Immunotherapy has greatly revolutionised cancer treatment by using the immune system's power to fight malignancies. However, not all patients respond equally to these therapies, and immunotherapy still doesn’t work for most patients, which leads to our focus on genetic biomarkers. These markers, with their potential to predict immunotherapy outcomes and guide treatment decisions, offer a hopeful future for cancer treatment. This article explores the various genetic factors that influence the efficacy of immune checkpoint inhibitors (ICIs) and their potential as predictive biomarkers.

Understanding Genetic Biomarkers

Genetic biomarkers serve as measurable indicators of biological processes, disease conditions, or responses to treatments. They offer critical insights into tumor characteristics relevant to clinical diagnosis, immune responses, and the likelihood of successful future treatments. Within the realm of immunotherapy, these biomarkers can assist in predicting which patients are most likely to respond positively to treatment and which may face varied outcomes.¹

Genetic Biomarkers: Guiding Lights in Immunotherapy

In the context of immunotherapy, they are powerful predictors of prognosis(outcome of patient over time) and treatment success. Several key biomarkers have been identified:

Tumour mutational Burden (TMB)

TMB refers to the number of mutations within a tumour's genome. Higher TMB is associated with increased neoantigen(a foreign particle which can be recognised as non-self by the body’s immune cells) production, potentially making tumours more visible to the immune system. Studies have shown that patients with high TMB often respond better to immune checkpoint inhibitors (ICIs) across various cancer types. TMB has been consistently associated with improved responses to immunotherapy across multiple cancer types. Higher TMB correlates with increased neoantigens, potentially enhancing tumour immunogenicity.² Studies have shown that patients with TMB ≥10 mutations/megabase demonstrate significantly higher response rates to ICIs than those with lower TMB[3]. This association has been observed in various cancers, including urothelial carcinoma, small-cell lung cancer, melanoma, and non-small cell lung cancer (NSCLC).³

Persistent tumour Mutational Burden (pTMB):

pTMB, defined as mutations in haploid or polyploid regions of the genome, maybe a better predictor of ICI response than overall TMB in some cancer types.⁴

However, it's important to note that TMB's predictive value can be context-dependent. While high TMB generally indicates better outcomes for ICI-treated patients, it may potentially correlate with a worse prognosis in non-ICI-treated individuals.³

The Immune Contexture

The concept of immune contexture, which refers to the types and arrangement of immune cells within the tumor, also plays a crucial role in determining immunotherapy outcomes. A favorable immune contexture, characterized by the presence of immune cells that can attack the tumor and a lack of cells that suppress the immune system, is often linked to improved patient survival and response to treatment. Genetic biomarkers can help identify these immune cell populations, providing insights into the tumor's immune landscape.

HLA Genes

Recent research has highlighted the role of human leukocyte antigen (HLA) genes in predicting immunotherapy outcomes. Specifically, the HLA-A*03 allele has been identified as a potential biomarker for reduced ICI efficacy. Patients carrying this allele showed shorter survival times after ICI treatment across various cancer types, including kidney, bladder, and skin cancers.

Interferon-γ Pathway Genes

Alterations in genes involved in the interferon-γ signalling pathway can affect ICI responsiveness. IFNGR1/2, JAK1/2, and IRF1 mutations have been associated with poor response to ICI treatment.⁶ These mutations can lead to downregulating or altering IFN-γ signalling, potentially allowing tumour cells to evade immune recognition.⁶

Epigenetic Factors

Emerging research suggests that epigenetic changes, which are alterations in gene expression that do not involve changes to the underlying DNA sequence, particularly DNA methylation patterns, may complement TMB in predicting immunotherapy efficacy. Studies in NSCLC have shown that tumors with high TMB often display more DNA methylation aberrations and copy number variations.

Clonal Neoantigens

• Clonal neoantigens (mutations present in all cancer cells of a tumour) appears to be more predictive of ICI response than overall mutation burden⁷
• Tumours with high genetic diversity (many different mutations in small fractions of cells) were less responsive to ICIs in mouse models⁷

Mismatch Repair Deficiency (MMR-D) 

• Tumours with MMR-D tend to have high TMB, which is generally associated with better response to immune checkpoint inhibitors (ICIs)
• However, MMR-D and high TMB alone are not always predictive of response, as less than 50% of patients with MMR-D tumours have long-lasting responses to pembrolizumab⁷

Microsatellite Instability (MSI)

Microsatellites are present at the end of each chromosome and serve as a marker in many physiological events of a cell. MSI-high tumours, characterised by defects in DNA mismatch repair, tend to accumulate mutations rapidly. These tumours are often more responsive to immunotherapy, leading to the approval of pembrolizumab for MSI-high cancers regardless of tumour origin.

PD-L1 Expression

The interaction between PD-1 and its ligand PD-L1 is a key immune checkpoint. Higher PD-L1 expression in tumours often correlates with better responses to PD-1/PD-L1 inhibitors. However, this biomarker has limitations due to heterogeneous expression and potential false negatives.

Other biomarkers

Mutational Signatures:

Certain mutational signatures, such as those associated with ageing, tobacco use, UV exposure, and POLE mutations, were significantly associated with ICI response.⁴

Gene Expression Profiles:

Various gene expression signatures have been developed to predict ICI response, including:

• The IMPRES score for melanoma⁴
• The innate anti-PD-1 resistant score (IPRES)⁴
• Immune-related gene expression profiles

Specific Gene Mutations:

Mutations in genes like STK11 have been linked to poor survival and may explain lack of treatment response in some cases.¹

Clinical Impact: Tailoring Treatment Strategies

Genetic biomarkers play a crucial role in patient selection and treatment optimisation(advancement):

Personalised Treatment Plans

By analysing a patient's genetic profile, oncologists can tailor immunotherapy regimens. For instance, patients with high TMB or MSI-high tumours may be prioritised for ICI treatment, while those with low TMB might benefit more from combination therapies.

Dynamic Monitoring

Genetic biomarkers allow for real-time monitoring of treatment efficacy. This dynamic monitoring capability instills confidence in the adaptability of cancer treatment, as changes in biomarker levels during therapy can guide decisions on treatment continuation or modification.

Challenges and Limitations

Despite their promise, genetic biomarkers face several challenges such as:

Tumour Heterogeneity

Intra-tumour heterogeneity can lead to sampling bias and inaccurate biomarker assessment. Single biopsies may not capture the full genetic landscape of a tumour.

Resistance Mechanisms

Even tumours with favourable biomarker profiles can develop resistance to immunotherapy. Understanding these resistance mechanisms is crucial for developing effective combination strategies.

Future Directions

The field of genetic biomarkers in immunotherapy is rapidly evolving with discoveries such as :

Emerging Biomarkers

Research is uncovering new genetic signatures that may predict immunotherapy response. For example, mutations in genes that create a response to DNA and specific immune-related gene expression profiles show promise as predictive biomarkers.

Artificial Intelligence in Biomarker Discovery

AI-driven approaches are being developed to identify novel genetic biomarkers and create predictive models for immunotherapy outcomes. These tools may help integrate complex genetic data to improve treatment decisions. Genetic biomarkers are transforming the landscape of cancer immunotherapy. By providing insights into tumour biology and immune interactions, they enable more precise patient selection and treatment strategies. As research advances, integrating genetic biomarkers into clinical practice will likely lead to improved outcomes and more personalised cancer care.

Combination of Biomarkers

Combining multiple biomarkers, such as TMB, PD-L1 expression, and immune cell infiltration, may provide more accurate prediction of treatment response than individual markers alone[3].

While several genetic biomarkers show promise in predicting immunotherapy outcomes, their effectiveness varies across cancer types and individual patients. Ongoing research aims to refine and combine these biomarkers to more accurately predict immunotherapy responses.

Conclusion

Integrating multiple genetic biomarkers holds promise for more accurate prediction of immunotherapy outcomes. While individual markers like TMB and specific gene mutations provide valuable insights, combining these with other factors such as HLA types, mutational signatures, and epigenetic profiles may offer a more constructive approach to patient selection for immunotherapy. As research increases, developing multi-factorial predictive models involving these genetic biomarkers could significantly enhance the precision of immunotherapy in cancer treatment. However, further validation through prospective clinical trials is essential to establish the clinical utility of these biomarkers and their combinations.