A simple blood test could hold the key to personalized cancer therapy, saving patients from unnecessary treatments and side effects.
Imagine facing a powerful cancer treatment without knowing whether it will work for you. For patients with esophageal squamous cell carcinoma—a particularly aggressive digestive tract cancer—this uncertainty has been the reality until now. But recent scientific breakthroughs have revealed that inexpensive, routine blood tests can predict treatment success with remarkable accuracy, potentially revolutionizing how doctors personalize cancer care.
Esophageal cancer ranks as the sixth most common malignant tumor worldwide and claims over 500,000 lives annually, with particularly high incidence rates in Asia. The squamous cell subtype accounts for approximately 90% of cases in Asian populations 3 .
Traditional treatments like chemotherapy and radiation have shown limited effectiveness against this aggressive cancer. However, the emergence of neoadjuvant immunochemotherapy (NICT)—which combines conventional chemotherapy with immunotherapy drugs called anti-PD-1 inhibitors—has dramatically changed the landscape. This approach activates the body's immune system to recognize and attack cancer cells while simultaneously targeting tumors with chemotherapy 1 .
The results have been remarkable—some patients experience complete disappearance of their tumors before surgery. This phenomenon, called pathologic complete response (pCR), represents the ideal outcome and is strongly linked to long-term survival 1 4 6 .
The challenge? Only a subset of patients achieves this optimal response, while others endure side effects without significant benefit. This dilemma sparked the search for biomarkers that could predict which patients would respond before treatment begins.
Scientists have long recognized the complex relationship between inflammation, our immune system, and cancer. Tumors don't just consist of cancer cells—they create elaborate environments populated by various immune cells that can either fight cancer or be tricked into supporting it 1 .
Systemic inflammation plays a crucial role in cancer progression. Certain immune cells, when influenced by tumors, can actually promote cancer spread and invasion rather than stopping it. This understanding led researchers to investigate whether markers of inflammation in routine blood tests could reveal how a patient might respond to immunotherapy 7 9 .
The theory was compelling: since immunotherapy works by reprogramming the immune system, and inflammation reflects immune system activity, perhaps inflammatory markers could predict treatment effectiveness.
In a groundbreaking 2025 study published in Scientific Reports, researchers from Beijing Friendship Hospital and Qingdao University investigated whether easily calculated inflammatory indexes from routine blood tests could predict pathological response in esophageal squamous cell carcinoma patients receiving anti-PD-1 immunochemotherapy 1 .
The research team retrospectively analyzed 116 ESCC patients who received NICT before surgery. They collected laboratory data from blood tests performed within one week before treatment initiation, focusing on standard components: white blood cells, lymphocytes, platelets, monocytes, neutrophils, and albumin 1 .
From these basic components, they calculated seven inflammatory indexes:
Patients were categorized based on their pathological response after surgery, with special attention to those achieving pathologic complete response (pCR)—the complete disappearance of cancer cells in the surgical specimen 1 .
116 ESCC patients receiving NICT
White blood cells, lymphocytes, platelets, monocytes, neutrophils, albumin
7 different indexes calculated
Pathologic complete response (pCR)
The results were striking. Among all inflammatory indexes analyzed, the Prognostic Nutritional Index (PNI)—calculated as serum albumin level plus 5 times lymphocyte count—emerged as the most consistent predictor of treatment response 1 .
| PNI Level | Probability of Pathological Complete Response |
|---|---|
| Low PNI | Significantly Lower |
| High PNI | Significantly Higher |
| PNI Cutoff Value | Predictive Purpose | AUC |
|---|---|---|
| 53.585 | Predicting Pathologic Complete Response (pCR) | 0.720 |
| 47.85 | Predicting Good Response (GR) | 0.723 |
| 47.85 | Predicting Any Response (R) | 0.629 |
Through sophisticated statistical analysis including LASSO regression and multivariable analysis, the research team confirmed that PNI was an independent predictor of achieving pCR, along with smoking status. For good response, PNI and platelet-to-lymphocyte ratio (PLR) both served as independent predictors 1 .
To make these findings clinically useful, researchers developed predictive nomograms—visual calculation tools that combine multiple factors to estimate an individual patient's probability of achieving pCR or good response. These models demonstrated excellent predictive accuracy, with AUC values shown in the table below:
| Cohort Type | Prediction Target | Area Under Curve (AUC) |
|---|---|---|
| Training | Pathologic Complete Response (pCR) | 0.795 |
| Training | Good Response (GR) | 0.763 |
| Validation | Pathologic Complete Response (pCR) | 0.907 |
| Validation | Good Response (GR) | 0.757 |
To understand how researchers make these discoveries, let's examine the key tools and reagents essential for this field of study:
The implications of this research extend far beyond esophageal cancer. Similar inflammatory indexes are being investigated in breast cancer, lung cancer, gastric cancer, and many other malignancies 4 7 9 . The approach represents a significant shift toward personalized medicine in oncology.
The beauty of these inflammatory biomarkers lies in their simplicity, affordability, and accessibility. Unlike complex genetic tests that can be expensive and technically challenging, these indexes derive from routine blood tests available in even basic healthcare settings. This makes them particularly promising for resource-limited environments 1 7 .
Combining various markers for improved accuracy
Combining blood markers with imaging data
Advanced algorithms for pattern recognition
Large-scale trials to confirm findings
As we advance toward increasingly personalized cancer care, the humble blood test—an ancient medical tool—may become one of our most sophisticated guides in determining the right treatment for the right patient at the right time.
The era of one-size-fits-all cancer therapy is slowly ending, replaced by approaches that respect the unique biological characteristics of each patient's disease. In this new paradigm, something as simple as a routine blood test could help thousands of cancer patients avoid ineffective treatments while steering them toward therapies that offer the greatest hope.