The efficacy of immunotherapy varies significantly across different tumour types, and antigen heterogeneity has emerged as a key factor determining treatment outcomes.

Tumours with high immunogenicity and a high mutational burden typically elicit strong immune responses and respond well to immune checkpoint blockade (ICB); however, when immunogenic subpopulations are limited, immune recognition and clearance are often restricted.

Intratumoural antigen heterogeneity not only weakens immune surveillance but also leads to chemotherapy resistance, posing a major challenge in cancer treatment.

In recent years, chemo-immunotherapy combination strategies have been considered effective for overcoming the impact of heterogeneity.

While chemotherapy has traditionally been viewed as immunosuppressive, increasing evidence indicates that it can also stimulate the immune system by releasing tumour antigens and remodelling the tumour microenvironment.

Nevertheless, the synergistic mechanisms of chemotherapy and immunotherapy in antigen-heterogeneous tumours remain poorly understood.

Existing mathematical models have revealed some aspects of tumour-immune dynamics but largely neglect neoantigen diversity and its dynamic evolution under treatment.

Recently, an article titled “Modelling Combination Chemo-Immunotherapy for Heterogeneous Tumours” in Quantitative Biology, establishing a quantitative modelling framework to elucidate how antigen heterogeneity, immune selection, and chemotherapy jointly determine tumour evolution and therapeutic outcomes.

As shown in Figure 1, the team proposed two models to simulate tumour responses under conditions of immune homogeneity (IHoM) and heterogeneity (IHeM).

Both stochastic and deterministic models were constructed to simulate the dynamic evolution of IHoM and IHeM tumours under immunotherapy and chemotherapy.

Results showed that the two tumour types exhibited markedly different responses to immunotherapy: homogeneous tumours were more sensitive, whereas heterogeneous tumours displayed significant treatment tolerance.

On the other hand, chemotherapy suppressed both tumour types, but relapse patterns differed.

Further analysis revealed that chemotherapy significantly reduced antigen mutations and antigen diversity features in heterogeneous tumour models.

This finding suggests that chemotherapy not only directly kills tumour cells but may also indirectly improve the immune microenvironment by decreasing antigen heterogeneity.

Based on this mechanism, the team emphasised the critical role of immune system recovery in combination therapy.

They ultimately recommended a sequential treatment strategy in clinical design, with chemotherapy administered first followed by immunotherapy, to fully leverage chemotherapy-induced remodelling of the tumour immune microenvironment and enhance overall treatment efficacy.

Key Findings

Chemotherapy reduces antigen heterogeneity and improves the immune microenvironment:

The mathematical models explored the role of tumour antigen heterogeneity in combination chemo-immunotherapy.

Model results indicate that when antigen heterogeneity is high, monotherapy with immunotherapy is often insufficient.

Chemotherapy reduces sensitive cell populations and antigen diversity, making residual cells more susceptible to immune recognition and clearance.

Thus, chemotherapy not only directly kills tumour cells but also acts as an immunological priming strategy, creating favourable conditions for combination therapy.

Immune system recovery is critical for combination therapy efficacy:

Even if chemotherapy reduces antigen heterogeneity, the benefits of combination therapy remain limited if the immune system has not recovered to an effective level.

Low-dose chemotherapy combined with moderate ICB achieves favourable outcomes, highlighting that coordination between immune status and treatment timing is a key determinant of efficacy.

Combination therapy outperforms monotherapy, and treatment should consider dynamic evolution:

The model compared monotherapy with chemotherapy, monotherapy with immunotherapy, and combination therapy.

Results showed that combination strategies significantly delay resistance and promote immune clearance.

The study emphasised that treatment should be considered a dynamic process, accounting for tumour heterogeneity evolution and timing of immune recovery.

Based on chemotherapy’s impact on immune heterogeneity and immune recovery, the authors proposed an alternating dosing scheme, recommending chemotherapy first followed by immunotherapy.

Future Applications:

This study provides a theoretical foundation for designing more effective chemo-immunotherapy regimens.

Future strategies could monitor immune recovery status to identify optimal timing for initiating immunotherapy post-chemotherapy and optimise individualised treatment based on tumour antigen heterogeneity and immune parameters.

The model can also be extended to evaluate sensitivity to dosage intensity, chemotherapy-immunotherapy intervals, and individual immune variations, guiding precise decisions on dosing and timing.

Furthermore, incorporating such models into clinical trial design may reduce failure rates, improve resource utilisation, and enable truly model-driven “digital treatment optimisation.” The framework also offers new perspectives for studying immune resistance, antigen escape, and digital twin-based therapy optimisation, with broad potential in precision oncology.

More broadly, it can serve as a foundation for developing individualised tumour-immune system digital twin models to support precise prediction and real-time optimisation of clinical treatment strategies.

Article: Modeling combination chemo-immunotherapy for heterogeneous tumors

Source: Higher Education Press