Breast cancer ribbon intertwined with a DNA strand.

Decoding Breast Cancer Genes: Fresh vs. Archived Tissues

Elliot Brynn in Health & Wellness December 2025 • 5 min read.

"Can we reliably use old tissue samples to guide modern breast cancer treatment?"

In the fight against breast cancer, understanding the unique genetic makeup of a tumor is crucial. Gene expression profiling, which analyzes the activity levels of different genes, has become a powerful tool for identifying potential diagnostic and therapeutic targets. This information can help doctors classify breast cancers, predict how they will respond to treatment, and tailor therapies accordingly.

However, a significant hurdle lies in the availability of suitable tissue samples for these analyses. While fresh frozen (FF) tissue provides the most accurate snapshot of a tumor's gene expression, it's often challenging to obtain and store in large quantities. On the other hand, formalin-fixed, paraffin-embedded (FFPE) samples, which are routinely collected and stored in pathology archives, represent a vast resource of clinical information. But, the process of preserving these FFPE samples can degrade RNA, the molecule that carries genetic information, making it difficult to analyze.

This article explores the challenges and potential solutions of using FFPE tissue for gene expression profiling. We'll dive into a study that compares gene expression data from matched FF and FFPE breast cancer samples, investigates different normalization strategies to correct for RNA degradation, and ultimately, helps guide the development of reliable diagnostic tests using archived tissues.

The FFPE Challenge: RNA Degradation and Normalization

Breast cancer ribbon intertwined with a DNA strand.

The main issue with FFPE samples is that the RNA within them is often degraded. Think of RNA as a delicate string of beads. The preservation process can chop those strings into smaller pieces, making it harder to get an accurate reading of gene expression. The study confirms this, showing a noticeable shift in raw cycle threshold (Cq) values – a measure of gene expression – in FFPE samples compared to FF samples. Higher Cq values in FFPE indicate lower RNA integrity due to degradation.

Fortunately, scientists have developed strategies to compensate for this degradation, primarily through normalization. Normalization acts like a volume control, adjusting the expression levels of genes to account for differences in RNA quality or quantity between samples. The goal is to ensure that any observed differences in gene expression are real biological variations, not artifacts of the preservation process.

Researchers tested several normalization methods, including:

geNorm: Identifies the most stable genes across a set of samples to use as controls.
NormFinder: Selects the best single control gene or combination of two genes, considering different sample subgroups (FF vs. FFPE).
Mean Cq per sample: Uses the average expression level of all genes in a sample as a normalization factor.
NorMean: A new model developed in the study that combines the coefficient of variation (CV) and Pearson correlation coefficient to identify stable control genes.

The study found that proper normalization can indeed improve the correlation between gene expression values in FF and FFPE samples. However, the effectiveness of normalization depends on the gene itself.

The Future of FFPE in Breast Cancer Diagnostics

The researchers found that normalization works best for genes that are moderately to highly expressed and show significant variation between samples. These genes are more likely to provide reliable information, even when extracted from FFPE tissue. However, some genes consistently failed to correlate between FF and FFPE samples, regardless of the normalization method used.

This highlights a critical point: not all genes are suitable for clinical tests based on FFPE samples. Genes that show poor correlation should be excluded from consideration to avoid inaccurate results and potentially flawed treatment decisions.

Ultimately, this research provides valuable guidance for developing clinical diagnostic tests that leverage the vast archive of FFPE tissues. By carefully selecting genes and applying appropriate normalization strategies, scientists can unlock the wealth of information stored within these samples, paving the way for more personalized and effective breast cancer treatments.

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This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.2144/000113388, Alternate LINK

Title: Comparison Of Gene Expression Profiling By Reverse Transcription Quantitative Pcr Between Fresh Frozen And Formalin-Fixed, Paraffin-Embedded Breast Cancer Tissues

Subject: General Biochemistry, Genetics and Molecular Biology

Journal: BioTechniques

Publisher: Future Science Ltd

Authors: Iker Sánchez-Navarro, Angelo Gámez-Pozo, Manuel González-Barón, Álvaro Pinto-Marín, David Hardisson, Rocío López, Rosario Madero, Paloma Cejas, Marta Mendiola, Enrique Espinosa, Juan Ángel Fresno Vara

Published: 2010-05-01

Everything You Need To Know

Why is fresh frozen tissue preferred for gene expression profiling in breast cancer?

Fresh frozen tissue, or FF, offers the most precise snapshot of a tumor's gene expression because it minimizes RNA degradation. However, obtaining and storing FF tissue in sufficient quantities for research and diagnostics can be difficult, limiting its accessibility for widespread use.

What is the primary challenge when using formalin-fixed, paraffin-embedded (FFPE) tissue samples for gene expression profiling, and how do normalization methods address this?

The main challenge is that formalin-fixed, paraffin-embedded samples, or FFPE, often have degraded RNA due to the preservation process. This degradation can affect the accuracy of gene expression profiling. Normalization methods, like geNorm, NormFinder, Mean Cq per sample, and NorMean, are used to compensate for this degradation by adjusting gene expression levels.

Can you explain how normalization methods like geNorm, NormFinder, Mean Cq per sample, and NorMean improve the accuracy of gene expression analysis using FFPE tissue?

Normalization methods, such as geNorm, which identifies stable genes, NormFinder, which selects control genes considering sample subgroups, Mean Cq per sample, which uses the average expression level, and NorMean, which combines coefficient of variation and Pearson correlation, help correct for variations caused by RNA degradation in FFPE samples. By applying these methods, researchers aim to differentiate genuine biological differences in gene expression from artifacts introduced during tissue preservation.

Does normalization work equally well for all genes when using FFPE tissue, and what factors influence its effectiveness?

The effectiveness of normalization depends on the specific gene being analyzed. Genes that are moderately to highly expressed and exhibit significant variation between samples tend to yield more reliable information from FFPE tissue after normalization. However, some genes may not correlate well between FF and FFPE samples regardless of the normalization method used, suggesting inherent limitations in using FFPE tissue for those specific genes.

What are the implications of this research for the future of breast cancer diagnostics, and does it mean FFPE samples can completely replace fresh frozen tissue?

The study highlights that while normalization techniques can improve the reliability of gene expression data from FFPE samples, not all genes are equally amenable to this approach. This implies that for certain critical diagnostic or therapeutic genes, fresh frozen tissue might still be necessary to ensure accurate profiling. Future research could focus on refining normalization methods or developing new techniques to mitigate RNA degradation in FFPE samples, potentially expanding the range of genes that can be reliably analyzed from archived tissues, and reduce reliance on FF tissues.