How Pathway Analysis Reveals the Disease's Hidden Networks
Breast cancer affects millions of women worldwide, with approximately 2.3 million new cases diagnosed globally each year 2 . For decades, scientists have known that breast cancer isn't a single disease but rather a collection of different conditions with varying genetic drivers and clinical outcomes. What makes this disease so challenging to combat is its remarkable complexity—not just one gene, but entire networks of genes and proteins work differently in cancer cells compared to healthy ones.
Enter pathway analysis, an innovative approach that doesn't just look at individual genes but examines how entire biological pathways are disrupted in cancer cells. Think of it as the difference between examining single bricks versus understanding the entire architecture of a building.
This powerful method is helping researchers identify previously overlooked connections and develop more targeted treatment strategies that could potentially help the thousands of women who still lose their lives to this disease each year 1 6 .
Multiple genes interact in complex networks to drive cancer progression.
Pathway analysis examines entire biological systems rather than isolated components.
Understanding pathway disruptions enables targeted, personalized treatments.
Biological pathways are like cellular highways that control how our cells function. Just as cities rely on complex transportation networks to move people and goods, our cells depend on these pathways to transmit signals, metabolize nutrients, and make critical decisions about when to grow, divide, or die. When these pathways malfunction—like a traffic system gone haywire—cells can begin dividing uncontrollably, leading to cancer.
These are intricate maps showing how different genes interact with each other in disease states. Rather than looking at genes in isolation, researchers can see how changes in one gene affect others in the same network.
This approach examines whether entire groups of genes in the same biological pathway are collectively altered in cancer cells. It's based on the understanding that genes don't work alone but through coordinated teams to perform cellular functions 6 .
Traditional approaches to understanding breast cancer often focused on single genes like BRCA1, BRCA2, and HER2. While these discoveries were crucial, they only revealed part of the picture. Pathway analysis provides a more comprehensive view because:
Research has shown that breast cancer involves multiple disrupted pathways beyond the well-known HER2 and estrogen receptor pathways, including critical systems that control cell death, DNA repair, and cellular signaling 1 .
In a groundbreaking 2014 study, researchers embarked on a comprehensive mission to map the disrupted pathways in breast cancer at both genetic and protein levels. Their approach was both innovative and meticulous, comparing normal breast tissue with invasive ductal carcinoma tissues from 33 patients 1 .
The team obtained 33 paired normal and tumor tissue samples from patients undergoing lumpectomy procedures, ensuring they could compare cancerous tissue with normal tissue from the same individual.
Using SmartChip Real-Time PCR Technology, the researchers analyzed the expression levels of 1,243 cancer pathway-related genes from 13 cancer and 5 benign breast tissue samples. This sophisticated technology allowed them to measure which genes were overactive or underactive in cancer cells.
Perhaps even more importantly, the team developed an innovative Protein Pathway Array to measure 131 cancer-related proteins and phosphoproteins in all 33 tissue pairs. This step was crucial because proteins, not genes, actually perform most cellular functions.
This dual approach—examining both mRNA (the intermediate between genes and proteins) and the proteins themselves—allowed the researchers to answer a critical question: Do genetic changes reliably predict what happens at the protein level? The results revealed a surprising disconnect that might explain why some treatments based solely on genetic information prove ineffective.
Cancer pathway-related genes analyzed
Cancer-related proteins and phosphoproteins measured
The findings revealed a staggering level of disruption in breast cancer cells. Of the 1,243 genes examined, 854 were detectable in breast cancer tissues, with 395 showing statistically significant differences compared to normal tissue 1 . Even more striking was the discovery that 105 genes were expressed only in cancer tissues, while 33 were found only in normal tissues, suggesting completely different genetic programs at work.
When researchers analyzed these genetic changes in the context of biological pathways, they identified more than 15 pathways that were significantly altered in breast cancer. Six of these were consistently disrupted at both the mRNA and protein levels, including the crucial p53 pathway (which normally prevents cancer by repairing DNA damage or triggering cell death) and the PI3K/AKT pathway (a key signaling route that drives cell growth and survival) 1 .
| Pathway Name | Normal Function | Role in Cancer | Detection Level |
|---|---|---|---|
| p53 Signaling | DNA repair, cell death prevention | Prevents damaged cells from dying | Both mRNA & Protein |
| PI3K/AKT | Cell growth, metabolism | Promotes excessive growth & survival | Both mRNA & Protein |
| PTEN | Suppresses tumor growth | Loss allows uncontrolled growth | Both mRNA & Protein |
| IL17 | Immune response | May create inflammatory environment | Both mRNA & Protein |
| HGF | Tissue repair & regeneration | May promote invasion & spread | Both mRNA & Protein |
| NGF | Nerve cell growth & maintenance | May influence cancer pain & growth | Both mRNA & Protein |
One of the most significant findings was that mRNA expression often doesn't correlate with protein levels, suggesting different regulation mechanisms between these two layers of biological information 1 . This discovery has profound implications for how we study and treat breast cancer.
For example, while three genes (CDK6, Vimentin, and SLUG) showed consistent changes at both mRNA and protein levels, six proteins (BCL6, CCNE1, PCNA, PDK1, SRC, and XIAP) were differentially expressed in tumors without corresponding differences at the mRNA level 1 . This means researchers would have completely missed these important cancer drivers if they had only studied genetic information.
| Gene/Protein | mRNA Level in Cancer | Protein Level in Cancer | Known Cancer-Related Function |
|---|---|---|---|
| CDK6 | Decreased | Decreased | Cell cycle regulation |
| Vimentin | Decreased | Decreased | Cell structure & movement |
| SLUG | Decreased | Decreased | Cancer spread & invasion |
| BCL6 | No change | Increased | Prevents cell death |
| CCNE1 | No change | Increased | Cell cycle progression |
| PCNA | No change | Increased | DNA replication & repair |
| PDK1 | No change | Increased | Cell metabolism & survival |
| SRC | No change | Increased | Cell signaling & growth |
| XIAP | No change | Increased | Prevents cell death |
mRNA expression often doesn't correlate with protein levels, suggesting different regulation mechanisms between these two layers of biological information.
Researchers would have missed important cancer drivers if they had only studied genetic information without examining protein expression.
Modern pathway analysis relies on sophisticated tools that allow researchers to simultaneously examine hundreds of molecular players. Key resources include:
| Tool/Reagent | Function | Application in Pathway Analysis |
|---|---|---|
| SmartChip Real-Time PCR System | Measures gene expression levels | Analyzing mRNA levels of 1,243 cancer genes |
| Protein Pathway Array | Detects proteins & phosphoproteins | Measuring 131 cancer-related proteins simultaneously |
| Primary Antibodies | Bind to specific target proteins | Identifying presence & modification of proteins |
| Lysis Buffer | Breaks open cells to release contents | Extracting proteins for pathway analysis |
| Protease Inhibitors | Prevent protein degradation | Maintaining protein integrity during analysis |
| Phosphatase Inhibitors | Preserve phosphorylation states | Detecting activated signaling pathways |
| Bioinformatics Software | Analyze complex molecular data | Identifying dysregulated pathways & networks |
The most powerful insights come from combining multiple approaches, as demonstrated by the 2014 study that used both genetic and proteomic methods 1 . This comprehensive strategy revealed the limitations of looking at only one type of data and highlighted the need to validate genetic findings at the protein level—a crucial consideration for drug development since most therapeutics target proteins rather than genes.
Emerging technologies like whole genome sequencing are further expanding this toolkit. Recent research from the University of Cambridge suggests that comprehensive genetic analysis could help match more breast cancer patients to tailored therapies and clinical trials 2 . Their findings indicate that 27% of breast cancer cases have genetic features that could immediately guide treatment decisions, potentially helping tens of thousands of women each year.
Examining DNA and RNA to identify mutations and expression changes.
Measuring protein levels and modifications to understand functional changes.
Using computational tools to integrate and analyze complex datasets.
The insights gained from pathway analysis are already beginning to transform how we approach breast cancer treatment. Instead of classifying tumors solely by traditional markers like estrogen receptors, researchers can now identify specific pathway disruptions and select treatments that target those precise vulnerabilities.
The potential clinical impact is substantial. For example, the Cambridge research team found that genetic markers could sometimes better predict patient outcomes than traditional measures like cancer stage or tumor grade 2 . This information could help doctors identify which patients need more aggressive treatment and which might safely avoid unnecessary therapies and their side effects.
Pathway analysis is also revolutionizing clinical trial design and patient matching. As Professor Serena Nik-Zainal from the University of Cambridge explains, "If we have patients' entire genetic readouts, we will no longer be restricted to single trials with a specific target. We could massively open up the potential for recruitment to multiple clinical trials in parallel, making recruitment to clinical trials more efficient, ultimately getting the right therapies to the right patients much faster" 2 .
This approach could be particularly impactful for patients with rare genetic profiles who might otherwise struggle to find appropriate clinical trials. Additionally, the National Comprehensive Cancer Network (NCCN) has recently launched new resources to help healthcare providers navigate the complex landscape of genetic testing and targeted treatments for breast cancer 7 .
While significant progress has been made, researchers continue to face challenges in fully mapping breast cancer's complex molecular networks. Future directions include:
As these efforts advance, the hope is that pathway analysis will continue to provide deeper insights into breast cancer's complexities, ultimately leading to more effective, personalized treatments that save lives and improve quality of life for patients worldwide.
The journey from viewing cancer as a disease of single genes to understanding it as a disruption of complex cellular networks represents one of the most important paradigm shifts in modern oncology—a shift that promises to fundamentally transform our approach to combating this devastating disease.