# Antibody-Drug Conjugates for Lymphoma: A Complete Review of the Clinical Trial Landscape

This systematic analysis examined 410 clinical trials of antibody-drug conjugates (ADCs) in lymphoma. Trial numbers have grown dramatically, from 2 in 2006 to 37 in 2025. However, most development remains concentrated in a few lymphoma subtypes and a limited number of drug platforms. Phase II trials dominated (193 trials, 47.1%), and 61.7% were single-arm studies without a comparison group. Among 50 unique ADC programs, 70% had not progressed beyond early-phase testing, and safety reporting varied widely across trials.

# Antibody-Drug Conjugates for Lymphoma: A Complete Review of the Clinical Trial Landscape

## Table of Contents

- Key Points
- Background: Why This Research Matters
- What Are Antibody-Drug Conjugates?
- How the Research Was Conducted
- Trial Growth Over Time
- Where Trials Are Conducted
- Who Funds Lymphoma ADC Trials
- Which Lymphoma Types Are Studied
- Target Antigens in Development
- Drug Platforms: Payloads and Linkers
- Trial Design and Endpoints
- Combination Strategies
- How Many ADC Programs Advance?
- Why Trials Stop: Inactive Development
- Safety Reporting Across Trials
- Landmark Trials in Context
- Clinical Implications for Patients
- Limitations of This Analysis
- Recommendations and Future Priorities
- Frequently Asked Questions
- Source Information

## Key Points

- Annual lymphoma antibody-drug conjugate trial starts rose from 2 in 2006 to 37 in 2025, peaking at 41 in 2023, with 201 trials beginning between 2020 and 2025.
- Of 410 trials, 253 (61.7%) were single-arm and 76 (18.5%) randomized; Phase II was largest at 193 trials (47.1%).
- Of 50 unique ADC programs, 35 (70%) had not progressed beyond Phase I or Phase I/II in lymphoma trials.
- DLBCL appeared in 213 of 837 trial-subtype pairs (25.4%) and classical Hodgkin lymphoma in 135 (16.1%), which also held most late-phase activity.
- In 177 safety-evaluable trials, grade 3 or higher adverse events were reported in 109, neutropenia in 98, infection in 79, and peripheral neuropathy in 69.

## Background: Why This Research Matters

Lymphomas are cancers that begin in white blood cells called lymphocytes. The term covers Hodgkin lymphoma and a broad group of mature B-cell, T-cell, and natural killer-cell cancers. Current classification systems emphasize major differences in cellular origin, clinical features, and molecular profiles across these diseases.

The incidence and mortality of lymphoma vary across disease subtypes and geographic regions. Even so, lymphomas remain an important global cancer burden. Treatment has improved through risk-adapted chemotherapy, radiotherapy, targeted agents, and immunotherapies.

Despite these advances, a clinically important proportion of patients develop relapsed or refractory disease. This means the cancer returns or stops responding to treatment. The problem is particularly significant in aggressive B-cell lymphomas and classical Hodgkin lymphoma (cHL).

These patients may need several lines of treatment. Each additional treatment adds cumulative toxicity, time burden, and pressure on quality of life. New treatments should therefore improve disease control without further increasing the overall treatment burden.

Antibody-drug conjugates (ADCs) provide one approach to this need. Individual ADCs have shown success in selected lymphoma settings. However, the success of individual agents does not fully describe the broader lymphoma ADC pipeline. Important questions remain about how mature this field is, how it is distributed across lymphoma subtypes, and whether it is generating confirmatory evidence.

## What Are Antibody-Drug Conjugates?

An antibody-drug conjugate (ADC) contains three main components: a monoclonal antibody, a chemical linker, and a cytotoxic payload. A monoclonal antibody is a laboratory-made protein that binds to a specific target on cancer cells. The payload is a cell-killing drug. The linker connects the antibody to the payload.

After the antibody binds to its target antigen, the ADC is usually internalized and processed within the cell. The payload is then released, leading to tumor-cell death. This design can increase tumor-directed drug delivery.

However, this design does not eliminate systemic exposure, off-target effects, or payload-related toxicity. In other words, ADCs can still cause side effects in healthy tissues.

The activity and safety of an ADC depend on several factors:

- Target expression (how much of the target is present on cancer cells)
- Antigen accessibility (how easily the antibody can reach the target)
- Internalization (how well the cancer cell takes in the ADC)
- Linker stability (how well the linker holds the payload until it reaches the cancer cell)
- Payload properties (how the cell-killing drug works)
- Tumor biology (the characteristics of the cancer itself)

Several factors may reduce treatment sensitivity. These include antigen heterogeneity (differences in target expression across cancer cells), changes in target expression, altered intracellular processing, and payload resistance. Agent-specific toxicities can also limit treatment exposure and further development.

Clinical trials have established the value of ADCs in several lymphoma settings. Brentuximab vedotin has produced long-term survival benefits in previously untreated stage III or IV classical Hodgkin lymphoma and CD30-positive peripheral T-cell lymphoma. Polatuzumab vedotin combined with rituximab, cyclophosphamide, doxorubicin, and prednisone improved progression-free survival compared with R-CHOP in previously untreated intermediate- or high-risk diffuse large B-cell lymphoma (DLBCL). The 5-year POLARIX analysis showed that this benefit was sustained. Loncastuximab tesirine has also produced durable responses in patients with relapsed or refractory DLBCL after at least two previous lines of systemic therapy.

## How the Research Was Conducted

Researchers conducted a systematic landscape analysis of registered interventional clinical trials evaluating antibody-drug conjugates in lymphoma. The study characterized temporal and geographic patterns, lymphoma subtype distribution, trial design, endpoint selection, ADC platform characteristics, inactive development, toxicity reporting, and the developmental maturity of individual ADC programs.

The study was not designed to estimate pooled treatment effects. In other words, researchers did not combine results from different trials to calculate an overall treatment effect.

Trial records were retrieved from the Trialtrove database through the Citeline Clinical Intelligence platform. This database compiles information from international and national trial registries, publications, conference materials, regulatory sources, and sponsor-reported updates.

The complete retrieval strategy used specific filters:

- Drug type: antibody-drug conjugate
- Disease: Hodgkin lymphoma or non-Hodgkin lymphoma
- Actual or anticipated start date on or before December 31, 2025
- Trial phase: I, I/II, II, II/III, III, III/IV, or IV

No restrictions were applied by country, sponsor type, or trial status. Both actual and anticipated start dates were included. The start year was defined by the actual start date when available and by the anticipated date for planned trials without an actual start date.

The database search and data export were completed on January 20, 2026. The prespecified trial-start cutoff was December 31, 2025. Study identification, selection, and reporting were guided by the PRISMA 2020 statement, a widely used guideline for reporting systematic reviews.

This study was a systematic clinical-trial landscape analysis, not a meta-analysis of intervention effects. Therefore, items that were not applicable and methods that were not undertaken are identified and justified in the checklist. The review protocol was not prospectively registered.

### Which Trials Were Eligible

Eligible studies were interventional Phase I–IV trials that evaluated at least one ADC and enrolled patients with Hodgkin lymphoma, non-Hodgkin lymphoma, or a defined lymphoma subtype. For eligibility, an ADC was defined as an antibody covalently conjugated through a chemical linker to a pharmacologically active payload.

Initial identification was based on the Trialtrove ADC filter, followed by manual confirmation using the trial record and external sources where available. Records in which the investigational intervention could not be confirmed as an ADC were excluded.

Trials were excluded if they did not involve an ADC, did not include patients with lymphoma, were non-interventional, or had ADC exposure or lymphoma eligibility that could not be confirmed.

Mixed hematologic-malignancy trials were retained when patients with lymphoma were eligible or a lymphoma-specific cohort could be identified. Trials involving acute lymphoblastic leukemia and lymphoblastic lymphoma were included only when lymphoblastic lymphoma involvement was confirmed. Multi-arm studies were counted as one trial in trial-level analyses.

Duplicate records were assessed using trial identifiers, sponsor protocol numbers, and study titles. No duplicate trial records were identified. Two reviewers independently screened all records, with disagreements resolved by consensus.

Missing payload, linker, endpoint, sponsor, or other platform information did not itself result in exclusion. Unconfirmed variables were coded as "undisclosed," "not reported," or "unclear."

### How Data Were Extracted and Classified

Two reviewers extracted trial identifier, ADC name, title, start year, phase, status, location, sponsor and collaborator information, lymphoma subtype, target, primary payload and payload class, primary linker type, regimen, arm structure, randomization, primary endpoints, result availability, toxicity reporting, and prior treatment exposure. Reviewers extracted prior treatment exposure when explicitly reported.

Trialtrove served as the primary data source. Information was externally verified, where available, using public trial registries, peer-reviewed publications, regulatory documents, and official sponsor materials. When sources differed, the most specific verifiable source was used. Unresolved variables were not inferred.

Sponsor types followed Trialtrove categories: Top-20 pharmaceutical companies, other pharmaceutical companies, academic institutions, government agencies, and cooperative groups.

Lymphoma subtypes were standardized into specific categories. These included classical Hodgkin lymphoma, nodular lymphocyte-predominant Hodgkin lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma or extranodal mucosa-associated lymphoid tissue lymphoma, chronic lymphocytic leukemia or small lymphocytic lymphoma, lymphoblastic lymphoma or acute lymphoblastic leukemia overlap, anaplastic large cell lymphoma, peripheral T-cell lymphoma, cutaneous T-cell lymphoma, Waldenström macroglobulinemia or lymphoplasmacytic lymphoma, primary mediastinal B-cell lymphoma, high-grade B-cell lymphoma, other B-cell malignancies, and unspecified or mixed lymphoma populations.

All confirmed ADC targets within a trial were retained. For trial-level payload and linker analyses, one primary ADC platform was assigned to each trial. This was defined as the principal investigational ADC identified in the trial record and confirmed through external sources when necessary. When no principal ADC could be distinguished, the platform was coded as unclear.

Payloads were recorded individually and grouped as tubulin inhibitors, DNA-damaging agents, topoisomerase I inhibitors, RNA-directed agents, other classes, or undisclosed. Exploratory toxicity analyses additionally used MMAE, MMAF, calicheamicin-derived, pyrrolobenzodiazepine-related, and maytansinoid platform families.

Linkers were classified as cleavable, non-cleavable, or undisclosed. Further chemical subclassification was not performed because linker details were inconsistently reported.

A unique ADC program was defined as a distinct ADC drug or development code. Alternative names and sponsor-specific codes for the same ADC were consolidated. Different ADCs directed against the same target were treated as separate programs.

Primary endpoints were classified into specific categories. These included safety-related endpoints, dose-limiting toxicity, maximum tolerated dose, recommended Phase II dose or recommended dose for expansion, pharmacokinetics, immunogenicity, objective response rate or response rate, complete response or complete metabolic response, duration of response, progression-free survival, overall survival, other time-to-event or relapse endpoints, minimal residual disease, biomarker or correlative endpoints, other response or progression endpoints, feasibility or transplant-related endpoints, and other or unclear assessments.

"Any safety-related endpoint" was a composite category encompassing adverse events, toxicity, tolerability, dose-limiting toxicity, maximum tolerated dose, vital signs, laboratory abnormalities, and organ-specific safety measures. Dose-limiting toxicity and maximum tolerated dose were also coded separately and therefore overlapped with the broader safety category. Trials with multiple primary endpoint types contributed to each applicable category.

Non-ADC combination partners were grouped as chemotherapy or corticosteroids, monoclonal antibodies or other immunotherapies, targeted small molecules or immunomodulatory agents, immune checkpoint inhibitors, bispecific antibodies or T-cell engagers, cellular therapy or transplant-related strategies, and other or unidentified agents. These categories were not mutually exclusive.

Treatment setting and line of therapy were not used as core quantitative variables because their definitions were incomplete and inconsistent across sources. Prior treatment exposure was retained only when explicitly reported and was used for contextual safety analyses.

### Units of Analysis and Denominators

The individual clinical trial was the primary analytical unit, with 410 eligible trials included.

For the regional analysis, participating countries were mapped to six continental regions: Africa, Asia, Europe, North America, Oceania, and South America. Each trial was assigned to one mutually exclusive continent or continent-combination category. Country-level mapping was conducted separately using trial–country records. Therefore, multinational trials contributed to more than one country on the map. Lymphoma subtype analyses used trial–subtype pairs. Target analyses used target–trial records. Sponsor involvement was assessed using mutually exclusive trial-level funding categories and non-mutually exclusive sponsor–trial records.

Overall endpoint frequencies were calculated at the trial level. Phase-stratified endpoint analyses used trial–endpoint category records. Subtype-stratified analyses used trial–subtype–endpoint category records. Trials could therefore contribute to multiple records when they included several subtypes, endpoint categories, or both.

Overall payload and linker analyses were based on the primary ADC platform, with each trial contributing no more than one payload and one linker assignment. Subtype-stratified analyses used trial–subtype–payload and trial–subtype–linker records. Subtype-stratified target analyses used trial–subtype–target records.

Program-level developmental maturity was assessed by assigning each unique ADC program to the highest clinical phase attained in lymphoma trials. This was not interpreted as a phase-transition rate or probability of developmental success. Because analytical units differed, some totals exceeded the 410 unique trials.

### Trial Status, Inactive Development, and Safety Reporting

Trialtrove status labels were standardized as open, completed, terminated, planned, closed, or temporarily closed. Completed status was considered an operational outcome and not evidence of clinical benefit. No trial was excluded on the basis of development status.

Trial status was assigned according to the information available on the database export date of January 20, 2026. Trials without an actual start date were retained as planned trials when an anticipated start date on or before December 31, 2025 was available. Suspended or withdrawn records, when present in the underlying sources, were retained under the corresponding standardized Trialtrove status rather than excluded.

Terminated, closed, and temporarily closed trials were analyzed as inactive-status records. Explicitly reported reasons were categorized as recruitment or feasibility problems, sponsor strategy or pipeline reprioritization, insufficient efficacy, safety or adverse effects, funding problems, administrative or other reasons, no further development reported, or no specific reason available. Reasons were not inferred from target, payload, linker, or sponsor type.

Safety analyses included trials with available results reporting at least one usable toxicity outcome by event type, grade, frequency, or clinical consequence. Categories included grade 3 or higher adverse events, neutropenia, anemia, thrombocytopenia, infection, peripheral neuropathy, hepatotoxicity, ocular toxicity, pneumonitis or interstitial lung disease, toxicity-related discontinuation, dose modification, and treatment-related death.

Because adverse-event definitions, populations, regimens, follow-up, and reporting formats varied, safety outcomes were summarized as the number and proportion of safety-evaluable trials reporting each category. Patient-level adverse-event incidence was not pooled.

### Statistical Analysis

Categorical variables were summarized as counts and percentages. Temporal patterns were assessed by trial-start year and predefined time periods. All analyses used the prespecified units and denominators described above.

No formal hypothesis testing or pooled treatment-effect estimation was performed. Therefore, p-values were not used. Formal risk-of-bias and certainty-of-evidence assessments were not applied because registered trials were included irrespective of result availability and comparative treatment effects were not synthesized. No sensitivity analysis was prespecified because no pooled or model-based estimate was generated.

Data were organized in Microsoft Excel 2019. Descriptive analyses and figures were produced using Python.

## Trial Growth Over Time

The search identified 482 records. Of these, 72 were excluded: 37 did not evaluate an ADC, 25 were observational, 5 did not include patients with lymphoma, and 5 lacked sufficient information to confirm the ADC intervention or lymphoma eligibility. The final dataset comprised 410 interventional trials.

Annual trial initiation increased from 2 trials in 2006 to 37 in 2025. The peak was 41 trials in 2023. Overall, 201 trials (49.0%) began between 2020 and 2025, compared with 26 during 2006–2010.

Phase II was the largest category, with 193 trials (47.1%). Phase I followed with 93 trials (22.7%), and Phase I/II with 63 trials (15.4%). Late-stage development included 34 Phase III trials (8.3%), 22 Phase IV trials (5.4%), and 5 Phase II/III trials (1.2%). No eligible Phase III/IV trial was identified.

This distribution shows that most lymphoma ADC trials are early-phase studies. Fewer trials reach the late stages needed to confirm clinical benefit.

## Where Trials Are Conducted

Geographic participation was broad. Of the 410 trials, 311 (75.9%) were conducted within a single continent. The most common single-continent locations were North America (161 trials, 39.3%), Asia (89 trials, 21.7%), and Europe (56 trials, 13.7%).

The remaining 99 trials (24.1%) involved multiple continents. Europe–North America was the most frequent combination (33 trials, 8.0%). Asia–Europe–North America followed (17 trials, 4.1%), then Asia–Europe–North America–Oceania (11 trials, 2.7%).

When all geographic participation was counted, North America was represented in 251 trials (61.2%), Europe in 148 trials (36.1%), and Asia in 143 trials (34.9%). Oceania, South America, and Africa were represented in 37, 18, and 9 trials, respectively.

These numbers show that lymphoma ADC research is concentrated in North America, Europe, and Asia. Africa, South America, and Oceania have much less representation.

## Who Funds Lymphoma ADC Trials

At the trial level, industry-only funding was most frequent (164 trials, 40.0%). Academic-only funding followed (94 trials, 22.9%), then academic–industry funding (75 trials, 18.3%).

Other funding arrangements were less common:

- Academic–government funding: 19 trials (4.6%)
- Cooperative-group funding: 14 trials (3.4%)
- Government-only funding: 10 trials (2.4%)
- Other mixed arrangements: 34 trials (8.3%)

Sponsor–trial categories were not mutually exclusive. Top-20 pharmaceutical companies were involved in 247 records. This includes trials where they were the sole sponsor and trials where they collaborated with other organizations.

## Which Lymphoma Types Are Studied

Among 837 trial–subtype pairs, diffuse large B-cell lymphoma (DLBCL) was the most frequent subtype. DLBCL appeared in 213 trial–subtype pairs (25.4%). Classical Hodgkin lymphoma (cHL) followed with 135 pairs (16.1%).

These two subtypes also contained the most late-phase activity. This means that Phase III and Phase IV trials were concentrated in DLBCL and cHL.

Other lymphoma subtypes were studied less frequently. These included follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, peripheral T-cell lymphoma, cutaneous T-cell lymphoma, and several other categories.

The concentration of trials in DLBCL and cHL means that other lymphoma subtypes have less access to ADC clinical trials. Patients with less common lymphoma types may have fewer opportunities to participate in research.

## Target Antigens in Development

Researchers identified the leading assigned targets across lymphoma ADC trials. The most common targets were:

- CD30: 181 trials
- CD79b: 104 trials
- CD19: 47 trials
- CD22: 29 trials

CD30 is a protein found on the surface of certain lymphoma cells, including classical Hodgkin lymphoma and some T-cell lymphomas. CD79b and CD19 are proteins found on B cells and many B-cell lymphomas. CD22 is another B-cell protein.

The concentration of trials on a small number of targets suggests that the field has focused on well-validated antigens. Other potential targets remain less explored.

## Drug Platforms: Payloads and Linkers

Of 50 unique ADC programs, 35 (70.0%) had not progressed beyond Phase I or Phase I/II. This means that most ADC programs in lymphoma remain in early-stage testing.

Tubulin inhibitors and cleavable linkers predominated among the ADC platforms studied. Tubulin inhibitors are payloads that interfere with cell division. Cleavable linkers are designed to release the payload under specific conditions inside or near the cancer cell.

Payloads were grouped into several classes:

- Tubulin inhibitors
- DNA-damaging agents
- Topoisomerase I inhibitors
- RNA-directed agents
- Other classes
- Undisclosed

Linkers were classified as cleavable, non-cleavable, or undisclosed. Further chemical subclassification was not performed because linker details were inconsistently reported across trials.

The predominance of tubulin inhibitors and cleavable linkers indicates that the field relies on a limited number of platform technologies. Greater platform diversity could lead to new treatment options for patients who do not respond to current ADCs.

## Trial Design and Endpoints

Among the 410 trials, 253 (61.7%) were single-arm studies. A single-arm study means all patients receive the same treatment, and there is no comparison group. Randomized trials, which assign patients to different treatment groups by chance, numbered 76 (18.5%).

This distribution shows that most lymphoma ADC trials are not randomized. Randomized trials provide stronger evidence because they can compare a new treatment directly against a standard treatment.

Primary endpoints were classified into multiple categories. Safety-related endpoints were common, especially in early-phase trials. Response-based endpoints, such as objective response rate and complete response, were also frequently used.

Survival-based endpoints, such as progression-free survival and overall survival, were less common. These endpoints require longer follow-up and larger trials.

Biomarker-driven endpoints, such as minimal residual disease, were also limited. Biomarkers are measurable indicators of disease or treatment response. Biomarker-guided patient selection could help identify which patients are most likely to benefit from a specific ADC.

## Combination Strategies

Combination trials, including studies of polatuzumab vedotin with bispecific antibodies, show increasing interest in integrating ADCs with other immune therapies. Bispecific antibodies are engineered proteins that can bind to two different targets at once.

Non-ADC combination partners were grouped into several categories:

- Chemotherapy or corticosteroids
- Monoclonal antibodies or other immunotherapies
- Targeted small molecules or immunomodulatory agents
- Immune checkpoint inhibitors
- Bispecific antibodies or T-cell engagers
- Cellular therapy or transplant-related strategies
- Other or unidentified agents

These categories were not mutually exclusive. A single trial could combine an ADC with more than one type of partner.

Combination regimens may add overlapping toxicity. They also make treatment effects harder to attribute to a single component. In other words, if a combination works, it may be difficult to know which drug is responsible for the benefit.

## How Many ADC Programs Advance?

Researchers assessed program-level developmental maturity by assigning each unique ADC program to the highest clinical phase attained in lymphoma trials. Of 50 unique ADC programs, 35 (70.0%) had not progressed beyond Phase I or Phase I/II.

This finding shows that trial growth has outpaced platform diversification and the generation of confirmatory evidence. Many ADC programs enter early-phase testing, but most do not advance to later-phase trials.

Mature development remains concentrated in DLBCL, cHL, and a limited number of target–payload–linker platforms. Randomized, survival-based, and biomarker-driven evidence remains limited.

It is important to note that this measure was not interpreted as a phase-transition rate or probability of developmental success. A program that has not advanced may still be under active development, or it may have been discontinued for reasons not captured in this analysis.

## Why Trials Stop: Inactive Development

Among 92 inactive trials, researchers identified the leading reported reasons for discontinuation. These were:

1. Recruitment or feasibility problems: 30 trials
1. Unspecified reasons: 28 trials
1. Sponsor strategy or pipeline reprioritization: 15 trials

Other reported reasons included insufficient efficacy, safety or adverse effects, funding problems, administrative or other reasons, and no further development reported.

Reasons were not inferred from target, payload, linker, or sponsor type. This means researchers only used explicitly reported reasons and did not guess why a trial stopped.

Recruitment problems were the most common reported reason. This finding suggests that many lymphoma ADC trials struggle to enroll enough patients. Feasibility problems, such as difficulty completing required procedures or meeting regulatory requirements, also contributed.

Unspecified reasons were the second most common category. This lack of transparency makes it harder to understand why some ADC programs do not advance.

## Safety Reporting Across Trials

In 177 safety-evaluable trials, researchers examined the frequency of specific toxicity categories. These trials had available results reporting at least one usable toxicity outcome.

The most commonly reported safety categories were:

- Grade 3 or higher adverse events: reported in 109 trials
- Neutropenia: reported in 98 trials
- Infection: reported in 79 trials
- Peripheral neuropathy: reported in 69 trials

Grade 3 or higher adverse events are serious side effects that require medical attention. Neutropenia is a low level of neutrophils, a type of white blood cell that fights infection. Peripheral neuropathy is nerve damage that can cause numbness, tingling, or pain, usually in the hands and feet.

Other safety categories included anemia, thrombocytopenia, hepatotoxicity, ocular toxicity, pneumonitis or interstitial lung disease, toxicity-related discontinuation, dose modification, and treatment-related death.

Because adverse-event definitions, populations, regimens, follow-up, and reporting formats varied across trials, safety outcomes were summarized as the number and proportion of safety-evaluable trials reporting each category. Patient-level adverse-event incidence was not pooled.

This means the numbers above show how many trials reported each toxicity category. They do not show how many patients experienced each side effect. Trial-level safety-reporting frequencies should not be interpreted as measures of clinical success or patient-level adverse-event incidence.

## Landmark Trials in Context

Researchers used selected landmark and contemporary studies to illustrate the evolution of lymphoma ADC development. Selection emphasized pivotal or practice-informing completed trials and recent late-phase programs across major subtypes and platforms.

Study-level efficacy and safety findings were verified from primary publications, regulatory documents, and public registries. These findings were not pooled.

Key landmark trials mentioned in the analysis include:

- Brentuximab vedotin trials in previously untreated stage III or IV classical Hodgkin lymphoma and CD30-positive peripheral T-cell lymphoma, which produced long-term survival benefits
- The POLARIX trial showed that polatuzumab vedotin combined with rituximab, cyclophosphamide, doxorubicin, and prednisone improved progression-free survival compared with R-CHOP in previously untreated intermediate- or high-risk DLBCL. The 5-year analysis showed sustained benefit.
- Loncastuximab tesirine trials, which produced durable responses in patients with relapsed or refractory DLBCL after at least two previous lines of systemic therapy

These trials demonstrate that ADCs can improve outcomes in specific lymphoma settings. However, they represent a small number of platforms and subtypes relative to the broader pipeline.

## Clinical Implications for Patients

Lymphoma ADC development has expanded significantly. However, trial growth has outpaced platform diversification and the generation of confirmatory evidence.

For patients, this means that while many new ADC trials are opening, most are early-phase studies. Only a limited number of ADCs have reached late-phase testing or regulatory approval.

Mature development remains concentrated in DLBCL, classical Hodgkin lymphoma, and a limited number of target–payload–linker platforms. Patients with these lymphoma types have more opportunities to participate in ADC trials. Patients with less common lymphoma subtypes may have fewer options.

The limited number of randomized trials means that strong comparative evidence is scarce. Many trials test an ADC without directly comparing it to the current standard of care. This makes it harder to know whether a new ADC is better than existing treatments.

The variability in safety reporting means that comparing side effects across trials is difficult. Patients and clinicians should interpret safety data from individual trials with caution.

Combination strategies are increasingly common. These regimens may offer better disease control but may also add overlapping toxicity. Patients considering combination trials should discuss the potential risks and benefits with their care team.

## Limitations of This Analysis

This study has several important limitations that patients and clinicians should understand.

First, the analysis was descriptive. Researchers did not pool treatment effects or patient-level adverse-event incidences. This means the study cannot provide an overall estimate of how well ADCs work or how often they cause side effects.

Second, no formal hypothesis testing was performed. Therefore, p-values were not used. The study describes patterns rather than testing whether differences are statistically significant.

Third, formal risk-of-bias and certainty-of-evidence assessments were not applied. Registered trials were included irrespective of result availability. Comparative treatment effects were not synthesized.

Fourth, the review protocol was not prospectively registered. This means the analysis plan was not publicly documented before the study began.

Fifth, treatment setting and line of therapy were not used as core quantitative variables because their definitions were incomplete and inconsistent across sources. This limits the ability to analyze how ADC development differs across treatment settings.

Sixth, trial status and trial-level safety-reporting frequencies should not be interpreted as measures of clinical success or patient-level adverse-event incidence. A completed trial is not necessarily a successful trial. A trial that reports a side effect does not indicate how many patients experienced that side effect.

Seventh, the analysis relied on the Trialtrove database and external verification where available. Unresolved variables were not inferred. Some information may be incomplete or outdated.

Eighth, because analytical units differed across analyses, some totals exceeded the 410 unique trials. This means that some numbers in the results describe trial–subtype pairs, target–trial records, or other units rather than unique trials.

## Recommendations and Future Priorities

Based on their findings, the researchers identified several priorities for the future of lymphoma ADC development.

1.
**Subtype-specific targeting:** Expand ADC development beyond DLBCL and classical Hodgkin lymphoma to address less common lymphoma subtypes.
1.
**Platform diversification:** Develop ADCs with new targets, payloads, and linker technologies beyond the current predominance of tubulin inhibitors and cleavable linkers.
1.
**Randomized comparative studies:** Conduct more randomized trials that directly compare ADCs against standard treatments.
1.
**Standardized safety reporting:** Improve consistency in how adverse events are defined and reported across trials.
1.
**Biomarker-guided patient selection:** Use biomarkers to identify which patients are most likely to benefit from a specific ADC.
1.
**Rational sequencing with immune and cellular therapies:** Study how best to combine and sequence ADCs with immunotherapies and cellular therapies such as CAR-T cell therapy.

For patients, these priorities suggest that the field is moving toward more personalized and more rigorous testing of ADCs. Patients interested in ADC clinical trials should discuss available options with their oncology team. They should ask about the phase of the trial, whether it is randomized, what safety data are available, and how the trial fits into their overall treatment plan.

Patients should also be aware that participation in a clinical trial offers both potential benefits and potential risks. Early-phase trials may offer access to new treatments, but they also carry more uncertainty about effectiveness and safety.

## Frequently Asked Questions

### What are antibody-drug conjugates and how do they work?

An antibody-drug conjugate has three parts: a monoclonal antibody that binds a target on cancer cells, a chemical linker, and a cell-killing payload. After binding, the conjugate is usually taken into the cell and processed, releasing the payload to kill tumor cells. This design can increase tumor-directed delivery, but it does not remove systemic exposure, off-target effects, or payload-related toxicity.

### Which lymphoma types have the most antibody-drug conjugate trials?

Among 837 trial-subtype pairs, diffuse large B-cell lymphoma was the most frequent, appearing in 213 pairs, or 25.4 percent. Classical Hodgkin lymphoma followed with 135 pairs, or 16.1 percent. These two subtypes also contained the most late-phase activity. Other subtypes, including follicular, mantle cell, marginal zone, peripheral T-cell, and cutaneous T-cell lymphoma, were studied less often, so patients with those types may have fewer trial opportunities.

### Are most lymphoma antibody-drug conjugate trials randomized?

No. Of 410 trials, 253, or 61.7 percent, were single-arm studies, meaning all patients received the same treatment with no comparison group. Randomized trials numbered 76, or 18.5 percent. Randomized trials provide stronger evidence because they can compare a new treatment directly against a standard treatment. The limited number of randomized trials means strong comparative evidence is scarce.

### What does it mean that 70 percent of ADC programs have not progressed beyond early-phase testing?

Of 50 unique antibody-drug conjugate programs, 35, or 70 percent, had not advanced beyond Phase I or Phase I/II in lymphoma trials. This means most programs remain in early-stage testing. It is not a phase-transition rate or a probability of success. A program that has not advanced may still be under active development, or it may have been discontinued for reasons not captured.

### What side effects were reported across antibody-drug conjugate trials?

In 177 safety-evaluable trials, the most commonly reported categories were grade 3 or higher adverse events in 109 trials, neutropenia in 98, infection in 79, and peripheral neuropathy in 69. These numbers show how many trials reported each category, not how many patients experienced a side effect. Because definitions, populations, regimens, and reporting formats varied, patient-level incidence was not pooled.

### Why do lymphoma antibody-drug conjugate trials stop?

Among 92 inactive trials, the most common reported reason was recruitment or feasibility problems, in 30 trials. Unspecified reasons followed in 28 trials, and sponsor strategy or pipeline reprioritization in 15. Other reported reasons included insufficient efficacy, safety or adverse effects, funding problems, and administrative reasons. Reasons were not inferred from target, payload, linker, or sponsor type, so only explicitly reported reasons were used.

### What should I ask my oncology team before joining an ADC trial?

Patients interested in antibody-drug conjugate trials should discuss available options with their oncology team. Ask about the phase of the trial, whether it is randomized, what safety data are available, and how the trial fits into your overall treatment plan. Be aware that participation offers both potential benefits and potential risks. Early-phase trials may offer access to new treatments but carry more uncertainty about effectiveness and safety.

### When should a patient with lymphoma considering an antibody-drug conjugate clinical trial seek a second opinion?

Most lymphoma ADC trials are early-phase: 47.1% were Phase II, 61.7% were single-arm without a comparison group, and 70% of the 50 unique ADC programs had not progressed beyond Phase I or I/II. Mature development is concentrated in DLBCL and classical Hodgkin lymphoma, and randomized comparative evidence remains limited. A second opinion can help clarify the trial's phase, whether it is randomized, what safety data exist, and how it fits your treatment plan, particularly for less common subtypes with fewer trials. Diagnostic Detectives Network provides independent expert second opinions.

## Source Information

**Original article title:** Antibody-drug conjugate development in lymphoma: a systematic clinical trial landscape analysis.

**Authors:** Zhang H, Li J, Yan H, Liu Y.

**Affiliation:** School of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China

**Journal:** Frontiers in Immunology, Volume 17, Article 1855476

**DOI:** 10.3389/fimmu.2026.1855476

**Publication dates:** Received April 14, 2026; Revised July 19, 2026; Accepted August 6, 2026; Published August 19, 2026

**Article type:** Systematic Review

**Data source:** Trialtrove database through the Citeline Clinical Intelligence platform

**Note:** This patient-friendly article is based on peer-reviewed research. The original study was a systematic landscape analysis of clinical trial records and did not pool treatment effects or patient-level adverse-event incidences. All numerical data, study findings, and conclusions are drawn directly from the original publication.


---
Publisher: Diagnostic Detectives Network (https://diagnosticdetectives.com) — independent multi-expert medical second opinions, worldwide, private-pay.
Author byline: Anton Titov, MD, PhD. Contact: https://diagnosticdetectives.com/pages/contact
Canonical page: https://diagnosticdetectives.com/products/antibody-drug-conjugates-for-lymphoma-a-complete-review-of-the-clinical-trial-landscape