How Antibodies are Used in Cancer Immunotherapy

Over the past century, treatments like chemotherapy and radiation therapy have been developed for cancer. However, these therapies often lack the necessary specificity to deliver high enough doses to eliminate cancer cells while preventing intolerable toxicity.¹ In recent years, monoclonal antibody (mAb) therapy has become a key treatment modality in oncology, leveraging their specificity and affinity. From improving antitumor efficacy to reducing adverse events, ongoing innovations are shaping the future of antibody-based therapeutics.

The Mechanisms of Antibody Therapy in Cancer Treatment

Antibody therapies target specific antigens on cancer cells, allowing precise binding to malignant cells while ideally sparing healthy tissue. The mechanisms of action include antibody-dependent cellular cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC), and blockade of immune checkpoints to stimulate immune responses against tumors.

ADCC involves the engagement of immune cells that kill cancer cells upon antibody binding. Studies have demonstrated that optimizing ADCC is a promising strategy to boost mAb efficacy, as enhanced ADCC directly correlates with improved tumor response. Engineering strategies that modulate the Fc region of antibodies can significantly influence ADCC by strengthening the interaction between the antibody and immune cells.

Immune checkpoint blockade (ICB) enhances the body's own immune response by inhibiting cancer cell “escape” mechanisms. ICB agents like anti-CTLA4 and anti-PD1 antibodies can reactivate immune surveillance against tumors, though only a subset of patients currently benefit from these therapies. Research continues to investigate combination therapies to enhance response rates, as discussed below.

Therapeutic Resistance Challenges

Despite the success of antibody therapies, resistance remains an issue. Resistance mechanisms can emerge from mutations within the target antigen or downstream signaling pathways associated with cancer progression. For example, mutations in tumor antigens such as EGFR or HER2 can render certain antibody therapies less effective. Therefore, assessing for mutations in antibody targets and related signaling molecules can serve as a biomarker to guide treatment adjustments or combination strategies. 

Combining mAbs with inhibitors of alternative signaling pathways has shown promise for mitigating resistance. For example, blocking parallel pathways that drive tumor growth when the main target is compromised can restore treatment efficacy. Understanding and monitoring resistance markers will be important for future treatments, allowing for the development of personalized therapies that minimize the risk of resistance and enhance long-term efficacy. 

Advances in Antibody Engineering: Emerging Formats and Approaches

Researchers are developing novel antibody formats such as bispecific and trispecific antibodies, which can target multiple antigens or immune cell receptors simultaneously, as well as single-domain antibodies (nanobodies) for enhanced stability and penetration into tumor tissues. 

Trispecific Antibodies

Trispecific antibodies are capable of binding three different antigens, providing a multifaceted approach to cancer targeting. For example, a trispecific antibody might bind two cancer-associated antigens (e.g., CD19 and CD20) on B-cell malignancies while simultaneously engaging T cells through the CD3 receptor. This approach enhances tumor cell killing, as cancer cells would need to lose expression of both CD19 and CD20 to evade detection—a rare occurrence. Additionally, trispecific antibodies designed to activate T cell receptors, like CD3 and CD28, can boost immune response strength, offering a potent new tool for cancers that exhibit treatment resistance. 

VHH Antibodies

VHH antibodies, also known as nanobodies or single-domain antibodies, are derived from camelid heavy-chain antibodies and have shown potential in oncology due to their small size, stability, and ability to target hidden epitopes on cancer cells. These features allow nanobodies to penetrate dense tumor tissues, such as solid tumors or viral antigens, and they are easier to produce at scale. Most recently, the FDA approved ciltacabtagene autoleucel (also known as cilta-cel or CARVYKTI) in 2022. The drug is a nanobody-based CAR T cell targeting BCMA for cancer treatment, and clinical trials are exploring nanobody-derived drugs targeting CD19, CD20, and HER2 for lymphomas and breast cancers. 

Related: Mechanisms of Action of Bispecific Antibodies in Cancer Therapy

Activatable Antibodies: Targeting Cancer’s Unique Environment

Activatable antibodies are sensitive to tumor-specific stimuli are capable of selectively recognizing targets found on tumor cells. By exploiting differences such as lower pH or specific protease activity, these antibodies become active only within the tumor. This can reduce off-target effects, improving safety and specificity.²

One approach incorporates histidine residues into antibody sequences, creating a pH-sensitive binding mechanism. For instance, HER2-targeting antibodies engineered for acidic tumor microenvironments demonstrate increased binding to HER2 in tumors compared to normal tissue. Another approach uses protease-sensitive masking moieties that block antibody binding until cleaved by tumor-specific enzymes, allowing the antibody to engage its target only within the tumor. Known as probody therapies, these antibodies show promise in enhancing targeting precision for proteins like PDL1, CTLA4, EGFR, and CD166.

Optimizing Antibody-Drug Conjugates (ADCs) and Conjugated Antibodies

Antibody-drug conjugates (ADCs) are designed to deliver cytotoxic agents directly to cancer cells, minimizing systemic toxicity. Advances in ADCs include optimizing the antibody structure, linker chemistry, and drug payload to enhance efficacy and stability. For instance, trastuzumab deruxtecan, an ADC targeting HER2 with a high drug-to-antibody ratio (DAR), has achieved positive outcomes even in patients with low HER2 expression, expanding the therapeutic reach of HER2-targeted treatments. 

New linkers that control drug release, such as the tetrapeptide linker used in trastuzumab deruxtecan, mask hydrophobic payloads and prevent premature release. Similar linker-payload combinations are being explored in ongoing clinical trials for other ADCs targeting tumor antigens like TROP2 and folate receptor α, with encouraging results across various cancer types. 

Immunotoxin and Radioisotope Conjugates

Immunotoxins combine antibodies with toxins like Pseudomonas exotoxin to target cancer cells selectively. Challenges remain in reducing immunogenicity to extend half-life, but advances in this area could revitalize immunotoxin therapies for solid and hematologic cancers. Radioisotope conjugates are also being re-evaluated, with α-particle emitters such as actinium-225 showing promising results in selectively damaging DNA within tumor cells while minimizing effects on healthy tissue. 

Combining Antibody Therapy with Other Cancer Treatments

The integration of monoclonal antibody therapy with traditional treatments like chemotherapy, radiation, and immune checkpoint blockade (ICB) therapies has demonstrated enhanced efficacy in clinical settings. For example, neoadjuvant ICB, combined with surgery or radiation, may achieve a more comprehensive and systemic antitumor effect. 

In preclinical studies, combining T cell-engaging bispecific antibodies with ICB has shown increased antitumor efficacy. This synergy arises from the unique abilities of each modality to stimulate different aspects of the immune response, creating a more potent combined effect. Additionally, blocking inflammatory cytokines may help reduce immune-related adverse events (irAE) from ICB while boosting antitumor activity. These combination strategies are increasingly tested in clinical trials and hold potential for improved patient outcomes. 

The Role of Computational Design and Machine Learning

Computational approaches such as deep sequencing of antibody repertoires and functional data are being used to train machine-learning models that can predict optimal antibody sequences for affinity maturation and humanization. These models enable efficient design of antibodies that maximize tumor specificity while minimizing off-target effects. Additionally, computational tools help design masks for prodrugs, ensuring that antibodies become active only in the tumor microenvironment. 

Such machine-learning models and computational tools are used for rapid identification and optimization of therapeutic antibodies, paving the way for a new generation of precision oncology treatments.  

Related: The Evolution of Antibody-Drug Conjugates (ADCs) in Cancer Therapy

References: 

1. Paul, S., Konig, M. F., Pardoll, D. M., Bettegowda, C., Papadopoulos, N., Wright, K. M., Gabelli, S. B., Ho, M., Van Elsas, A., & Zhou, S. (2024). Cancer therapy with antibodies. Nature Reviews Cancer, 24(6), 399-426. https://doi.org/10.1038/s41568-024-00690-x

2. Lucchi, R., Jordi Bentanachs, & Benjamí Oller-Salvia. (2021). The Masking Game: Design of Activatable Antibodies and Mimetics for Selective Therapeutics and Cell Control. ACS Central Science, 7(5), 724–738. https://doi.org/10.1021/acscentsci.0c01448