Why Does Cancer Treatment Not Have the Same Effect in Everyone?

The Same Stage, Different Clinical Courses: Hidden Variables Determining Oncology Responses

A molecular and systemic analysis of the oncology paradox: A comprehensive review of biological, ecological, and host-related determinants of response beyond anatomical staging.

 The Limits of Anatomical Staging and the Biological Context

The traditional TNM (Tumor–Node–Metastasis) system was designed to predict surgical resectability and anatomical spread. Oncology’s ‘molecular revolution,’ however, has demonstrated that tumors at the same stage can have entirely different genetic signatures from one another.

For example, a stage II colon cancer that is MSI-High (high Microsatellite Instability) may respond dramatically to immunotherapy, while an MSS (Microsatellite Stable) tumor at the same stage may remain completely resistant. ‘Stage’ is therefore no longer the sole determinant of treatment, but a starting parameter.

 Tumor Heterogeneity: Chaos Evolving Within

II.A Intratumoral Heterogeneity (ITH)

A tumor contains billions of cells, not all sharing the same genetic sequence. According to the ‘branching evolution’ model, subclones with different mutational characteristics coexist within it.

Starting treatment destroys sensitive clones; however, a ‘resistant’ subclone initially representing only one in a thousand cells may survive treatment pressure, laying the foundation for a new, more aggressive recurrence. This is Charles Darwin’s natural selection operating microscopically within tumor tissue.

II.B Plasticity and Changes in Cellular State

Cells acquire resistance through phenotypic changes (plasticity) as well as genetic mutations. A lung cancer cell, for example, may escape a targeted drug by changing type (transitioning from an epithelial to a mesenchymal state). This biological ‘chameleon’ capability varies considerably among patients.

 The Tumor Microenvironment (TME): A ‘Shield’ Against Drugs

A tumor is not a cell mass growing in isolation: it communicates continuously with surrounding vessels, connective tissue (stroma), and immune cells. This environment can create physical and chemical barriers preventing drugs from reaching the tumor.

III.A Physical Permeability and Interstitial Pressure

Some patients’ tumor vessels are so abnormal that circulating drugs cannot penetrate deeply. Dense stroma (fibrosis) further restricts movement through tissue. Two patients receiving the ‘same intravenous dose’ may therefore have dramatically different ‘actual drug amounts’ reaching tumor cells.

III.B The Immune-Cold versus Immune-Hot Distinction

Differences in immunotherapy response arise from the tumor’s ‘immune geography.’ ‘Hot’ tumors are filled with immune cells (T lymphocytes) and ready for treatment. ‘Cold’ tumors have built a shield excluding immune cells. This microscopic difference in ‘border security’ often explains why one of two breast cancer patients at the same stage benefits from immunotherapy while the other does not.

 Host (Patient) Factors and the Microbiota as a ‘Second Genome’

IV.A Individual Immune Reserve and Immunosenescence

Cancer treatment involves not only killing the tumor but also reactivating immunity. Age, nutritional status, and chronic stress determine immune-cell quality. An aging immune system (immunosenescence) may struggle to respond even to the strongest drugs.

IV.B Gut Microbiota: The Invisible Intermediary

One of the most striking recent discoveries is that gut bacteria determine immunotherapy success. A lack of certain beneficial species makes it harder to ‘ignite’ immunity against cancer. Inappropriate antibiotic use before treatment may disrupt flora and completely eliminate treatment response.

 Pharmacogenomics: Is the ‘Same Dose’ the Same for Everyone?

Pharmacogenomic data show that genetic differences in drug-metabolizing enzymes (e.g., the CYP family, DPYD) can alter blood drug levels by more than 50%. One patient may eliminate a drug rapidly, while another accumulates it, increasing toxicity, or never reaches therapeutic levels.

 RCTs versus Real-World Practice: Statistics vs. Reality

Clinical trials (RCTs) generally select ‘ideal’ patients. In real-world data (RWD), patients are older, have comorbidities, and take multiple medications. This heterogeneity explains why a drug achieving 80% success in trials may deliver lower or different real-world results.

 Conclusion: The Individual Paradigm in Oncology

‘The same stage, the same treatment’ was a simple assumption from oncology’s infancy. We now know that every treatment is a four-dimensional equation involving: Tumor genetics + Microenvironment ecology + The patient’s biological reserve + Pharmacological exposure.

Treatment success or failure is therefore not ‘luck’: it results from complex interactions among these four dimensions. Improving clinical success requires more than selecting a drug; it requires accurate analysis of these variables to personalize treatment.