were analyzed from March 2016 to March 2018. Written in- formed consent for the molecular analysis and the collection and use of the clinical data was obtained from all patients within ENSAT. The present analysis was approved by the lo- cal institutional review board of each clinical center. All data were deidentified.
Pan-genomic Molecular Measures
A molecular classification was determined on the training cohort, previously characterized by pan-genomic ap-
Key Points
Question Is the prognostic molecular classification of adrenocortical carcinoma (ACC) more accurate than other known prognostic factors?
Findings In this prognostic study of 368 patients who had undergone surgical removal of ACC localized tumors, molecular classification was an independent prognostic factor in patients stage I to III disease after complete surgery, and the combination of tumor stage, tumor grade, and molecular class provided the best prognostic model of disease-free survival. The prognostic value of molecular classification was confirmed using targeted molecular measurements in a large independent cohort.
Meaning The findings suggest that in patients with low-grade and stage I to III ACC, molecular classification can better discriminate poor outcome ACC, which may spare patients from unnecessary adjuvant treatment.
proaches. Tumors that accumulated genomic alterations associated with poor outcome (C1A transcriptome, CIMP methylome, and noisy chromosome alteration profiles) were assigned to a poor outcome class, in agreement with previ- ous reports.10,11 Conversely, tumors with the C1B transcrip- tome profile were assigned to a better outcome class.10,11 For other tumors, prognostic classes were defined as the best combinations of pan-genomic measures that fit disease- free survival (DFS) and overall survival (OS) (eMethods and eFigure 1 in the Supplement).
Targeted Molecular Profiling
Targeted markers, aiming to recapitulate pan-genomic pro- files, were assessed for 72 patients with ACC from the train- ing cohort and for the entire validation cohort.
Gene Expression
Targeted gene expression consisted of a 2-gene molecular predictor measuring the differential expression of BUB1B (OMIM 602860) and PINK1 (OMIM 608309) by quantitative reverse-transcriptase polymerase chain reaction (eMethods in the Supplement), classifying tumors into poor and better outcome subgroups, as previously published.12,16
DNA Methylation
Targeted methylation was measured for 4 genes (PAX5 [OMIM 167414], GSTP1 [OMIM 134660], PYCARD [OMIM 606838], and PAX6 [OMIM 607108]) by methylation-specific multiplex ligation-dependent probe amplification (eMethods in the Supplement), classifying tumors into poor and better out- come subgroups, as previously described.17
Gene Mutations, Homozygous Deletions, and Amplifications
Eighteen driver genes previously reported to be recurrently altered in ACC10,11,18 were sequenced by targeted next- generation sequencing (NGS; Life Technologies) with a dedi- cated panel (eMethods in the Supplement). In addition to sequence variations, homozygous deletions and gene amplifi- cations were assessed by analyzing both single-nucleotide poly- morphism array data and the DNA copy number from NGS using
jamaoncology.com
TARGOMICs.19 Tumors were classified into poor or better out- come subgroups, depending on the mutational status (at least 1 vs no mutation) in pathways associated with prognostic value, that is, cell-cycle (TP53 [OMIM 191170], RB1 [OMIM 614041], CDK4 [OMIM 123829], CCNE1 [OMIM 123837], and CDKN2A [OMIM 615914]) and Wnt/B-catenin genes (CTNNB1 [OMIM 116806] and ZNRF3 [OMIM 612062]) (eTable 3 in the Supplement).
Chromosome Alterations
Chromosome alteration profiles were determined by single- nucleotide polymorphism array, based on the targeted status of 9 chromosome arms (eMethods in the Supplement). Tumors were classified into poor or better outcome sub- groups, depending on the chromosome profile (noisy vs chromosomal or quiet).
Targeted Molecular Classifiers
Targeted molecular measures were combined into 2 distinct molecular classifiers that recapitulate the pan-genomic clas- sification: a 3-dimensional (3-D) targeted classifier or a DNA- based targeted classifier. The 3-D targeted classifier used tumor RNA and DNA and combined targeted gene expres- sion, targeted methylation, and targeted measures of chro- mosome alterations (eMethods, eFigure 1, and eFigure 2A in the Supplement). The DNA-based targeted classifier used tumor DNA only and combined targeted methylation, tar- geted chromosome alteration profile, and mutational status (eMethods, eFigure 1, and eFigure 2B in the Supplement). For both the 3-D and DNA-based targeted classifiers, poor, intermediate, and better outcome prognostic classes were defined by the combination of targeted statuses that showed the best concordance with the pan-genomic classification using the k coefficient.
Statistical Analysis
Calculations were performed using R statistical software (stats, irr, pheatmap, survival, and survcomp packages; R Foundation) (source codes are given in the eMethods in the Supplement). Comparisons between groups were assessed using the 2-tailed, unpaired t test or parametric analysis of variance for quantitative variables and the Fisher or x2 test for qualitative variables. Correlations were assessed using the Spearman correlation coefficient. The Cohen K concor- dance coefficient was used to determine the best combina- tions of targeted molecular measurements as surrogates for pan-genomic classification.
Survival curves were obtained with Kaplan-Meier esti- mates and compared using the log-rank test. Cox propor- tional hazards regression was used for the DFS and OS analy- ses. Overall survival was defined as the time from diagnosis until death or last follow-up. Disease-free survival was ana- lyzed only in patients with stage I to III disease after com- plete surgery and was defined as the time from primary tu- mor resection until recurrence or last follow-up. Univariate analyses tested the association of demographic and known prognostic factors with DFS and OS, along with molecular clas- sification. Significant variables were combined into multivari-
able models. Performance of the prognostic models was fur- ther quantified by the Harrell C index. The best prognostic model was then tested in the validation cohort. The propor- tional hazards assumption was confirmed for each model constructed. All P values were 2-sided, and the level of significance was set at P < . 05.
Results
Patient Characteristics
Of the 368 patients (mean [SD] age, 49 [16] years), 144 were in the training cohort (100 [69.4%] female) and 224 were in the validation cohort (142 [63.4%] female). The training cohort patients were from the ENSAT and TCGA genomic studies,10,11 and the validation cohort patients were from ENSAT only. The baseline characteristics of the patients are presented in eTable 4 in the Supplement. The median follow-up was 39 months for the training cohort and 35 months for the validation cohort, with 16 patients (11.1%) in the training cohort and 23 patients (10.3%) in the validation cohort unavailable for follow-up. Patients in the validation cohort were older (mean age, 51 vs 47 years), had a higher tumor proliferation index (77 [43%] of 179 vs 30 [26%] of 116 high-proliferation tumors), and received less adjuvant mito- tane (73 [57%] of 129 vs 61 [80%] of 76).
Pan-genomic Classification of ACC (Training Cohort)
Meta-analysis of ENSAT and TCGA Pan-genomic Studies
The ENSAT and TCGA consortia had reported concordant mo- lecular classes of ACC. These 2 cohorts were thus merged into 1 because they did not include any patients in common. Each tumor was classified according to its transcriptomic (C1A or C1B), chromosome alteration (noisy or chromosomal/quiet), and methylome profile (CIMP or non-CIMP). Combining these 3 omics-based profiles resulted in discrete molecular classes (A1, A2, and A3-B) (eFigure 1 in the Supplement) with major differences in outcomes (Figure, A and B): 5-year OS was 9% for the A1 group, 45% for the A2 group, and 82% for the A3 to B group (log-rank P < . 001). This 3-D pan-genomic classifica- tion correlated with cortisol secretion (r = 0.47), ENSAT stage (r = 0.40), and tumor proliferation (r = 0.40) (eTable 5 and eTable 6 in the Supplement).
Prognostic Value of the Pan-genomic Classification
For patientswith localized ACC (R0 and stage I-III), ENSAT stage III, a high tumor proliferation index, cortisol secretion, and pan- genomic groups A1 and A2 were identified as adverse prog- nostic factors of DFS by univariate analysis (eTable 7 in the Supplement). In the multivariable model (eFigure 3 in the Supplement), the 3-D pan-genomic classification was the only independent prognostic factor of DFS (group A1 vs A3-B), with a hazard ratio (HR) of 55.91 (95% CI, 8.55-365.40; P < . 001) (eTable 8 in the Supplement). The 3-D pan-genomic classifi- cation remained associated with DFS after adjustment of ad- juvant mitotane treatment (group A1 vs A3-B: HR, 55.06; 95% CI, 5.80-467.10; P < . 001). The combination of ENSAT stage, tumor proliferation index, and 3-D pan-genomic classifica-
A
Pan-genomic classifier DFS (training cohort)
100
B
80
A3
DFS, %
60
A2
40
20
A1
Log-rank P <. 001
0
0
50
100
150
Time From Surgery, mo
| No. at risk | ||||
|---|---|---|---|---|
| A1 | 14 | 0 | 0 | 0 |
| A2 | 15 | 4 | 2 | 0 |
| A3 | 6 | 4 | 3 | 1 |
| B | 43 | 24 | 11 | 3 |
C 3-D targeted classifier DFS (validation cohort)
100
80
Better
DFS, %
60
40
Intermediate
20
Poor
Log-rank P <. 001
| 0 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| Time From Surgery, mo | |||||
| No. at risk | |||||
| Better | 37 | 17 | 5 | 1 | |
| Intermediate | 30 | 8 | 5 | 1 | |
| Poor | 9 | 1 | 0 | 0 | |
B Pan-genomic classifier OS (training cohort)
100
B
80
A3
60
OS, %
40
A2
20
Log-rank P <. 001
A1
A, Disease-free survival (DFS) among patients with R0 stage I to III disease, according to molecular group, using pan-genomic classifier (training cohort). B, Overall survival (OS) among patients with stage I to IV disease, according to molecular group, using pan-genomic classifier (training cohort). C, DFS among patients with R0 stage I to III disease, according to molecular group, using the
| 0 0 | 50 | 100 | 150 | |
|---|---|---|---|---|
| Time From Surgery, mo | ||||
| No. at risk | ||||
| A1 | 27 | 3 | 0 | 0 |
| A2 | 28 | 9 | 2 | 0 |
| A3 | 8 | 6 | 4 | 1 |
| B | 50 | 31 | 12 | 4 |
D DNA-based targeted classified DFS (validation cohort)
100
80
DFS, %
60
Better
40
Intermediate
20
Log-rank P <. 001
Poor
0
0
50
100
150
200
Time From Surgery, mo
No. at risk Better
93
39
16
3
Intermediate
34
5
2
2
Poor
23
4
1
1
3-dimensional (3-D) targeted classifier (validation cohort). D, DFS among patients with R0 stage I to III disease, according to molecular group, using the DNA-based targeted classifier (validation cohort). Survival curves were obtained with Kaplan-Meier estimates.
tion provided the most discriminant prognostic model (C index, 0.88) (eFigure 4 in the Supplement).
In contrast to localized ACC, the prognostic value of the 3-D pan-genomic classification was weaker for patients with stage IV disease (univariate analysis of OS for group A1 vs A3-B: HR, 5.40; 95% CI, 1.16-25.11; P = . 03) (eTable 8 and eFigure 7 in the Supplement).
Targeted Molecular Classification Using Tumor DNA and RNA
Targeted measures were adopted, reflecting each dimension of the 3-D pan-genomic classifier. These measures included the expression of 2 genes, BUB1B and PINK1, quantified by quan- titative reverse-transcriptase polymerase chain reaction, and DNA methylation in CpG islands of 4 genes, GSTP1, PYCARD, PAX6, and PAX5, assayed by methylation-specific multiplex
ligation-dependent probe amplification, along with the chromosome alteration profile.
From 3-D Pan-genomic to 3-D Targeted Classification (Training Cohort)
In the training cohort, targeted molecular measures of gene expression and DNA methylation were associated with the pan-genomic classes (eFigure 1 in the Supplement). The 3-D targeted classifier, which combined targeted gene expres- sion, targeted DNA methylation, and the chromosome altera- tion profile, was concordant with the pan-genomic classes (K = 0.76, P < . 001) (eFigure 1 in the Supplement).
Prognostic Value of the 3-D Targeted Classifier (Validation Cohort) For patients with localized ACC (R0 and stage I-III), the molecular class determined by the 3-D targeted classifier (eFig-
| Table. Multivariable Prognostic Models of Disease-Free Survival in the Validation Cohorta | ||||||||
|---|---|---|---|---|---|---|---|---|
| Variable | 3-D Targeted Classifier | DNA-Based Targeted Classifier | Expression Marker | Methylation Marker | ||||
| HR (95% CI) | P Value | HR (95% CI) | P Value | HR (95% CI) | P Value | HR (95% CI) | P Value | |
| ENSAT stage III vs I-II | 1.48 (0.97-2.25) | .07 | 1.50 (1.13-1.99) | .005 | 1.56 (1.03-2.36) | .03 | 1.52 (1.14-2.02) | .004 |
| Tumor proliferation index high vs low | 3.30 (1.42-7.66) | .005 | 2.04 (1.13-3.69) | .02 | 2.88 (1.28-6.48) | .01 | 2.13 (1.18-3.83) | .01 |
| Molecular class | ||||||||
| Intermediate vs better | 3.43 (1.22-9.67) | .02 | 1.85 (0.94-3.64) | .08 | NA | NA | NA | NA |
| Poor vs better | 5.96 (1.81-19.58) | .003 | 2.61 (1.31-5.19) | .006 | 3.71 (1.37-10.00) | .01 | 2.41 (1.35-4.32) | .003 |
Abbreviations: 3-D, 3-dimensional; ENSAT, European Network for the Study of Adrenal Tumors; HR, hazard ratio; NA, not applicable.
a Multivariable models were generated for patients with R0 stage I to III disease. The HR for recurrence and the 95% CI are based on a Cox proportional hazards regression model with ENSAT stage, tumor proliferation index, and molecular
classifiers as explanatory variables. High tumor proliferation index was defined by mitotic count of 20 or more per 50 high-power fields and/or Ki67 staining in 20% of cells or more. Molecular class was determined by targeted classifiers using tumor RNA and DNA (3-D targeted classifier) or tumor DNA only (DNA-based targeted classifier) (details in eMethods in the Supplement).
ure 2A in the Supplement) was confirmed to be a prognostic factor in univariate analysis (Figure, C, and eTable 9 and eFig- ure 6A in the Supplement). The median DFS was 14 months for the poor prognostic group, 29 months for the intermedi- ate prognostic group, and not reached (exceeding the follow-up period) for the better prognostic group (log-rank P < . 001) (Figure, C). After multivariable modeling, including tumor stage and tumor proliferation index (eFigure 3 in the Supplement), the 3-D targeted classifier was identified as an independent prognostic factor (Table). The HRs for recur- rence were 3.43 (95% CI, 1.22-9.67; P = . 02) for the interme- diate and 5.96 (95% CI, 1.81-19.58; P = . 003) for the poor prog- nostic groups. Combination of the ENSAT stage, tumor proliferation index, and molecular class, determined by the 3-D targeted classifier, was confirmed to be the best discriminant model (C index, 0.77). In contrast to patients with localized ACC, the prognostic value of the 3-D targeted classifier was not significant for patients with stage IV ACC (eTable 10 and eFigure 6B in the Supplement).
Targeted Molecular Classification
Using Tumor DNA Only
Compared with the 3-D targeted classifier, the DNA-based targeted classifier did not consider gene expression but included instead the mutational status of the selected genes.
From 3-D Pan-genomic to DNA-Based Targeted Classification (Training Cohort)
Somatic mutations within pathways associated with the prog- nostic value (eTable 3 in the Supplement), that is, cell cycle and Wnt/B-catenin, were associated with the pan-genomic classes (eFigure 1 in the Supplement). The DNA-based targeted clas- sifier, which combined targeted measures of methylation, chro- mosome alteration profiles, and mutations, was concordant with the pan-genomic classes (K = 0.73, P < . 001) (eFigure 1 in the Supplement).
Prognostic Value of the DNA-Based Targeted Classifier (Validation Cohort)
In patients with localized ACC (R0 and stage I-III), the mo- lecular class determined by the DNA-based targeted classifier (eFigure 2B in the Supplement) was confirmed to be a prog-
nostic factor in univariate (Figure, D, and eTable 7 and eFigure 6C in the Supplement) and multivariable models (Table). In contrast to patients with localized ACC, the prog- nostic value of the DNA-based targeted classifier was not as strong for patients with stage IV ACC (eTable 8 and eFigure 6D in the Supplement).
Discussion
The study findings support the prognostic value of targeted molecular classifiers, reflecting the pan-genomic classifica- tion of ACC. To date, clinical factors, including tumor stage and tumor proliferation index, are considered to be the strongest and most consensual prognostic factors for patients with ACC.2º However, risk stratification within sub- groups is still variable.21-23 In particular, the individual risk of recurrence for patients with stage I to III disease after complete surgery cannot be properly estimated. In addition, these prognostic factors are not specific to ACC. Tumor stage is a globally obvious prognostic factor, especially the pres- ence of metastases, and the tumor proliferation index is associated with survival for most cancers.24 We proposed complementing these standard prognostic factors with an approach based primarily on the molecular biology of ACC. Molecular subgroups are supported by distinct pathophysi- ologic mechanisms and translate into clinically relevant dif- ferences in outcome. The results of 2 independent inte- grated genomic studies,10,11 one from ENSAT and the other from the TCGA consortium, converged on a molecular classi- fication of ACC mainly based on transcriptomic profiles. Concordance between the 2 studies10,11 made it possible to merge the genomic data into a single meta-analysis to define a unique molecular classification for these 2 cohorts.
This study supports the prognostic value of molecular classification in localized ACC. Conversely, the prognostic value of molecular classification was weaker in patients with stage IV disease. The presence of metastases appeared to be the strongest pejorative prognostic factor in the cohort, in line with previous clinical studies.1,7 Of note, almost all metastatic tumors were classified into molecular classes of poor and intermediate outcome. Thus, the association
between molecular class and survival was hard to assess, especially considering the limited size of the cohort with stage IV ACC .
For the transfer to clinical routine, we developed 2 com- binations of targeted measures, the 3-D targeted classifier and the DNA-based targeted classifier. The former provided information closest to the pan-genomic classification but could be evaluated for only half of the patients because of limited access to tumor RNA in the retrospective cohort. This is a potentially important limitation for routine trans- fer. However, many centers propose systematic freezing of tumor samples. In addition, techniques measuring RNA lev- els on formalin-fixed paraffin-embedded samples have been developed, adapted to the degraded nature of RNA in these samples,25 with promising results in cancer oncogenetics.26 In ACC, adaption of the targeted RNA signature to formalin- fixed paraffin-embedded samples remains to be done and tested. Considering this potential limitation, the DNA-based classifier was developed. This classifier can be assessed using frozen or formalin-fixed paraffin-embedded samples27 and measured by targeted NGS.19 Finally, beyond the intrinsic performance of any molecular classifier, the final choice of a technique was also determined by its local availability and feasibility, with major differences among centers.
Combining molecular classification with standard prog- nostic factors, such as tumor stage and proliferation index, provided the best prognostic discrimination in patients with localized ACC in the training (C index, 0.88) and validation (C index, 0.77) cohorts. Therefore, the findings suggest that molecular classification should be integrated into patient care. This method raises several questions concerning the consequences for patients. For example, should patients with a better outcome receive a simplified follow-up after surgery for R0 disease and receive locoregional treatments in case of recurrence? In more common cancers, such as breast carci- noma, molecular classification has radically changed prac- tice, resulting in less use of aggressive and costly treatments for less aggressive tumors.28,29 Thus, patients with a better outcome would probably benefit from a less aggressive man- agement after complete tumor removal and avoid toxic adju- vant treatment, such as mitotane and/or cytotoxic chemo- therapy. Conversely, perhaps it would be possible for patients with an intermediate or poor outcome to be followed up more closely and receive more aggressive systemic adjuvant therapies. In addition to mitotane, adjuvant cytotoxic che- motherapy in patients with poor outcomes could be consid- ered. Clinical trials are needed to address these questions, and future randomized trials in ACC, especially in the adju-
vant setting, should add molecular classification to the set of standard prognostic factors.
Limitations
A limitation of this study includes the number of patients un- available for follow-up, approximately 10% for both the train- ing and validation cohorts, followed up for less than 2 years. However, unavailability of patient follow-up does not affect the interaction of the molecular markers with survival and other prognostic factors. The differences in baseline charac- teristics between the training and validation cohorts may have also affected the results of this study. The tumor proliferation index was significantly higher in the validation cohort. How- ever, the overall prognosis and prognostic value of tumor pro- liferation were similar in the training and validation cohorts. Thus, the differences between the 2 cohorts may reflect the evolution of the techniques for determining tumor prolifera- tion. First, mitotic count was used for the oldest tumors (train- ing cohort) and Ki67 for the most recent ones (validation co- hort). Second, Ki67 is globally increasing over time (Pearson r = 0.37, P < . 001). An additional limitation is related to the Ki67 cutoff chosen to discriminate high- and low-proliferation in- dexes. Both Ki67 cutoffs at 10% and 20% have been investi- gated previously.4,7,9 In our study, the Ki67 cutoff at 20% was more concordant with a cutoff of more than 20 mitoses per 50 high-power fields to define high proliferation index tumors and was retained for this reason. Another limitation is the lack of standardization of adjuvant and palliative treatments. How- ever, therapeutic decisions were not based on molecular mark- ers. In addition, the limited efficacy of current treatments sug- gests a limited influence of this potential bias on this study. Third, these limitations are intrinsic to a retrospective and mul- ticentric design, which is shared by all large prognostic stud- ies on ACC.5,7,30 Given the rarity of the disease, these limita- tions are challenging to overcome. Beyond these limitations, to our knowledge, this cohort was the largest collection of ad- renal cancers investigated at the molecular level.
Conclusions
The findings suggest that molecular classifiers can be consid- ered as independent prognostic markers in localized ACC. Combining these markers with tumor stage and proliferation index may provide the most accurate individual prognostic de- termination and may thus orient adjuvant therapy decisions after complete surgery. Molecular markers appear to be suit- able for clinical routine use and may be proposed in standard care for patients with ACC.
| ARTICLE INFORMATION | Sibony, Tissier, Groussin, Bertagna, Bertherat); Endocrinology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France (Assié, Jouinot, Libé, Groussin, Bertagna, Bertherat); Medical Oncology, Assistance Publique Hôpitaux de Paris, | Sbiera, Ronchi); Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany (Fassnacht, Kroiss); Department of Oncogenetics, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France (Hamzaoui); |
| Accepted for Publication: March 29, 2019. | ||
| Published Online: July 11, 2019. doi:10.1001/jamaoncol.2019.1558 | ||
| Author Affiliations: Institut Cochin, INSERM | Hôpital Cochin, Paris, France (Jouinot); Division of Endocrinology and Diabetes, Department of Internal Medicine I, University Hospital, University of Würzburg, Würzburg, Germany (Fassnacht, | Department of Pathology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France (Sibony); Department of Pathology, Assistance Publique Hôpitaux de Paris, Hôpital Pitié |
| U1016, CNRS UMR8104, Paris Descartes University, Paris, France (Assié, Jouinot, Libé, Garinet, Jacob, Neou, Sakat, de La Villéon, Perlemoine, Ragazzon, |
Salpétrière, Paris, France (Tissier); Department of Digestive and Endocrine Surgery, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France (Gaujoux, Dousset); Institute of Metabolism and System Research, University of Birmingham, Birmingham, United Kingdom (Ronchi); Department of Pathology, Erasmus MC University Medical Center, Rotterdam, the Netherlands (Korpershoek, De Krijger); Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands (De Krijger); Department of Surgery, University Hospital Giessen and Marburg, Campus Marburg, Marburg, Germany (Waldmann); Endocrinology in Charlottenburg, Berlin, Germany (Quinkler); Department of Endocrinology, Diabetes and Metabolic Diseases, University Hospital of Bordeaux, Bordeaux, France (Haissaguerre, Tabarin); Department of Endocrinology, University Hospital of Grenoble, Grenoble, France (Chabre); Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy (Luconi, Mannelli); Department of Nuclear Medicine and Endocrine Oncology, Institut Gustave Roussy, Villejuif, France (Baudin); Hypertension Unit, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France (Amar); Biostatistics and Epidemiology Unit, Hôtel Dieu, Assistance Publique-Hôpitaux de Paris, Paris, France (Coste); Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-Universität München, Munich, Germany (Beuschlein); Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitätsspital Zürich, Zurich, Switzerland (Beuschlein).
Author Contributions: Drs Assié and Jouinot contributed equally to this work. Drs Assié and Jouinot had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Assié, Jouinot, Fassnacht, de La Villéon, Dousset, Mannelli, Coste, Bertherat. Acquisition, analysis, or interpretation of data: Assié, Jouinot, Fassnacht, Libé, Garinet, Jacob, Hamzaoui, Neou, Sakat, de La Villéon, Perlemoine, Ragazzon, Sibony, Tissier, Gaujoux, Sbiera, Ronchi, Kroiss, Korpershoek, De Krijger, Waldmann, Quinkler, Haissaguerre, Tabarin, Chabre, Luconi, Groussin, Bertagna, Baudin, Amar, Coste, Beuschlein, Bertherat.
Drafting of the manuscript: Assié, Jouinot, Sakat, Tissier, Korpershoek, Groussin, Bertherat. Critical revision of the manuscript for important intellectual content: Jouinot, Fassnacht, Libé, Garinet, Jacob, Hamzaoui, Neou, de La Villéon, Perlemoine, Ragazzon, Sibony, Gaujoux, Dousset, Sbiera, Ronchi, Kroiss, De Krijger, Waldmann, Quinkler, Haissaguerre, Tabarin, Chabre, Luconi, Mannelli, Bertagna, Baudin, Amar, Coste, Beuschlein, Bertherat.
Statistical analysis: Assié, Jouinot, Jacob, Neou, Coste. Obtained funding: Fassnacht, Sakat, Sbiera, Bertherat.
Administrative, technical, or material support: Jouinot, Fassnacht, Perlemoine, Sibony, Tissier, Gaujoux, Sbiera, Ronchi, Korpershoek, De Krijger, Haissaguerre, Tabarin, Luconi, Mannelli, Groussin, Bertagna, Amar, Beuschlein, Bertherat. Supervision: Assié, Jouinot, Libé, Gaujoux, Dousset, Waldmann, Baudin, Bertherat.
Conflict of Interest Disclosures: Dr Assie reported receiving personal fees from Novartis Pharma and
HRA Pharma outside the submitted work and having a patent pending. Dr Jouinot reported receiving grants from the French National Cancer Institute (L’Institut Thématique Multi-Organisme Cancer d’Aviesan) during the conduct of the study. Dr Fassnacht reported receiving grants from the German Research Foundation during the conduct of the study and grants from HRA Pharma, Millendo, German Cancer Aid, and the German Research Foundation outside the submitted work. Dr Sbiera reported receiving grants from Else Kroner-Fresenius Stiftung during the conduct of the study. Dr Chabre reported receiving personal fees and registration fee to congress from Novartis, HRA Pharma, and IPSEN outside the submitted work. Dr Beuschlein reported receiving grants from FP7 during the conduct of the study. Dr Bertherat reported grants from the French Ministry of Health, European Community, and French National Cancer Institute during the conduct of the study and grants, personal fees, and nonfinancial support from Novartis, personal fees from HRA Pharma and Atterocor, and nonfinancial support from Ipsen outside the submitted work. No other disclosures were reported.
Funding/Support: This work was supported by the European Network for the Study of Adrenal Tumor (ENSAT) Cancer Health F2-2010-259735 program (FP7 program) to ENSAT, the Programme Hospitalier de Recherche Clinique to the Cortico Medullo-surrénale Tumeur Endocrines (COMETE) network (Towards an Easy-to-use Adrenal Cancer/ Tumor Identity Card), the Brou de Lauriere Foundation (to Dr Bertherat’s laboratory), the Promex Foundation (to Dr Bertherat’s laboratory), the Institut National de la Santé et de la Recherche Médicale (Dr Assié received a contrat d’interface), and L’Institut Thématique Multi-Organisme Cancer d’Aviesan as part of the Plan Cancer 2014-2019 (Dr Jouinot received a PhD grant). This work was also supported in part by the Deutsche Forschungsgemeinschaft within the CRC/ Transregio 205/1 (The Adrenal: Central Relay in Health and Disease) (Drs Fassnacht, Kroiss, and Beuschlein).
Role of the Funder/Sponsor: The funding
sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: The tumor bank of Cochin Hospital (Benoit Terris), the Centre de Ressources Biologiques of Bordeaux University Hospital, the Endocrinology Departments of Ambroise Paré Hospital (Marie Laure Raffin-Sanson), Le Mans Hospital (Noha Saad), St Nazaire Hospital (Sylvie Regnier-Le Coz), Lyon University Hospital (Francoise Borson-Chazot, MD, PhD, and Jean Louis Peix, MD, PhD), Toulouse University Hospital (Delphine Vezzosi, MD), La Rochelle Hospital (Frederique Duengler, MD), and the Oncogenetic Unit of Cochin Hospital (Michel Vidaud, PharmD, PhD) helped with sample collection. The members of our laboratories and COMETE and ENSAT provided support and discussions, and the staff of the clinical and pathology departments were involved in patient care. No compensation was given for this work.
REFERENCES
1. Fassnacht M, Johanssen S, Quinkler M, et al; German Adrenocortical Carcinoma Registry Group; European Network for the Study of Adrenal Tumors. Limited prognostic value of the 2004 International Union Against Cancer staging classification for adrenocortical carcinoma: proposal for a revised TNM classification. Cancer. 2009;115(2):243-250. doi:10.1002/cncr.24030
2. Weiss LM, Medeiros LJ, Vickery AL Jr. Pathologic features of prognostic significance in adrenocortical carcinoma. Am J Surg Pathol. 1989; 13(3):202-206. doi:10.1097/00000478- 198903000-00004
3. Miller BS, Gauger PG, Hammer GD, Giordano TJ, Doherty GM. Proposal for modification of the ENSAT staging system for adrenocortical carcinoma using tumor grade. Langenbecks Arch Surg. 2010;395(7):955-961. doi:10.1007/s00423-010- 0698-y
4. Beuschlein F, Weigel J, Saeger W, et al. Major prognostic role of Ki67 in localized adrenocortical carcinoma after complete resection. J Clin Endocrinol Metab. 2015;100(3):841-849. doi:10. 1210/jc.2014-3182
5. Abiven G, Coste J, Groussin L, et al. Clinical and biological features in the prognosis of adrenocortical cancer: poor outcome of cortisol-secreting tumors in a series of 202 consecutive patients. J Clin Endocrinol Metab. 2006;91(7):2650-2655. doi:10.1210/jc.2005- 2730 6. Berruti A, Fassnacht M, Haak H, et al. Prognostic role of overt hypercortisolism in completely operated patients with adrenocortical cancer. Eur Urol. 2014;65(4):832-838. doi:10.1016/j.eururo.2013. 11.006
7. Libé R, Borget I, Ronchi CL, et al; ENSAT network. Prognostic factors in stage III-IV adrenocortical carcinomas (ACC): a European Network for the Study of Adrenal Tumor (ENSAT) study. Ann Oncol. 2015;26(10):2119-2125. doi:10. 1093/annonc/mdv329
8. Terzolo M, Angeli A, Fassnacht M, et al. Adjuvant mitotane treatment for adrenocortical carcinoma. N Engl J Med. 2007;356(23):2372-2380. doi:10. 1056/NEJMoa063360
9. Fassnacht M, Dekkers O, Else T, et al. European Society of Endocrinology clinical practice guidelines on the management of adrenocortical carcinoma in adults, in collaboration with the European Network for the Study of Adrenal Tumors. Eur J Endocrinol. 2018;(July). doi:10.1530/EJE-18-0608
10. Assié G, Letouzé E, Fassnacht M, et al. Integrated genomic characterization of adrenocortical carcinoma. Nat Genet. 2014;46(6): 607-612. doi:10.1038/ng.2953
11. Zheng S, Cherniack AD, Dewal N, et al; Cancer Genome Atlas Research Network. Comprehensive pan-genomic characterization of adrenocortical carcinoma. Cancer Cell. 2016;29(5):723-736. doi:10. 1016/j.ccell.2016.04.002
12. de Reyniès A, Assié G, Rickman DS, et al. Gene expression profiling reveals a new classification of adrenocortical tumors and identifies molecular predictors of malignancy and survival. J Clin Oncol. 2009;27(7):1108-1115. doi:10.1200/JCO.2008.18. 5678
13. Giordano TJ, Kuick R, Else T, et al. Molecular classification and prognostication of adrenocortical tumors by transcriptome profiling. Clin Cancer Res. 2009;15(2):668-676. doi:10.1158/1078-0432.CCR- 08-1067
14. Tombol Z, Szabó PM, Molnár V, et al. Integrative molecular bioinformatics study of human adrenocortical tumors: microRNA, tissue-specific target prediction, and pathway analysis. Endocr Relat Cancer. 2009;16(3):895-906. doi:10.1677/ ERC-09-0096
15. Barreau O, Assie G, Wilmot-Roussel H, et al. Identification of a CpG island methylator phenotype in adrenocortical carcinomas. J Clin Endocrinol Metab. 2013;98(1):E174-E184. doi:10. 1210/jc.2012-2993
16. Fragoso MCBV, Almeida MQ, Mazzuco TL, et al. Combined expression of BUB1B, DLGAP5, and PINK1 as predictors of poor outcome in adrenocortical tumors: validation in a Brazilian cohort of adult and pediatric patients. Eur J Endocrinol. 2012;166(1):61-67. doi:10.1530/EJE-11- 0806
17. Jouinot A, Assie G, Libe R, et al. DNA methylation is an independent prognostic marker of survival in adrenocortical cancer. J Clin Endocrinol Metab. 2017;102(3):923-932. doi:10.1210/jc.2016- 3205
18. Juhlin CC, Goh G, Healy JM, et al. Whole-exome sequencing characterizes the landscape of somatic mutations and copy number alterations in adrenocortical carcinoma. J Clin Endocrinol Metab. 2015;100(3):E493-E502. doi:10.1210/jc.2014-3282
19. Garinet S, Néou M, de La Villeon B, et al. Calling chromosome alterations, DNA methylation statuses, and mutations in tumors by simple targeted next-generation sequencing: a solution for transferring integrated pangenomic studies into routine practice? J Mol Diagn. 2017;19(5):776-787. doi:10.1016/j.jmoldx.2017.06.005
20. Jouinot A, Bertherat J. Management of endocrine disease: adrenocortical carcinoma: differentiating the good from the poor prognosis tumors. Eur J Endocrinol. 2018;178(5):R215-R230. doi:10.1530/EJE-18-0027
21. Ayala-Ramirez M, Jasim S, Feng L, et al. Adrenocortical carcinoma: clinical outcomes and prognosis of 330 patients at a tertiary care center. Eur J Endocrinol. 2013;169(6):891-899. doi:10.1530/ EJE-13-0519
22. Else T, Williams AR, Sabolch A, Jolly S, Miller BS, Hammer GD. Adjuvant therapies and patient and tumor characteristics associated with survival of adult patients with adrenocortical carcinoma. J Clin Endocrinol Metab. 2014;99(2):455-461. doi:10.1210/ jc.2013-2856
23. Kerkhofs TMA, Verhoeven RHA, Van der Zwan JM, et al Adrenocortical carcinoma: a population-based study on incidence and survival in the Netherlands since 1993. Eur J Cancer. 19902013;49(11):2579-2586. doi:10.1016/j.ejca. 2013.02.034
24. Yerushalmi R, Woods R, Ravdin PM, Hayes MM, Gelmon KA. Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol. 2010;11(2):174- 183. doi:10.1016/S1470-2045(09)70262-1
25. Narrandes S, Xu W. Gene expression detection assay for cancer clinical use. J Cancer. 2018;9(13): 2249-2265. doi:10.7150/jca.24744
26. Kwa M, Makris A, Esteva FJ. Clinical utility of gene-expression signatures in early stage breast cancer. Nat Rev Clin Oncol. 2017;14(10):595-610. doi:10.1038/nrclinonc.2017.74
27. de Leng WWJ, Gadellaa-van Hooijdonk CG, Barendregt-Smouter FAS, et al. Targeted next generation sequencing as a reliable diagnostic assay for the detection of somatic mutations in tumours using minimal DNA amounts from formalin fixed paraffin embedded material. PLoS One. 2016;11(2):e0149405. doi:10.1371/journal.pone. 0149405
28. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351(27):2817-2826. doi:10.1056/NEJMoa041588
29. Sparano JA, Gray RJ, Makower DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med. 2018;379(2):111-121. doi:10.1056/NEJMoa1804710
30. Assié G, Antoni G, Tissier F, et al. Prognostic parameters of metastatic adrenocortical carcinoma. J Clin Endocrinol Metab. 2007;92(1):148-154. doi: 10.1210/jc.2006-0706