The VIO Score: A New Tool for Smarter Cancer Trials

How a simple scoring system is helping doctors match the right patients with revolutionary new immunotherapies.

Immuno-Oncology Clinical Trials Patient Selection

For patients with advanced cancer who have exhausted standard treatments, phase I clinical trials represent a beacon of hope. These early-stage studies test innovative therapies, including cutting-edge immuno-oncology drugs that harness the immune system to fight cancer. However, these trials come with significant uncertainties — both about the experimental treatment's effectiveness and about whether a patient is well enough to participate. This is where the Vall d'Hebron Immuno-Oncology (VIO) Prognostic Index emerges as a revolutionary tool, bringing precision to patient selection and potentially improving outcomes for everyone involved.

The Critical Challenge: Selecting the Right Patients

Phase I oncology trials represent the first time a new drug is tested in humans, with primary goals of assessing safety and determining appropriate dosage. Historically, patient selection for these trials has been challenging, with doctors relying heavily on clinical judgment and limited objective criteria4 .

Early Mortality Risk

Approximately 16% of patients in phase I trials experience early mortality (within 90 days), mostly due to disease progression rather than drug toxicity4 .

Early Discontinuation

There is up to a 40% risk of early discontinuation in early-phase clinical trials, mostly due to cancer-related complications7 .

What is the VIO Prognostic Index?

The VIO Prognostic Index (VIO Score) is a structured scoring system developed to objectively predict survival outcomes for cancer patients considering immuno-oncology treatments. Developed by researchers at Vall d'Hebron Institute of Oncology, this tool helps clinicians identify patients most likely to benefit from these innovative therapies while avoiding unnecessary risks for those unlikely to withstand treatment.

VIO Score Components

The VIO Score evaluates five straightforward clinical factors that reflect a patient's overall disease burden and physiological reserve:

  • Low albumin levels
  • Elevated lactate dehydrogenase
  • Elevated neutrophil ratio
  • Multiple metastatic sites
  • Presence of liver metastases

Each factor contributes to a final score that categorizes patients into distinct prognostic groups, enabling more informed decision-making about trial participation3 .

A Closer Look: Validating the VIO Score

The development and validation of any prognostic tool requires rigorous testing in real patient populations. For the VIO Score, researchers conducted a retrospective study involving patients with metastatic urothelial carcinoma who were treated with immune checkpoint inhibitors3 .

Methodology Step-by-Step

Patient Cohort

The research team analyzed data from 86 patients with metastatic urothelial carcinoma treated between May 2015 and May 2018. Of these, 72 had complete data for all VIO Score components and were included in the final analysis3 .

Data Collection

For each patient, researchers collected data on the five VIO factors and assigned patients to three prognostic clusters based on their scores.

Statistical Analysis

The researchers tracked overall survival across these groups using statistical methods including Kaplan-Meier survival curves and log-rank tests for comparison3 .

Key Findings and Significance

The results demonstrated striking differences in survival outcomes based on VIO Score categories:

Prognostic Group Percentage of Patients Median Overall Survival 3-Month Overall Survival Rate
Good (0-1 factors) 62.5% 19.3 months 95.6%
Intermediate (2 factors) 23.6% 8.2 months 82.4%
Poor (≥3 factors) 13.9% 2.9 months 50.0%

95.6%

3-month survival in good prognosis group

82.4%

3-month survival in intermediate group

50.0%

3-month survival in poor prognosis group

The dramatic separation in survival curves, with a statistically significant difference (p < 0.001), confirmed the VIO Score's ability to stratify patients by expected outcomes3 . Particularly noteworthy was the 3-month survival rate — a crucial timeframe for early-phase trials — which ranged from 95.6% in the good prognosis group to just 50% in the poor prognosis group.

The Scientist's Toolkit: Key Components of the VIO Score

The VIO Score utilizes routinely available clinical parameters, making it practical for widespread implementation. Each component provides insight into different aspects of a patient's health status and disease burden:

Component Threshold Biological Significance
Albumin <3.5 g/dl Reflects nutritional status and overall physiological reserve; low levels indicate cachexia
Lactate Dehydrogenase (LDH) >Upper limit of normal Indicates tumor cell turnover and burden; elevated in aggressive disease
derived Neutrophil-Lymphocyte Ratio (dNLR) >3 Measures systemic inflammation; higher ratios suggest immune dysfunction
Metastatic Sites >2 Represents overall disease burden and dissemination
Liver Metastases Presence Specific organ involvement known to correlate with poorer outcomes

These biomarkers collectively paint a comprehensive picture of a patient's disease trajectory beyond what performance status alone can provide. The inflammatory markers (dNLR) particularly reflect the immune system's role in cancer progression, which may have special relevance for patients considering immuno-oncology therapies3 5 .

Beyond VIO: The Evolving Landscape of Prognostic Tools

The VIO Score is part of a growing movement to bring greater precision to patient selection for early-phase trials. Other institutions have developed similar tools, such as the Princess Margaret Immuno-oncology Prognostic Index (PM-IPI), which uses performance status, number of metastatic sites, and albumin levels to predict outcomes6 .

Prognostic Index Key Components Cancer Types Validated Primary Outcome Measured
VIO Score Albumin, LDH, dNLR, metastatic sites, liver mets Metastatic urothelial carcinoma 3-month and overall survival
PM-IPI Performance status, number of metastatic sites, albumin Various solid tumors 90-day mortality, overall survival
Royal Marsden Hospital Score Albumin, LDH, alkaline phosphatase, lymphocytes, number of metastases Various solid tumors 90-day mortality
Impact on Trial Mortality

Research has shown that using such objective scoring systems could reduce non-drug-related 90-day mortality in phase I trials by approximately 50%4 .

Future Biomarkers

The field continues to evolve with investigations into more sophisticated biomarkers including serum immuno-oncology proteins and 27-gene immuno-oncology tests5 .

The Future of Patient Selection in Early-Phase Trials

The integration of prognostic tools like the VIO Score represents a significant step toward more personalized and ethical cancer drug development. New approaches to optimize the early-phase clinical trial referring process include automatic trial matching and better prediction of patient eligibility7 .

Expected Benefits

  • More precise patient selection
  • Reduced early discontinuation rates
  • Faster drug development
  • Improved patient experiences

Implementation Progress

Clinical Validation: 65%
Clinical Adoption: 40%
Integration with EMR: 25%

Conclusion: Toward More Personalized Cancer Care

The VIO Prognostic Index exemplifies how straightforward clinical parameters, when systematically analyzed, can yield powerful insights for personalizing cancer care. By bringing objective data to the complex decision of trial participation, tools like the VIO Score help ensure that precious opportunities to test innovative therapies are directed toward those most likely to benefit while protecting vulnerable patients from unnecessary risks.

Balancing Innovation and Protection

As immuno-oncology continues to revolutionize cancer treatment, such prognostic tools will play an increasingly vital role in connecting the right patients with the right trials — accelerating progress while never losing sight of the individual patient at the heart of every clinical decision. The future of cancer drug development lies in this delicate balance between innovation and protection, between statistical probabilities and human realities.

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