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Background <p>Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment, particularly in advanced non-small cell lung cancer (NSCLC) and muscle-invasive bladder cancer (MIBC). However, identifying reliable predictive biomarkers for ICI response remains a significant challenge. In this study, we analyzed real-world cohorts of advanced NSCLC and MIBC patients treated with ICI as first-line therapy.</p> Methods <p>Tumor samples underwent Whole Genome Sequencing (WGS) to identify specific somatic variants and assess tumor mutational burden (TMB). Additionally, mutational signature extraction and pathway enrichment analyses were performed to uncover the underlying mechanisms of ICI response. We also characterized HLA-I haplotypes and investigated LINE-1 retrotransposition.</p> Results <p>Distinct mutation patterns were identified in patients who responded to treatment, suggesting potential biomarkers for predicting ICI effectiveness. In NSCLC, tumor mutational burden (TMB) did not differ significantly between responders and non-responders, while in MIBC, higher TMB was linked to better responses. Specific mutational signatures and HLA haplotypes were associated with ICI response in both cancers. Pathway analysis showed that NSCLC responders had active inflammatory and immune pathways, while pathways enriched in non-responders&#xa0;related to FGFR3 and neural crest differentiation, associated to resistance mechanisms. In MIBC, responders had alterations in DNA repair, leading to more neoantigens and a stronger ICI response. Importantly, for the first time, we found that LINE-1 activation was positively linked to ICI response, especially in MIBC.</p> Conclusion <p>These findings reveal promising biomarkers and mechanistic insights, offering a new perspective on predicting ICI response and opening up exciting possibilities for more personalized immunotherapy strategies in NSCLC and MIBC.</p>

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Genomics guiding personalized first-line immunotherapy response in lung and bladder tumors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月5日

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  • Jenifer Brea-Iglesias,
  • María Gallardo-Gómez,
  • Ana Oitabén,
  • Martin E. Lázaro-Quintela,
  • Luis León,
  • Joao M. Alves,
  • Manuel Pino-González,
  • Laura Juaneda-Magdalena,
  • Carme García-Benito,
  • Ihab Abdulkader,
  • Laura Muinelo,
  • Jesús M. Paramio,
  • Mónica Martínez-Fernández

摘要

Background

Immune checkpoint inhibitors (ICI) have revolutionized cancer treatment, particularly in advanced non-small cell lung cancer (NSCLC) and muscle-invasive bladder cancer (MIBC). However, identifying reliable predictive biomarkers for ICI response remains a significant challenge. In this study, we analyzed real-world cohorts of advanced NSCLC and MIBC patients treated with ICI as first-line therapy.

Methods

Tumor samples underwent Whole Genome Sequencing (WGS) to identify specific somatic variants and assess tumor mutational burden (TMB). Additionally, mutational signature extraction and pathway enrichment analyses were performed to uncover the underlying mechanisms of ICI response. We also characterized HLA-I haplotypes and investigated LINE-1 retrotransposition.

Results

Distinct mutation patterns were identified in patients who responded to treatment, suggesting potential biomarkers for predicting ICI effectiveness. In NSCLC, tumor mutational burden (TMB) did not differ significantly between responders and non-responders, while in MIBC, higher TMB was linked to better responses. Specific mutational signatures and HLA haplotypes were associated with ICI response in both cancers. Pathway analysis showed that NSCLC responders had active inflammatory and immune pathways, while pathways enriched in non-responders related to FGFR3 and neural crest differentiation, associated to resistance mechanisms. In MIBC, responders had alterations in DNA repair, leading to more neoantigens and a stronger ICI response. Importantly, for the first time, we found that LINE-1 activation was positively linked to ICI response, especially in MIBC.

Conclusion

These findings reveal promising biomarkers and mechanistic insights, offering a new perspective on predicting ICI response and opening up exciting possibilities for more personalized immunotherapy strategies in NSCLC and MIBC.