# Evaluation of the effect of patient-context factors and sample acquisition on the quality and analytical performance of cell-free DNA and circulating tumor cells profiling assays in prostate cancer pa

> **NIH NIH U01** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $404,888

## Abstract

Summary
The analysis of liquid biopsy (eg, cell-free DNA [cfDNA]; circulating tumor cells [CTC]) in blood is increasingly
integrated in clinical contexts including diagnosis, disease monitoring, understanding resistance, and early
detection of relapse. The key challenge of detecting these analytes is that they are present at a very low
proportion of the biospecimens, and therefore are heavily influenced by pre-analytical factors associated with
acquisition and processing. Understanding effects of these pre-analytical variables on the quality of data
generated in downstream molecular CTC and cfDNA assays is critical for robust clinical implementation of
liquid biopsy tests. To date, research efforts have focused on effects of preservation methods, processing time,
storage temp, and shipment conditions on quality of CTC and cfDNA in blood plasma. There are no studies
reported on effects of patient-specific context such as fasting, administration of anti-emetics, or biospecimen
acquisition procedures (eg, order of blood collection aliquots, time of day when blood is drawn, etc.) There is a
lack of data on this type of pre-analytical variable that impacts design of clinical trials such as optimal timing for
blood draw and interpretation of data to distinguish technical variables introduced by these pre-analytical
factors from the biological signals being evaluated. We propose to address this gap by extending the work
done by our team members on evaluating effects of sample processing protocols on cfDNA and CTC analysis,
to further investigate effect of patient-specific context. Hypothesis: Pre-analytic variables may affect signal-to-
noise ratio in cfDNA and CTC analysis and thus have a higher impact on quantification at levels close to the
assay limit of detection. Aim 1: Determine the effect of patient-specific context on the quality of cell-free DNA
(cfDNA) and circulating tumor cells (CTC) in prostate cancer patients. Aim 2: Evaluate the impact of these
variables on the performance of downstream cfDNA and CTC molecular profiling assays. We will apply an
adaptive design in which we perform initial analysis with 20 patients per cohort, then adjust as needed. In a
foundation-funded pilot study, we focused on one cohort to study effect of draw order. Results confirm the
variability of biomarkers quantification as a result of pre-analytical variables.
Significance: Results will elucidate effects of multiple pre-analytical variables specific to individual patient
context on performance of blood-based biomarker analysis in cfDNA and CTC. These data will inform the
design of liquid biopsy-incorporated clinical trials by identifying optimal timing of blood collection to minimize
effects of pre-analytical variables. Innovation: This will be the first study to examine the effect of patient-
specific context on quality of liquid biopsy data. We will collaborate closely with commercial liquid biopsy test
developers as part of the Blood Profiling Atlas in Canc...

## Key facts

- **NIH application ID:** 10054009
- **Project number:** 1U01CA253217-01
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Maria E Arcila
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $404,888
- **Award type:** 1
- **Project period:** 2020-09-08 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10054009

## Citation

> US National Institutes of Health, RePORTER application 10054009, Evaluation of the effect of patient-context factors and sample acquisition on the quality and analytical performance of cell-free DNA and circulating tumor cells profiling assays in prostate cancer pa (1U01CA253217-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10054009. Licensed CC0.

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