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The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Concurrent tissue and circulating tumor DNA analysis in renal cell carcinoma: insights from a multimodal database
Chinmay T Jani1, Elizabeth Tran2, Ellen Jaeger3
1University of Miami/Sylvester Comprehensive Cancer Center, Miami, FL, 33136, United States.
Introduction:
Circulating tumor DNA (ctDNA) sequencing complements tissue-based next-generation sequencing (NGS), offering noninvasive and serial testing. We explore the mutational landscape of renal cell carcinoma (RCC) using matched tissue and ctDNA data to assess complementarity and clinical significance of molecular alterations.
Methods:
From the Tempus multimodal database, we retrospectively analyzed de-identified data from patients with RCC with concurrent tissue (Tempus xT) and ctDNA testing (Tempus xF). Patients with xT and xF matched samples (collected ±90 days of one another) were included. We evaluated socio-demographic and clinical characteristics and selected pathogenic somatic short variants (PSSVs) and copy number variants (CNV). Analyses were restricted to the 104 genes shared by all assays.
Results:
Among 392 patients, 66% (n = 259) had metastatic disease. The median time from tissue to blood collection was 21 days. The most common tissue sites were kidney (49%, n = 189) and bone (11%, n = 43). Frequently altered tissue-tested genes were: VHL (59%), PBRM1 (32%), and SETD2 (23%). Most frequently altered genes in ctDNA were TP53 (23%), VHL (18%), BAP1 (6%), and PBRM1 (5%); notably, 176 patients did not have any pathogenic or likely pathogenic variants detected in the 104 genes analyzed. Complementary ctDNA and tissue testing detected 6% more alterations than tissue testing alone, with greater concordance in metastatic cases.
Conclusion:
ctDNA testing offers complementary insights to tissue NGS in RCC, particularly in metastatic disease, suggesting the potential utility of ctDNA in advanced RCC. Longitudinal analysis may enhance delineation of biomarkers of response and resistance at mutation and ctDNA fraction levels.
Insights
Circulating tumor DNA (ctDNA) sequencing provides valuable insights complementing tissue-based next-generation sequencing (NGS) for renal cell carcinoma (RCC). This noninvasive approach shows particular promise in detecting molecular alterations in advanced or metastatic RCC.
Area of Science:
- Oncology
- Genomics
- Molecular Diagnostics
Background:
- Next-generation sequencing (NGS) of tissue is standard for renal cell carcinoma (RCC) molecular profiling.
- Circulating tumor DNA (ctDNA) sequencing offers a noninvasive, serial alternative for detecting genetic alterations.
- Understanding the complementarity of tissue and ctDNA sequencing is crucial for advancing RCC diagnostics.
Purpose of the Study:
- To explore the mutational landscape of RCC using matched tissue and ctDNA data.
- To assess the complementarity and clinical significance of molecular alterations detected by both methods.
- To evaluate the utility of ctDNA sequencing in RCC patient management.
Main Methods:
- Retrospective analysis of de-identified data from the Tempus multimodal database.
- Inclusion of RCC patients with concurrent tissue (Tempus xT) and ctDNA testing (Tempus xF) within 90 days.
- Evaluation of socio-demographic, clinical characteristics, and pathogenic somatic variants (PSSVs) and copy number variants (CNVs) in 104 shared genes.
Main Results:
- Analysis included 392 RCC patients, 66% with metastatic disease.
- Frequently altered genes in tissue: VHL (59%), PBRM1 (32%), SETD2 (23%).
- Frequently altered genes in ctDNA: TP53 (23%), VHL (18%), BAP1 (6%), PBRM1 (5%).
- Combined ctDNA and tissue testing detected 6% more alterations than tissue alone, with higher concordance in metastatic cases.
Conclusions:
- ctDNA sequencing offers complementary molecular insights to tissue NGS in RCC.
- ctDNA testing demonstrates potential utility in advanced and metastatic RCC.
- Longitudinal ctDNA analysis may help delineate biomarkers for treatment response and resistance.

