Société Internationale d’Urologie Journal (Jul 2021)

Primary Adult Retroperitoneal Sarcoma: A Comprehensive Genomic Profiling Study

  • Andrea Necchi,
  • Giuseppe Basile,
  • Filippo Pederzoli,
  • Marco Bandini,
  • Petros Grivas,
  • Gennady Bratslavsky,
  • Philippe E. Spiess,
  • J. Keith Killian,
  • Douglas I. Lin,
  • Erik Williams,
  • Shakti Ramkissoon,
  • Eric A. Severson,
  • Brian M. Alexander,
  • Jeffrey Venstrom,
  • Prasanth Reddy,
  • Kimberley McGregor,
  • Julia A. Elvin,
  • Alexa B. Schrock,
  • Dean V. Pavlick,
  • Dexter X. Jin,
  • Sally E. Trabucco,
  • Natalie Danzinger,
  • Jeffrey S. Ross

DOI
https://doi.org/10.48083/VOGF2319
Journal volume & issue
Vol. 2, no. 4
pp. 216 – 228

Abstract

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BackgroundAdult primary retroperitoneal sarcomas (RPSs) are a group of heterogeneous tumors with different histological subtypes. Comprehensive genomic profiling (CGP) analyses have recently provided significant insights into the biology of sarcomas by identifying genomic alterations (GAs) which could benefit from targeted therapies. MethodsRPS were evaluated by CGP using next-generation sequencing of up to 406 cancer-related genes. Tumor mutational burden (TMB) was determined on 0.83 to 1.14 mut/Mb of sequenced DNA. Finally, PD-L1 expression was determined. ResultsOverall, 296 cases of primary RPS were analyzed. Liposarcoma (LPS) subtype had more GA/tumor than leiomyosarcoma (LMS) subtypes, with follicular dendritic cell sarcomas harboring the highest and synovial sarcomas the lowest. TP53 and Rb1 alterations were the highest in LMS, and CDK4/6 and MDM2 in LPS. However, both the TMB and targetable GA rates were low across subtypes. PD-L1 immunostaining was low positive in 21% and high positive in 5% of patients, respectively. ConclusionsCGP analysis revealed that potentially actionable genomic targets were rare in our cohort of RPS. Moreover, RPSs seem less likely to respond to immune checkpoint inhibitors based on putative biomarkers status. Nevertheless, genomic stratification according to histological subtypes led to description of GAs that can inform future clinical trials design.

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