PD-L1 vs TMB vs MSI-H: Which Biomarker Best Predicts Immunotherapy Response?

By Editorial Team

Quick Answer

PD-L1, Tumor Mutation Burden (TMB), and Microsatellite Instability-High (MSI-H) are among the most important biomarkers used to predict whether a cancer patient is likely to benefit from immunotherapy. While MSI-H is generally considered the strongest single predictor of response, no biomarker is perfect. Many oncologists now combine PD-L1, TMB, MSI-H, genomic profiling, and clinical factors to make treatment decisions.

In general:

  • MSI-H often provides the strongest prediction of immunotherapy response.

  • High TMB may increase the likelihood of benefit from checkpoint inhibitors.

  • High PD-L1 expression can help identify patients more likely to respond, especially in lung cancer.

  • The best predictions often come from combining multiple biomarkers rather than relying on just one.

Why Biomarkers Matter in Immunotherapy

Immunotherapy has transformed cancer treatment. Some patients with advanced cancer achieve long-lasting remissions that were once considered impossible.

However, not all patients benefit.

Some experience dramatic responses.

Others experience little or no benefit despite receiving the same treatment.

This variability has driven the search for biomarkers—biological signals that help predict who is most likely to respond.

Today, three biomarkers dominate clinical practice:

  • PD-L1

  • Tumor Mutation Burden (TMB)

  • Microsatellite Instability-High (MSI-H)

The challenge is determining which biomarker provides the most useful information.

Understanding PD-L1

PD-L1 (Programmed Death Ligand 1) is a protein found on some cancer cells.

Tumors use PD-L1 as a shield to suppress immune attack.

When PD-L1 binds to PD-1 receptors on T-cells, immune activity is reduced.

Checkpoint inhibitors block this interaction and reactivate anti-cancer immunity.

What High PD-L1 Means

Generally:

  • Higher PD-L1 expression increases the probability of response.

  • Lower PD-L1 expression decreases the probability but does not eliminate it.

Many lung cancer treatment decisions are based on PD-L1 levels.

Examples:

  • PD-L1 ≥50% often predicts better responses to single-agent immunotherapy.

  • PD-L1 ≥1% may still qualify patients for treatment.

  • PD-L1 0% does not automatically exclude benefit.

Strengths of PD-L1

  • Widely available.

  • Relatively inexpensive.

  • Established clinical utility.

  • Strong evidence in lung cancer.

Limitations of PD-L1

  • Expression can change over time.

  • Different laboratories use different testing methods.

  • Tumor samples may not represent the entire cancer.

  • Some high-PD-L1 patients fail to respond.

  • Some low-PD-L1 patients respond exceptionally well.

Understanding Tumor Mutation Burden (TMB)

Tumor Mutation Burden measures the number of genetic mutations present within a cancer.

The theory is simple:

More mutations create more abnormal proteins known as neoantigens.

More neoantigens provide more targets for immune cells.

As a result, tumors with high mutation burdens may be easier for immunotherapy to recognize and destroy.

High-TMB Cancers

Common examples include:

  • Melanoma

  • Smoking-related lung cancer

  • Bladder cancer

  • Head and neck cancer

  • Cutaneous squamous cell carcinoma

Strengths of TMB

  • Measures overall tumor genetic complexity.

  • Can identify responders missed by PD-L1 testing.

  • Applicable across multiple cancer types.

  • Supported by FDA tissue-agnostic approvals.

Limitations of TMB

  • No universal cutoff exists.

  • Different testing platforms may produce different results.

  • High TMB does not guarantee response.

  • Low TMB does not exclude benefit.

Understanding MSI-H

Microsatellite Instability-High (MSI-H) occurs when a tumor loses its ability to repair DNA replication errors.

As these errors accumulate, mutation rates rise dramatically.

Many MSI-H tumors become highly visible to the immune system.

This is one reason MSI-H cancers often respond exceptionally well to checkpoint inhibitors.

Common MSI-H Cancers

  • Colorectal cancer

  • Endometrial cancer

  • Gastric cancer

  • Small bowel cancer

  • Certain ovarian cancers

Strengths of MSI-H

  • Strong biological rationale.

  • Consistently predicts immunotherapy sensitivity.

  • FDA-recognized biomarker.

  • High response rates observed across multiple tumor types.

Limitations of MSI-H

  • Relatively uncommon in many cancers.

  • Not all MSI-H tumors respond.

  • Cannot predict response in most patients because prevalence is low.

Which Biomarker Performs Best?

The answer depends on what you mean by "best."

If the goal is identifying the highest probability responders, MSI-H generally performs the strongest.

If the goal is identifying a larger population of potential responders, TMB may provide broader utility.

If the goal is selecting treatment in lung cancer, PD-L1 remains extremely valuable.

Relative Predictive Strength

Overall, MSI-H is often viewed as the most powerful standalone biomarker because response rates can be remarkably high when mismatch repair deficiency is present.

Why No Single Biomarker Is Enough

Modern oncology increasingly recognizes that cancer is too complex to be explained by a single test.

A patient may have:

  • High PD-L1

  • High TMB

  • MSI-H

Such patients often represent ideal immunotherapy candidates.

Conversely, a patient may have:

  • Low PD-L1

  • Low TMB

  • Microsatellite stable disease

Yet still respond to treatment.

Other factors influence outcomes, including:

  • Tumor microenvironment

  • T-cell infiltration

  • Genomic alterations

  • Gut microbiome

  • Metabolic health

  • Previous treatments

  • Immune exhaustion

This explains why biomarker panels are increasingly replacing single-marker approaches.

The Emerging Role of Combination Biomarkers

Researchers are now combining:

  • PD-L1

  • TMB

  • MSI-H

  • Circulating tumor DNA (ctDNA)

  • Tumor-infiltrating lymphocytes (TILs)

  • Gene-expression signatures

  • LAG-3

  • TIGIT

  • Metabolic biomarkers

The future of precision immunotherapy will likely involve integrated biomarker models rather than individual tests.

Real-World Example

Consider three patients:

Patient A

  • PD-L1: 90%

  • TMB: Low

  • MSI-H: Negative

Likely candidate for immunotherapy, particularly in lung cancer.

Patient B

  • PD-L1: 5%

  • TMB: High

  • MSI-H: Negative

May still derive substantial benefit from checkpoint inhibition.

Patient C

  • PD-L1: 0%

  • TMB: High

  • MSI-H: Positive

Potentially among the strongest immunotherapy candidates despite negative PD-L1 expression.

This illustrates why relying on a single biomarker may lead to missed opportunities.

Frequently Asked Questions

Is MSI-H better than PD-L1?

Generally yes. MSI-H is often considered a stronger predictor of immunotherapy response, although it is less common.

Is TMB better than PD-L1?

Neither is universally superior. Their usefulness depends on cancer type and clinical context.

Can low PD-L1 patients still respond?

Yes. Some patients with low or absent PD-L1 expression achieve durable responses.

Can high TMB predict immunotherapy success?

High TMB increases the probability of response but does not guarantee it.

What is the best biomarker overall?

Currently, MSI-H is often regarded as the strongest single biomarker. However, combining PD-L1, TMB, MSI-H, and other biomarkers provides the most accurate assessment.

Final Thoughts

The question is no longer whether PD-L1, TMB, or MSI-H is the "best" biomarker. The future of precision oncology lies in understanding how these biomarkers work together.

MSI-H remains one of the strongest predictors of immunotherapy sensitivity. High TMB provides valuable information about tumor immunogenicity. PD-L1 continues to guide treatment decisions across many cancers, particularly lung cancer.

For patients considering immunotherapy, the most informative strategy is often comprehensive biomarker testing rather than reliance on a single marker. As precision oncology advances, integrated biomarker models will likely replace today's one-test-fits-all approach, helping more patients receive the treatments most likely to benefit them.

References

  1. OneDayMD: Latest Breakthroughs in Cancer Treatment

  2. How to Read a Cancer Study Without Being Misled (2026 Guide)

  3. Why Some Patients Respond Miraculously to Immunotherapy

  4. PD-L1 Explained for Patients: What Your Biomarker Test Really Means

  5. Gastric Cancer and the Immunotherapy Revolution: How Checkpoint Inhibitors Are Changing Survival Outcomes

  6. Tumor Mutation Burden (TMB) Explained: Who Responds Best to Immunotherapy?

  7. Cold vs Hot Tumors Explained: Why the Tumor Microenvironment Determines Immunotherapy Success

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