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Biomarkers: Improving survival prediction for melanoma

  1. Mykyta Artomov  Is a corresponding author
  1. Massachusetts General Hospital, United States
  2. Broad Institute, United States
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Cite this article as: eLife 2019;8:e48145 doi: 10.7554/eLife.48145


The survival of patients with cutaneous melanoma can be accurately predicted using just four DNA methylation marks.

Main text

Predicting the risk of outcomes in patients with cancer has traditionally relied on clinical observations: the age of the patient, the size of the tumor, how far it spreads, and how the tumor cells look under the microscope. The accuracy of these clinical evaluations depends on the type of cancer: this approach usually delivers good predictions for cancers that do not spread, but once the cancer metastasizes, the predictive power of this approach declines rapidly.

One of the most challenging cancers to make predictions for is cutaneous melanoma because it progresses rapidly and often spreads into the lymph nodes and other distant organs (Homsi et al., 2005). Cutaneous melanoma is the deadliest skin cancer (Miller and Mihm, 2006), so it is important to be able to manage patient expectations. This means that we need methods other than those based on clinical observations that can predict patient survival.

One alternative approach is based on biomarkers – biological properties within tumors that are associated with melanoma survival. For instance, research showed that several drugs for the treatment of melanoma only targeted tumors that carried a specific mutation in the BRAF gene: the presence of this mutation in a patient is therefore associated with a higher chance of survival due to a positive drug response (Figure 1). Indeed, subsequent research has shown that the higher the mutational 'burden' in the melanoma, the better the response to treatment (Goodman et al., 2017; Figure 1). The interaction between the transcription of genes in the tumor and the immune system is also important: depending on the melanoma tumor type, low levels of transcription of a gene called MITF results in fewer immune cells being attracted to the tumor, which leads to an acceleration in tumor growth (Wiedemann et al., 2019). Taken together, these findings highlight that understanding the biological characteristics of melanoma tumors is critical for predicting outcomes and developing new treatments.

Different ways to predict survival rates for patients with melanoma.

Some patients with a given cancer have higher survival rates than other patients with the same type of cancer: the discovery of signatures for higher (green line in graph) or lower (red line) survival rates would help doctors to manage the expectations of their patients. Survival predictions for cutaneous melanoma were originally based on clinical parameters: tumor location, Breslow thickness (how deep it spreads into the skin), stage (size and distance spread), and grade (how its cells look under the microscope). Advances in cancer genetics led to the discovery of biomarkers (such as the V600E mutation in the BRAF gene) that enabled more accurate predictions. Advances in transcriptomics also led to biomarkers, such as the level of transcription of a gene called MITF. Guo et al. complemented these approaches by analyzing epigenomics data to identify a biomarker based on DNA methylation marks (orange box): the predictive power of the new biomarkers is higher than that of previous biomarkers.

IMAGE CREDIT:Rw251 [CC0 1.0].

To continue the search for better biomarkers researchers went from studying genomics and transcriptomics to studying epigenomic changes such as DNA methylation (Figure 1). Multiple studies have shown that the addition of methyl group to certain DNA nucleotides plays important roles in tumor formation and cancer progression. Furthermore, these methyl markers are easily detectable and remain stable in biological samples, making them clinically useful as biomarkers (Keeley et al., 2013). Now, in eLife, Qiang Wang, Jian-Qun Chen and co-workers at Nanjing University and Shanghai University – including Wenna Guo and Liucun Zhu as joint first authors – report the discovery of a biomarker based on DNA methylation that provides the most accurate predictions of melanoma survival to date (Guo et al., 2019).

Guo et al. studied the methylation profile of 461 cutaneous melanoma patients from the Cancer Genome Atlas Project (International Cancer Genome Consortium et al., 2010). Regression analysis of this dataset revealed 4,454 DNA methylation sites that were associated with overall melanoma survival. Exploring all possible combinations of these markers identified a combination of four methylation marks that could optimally predict the survival of melanoma patients (Figure 1). Intriguingly, two out of the four methylation marks are in close proximity to two genes that are known to be associated with cutaneous melanoma: OCA2, which was found to be genetically varied in melanoma patients (Law et al., 2015), and RAB37, which is a member of an oncogene family.

Understanding the biological basis of the link between these methylation marks and survival will be challenging. DNA methylation could be controlling gene expression: however, the direction of this effect would need to be determined on gene by gene basis. Interestingly, Guo et al. also found that their four-methylation-mark signature has similarities to a signature used in cancer immunotherapy. The predictive power of the new biomarker is also higher than that of other biomarkers, including the five-DNA methylation signature that can predict the immune response to tumors (Jeschke et al., 2017).

Improvements in our ability to predict disease outcome are valuable in their own right. Moreover, a better understanding of the biology responsible for the correlations observed between the methylation signature, gene expression and immunotherapy targets has the potential to contribute to the global efforts to find a cure for melanoma.


    1. International Cancer Genome Consortium
    2. Hudson TJ
    3. Anderson W
    4. Artez A
    5. Barker AD
    6. Bell C
    7. Bernabé RR
    8. Bhan MK
    9. Calvo F
    10. Eerola I
    11. Gerhard DS
    12. Guttmacher A
    13. Guyer M
    14. Hemsley FM
    15. Jennings JL
    16. Kerr D
    17. Klatt P
    18. Kolar P
    19. Kusada J
    20. Lane DP
    21. Laplace F
    22. Youyong L
    23. Nettekoven G
    24. Ozenberger B
    25. Peterson J
    26. Rao TS
    27. Remacle J
    28. Schafer AJ
    29. Shibata T
    30. Stratton MR
    31. Vockley JG
    32. Watanabe K
    33. Yang H
    34. Yuen MM
    35. Knoppers BM
    36. Bobrow M
    37. Cambon-Thomsen A
    38. Dressler LG
    39. Dyke SO
    40. Joly Y
    41. Kato K
    42. Kennedy KL
    43. Nicolás P
    44. Parker MJ
    45. Rial-Sebbag E
    46. Romeo-Casabona CM
    47. Shaw KM
    48. Wallace S
    49. Wiesner GL
    50. Zeps N
    51. Lichter P
    52. Biankin AV
    53. Chabannon C
    54. Chin L
    55. Clément B
    56. de Alava E
    57. Degos F
    58. Ferguson ML
    59. Geary P
    60. Hayes DN
    61. Hudson TJ
    62. Johns AL
    63. Kasprzyk A
    64. Nakagawa H
    65. Penny R
    66. Piris MA
    67. Sarin R
    68. Scarpa A
    69. Shibata T
    70. van de Vijver M
    71. Futreal PA
    72. Aburatani H
    73. Bayés M
    74. Botwell DD
    75. Campbell PJ
    76. Estivill X
    77. Gerhard DS
    78. Grimmond SM
    79. Gut I
    80. Hirst M
    81. López-Otín C
    82. Majumder P
    83. Marra M
    84. McPherson JD
    85. Nakagawa H
    86. Ning Z
    87. Puente XS
    88. Ruan Y
    89. Shibata T
    90. Stratton MR
    91. Stunnenberg HG
    92. Swerdlow H
    93. Velculescu VE
    94. Wilson RK
    95. Xue HH
    96. Yang L
    97. Spellman PT
    98. Bader GD
    99. Boutros PC
    100. Campbell PJ
    101. Flicek P
    102. Getz G
    103. Guigó R
    104. Guo G
    105. Haussler D
    106. Heath S
    107. Hubbard TJ
    108. Jiang T
    109. Jones SM
    110. Li Q
    111. López-Bigas N
    112. Luo R
    113. Muthuswamy L
    114. Ouellette BF
    115. Pearson JV
    116. Puente XS
    117. Quesada V
    118. Raphael BJ
    119. Sander C
    120. Shibata T
    121. Speed TP
    122. Stein LD
    123. Stuart JM
    124. Teague JW
    125. Totoki Y
    126. Tsunoda T
    127. Valencia A
    128. Wheeler DA
    129. Wu H
    130. Zhao S
    131. Zhou G
    132. Stein LD
    133. Guigó R
    134. Hubbard TJ
    135. Joly Y
    136. Jones SM
    137. Kasprzyk A
    138. Lathrop M
    139. López-Bigas N
    140. Ouellette BF
    141. Spellman PT
    142. Teague JW
    143. Thomas G
    144. Valencia A
    145. Yoshida T
    146. Kennedy KL
    147. Axton M
    148. Dyke SO
    149. Futreal PA
    150. Gerhard DS
    151. Gunter C
    152. Guyer M
    153. Hudson TJ
    154. McPherson JD
    155. Miller LJ
    156. Ozenberger B
    157. Shaw KM
    158. Kasprzyk A
    159. Stein LD
    160. Zhang J
    161. Haider SA
    162. Wang J
    163. Yung CK
    164. Cros A
    165. Cross A
    166. Liang Y
    167. Gnaneshan S
    168. Guberman J
    169. Hsu J
    170. Bobrow M
    171. Chalmers DR
    172. Hasel KW
    173. Joly Y
    174. Kaan TS
    175. Kennedy KL
    176. Knoppers BM
    177. Lowrance WW
    178. Masui T
    179. Nicolás P
    180. Rial-Sebbag E
    181. Rodriguez LL
    182. Vergely C
    183. Yoshida T
    184. Grimmond SM
    185. Biankin AV
    186. Bowtell DD
    187. Cloonan N
    188. deFazio A
    189. Eshleman JR
    190. Etemadmoghadam D
    191. Gardiner BB
    192. Gardiner BA
    193. Kench JG
    194. Scarpa A
    195. Sutherland RL
    196. Tempero MA
    197. Waddell NJ
    198. Wilson PJ
    199. McPherson JD
    200. Gallinger S
    201. Tsao MS
    202. Shaw PA
    203. Petersen GM
    204. Mukhopadhyay D
    205. Chin L
    206. DePinho RA
    207. Thayer S
    208. Muthuswamy L
    209. Shazand K
    210. Beck T
    211. Sam M
    212. Timms L
    213. Ballin V
    214. Lu Y
    215. Ji J
    216. Zhang X
    217. Chen F
    218. Hu X
    219. Zhou G
    220. Yang Q
    221. Tian G
    222. Zhang L
    223. Xing X
    224. Li X
    225. Zhu Z
    226. Yu Y
    227. Yu J
    228. Yang H
    229. Lathrop M
    230. Tost J
    231. Brennan P
    232. Holcatova I
    233. Zaridze D
    234. Brazma A
    235. Egevard L
    236. Prokhortchouk E
    237. Banks RE
    238. Uhlén M
    239. Cambon-Thomsen A
    240. Viksna J
    241. Ponten F
    242. Skryabin K
    243. Stratton MR
    244. Futreal PA
    245. Birney E
    246. Borg A
    247. Børresen-Dale AL
    248. Caldas C
    249. Foekens JA
    250. Martin S
    251. Reis-Filho JS
    252. Richardson AL
    253. Sotiriou C
    254. Stunnenberg HG
    255. Thoms G
    256. van de Vijver M
    257. van't Veer L
    258. Calvo F
    259. Birnbaum D
    260. Blanche H
    261. Boucher P
    262. Boyault S
    263. Chabannon C
    264. Gut I
    265. Masson-Jacquemier JD
    266. Lathrop M
    267. Pauporté I
    268. Pivot X
    269. Vincent-Salomon A
    270. Tabone E
    271. Theillet C
    272. Thomas G
    273. Tost J
    274. Treilleux I
    275. Calvo F
    276. Bioulac-Sage P
    277. Clément B
    278. Decaens T
    279. Degos F
    280. Franco D
    281. Gut I
    282. Gut M
    283. Heath S
    284. Lathrop M
    285. Samuel D
    286. Thomas G
    287. Zucman-Rossi J
    288. Lichter P
    289. Eils R
    290. Brors B
    291. Korbel JO
    292. Korshunov A
    293. Landgraf P
    294. Lehrach H
    295. Pfister S
    296. Radlwimmer B
    297. Reifenberger G
    298. Taylor MD
    299. von Kalle C
    300. Majumder PP
    301. Sarin R
    302. Rao TS
    303. Bhan MK
    304. Scarpa A
    305. Pederzoli P
    306. Lawlor RA
    307. Delledonne M
    308. Bardelli A
    309. Biankin AV
    310. Grimmond SM
    311. Gress T
    312. Klimstra D
    313. Zamboni G
    314. Shibata T
    315. Nakamura Y
    316. Nakagawa H
    317. Kusada J
    318. Tsunoda T
    319. Miyano S
    320. Aburatani H
    321. Kato K
    322. Fujimoto A
    323. Yoshida T
    324. Campo E
    325. López-Otín C
    326. Estivill X
    327. Guigó R
    328. de Sanjosé S
    329. Piris MA
    330. Montserrat E
    331. González-Díaz M
    332. Puente XS
    333. Jares P
    334. Valencia A
    335. Himmelbauer H
    336. Himmelbaue H
    337. Quesada V
    338. Bea S
    339. Stratton MR
    340. Futreal PA
    341. Campbell PJ
    342. Vincent-Salomon A
    343. Richardson AL
    344. Reis-Filho JS
    345. van de Vijver M
    346. Thomas G
    347. Masson-Jacquemier JD
    348. Aparicio S
    349. Borg A
    350. Børresen-Dale AL
    351. Caldas C
    352. Foekens JA
    353. Stunnenberg HG
    354. van't Veer L
    355. Easton DF
    356. Spellman PT
    357. Martin S
    358. Barker AD
    359. Chin L
    360. Collins FS
    361. Compton CC
    362. Ferguson ML
    363. Gerhard DS
    364. Getz G
    365. Gunter C
    366. Guttmacher A
    367. Guyer M
    368. Hayes DN
    369. Lander ES
    370. Ozenberger B
    371. Penny R
    372. Peterson J
    373. Sander C
    374. Shaw KM
    375. Speed TP
    376. Spellman PT
    377. Vockley JG
    378. Wheeler DA
    379. Wilson RK
    380. Hudson TJ
    381. Chin L
    382. Knoppers BM
    383. Lander ES
    384. Lichter P
    385. Stein LD
    386. Stratton MR
    387. Anderson W
    388. Barker AD
    389. Bell C
    390. Bobrow M
    391. Burke W
    392. Collins FS
    393. Compton CC
    394. DePinho RA
    395. Easton DF
    396. Futreal PA
    397. Gerhard DS
    398. Green AR
    399. Guyer M
    400. Hamilton SR
    401. Hubbard TJ
    402. Kallioniemi OP
    403. Kennedy KL
    404. Ley TJ
    405. Liu ET
    406. Lu Y
    407. Majumder P
    408. Marra M
    409. Ozenberger B
    410. Peterson J
    411. Schafer AJ
    412. Spellman PT
    413. Stunnenberg HG
    414. Wainwright BJ
    415. Wilson RK
    416. Yang H
    (2010) International network of cancer genome projects
    Nature 464:993–998.
    1. Miller AJ
    2. Mihm MC
    (2006) Melanoma
    New England Journal of Medicine 355:51–65.

Article and author information

Author details

  1. Mykyta Artomov

    Mykyta Artomov is in the Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, and the Broad Institute, Cambridge, United States

    For correspondence
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5282-8764

Publication history

  1. Version of Record published: June 6, 2019 (version 1)


© 2019, Artomov

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.


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