In a world where personalized medicine is becoming a reality, computational technologies are opening new horizons in the treatment of one of the most common cancers in men.
Prostate cancer holds one of the leading positions in the structure of oncological morbidity among men worldwide. Particularly challenging to treat are highly malignant tumors, which have the ability to metastasize and develop therapy resistance.
Traditional chemotherapy approaches often resemble shooting blindfolded - the same drug is prescribed to different patients in hopes that it will work for at least some of them.
Clinical trials of most anticancer drugs do not account for the genetic characteristics of tumors, which explains their low effectiveness in diverse patient populations.
This is where modern computer technologies come to the rescue, particularly the in silico approach - using computational methods to predict the effectiveness of drugs based on tumor genetic data.
The term "in silico" means conducting experiments on a computer, rather than in a test tube (in vitro) or living organism (in vivo). In the context of oncology, this approach involves analyzing big data on tumor genetic profiles and their sensitivity to various chemotherapeutic agents.
The main goal is to identify molecular markers that can predict tumor response to a specific drug, allowing a transition from a universal treatment approach to personalized therapy that considers the genetic characteristics of each patient's tumor.
Leveraging advanced algorithms to analyze complex biological data
In 2012, a landmark study was published demonstrating the practical value of the in silico approach for predicting the sensitivity of prostate cancer to oxaliplatin 1 .
Scientists used a multi-stage approach combining computational analysis and experimental validation:
Researchers started with a published signature of 86 genes capable of differentiating high-grade and low-grade prostate tumors
Using US National Cancer Institute databases containing gene expression profiles of 60 tumor cell lines and data on their sensitivity to chemotherapeutic drugs, scientists identified correlations between expression levels of certain genes and sensitivity to oxaliplatin
Predictions obtained from computational analysis were tested in experiments on prostate cancer cell lines DU145, LNCaP and C4-2B using knockdown (suppression of expression) of identified genes
Researchers managed to identify a six-gene signature whose expression levels reliably predicted the sensitivity of prostate cancer cells to oxaliplatin, but not to cisplatin - another platinum drug 1 .
Resistance to different platinum drugs is mediated by different molecular mechanisms, even if these drugs belong to the same class of chemotherapeutic agents.
| Parameter | Oxaliplatin | Cisplatin |
|---|---|---|
| Class | Third-generation platinum derivative | First-generation platinum derivative |
| Structure | Contains diaminocyclohexane ring | Contains ammonia ligands |
| Application in Prostate Cancer | Effective against high-grade tumors | Limited effectiveness |
| Mechanism of Action | Formation of DNA cross-links | Formation of DNA cross-links |
| Resistance | Determined by specific gene set | Determined by other mechanisms |
Understanding the mechanisms underlying the predictive ability of the gene signature is of crucial importance for developing new therapeutic strategies.
Studies on other cancer types, such as colorectal cancer, have identified several key genes and pathways associated with oxaliplatin resistance:
Phospholipase C beta 4 - low expression associated with worse survival and resistance to oxaliplatin 4
microRNA regulating PLCB4 expression; high expression associated with poor prognosis 4
Play pivotal role in regulating apoptotic response to oxaliplatin
Cell's ability to repair DNA damage caused by oxaliplatin
| Gene | Function | Effect on Resistance |
|---|---|---|
| PLCB4 | Involved in cellular signaling | Low expression associated with resistance |
| AKT3 | Involved in cell survival and proliferation | High expression associated with resistance |
| TGFB1 | Regulates cell growth and differentiation | High expression associated with resistance |
| Bax | Pro-apoptotic protein | Low expression associated with resistance |
| p53 | Tumor suppressor protein | Inactivation associated with resistance |
Modern research in the field of in silico oncology relies on a whole arsenal of bioinformatics tools and databases.
Allows simultaneous assessment of expression of thousands of genes
Provide information on known and predicted protein interactions (STRING) 4
Allow assessment of prognostic value of genes based on clinical data (R2, OncoLnc) 4
Provide information on interactions between microRNAs and messenger RNAs (ENCORI) 4
Algorithms capable of integrating heterogeneous data and identifying complex patterns associated with drug sensitivity 3
| Tool/Resource | Purpose | Example Application |
|---|---|---|
| GEO (Gene Expression Omnibus) | Archive of gene expression data | Obtaining data on gene expression in tumor cells |
| STRING | Protein-protein interaction database | Building networks of gene/protein interactions |
| Cytoscape | Visualization of biological networks | Analysis and visualization of gene/protein interaction networks |
| DAVID | Functional annotation of genes | Identifying biological pathways enriched with a given gene set |
| R2: Genomics Platform | Analysis of genomic data and survival | Validation of prognostic value of genes on clinical data |
The in silico approach represents a powerful tool in the arsenal of modern oncology, allowing a transition from universal chemotherapy to personalized treatment that considers the genetic characteristics of each patient's tumor.
Identification of the six-gene signature predicting response to oxaliplatin in prostate cancer 1 is just one example of how computer technologies are revolutionizing oncological practice.
The future direction of research lies in integrating in silico methods with other advanced technologies, such as artificial intelligence 3 and CRISPR-Cas9 genome editing 3 , which will allow not only predicting sensitivity to existing drugs but also developing new strategies to overcome drug resistance.
Combining the efforts of bioinformaticians, molecular biologists, and clinicians opens new horizons in the fight against one of the most common oncological diseases in men, offering hope for more effective and less toxic treatment.