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Poster Display session 3

2157 - Immune status defined by molecular information layers predicts response to pembrolizumab treatment in advanced melanoma

Date

30 Sep 2019

Session

Poster Display session 3

Presenters

Guillermo Prado-Vázquez

Citation

Annals of Oncology (2019) 30 (suppl_5): v533-v563. 10.1093/annonc/mdz255

Authors

G. Prado-Vázquez1, L. Trilla-Fuertes2, A. Gámez-Pozo2, R. López-Vacas2, E. López-Camacho2, A. Zapater-Moros2, J. M. Arevalillo3, H. Navarro3, M. Díaz-Almirón4, P. Maín5, J. Feliu Batlle6, J.A. Fresno Vara2, E.A. Espinosa6

Author affiliations

  • 1 Molecular Oncology, Hospital Universitario La Paz, 28046 - Madrid/ES
  • 2 Molecular Oncology, Hospital Universitario La Paz, 28036 - Madrid/ES
  • 3 Operational Research And Numerical Analysis, National Distance Education University (UNED), 28040 - Madrid/ES
  • 4 Biostatistics, Hospital Universitario La Paz, 28036 - Madrid/ES
  • 5 Department Of Statistics And Operations Research, Faculty of Mathematics, Complutense University of Madrid, 28040 - Madrid/ES
  • 6 Medical Oncology, Hospital Universitario La Paz, 28046 - Madrid/ES
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Resources

Abstract 2157

Background

The molecular analysis of melanoma has improved our understanding of the disease. The Cancer Genome Atlas (TCGA) Network proposed the molecular classification of melanoma in three subtypes: keratin-high, immune-high and membrane-low. However, this classification has not translated into therapeutic advances so far. Immunotherapy has contributed to improve survival, yet the mechanisms explaining differences in efficacy have not been elucidated. The aim of this study is to characterize the immune status of melanoma tumors through gene expression, and to analyze if these differences have an impact in the response to immunotherapy.

Methods

A probabilistic graphical model, followed by successive sparse k-means and consensus cluster analyses, was used to classify melanoma tumor samples from the TCGA cohort. Findings were translated into a cohort of patients treated with anti-PD1 antibodies (GSE78220, Hugo W et al) as a validation dataset.

Results

A probabilistic graphical model, including the 2,971 more variable genes from 472 melanoma samples from the TCGA dataset was built, and the resulting graph was processed to seek functional structures. Sparse k-means selected 119 genes. Gene ontology analysis showed that these genes were mainly related with immune processes. Immune genes split the population into two groups with different immune status. The so-called immune-high group included 232 patients (49%) and the immune-low group groups 238 patients (51%). The validation dataset GSE78220 provided mRNA expression in melanomas being treated with anti-PD-1 antibodies (28 biopsies belonging to 27 patients). The immune layer was translated to the new cohort by centroid method: 9 patients had immune-low tumors, whereas the remaining 18 had immune-high tumors. Kaplan Meier analysis using the clinical data from the GSE78220 cohort found a favorable response in patients with immune-low tumors (90% long-term survival).

Conclusions

We found a gene signature related to the tumor immune status that split the TCGA cohort in two groups. When applied to a cohort of patients treated with anti-PD1 antibodies, the group with immune-low tumors had 90% of long-term survival.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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