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Tumour gene expression signature in primary melanoma predicts long-term outcomes.

ABSTRACT: Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR?=?1.63, p?=?5.24 × 10^-5) and overall survival (HR?=?1.61, p?=?1.67 × 10^-4), was validated in 175 regional lymph nodes metastasis as well as two externally ascertained datasets. The machine learning classification models trained using the signature genes performed significantly better in predicting metastases than models trained with clinical covariates (p_AUROC = 7.03 × 10^-4), or published prognostic signatures (p_AUROC < 0.05). The signature score negatively correlated with measures of immune cell infiltration (??=?-0.75, p?-16), with a higher score representing reduced lymphocyte infiltration and a higher 5-year risk of death in stage II melanoma. Our expression signature identifies melanoma patients at higher risk of metastases and warrants further evaluation in adjuvant clinical trials.

SUBMITTER: Garg M

PROVIDER: S-EPMC7893180 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Tumour gene expression signature in primary melanoma predicts long-term outcomes.

Garg Manik M Couturier Dominique-Laurent DL Nsengimana Jérémie J Fonseca Nuno A NA Wongchenko Matthew M Yan Yibing Y Lauss Martin M Jönsson Göran B GB Newton-Bishop Julia J Parkinson Christine C Middleton Mark R MR Bishop D Timothy DT McDonald Sarah S Stefanos Nikki N Tadross John J Vergara Ismael A IA Lo Serigne S Newell Felicity F Wilmott James S JS Thompson John F JF Long Georgina V GV Scolyer Richard A RA Corrie Pippa P Adams David J DJ Brazma Alvis A Rabbie Roy R

Nature communications 20210218 1

Adjuvant systemic therapies are now routinely used following resection of stage III melanoma, however accurate prognostic information is needed to better stratify patients. We use differential expression analyses of primary tumours from 204 RNA-sequenced melanomas within a large adjuvant trial, identifying a 121 metastasis-associated gene signature. This signature strongly associated with progression-free (HR = 1.63, p = 5.24 × 10<sup>-5</sup>) and overall survival (HR = 1.61, p = 1.67 × 10<sup> ...[more]

PMID: 33602918

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Project description:BackgroundPatients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes.Materials and methodsIn a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190).ResultsThe post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF.ConclusionPost-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.

Dataset Information

Tumour gene expression signature in primary melanoma predicts long-term outcomes.

Publications

Tumour gene expression signature in primary melanoma predicts long-term outcomes.

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