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Abstract Background Risk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear. Methods Data from patients with clear cell renal cell carcinoma (ccRCC) was downloaded from The Cancer Genome Atlas. Clinicopathologic variables were used to calculate SSIGN (stage, size, grade, and necrosis) scores. The 16 gene recurrence score (RS) signature was generated using RNA‐seq data. Transcriptomic risk groups were calculated using the original thresholds. SSIGN groups were divided into low, intermediate, and high risk. Disease‐free status was the primary endpoint assessed. Results SSIGN and RS were calculated for 428 patients with non‐metastatic ccRCC. SSIGN low‐, intermediate‐, and high‐risk groups demonstrated 2.7%, 15.2%, and 27.5%, 3‐year recurrence risk, respectively. On multivariable analysis, the RS was associated with disease‐free status (sub‐distribution hazard ratio (sHR) 1.43 per 25 RS [95% CI (1.00–1.43)], p = 0.05). By risk groups, RS further risk stratified the SSIGN intermediate‐risk group (sHR 2.22 [95% CI 1.10–4.50], p = 0.03). SSIGN intermediate‐risk patients with low and high RS had a 3‐year recurrence rate of 8.0% and 25.2%, respectively. Within this risk group, the area under the curve (AUC) at 3 years was 0.69 for SSIGN, 0.74 for RS, and 0.78 for their combination. Conclusions Transcriptomic recurrence scores improve risk prediction even when controlling for clinicopathologic factors. Utility may be best suited for intermediate‐risk patients who have heterogeneous outcomes and further refinement for clinical utility is warranted.

Summary: Background: Inflammatory and immune responses are essential and dynamic biological processes that protect the body against acute and chronic adverse stimuli. While conventional protein markers have been used to evaluate systemic inflammatory response, the immunological response to stimulation is complex and involves modulation of a large set of genes and interacting signalling pathways of innate and adaptive immune systems. There is a need for a non-invasive tool that can comprehensively evaluate and monitor molecular dysregulations associated with inflammatory and immune responses in circulation and in inaccessible solid organs. Methods: Here we utilized cell-free messenger RNA (cf-mRNA) RNA-Seq whole transcriptome profiling and computational biology to temporally assess lipopolysaccharide (LPS) induced and JAK inhibitor modulated inflammatory and immune responses in mouse plasma samples. Findings: Cf-mRNA profiling displayed a pattern of systemic immune responses elicited by LPS and dysregulation of associated pathways. Moreover, attenuation of several inflammatory pathways, including STAT and interferon pathways, were observed following the treatment of JAK inhibitor. We further identified the dysregulation of liver-specific transcripts in cf-mRNA which reflected changes in the gene-expression pattern in this generally inaccessible biological compartment. Interpretation: Using a preclinical mouse model, we demonstrated the potential of plasma cf-mRNA profiling for systemic and organ-specific characterization of drug-induced molecular alterations that are associated with inflammatory and immune responses. Funding: Molecular Stethoscope.

Circulating cell-free mRNA holds great promise as a non-invasive diagnostic biomarker. Here the authors show that cell-free mRNA captures transcripts from the bone marrow and can be used to non-invasively monitor dynamic changes in bone marrow physiology.

Abstract Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.