Abstract 114: Unraveling tumor- and tissue-specific gene expression patterns from plasma-derived cfRNA.

Abstract Minimally invasive plasma-derived cell-free RNA (cfRNA) profiling captures blood-derived clinical markers when white blood cells (WBCs) are unavailable, harboring potential for molecular diagnostics and disease monitoring. Our study explored cfRNA analysis as a promising method for deconvolving signals from tumors and various tissues from a single blood draw, expanding its utility to include functional genomics that reveals gene expression patterns and their impact on disease status. Plasma cfRNA was obtained from 207 healthy donors and 214 cancer patients. For comparison, 96 paired tumor whole exome sequencing (WES) and 208 paired WBC RNA-seq samples were used. Gene expression was quantified using pseudo-alignment with Kallisto and signature scores were computed by single-sample gene set enrichment analysis. Statistical significance was assessed with the Mann-Whitney U test. Somatic single nucleotide variants (SNVs) identified by tumor WES were cross-checked for detection in matched plasma cfRNA. Extracellular matrix organization, cell proliferation, and T- and B-cell recruitment signatures were significantly elevated in cfRNA compared with WBCs (p values 1.1×10-55, 3.8×10-63, and 2.8×10-62, respectively). These cfRNA (but not WBC) signatures effectively distinguished cancer patients from healthy donors (p value 6.5×10-11, 1.1×10-4, and 2.3×10-5, respectively). The epithelial signature was enriched in carcinoma cfRNA, with higher scores corresponding to larger numbers of metastatic sites (p value 1.5×10-7). Conversely, this score remained low in sarcomas originating from non-epithelial tissues. SNVs identified by tumor WES were detected in cfRNA in 20/29 (68.9%) of breast, 8/14 (57.1%) of colorectal, 7/13 (53.8%) of lung, and 5/11 (45.4%) of pancreatic cancer cases. The number of variants detected per patient trended positively with cancer stage. In colorectal cancer, samples with detected variants had higher epithelial and colon tissue signature scores, which aligns with reported cfDNA shedding levels for the diagnosis . Fisher’s exact test showed the cfRNA-tumor variant overlap to occur significantly more often (p-value = 9×10-129) in matched pairs than in random sample comparisons, indicative of the reliability of our approach for detecting tumor-related signals. This proof-of-concept study shows that cfRNA harbors tumor- and tissue-specific expression patterns that complement blood cell-based RNA-seq, providing additional biological and clinical information. Our framework integrates genomic and transcriptomic data to deconvolve tumor-related and systemic tissue signals from plasma, supporting cfRNA signatures as a clinically relevant resource for patient stratification, discovery of novel drug targets and plasma-based biomarkers, and disease progression and treatment response monitoring, with prospects for better trial design and treatment optimization. Citation Format: Maria Savchenko, Tatiana Nemchaninova, Alexey Dudakov, Aleksandr Serdiukov, Daria Shafranskaya, Artemiy Nikitin, Anastasia Tarabarova, Desiree Schenk, Alexandr Zaitsev, Anastasia Yudina, Michael F. Goldberg, Alexander Bagaev. Unraveling tumor- and tissue-specific gene expression patterns from plasma-derived cfRNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 114.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-114
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 114: Unraveling tumor- and tissue-specific gene expression patterns from plasma-derived cfRNA.

Desiree Schenk, Daria Shafranskaya, Anastasia Yudina, Michael Goldberg et al.
Cancer Research
Cancer Genomics and Diagnostics
article

Abstract 114: Unraveling tumor- and tissue-specific gene expression patterns from plasma-derived cfRNA.

Desiree Schenk, Daria Shafranskaya, Anastasia Yudina, Michael Goldberg, Maria Savchenko, Alexey Dudakov, Alexander Bagaev, Alexandr Zaitsev, Anastasia Tarabarova, Aleksandr Serdiukov, Artemiy Nikitin, Tatiana Nemchaninova
article en

Abstract

Abstract Minimally invasive plasma-derived cell-free RNA (cfRNA) profiling captures blood-derived clinical markers when white blood cells (WBCs) are unavailable, harboring potential for molecular diagnostics and disease monitoring. Our study explored cfRNA analysis as a promising method for deconvolving signals from tumors and various tissues from a single blood draw, expanding its utility to include functional genomics that reveals gene expression patterns and their impact on disease status. Plasma cfRNA was obtained from 207 healthy donors and 214 cancer patients. For comparison, 96 paired tumor whole exome sequencing (WES) and 208 paired WBC RNA-seq samples were used. Gene expression was quantified using pseudo-alignment with Kallisto and signature scores were computed by single-sample gene set enrichment analysis. Statistical significance was assessed with the Mann-Whitney U test. Somatic single nucleotide variants (SNVs) identified by tumor WES were cross-checked for detection in matched plasma cfRNA. Extracellular matrix organization, cell proliferation, and T- and B-cell recruitment signatures were significantly elevated in cfRNA compared with WBCs (p values 1.1×10-55, 3.8×10-63, and 2.8×10-62, respectively). These cfRNA (but not WBC) signatures effectively distinguished cancer patients from healthy donors (p value 6.5×10-11, 1.1×10-4, and 2.3×10-5, respectively). The epithelial signature was enriched in carcinoma cfRNA, with higher scores corresponding to larger numbers of metastatic sites (p value 1.5×10-7). Conversely, this score remained low in sarcomas originating from non-epithelial tissues. SNVs identified by tumor WES were detected in cfRNA in 20/29 (68.9%) of breast, 8/14 (57.1%) of colorectal, 7/13 (53.8%) of lung, and 5/11 (45.4%) of pancreatic cancer cases. The number of variants detected per patient trended positively with cancer stage. In colorectal cancer, samples with detected variants had higher epithelial and colon tissue signature scores, which aligns with reported cfDNA shedding levels for the diagnosis . Fisher’s exact test showed the cfRNA-tumor variant overlap to occur significantly more often (p-value = 9×10-129) in matched pairs than in random sample comparisons, indicative of the reliability of our approach for detecting tumor-related signals. This proof-of-concept study shows that cfRNA harbors tumor- and tissue-specific expression patterns that complement blood cell-based RNA-seq, providing additional biological and clinical information. Our framework integrates genomic and transcriptomic data to deconvolve tumor-related and systemic tissue signals from plasma, supporting cfRNA signatures as a clinically relevant resource for patient stratification, discovery of novel drug targets and plasma-based biomarkers, and disease progression and treatment response monitoring, with prospects for better trial design and treatment optimization. Citation Format: Maria Savchenko, Tatiana Nemchaninova, Alexey Dudakov, Aleksandr Serdiukov, Daria Shafranskaya, Artemiy Nikitin, Anastasia Tarabarova, Desiree Schenk, Alexandr Zaitsev, Anastasia Yudina, Michael F. Goldberg, Alexander Bagaev. Unraveling tumor- and tissue-specific gene expression patterns from plasma-derived cfRNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 114.

Cancer ResearchVol. 86(7_Supplement)
Repligen (United States) (US)
Good health and well-being
Openalex Percentile: Top 12%
Cancer Genomics and Diagnostics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.