Molecular Profiling of Tisotumab Vedotin–Treated Patients Identifies Immune Pathways Associated with Clinical Activity

Abstract

Purpose:

Tisotumab vedotin (TV) is a tissue factor (TF)–specific antibody–drug conjugate (ADC) that was evaluated in the innovaTV 204 study in patients with recurrent or metastatic cervical cancer (r/mCC). Although TF expression is required for TV binding to tumor cells, clinical responses were found to be independent of TF levels. This study aimed to evaluate whether baseline tumor gene expression signatures consistent with the proposed mechanisms of action of TV were associated with clinical outcomes.

Experimental Design:

Pretreatment tumor samples from patients enrolled in innovaTV 204 (NCT03438396), a phase II, single-arm, multicenter trial of TV in r/mCC following chemotherapy, were profiled for molecular biomarkers. Gene expression analyses of immune-related signatures were stratified by clinical response. In vitro antibody-dependent cellular phagocytosis (ADCP) and antibody-dependent cellular cytotoxicity (ADCC) assays were performed using tumor cell lines with variable TF expression.

Results:

TF expression was confirmed in most patients but did not associate with response. Higher expression of gene signatures linked to natural killer (NK) cells and myeloid cells was associated with improved clinical outcomes. In vitro, TV induced tumor cell death through myeloid-mediated ADCP and NK-mediated ADCC across various TF expression levels.

Conclusions:

This work provides supportive evidence for TV mechanisms of action that involve antitumor effects through direct cytotoxic and immune effector–mediated mechanisms. The association of immune-related gene signatures with improved clinical outcomes supports a multimodal mechanism of action for TV in the clinic and provides further rationale for ADC efficacy in tumors with heterogeneous target expression.

Translational Relevance.

Tisotumab vedotin (TV) is an antibody–drug conjugate (ADC) targeting tissue factor (TF) with clinical activity in recurrent or metastatic cervical cancer, independent of TF expression levels. This study integrates transcriptomic and functional analyses to support roles for immune effector mechanisms, including antibody-dependent cellular phagocytosis (ADCP) and antibody-dependent cellular cytotoxicity (ADCC), in TV’s antitumor activity. Expression of gene signatures linked to natural killer and myeloid cells from clinical trial biopsies was nominally associated with improved survival, and in vitro assays confirmed TV-induced ADCP and ADCC across tumor cell lines with variable TF expression. These findings suggest that immune effector engagement may complement direct cytotoxicity, supporting a potential multimodal mechanism of action and providing a biological rationale for ADC efficacy in tumors with heterogeneous target expression.

Introduction

Over the past several years, tumor-targeted therapy with antibody–drug conjugates (ADC) has shown enhanced clinical efficacy compared with standard of care across multiple indications, while minimizing systemic toxicity because of more precise targeting of tumor cells, and has led to various new drug approvals (1). There is evidence that ADCs exert antitumor activity through multiple compound-specific mechanisms. First, ADCs bind to their target antigen on target-positive tumor cells, followed by internalization and trafficking to lysosomes, where proteolytic processing results in the release of the cytotoxic payload and subsequent tumor cell death. In addition, ADCs may mediate bystander killing of adjacent target-low and target-negative tumor cells in the tumor microenvironment (TME) and can engage antibody Fc-mediated effector functions (2). Moreover, ADC-induced cell death can be immunogenic, thereby promoting antitumor immunity (2).

Despite the recent clinical successes of several ADCs, the mechanisms driving their activity in the clinic remain incompletely understood, particularly when responses occur irrespective of target expression levels, as observed with the tissue factor (TF)–targeting ADC tisotumab vedotin (TV). Under physiologic conditions, TF is the primary initiator of the coagulation cascade following vascular injury (3). In tumors, aberrant TF expression has been reported across a range of solid malignancies, including cervical cancer (4), in which it may contribute to angiogenesis, tumor progression, and metastasis. Correspondingly, elevated TF levels have been linked to poor clinical outcomes (3, 5).

TV was designed to exploit the upregulation of TF in cancer cells to deliver a potent cytotoxic payload directly into the TME (6, 7). Upon binding and internalization, the valine–citrulline linker of TV is cleaved by intracellular proteases, releasing monomethyl auristatin E (MMAE), a microtubule-disrupting agent that induces cell-cycle arrest and apoptotic cell death in actively dividing cells. In addition to direct cytotoxicity in TF-positive tumor cells, free MMAE can diffuse across cell membranes to kill neighboring proliferating tumor cells through bystander killing (8). Preclinical studies further demonstrated that TV can engage Fc-mediated effector functions, including antibody-dependent cellular phagocytosis (ADCP) and antibody-dependent cellular cytotoxicity (ADCC; refs. 6, 8). Finally, TV-induced cell death was shown to be immunogenic (9, 10).

The activity and safety of TV were evaluated in the multicenter, open-label, single-arm, phase II study innovaTV 204 (GOG-3023/ENGOT-cx6; ClinicalTrials.gov identifier NCT03438396) in women with previously treated recurrent or metastatic cervical cancer (r/mCC) who had progressed on or after chemotherapy (11). TF expression in cervical cancer was confirmed by immunohistochemistry (IHC) in a subset of innovaTV 204 patients, but no clear association was observed between TF membrane histologic (H)-scores and confirmed best overall response. To further evaluate mechanisms of action of TV beyond direct cytotoxicity in a clinical context, pretreatment tumor biopsies were profiled by RNA sequencing (RNA-seq) to characterize the expression of immune and canonical pathway signatures and to evaluate their association with clinical outcomes. Given the limited sample size and exploratory nature of the dataset, these analyses were not intended to enable de novo biomarker discovery but to evaluate whether clinical signals are consistent with mechanisms of action previously defined in preclinical studies. Among the gene expression signatures most strongly associated with improved overall survival (OS) and progression-free survival (PFS) were immune cell signatures linked to the Fc-mediated mechanisms of action, ADCP (monocytes and macrophages) and ADCC [natural killer (NK) cells]. Based on these associations, we further investigated the capacity of TV to induce ADCP and ADCC in vitro using tumor cell lines with varying levels of TF expression. Together, these analyses represent a reverse translational approach to characterize TV mechanisms of action beyond direct cytotoxicity, integrating clinical biomarker data with in vitro functional validation.

Materials and Methods

Study design and patients

The innovaTV 204 study was a multicenter, open-label, single-arm, phase II trial that enrolled patients ≥18 years of age with r/mCC who had progressed on or after prior treatment with doublet chemotherapy. Details on the study design and primary endpoints assessed are described in Coleman and colleagues (11). The clinical protocol for this study was approved by an independent ethics committee or institutional review board and conducted in accordance with the International Conference on Harmonization Good Clinical Practice Guidelines, the Declaration of Helsinki, and all applicable regulatory requirements and according to the European Network of Gynaecological Oncological Trial Groups and Gynecologic Oncology Group Model C. All patients provided written informed consent.

Transcriptomic and IHC profiling

One hundred one patients were assessed in the intent-to-treat population from the innovaTV 204 study (Table 1). As described by Coleman and colleagues (11), assessment of TF protein expression via IHC using an analytically validated assay was successfully performed on fresh (i.e., after the last systemic therapy) or archival (i.e., before the last systemic therapy) baseline (prior to TV treatment) tumor biopsies from 80 of these patients, 76 of whom patients had evaluable responses by Response Evaluation Criteria in Solid Tumors (RECIST) v1.1. Membrane and cytoplasmic TF levels were assessed via IHC, using an H-score that combined the percentage and intensity [low (1+), intermediate (2+), and high (3+)] of TF staining in these samples as described previously (12), based on the equation: H-score = [1 × (% cells 1+)] + [2 × (% cells 2+)] + [3 × (% cells 3+)].

Table 1.

Patient disposition in overall trial and biomarker-evaluable populations.

Patient characteristic	Study population (n = 101)	IHC study population (n = 80)	RNA-seq study population (n = 41)
Age, years	50 (43–58)	50 (43–58)	50 (43–58)
Race	​	​	​
White	95 (94%)	75 (94%)	37 (90%)
Asian	2 (2%)	2 (2%)	1 (2%)
Black or African American	1 (1%)	1 (1%)	1 (2%)
Other	3 (3%)	2 (2%)	2 (5%)
ECOG performance status	​	​	​
0	59 (58%)	47 (59%)	22 (54%)
1	42 (42%)	33 (41%)	19 (46%)
Histology	​	​	​
Squamous cell carcinoma	69 (68%)	59 (74%)	27 (66%)
Adenocarcinoma	27 (27%)	17 (21%)	11 (27%)
Adenosquamous cell carcinoma	5 (5%)	4 (5%)	3 (7%)
Positive TF expression	​	​	​
Membrane	77/80 (96%)	77/80 (96%)	39/40 (98%)
Objective response rate (95% CI)	24% (16–33)	25% (16–36)	24% (12–40)
CR	7 (7%)	6 (8%)	3 (7%)
PR	17 (17%)	14 (18%)	7 (17%)
SD	49 (49%)	39 (49%)	19 (46%)
PD	24 (24%)	17 (21%)	8 (20%)
Not evaluable	4 (4%)	4 (5%)	4 (10%)
Prior lines of therapy	​	​	​
1 prior line	71 (70%)	57 (71%)	28 (68%)
2 prior lines	30 (30%)	23 (29%)	13 (32%)

Abbreviations: CI, confidence interval; ECOG, Eastern Cooperative Oncology Group.

Biopsies with ≥1% of tumor cells expressing TF were considered positive. Differences in TF IHC metrics between response groups [complete response (CR) and partial response (PR) vs. stable disease (SD) and progressive disease (PD)] and between histologic subtypes (nonsquamous vs. squamous) were analyzed using a Wilcoxon rank-sum test.

Transcriptomic profiling (RNA-seq) was performed on pretreatment biopsies from a subset of 41 innovaTV 204 patients (Table 1) using the Tempus xE assay (RRID: SCR_025832) and associated genomics pipeline (Tempus Labs). All available pretreatment biopsy samples were submitted for analysis; however, only 41 samples met the vendor-defined quality control criteria and yielded evaluable RNA-seq data. Gene-level expression values (in transcripts per million) were generated for each evaluable sample. Differentially expressed genes and signatures (scored using gene set variation analysis) associated with OS and PFS were identified using a Cox proportional hazards model, grouping patients based on above- or below-median signature scores. Given the limited sample size of the RNA-seq dataset, these analyses were not powered for predictive biomarker discovery. Instead, they were designed to evaluate predefined immune-related signatures associated with known mechanisms of action of TV. Differences in baseline tumor volume (sum of diameters) were compared between biomarker-evaluable cohorts using a Kruskal–Wallis test.

Antibody generation

The unconjugated parental antibody tisotumab was generated as previously described (6). A nonbinding immunoglobulin (Ig) G1 control antibody (IgG1-ctrl) was derived from the gp120-specific antibody IgG1-b12 (13). To abrogate Fcγ receptor–mediated effector function, the Fc-silencing mutations L234F/L235E/D265A (FEA; ref. 14) were introduced, yielding tisotumab-FEA and IgG1-ctrl-FEA. For generation of ADCs, antibodies were conjugated with MMAE through a protease-cleavable linker as described previously (15, 16), resulting in TV, TV-FEA, and IgG1-ctrl-MMAE.

Cell lines and culture conditions

Tumor cell lines and culture conditions used for in vitro studies are described in Supplementary Tables S1 and S2. All cell lines maintained in culture were tested monthly for Mycoplasma contamination using the MycoAlert Mycoplasma Detection Kit and MycoAlert Control Set (Lonza Bioscience), according to the manufacturer’s instructions. Cell lines were obtained from the American Type Culture Collection (ATCC) or the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ; Supplementary Table S1). Cell line authenticity was confirmed by short tandem repeat profiling by the supplier, and the cell lines were not reauthenticated after purchase. Cells were used for a maximum of 20 passages, after which new aliquots were thawed for subsequent experiments.

Semiquantitative flow cytometry

Suspension cells were harvested directly, whereas adherent cell lines were detached using trypsinization prior to collection. Cells were washed with fluorescence-activated cell sorting (FACS) buffer [phosphate-buffered saline (PBS), prepared by diluting PBS 10× (Capricorn Scientific or Gibco, 70011-044) in Milli-Q water and supplemented with 0.1% weight per volume bovine serum albumin fraction V (Sigma-Aldrich, 126609, or Roche, 10735086001) with or without 0.02% weight per volume sodium azide (NaN₃; Morphisto, 13553)], passed through a 40-μm cell strainer (Falcon, 352340), and seeded in round-bottom 96-well plates (Greiner Bio-One, 650180) at 100,000 cells/well. After centrifugation and removal of supernatant, cells were resuspended and incubated with 50 μL of FACS buffer containing 10 μg/mL mouse antihuman TF antibody (R&D Systems, MAB2339, clone 323514, RRID: AB_2101338) or an equal concentration of mouse IgG2a isotype control antibody (BD Pharmingen, 555571, RRID: AB_395950) at 4°C for 30 to 45 minutes. In parallel, calibration beads from the Mouse QIFIKIT (Dako, K0078) were added to separate wells to prepare a standard curve (covering a range of 1,400–689,000 molecules/bead), essentially according to the manufacturer’s instructions. Cells and beads were washed 2 to 3 times with FACS buffer and incubated with 50 μL of fluorescein isothiocyanate–conjugated goat anti–mouse IgG secondary antibody (diluted 1:50 in FACS buffer; Dako, K0078) at 4°C for 30 to 45 minutes. After 2 additional washes with FACS buffer, cells were resuspended in FACS buffer or in FACS buffer supplemented with 2 mmol/L EDTA (ethylenediaminetetraacetic acid, Sigma-Aldrich, 03690) and containing 1:1,000 DRAQ7 viability dye (BioLegend, 424001) and measured on a BD FACSCelesta Flow Cytometer (BD Biosciences, RRID: SCR_019597). Data were processed using FlowJo software (FlowJo LLC, RRID: SCR_008520), and the standard curve was fitted using linear regression analysis [GraphPad Prism (GraphPad Software, RRID: SCR_002798) or Excel (Microsoft)]. The standard curve was used to interpolate the number of antibody molecules bound per cell, representing the estimated number of cell surface–expressed TF molecules per cell.

Peripheral blood mononuclear cell isolation

Human peripheral blood mononuclear cells (PBMC) were freshly isolated from buffy coats of healthy donors (Sanquin Blood Bank) by density centrifugation over Lymphocyte Separation Medium (MP Biomedicals, 50494; PromoCell, C-44010; or Corning, 25-072-CL), according to the manufacturer’s instructions. PBMCs were resuspended in RPMI 1640 (Gibco, A10491-01) supplemented with 10% donor bovine serum with iron (DBSI; Gibco, 20371-030) and counted. Subsequently, PBMCs were either cryopreserved in a 1:1 mixture of culture medium with either Cryoprotective Freezing Medium (Lonza Bioscience, 12-132A) or with 20% dimethyl sulfoxide (Sigma-Aldrich, D2438-50ML) in DBSI until use in ADCC experiments or used fresh for monocyte isolation in ADCP assays.

In vitro ADCP experiments

Monocytes were isolated from freshly isolated PBMCs using CD14 MicroBeads (Miltenyi Biotec, 130-050-201, RRID: AB_2665482) and LS Columns (Miltenyi Biotec, 130-042-401) according to the manufacturer’s instructions. Purity of monocytes was confirmed using flow cytometry. The freshly isolated monocytes were resuspended at 0.8 × 10⁶ cells/mL in Good Manufacturing Practice (GMP) Dendritic Cell (DC) medium (CellGenix, 20801-0500) supplemented with 50 ng/mL macrophage colony-stimulating factor (CSF; Gibco, PHC9501) and plated in UpCell dishes (Thermo Fisher Scientific, 174902). Cells were cultured at 37°C and 5% CO₂ for 7 days. After 2 to 3 days, 2 mL fresh GMP DC medium containing 250 ng/mL macrophage CSF was added to each dish. On day 6 or 7, cells were incubated at room temperature for 30 to 40 minutes to let the cells detach. Human monocyte-derived macrophages (hMDM) were collected in GMP DC medium, counted, and resuspended in GMP DC medium at 1 × 10⁵ cells/mL.

Target tumor cell lines were harvested (1 × 10⁶ cells/mL) and labeled with 1 μg/mL of the pH-sensitive, amine-reactive pHrodo Red Cell Labeling Dye for Incucyte (Sartorius, 6500-0039) according to the manufacturer’s instructions.

hMDMs were seeded at 1 × 10⁴ cells/well (100 μL) in 96-well flat-bottom plates (Greiner Bio-One, cat. #655180) and preincubated at 37°C for 20 minutes. pHrodo-labeled target cells were added at 5 × 10³ cells/well (50 μL) in GMP DC medium, resulting in an effector to target cell (E:T) ratio of 2:1. Next, 50 μL of the test antibodies (final concentration range: 0.000128–10 μg/mL in RPMI 1640 supplemented with 10% DBSI) was added. Automated live-cell imaging was initiated on an Incucyte S3 Live-Cell Analysis System (Sartorius, RRID: SCR_023147) at 45-minute intervals for 12 hours. Image analysis was performed using integrated Incucyte S3 software. For each cell line, a minimum threshold was set to count all target cells, whereas a fluorescence intensity threshold was set to define engulfed, acidified targets. For each well, the total number of pHrodo-positive objects and the integrated intensity (expressed as Red Calibrated Unit (RCU) × μm²/image) were quantified. The percentage of ADCP was calculated as % ADCP = counts/all target cell counts at t = 0 × 100%. The fold-change induction of ADCP was calculated relative to the no antibody control.

In vitro ADCC experiments

Frozen PBMCs were thawed in assay medium [RPMI 1640 (Capricorn Scientific, RPMI-HA) supplemented with 10% DBSI (Gibco, 20371-030)], washed with assay medium, and allowed to recover in assay medium overnight at 37°C. Tumor cells were harvested and labeled with CellTrace Violet (CTV; Invitrogen, C34557) at a 1:20,000 to 1:50,000 dilution in PBS (Capricorn Scientific, SP-2121) at 37°C for 20 minutes. A 4-fold excess of assay medium was added to quench the dye, after which cells were washed with assay medium.

CTV-labeled tumor cells were seeded in 50 μL assay medium at 10,000 cells/well in round-bottom 96-well plates (Greiner Bio-One, 650180) and incubated with 50 μL of an antibody concentration range (final assay concentrations: 0.000128−10 μg/mL in 5-fold increments) at room temperature for 15 minutes while lightly shaking to allow target cell opsonization. Next, 100 μL of assay medium containing PBMCs as effector cells was added at an E:T ratio of 30:1. Plates were centrifuged at 300 × g for 1 minute and incubated overnight at 37°C. The plates were spun down (300 × g, 5 minutes) and washed once with PBS before cells were incubated with 30 μL 0.05% trypsin-EDTA (Gibco, 25300-054) at 37°C for 5 to 10 minutes. Trypsin was inactivated by adding 70 μL assay medium, and cells were harvested by pipetting up and down. Next, 10 μL of 50 μg/mL propidium iodide (PI; Sigma-Aldrich, P4864) in PBS was added, and cells were incubated at room temperature for 5 minutes while gently shaking. Cells were washed with PBS or FACS buffer and measured on a BD FACSCelesta Flow Cytometer (BD Biosciences, RRID: SCR_019597) or BD LSRFortessa Cell Analyzer (BD Biosciences, RRID: SCR_018655). PI-negative, CTV-positive cells were identified as viable target cells. Counting beads were used to normalize target cell counts, and the normalized percentage of target cell killing was calculated using the following formula:

$$\text{Normalized}\hspace{0.17em}\text{percentage}\hspace{0.17em}\text{of}\hspace{0.17em}\text{target}\hspace{0.17em}\text{cell}\hspace{0.17em}\text{killing}\hspace{0.17em}=\hspace{0.17em}\left[\mathrm{1}\hspace{0.17em}\mathrm{-}\hspace{0.17em}(\text{normalized}\hspace{0.17em}\text{viable}\hspace{0.17em}\text{target}\hspace{0.17em}\text{cell}\hspace{0.17em}\text{counts}/\text{average}\hspace{0.17em}\mathrm{“}\text{no}\hspace{0.17em}\text{antibody}\mathrm{”}\hspace{0.17em}\text{normalized}\hspace{0.17em}\text{viable}\hspace{0.17em}\text{target}\hspace{0.17em}\text{cell}\hspace{0.17em}\text{counts})\right]\hspace{0.17em}\times \hspace{0.17em}100\mathrm{\%}$$

Target cell killing was statistically compared between experimental conditions using an ordinary one-way ANOVA followed by the Fisher least significant difference post hoc test.

Results

These analyses were conducted using pretreatment tumor samples and associated clinical outcomes from patients enrolled in the phase II innovaTV 204 trial, which included women with r/mCC who had progressed on or after prior chemotherapy.

TF expression levels and clinical response to TV

It was previously reported that TF expression, as measured by membrane H-score, did not associate with confirmed best overall response (CR, PR, SD, or PD) according to RECIST v1.1 in the innovaTV 204 trial (11). To further examine the relationship between TF expression and clinical response, we compared various TF IHC parameters between responders (patients with confirmed CR or PR) and nonresponders (patients with SD or PD). TF expression was assessed separately in the membrane and cytoplasmic compartments and quantified using both H-score (Fig. 1A and B) and the percentage of TF-positive tumor cells (Fig. 1C and D) across 80 patients with available TF IHC measurements (76 of whom had evaluable response). Most patients (96%) showed positive tumor membrane staining for TF, with a broad distribution of expression (Fig. 1C). When assessing membrane and cytoplasmic H-scores, as well as the percentage of positive cells in the membrane and cytoplasm, no significant difference was observed between TV responders and nonresponders. TV responders consisted of patients with both high and low levels of TF protein across all measurements assessed (Fig. 1). Additionally, in a subset of patients (n = 40) for whom both TF IHC and RNA-seq were performed, TF protein expression by IHC, on the membrane and in the cytoplasm, was highly correlated with mRNA expression of the gene encoding TF (F3; Supplementary Fig. S1).

Figure 1.

Grouped violin plots showing the distribution of (A) TF membrane and (B) cytoplasmic H-scores and the percentage of tumor cells positive for (C) TF membrane and (D) cytoplasmic staining in pretreatment biopsies from responders and nonresponders in the innovaTV 204 trial. Each point represents an individual patient, colored by best overall response. The width of each violin reflects the data distribution, and the mean values are indicated within the violins. The fold change (FC) in mean values and the P value obtained from the Wilcoxon rank-sum test are provided below each plot. NE, not evaluable.

These data, derived from the same innovaTV 204 dataset analyzed by Coleman and colleagues (11), confirm the previously reported lack of association between membrane TF H-score and clinical response and further extend these findings by demonstrating a similar absence of correlation between clinical response and cytoplasmic TF H-score, as well as the percentages of cytoplasmic and membrane TF-positive tumor cells. No differences in TF expression were observed across subgroup analyses based on biopsy acquisition time (before or after last systemic therapy), Eastern Cooperative Oncology Group performance status, or ethnicity (data not shown). Interestingly, TF membrane and cytoplasmic expression levels seemed higher in nonsquamous histologic subtypes, including adenocarcinoma and adenosquamous carcinoma, compared with squamous cervical cancer (Wilcoxon rank-sum P values < 0.01; Supplementary Fig. S2).

Immune cell gene expression signatures linked to ADCP and ADCC correlate with improved survival

To characterize pretreatment expression of immune-related and canonical biological pathway signatures (17) in the context of the proposed mechanisms of action of TV and to evaluate their association with clinical outcomes in the innovaTV 204 trial, RNA-seq was performed on pretreatment biopsies from a subset of patients (n = 41; Table 1). This subset had baseline characteristics comparable with those of the overall study population (Table 1), with similar mean baseline tumor volumes (Supplementary Table S3). Distributions of overall transcript abundance, global gene expression profiles, and expression levels of F3 and immune signatures of interest were comparable between biopsies acquired either before or after the patients’ most recent systemic therapy (Supplementary Figs. S3 and S4). Several immune-related gene signatures (scored per patient using gene set variation analysis) were nominally associated with longer median OS (Supplementary Table S4), including signatures linked to Fc-mediated ADCP (M0 and M1 macrophages and monocytes) and ADCC (NK cells; Fig. 2A), as well as multiple gene signatures associated with DCs (Supplementary Table S4). Additional immune signatures corresponding to myeloid-derived cell types (neutrophils and eosinophils), as well as T-cell signatures associated with cytokine-mediated regulation of myeloid immune responses (Th2), also trended in a similar direction based on nominal P values (Supplementary Table S4). TF protein expression, assessed either at the membrane or in the cytoplasm, did not show a significant correlation with the enrichment of these immune-related gene signatures (Supplementary Fig. S5 and data not shown). Patients with above-median scores for gene signatures linked to ADCP and ADCC had longer median OS compared with those with lower scores (Fig. 2B–E). High levels of NK and DC signatures were also nominally associated with longer median PFS (Supplementary Table S5). Immune signatures associated with better OS tended to be correlated with one another but not with TF transcript levels (Supplementary Fig. S6).

Figure 2.

Association of selected immune-related gene signatures with OS in the innovaTV 204 trial. A, Association of the indicated gene signatures in pretreatment biopsies with OS. Median OS (mOS) for patients with above-median (high) or below-median (low) signature levels in months, along with the hazard ratio (HR) and 95% confidence interval (CI) for the high versus low groups. B–E, Kaplan–Meier survival curves with CIs for patients stratified by high (above-median) and low (below-median) expression of the indicated gene signatures: (B) M0 macrophages, (C) M1 macrophages, (D) monocytes, and (E) NK cells. mOS values for the high and low groups are indicated within each plot.

TV induces ADCP and ADCC in tumor cell lines with varying levels of TF expression in vitro

Given these clinical observations, we next investigated whether TV activity could be mediated by Fc effector functions independent of the TF expression level using in vitro ADCP and ADCC assays across a panel of tumor cell lines (n = 10) with varying TF expression levels.

TV-mediated induction of ADCP was evaluated in a fluorescence-based phagocytosis assay, in which hMDMs were cocultured with antibody-opsonized, pHrodo-labeled target cells at an E:T ratio of 2:1 (Fig. 3A). Phagocytosis of antibody-opsonized tumor cells was monitored by live-cell imaging using the pH-sensitive dye pHrodo, for which fluorescence intensity increases as internalized cells traffic to the acidic lysosomal compartment. TV induced ADCP in all 3 tumor cell lines with high TF (TF^hi) expression [>100,000 specific antibody–binding capacity (sABC)/cell], in 2 of 2 cell lines with intermediate TF (TF^mid) expression (30,000–100,000 sABC/cell), and in 2 of 5 cell lines with low TF (TF^lo) expression (<30,000 sABC/cell; Fig. 3B).

Figure 3.

ADCP induction was evaluated in a fluorescence-based phagocytosis assay. A, hMDMs were cocultured with antibody-opsonized, pHrodo-labeled target cells at an E:T ratio of 2:1. When target cells are internalized by macrophages (Mφ) and trafficked to the acidic phagolysosomes, fluorescence intensity of the pH-sensitive pHrodo dye increases, which was quantitatively monitored using the Incucyte live-cell imaging system. B, Fold change in ADCP in Red Calibrated Units (RCU) induced by 80 ng/mL TV relative to the no antibody control after 6 hours of incubation, the timepoint when maximal RCU was typically observed. Cell lines are colored by the TF expression level and ranked in ascending order according to TF expression, as determined by semiquantitative flow cytometry (Supplementary Table S1). C, Total RCU per cell over time induced by 80 ng/mL of the indicated antibodies.

To confirm that the observed phagocytosis was dependent on Fc-mediated effector functions, Fc-silenced variants of TV and tisotumab were generated by introducing the FEA (L234F/L235E/D265A) mutations (14). In cocultures of hMDMs with MDA-MB-231 cells (TF^hi), TV and the unconjugated parental antibody tisotumab induced a comparable extent of ADCP, whereas the Fc-silenced TV-FEA and tisotumab-FEA antibody variants did not induce ADCP (Fig. 3C).

To exclude potential interference of payload-mediated cytotoxicity, the unconjugated parental antibody tisotumab was used for ADCC assays. Healthy human donor PBMCs (containing NK cells) were cocultured overnight with fluorescently labeled, antibody-opsonized target cells at an E:T ratio of 30:1, and target cell killing was quantified by flow cytometry (Fig. 4A). Tisotumab induced concentration-dependent ADCC in all 3 tumor cell lines with TF^hi protein expression (>100,000 sABC/cell) and was also capable of inducing ADCC in 1 of 2 cell lines with intermediate (30,000–100,000 sABC/cell) and 2 of 5 cell lines with low (<30,000 sABC/cell) TF expression (Fig. 4B; Supplementary Fig. S7A). To further assess the relevance of these findings, ADCC activity was evaluated in an additional panel of 7 cervical cancer cell lines with low (<30,000 sABC/cell) or intermediate (30,000–100,000 sABC/cell) TF expression. Consistent with the broader tumor cell line panel, tisotumab induced ADCC in multiple cervical cancer cell lines, demonstrating that ADCC induction is not restricted to tumor cells with TF^hi expression levels (Supplementary Fig. S7B).

Figure 4.

ADCC induction in tumor cell lines with varying TF expression levels in vitro. A, Healthy human donor PBMCs were cocultured overnight with antibody-opsonized, fluorescently labeled target cells at an E:T ratio of 30:1, and tumor cell viability was assessed by flow cytometry. APC, antigen-presenting cells; MO, monocyte; O/N, overnight. B, Normalized target cell killing (%) induced by 10 μg/mL tisotumab across the tumor cell line panel. Cell lines are colored by the TF expression level and ranked in ascending order according to TF expression, as determined by semiquantitative flow cytometry (Supplementary Table S1). Each point represents an individual measurement, with the median indicated by a horizontal black line. C, Normalized target cell killing (%) induced by 10 μg/mL of the indicated antibodies in the MDA-MB-231 (TF^hi) breast cancer cell line. Each point represents an individual measurement, with the median indicated by a horizontal black line. Statistical comparisons between conditions were performed using a one-way ANOVA followed by the Fisher least significant difference post hoc test (*, P < 0.05; **, P < 0.01; ns, nonsignificant).

In ADCC assays with MDA-MB-231 (TF^hi) breast cancer cells, TV and tisotumab induced a comparable extent of target cell killing (Fig. 4C). TV-FEA showed a nonsignificant trend toward reduced cytotoxicity compared with TV, whereas tisotumab-FEA showed significantly decreased ADCC relative to tisotumab, reaching levels comparable with the negative controls (no antibody and nonbinding antibody IgG1-ctrl). Similar results were obtained in ADCC assays using NCI-H292 (TF^mid) lung cancer cells and HCT-116 (TF^lo) colon cancer cells as target cells (Supplementary Fig. S7C and S7D).

These in vitro results demonstrate that TV can induce ADCP and ADCC across tumor cell lines spanning a broad range of TF expression levels.

Discussion

In the innovaTV 204 trial, TF membrane expression on tumor cells was detected in most cervical cancer samples (96%), with a wide range of expression levels (11). We found no association between clinical response and the percentage of membrane or cytoplasmic TF-positive tumor cells, even after adjusting for relevant clinical covariates, such as histology. TF membrane and cytoplasmic expression levels seemed higher in nonsquamous histologic subtypes compared with squamous cervical cancer. However, even with this difference, a comparable number of responses were observed in both groups, in line with the observed lack of association between TF expression and clinical response.

The finding that TV can induce clinical responses irrespective of the TF expression level is consistent with observations for several other ADCs targeting different antigens, including trastuzumab deruxtecan (HER2), enfortumab vedotin (nectin-4), and sacituzumab govitecan (Trop-2), for which objective responses have also been observed in patients with low target expression (18). Notably, enfortumab vedotin shares the same payload–linker chemistry as TV. Rapid internalization of the target protein once bound by antibody has been proposed to enhance ADC effectiveness and may contribute to ADC efficacy even at low target expression levels (18). TF has been shown to have a rapid internalization rate in nonclinical studies (7), which may therefore contribute to the observed clinical activity of TV regardless of the TF expression level.

The mechanisms underlying TV activity include direct and indirect mechanisms of action, such as ADCC, ADCP, and immunogenic cell death that occurs when direct cytotoxicity leads to apoptosis and release of tumor-derived antigens, thereby promoting activation of immune cells within the TME. In contrast, ADCC and ADCP depend on engagement of Fcγ receptors on immune effector cells by the antibody component of the ADC.

Preclinical studies have demonstrated that TV can induce both ADCC and ADCP (6, 8), and these mechanisms are mentioned in the drug’s prescribing information. Here, transcriptomic profiling of pretreatment tumor biopsies revealed that related gene signatures associated with ADCP (monocytes and macrophages) and ADCC (NK cells) were associated with improved median OS and PFS, supporting a role for these immune mechanisms in mediating clinical benefit. Interestingly, gene signatures for DCs and Th2 cells were also enriched in patients with longer median OS, which suggests that improved outcomes may be associated with enhanced antigen presentation and subsequent T-cell activation. Given the single-arm design of this phase II study, these signatures cannot be considered predictive of response to TV and may instead reflect a more general immune-inflamed tumor phenotype. However, the association of ADCC- and ADCP-related immune cell signatures with clinical outcomes is consistent with the Fc-mediated mechanisms of action of TV demonstrated preclinically, supporting their potential contribution to clinical activity.

Notably, the biomarker-evaluable population represented a smaller subset of the total study population, and the limited sample size constrains interpretation. Although associations between immune signature expression and survival passed standard threshold based on nominal P values, after adjustment for multiple hypothesis testing using the Benjamini–Hochberg method, none of the signature-level associations remained significant. The observed associations should be considered exploratory and reflect the presence of specific immune cell populations before treatment, which may indicate their potential to mediate TV-induced ADCP or ADCC. To further evaluate TV-induced ADCP and ADCC activity in clinical samples, additional studies are needed, preferably using paired pretreatment and on-treatment samples to allow more direct assessment of treatment-related biomarker changes within the TME.

Establishing whether these signatures are predictive of clinical benefit would require independent validation in a larger, adequately powered cohort with sufficient responders and nonresponders and/or survival events to estimate effect sizes with acceptable precision, as well as a prespecified signature definition, clinically deployable assay, and demonstration of reproducibility across datasets. An additional consideration is the timing of biopsy collection, as 80.5% of samples were obtained prior to the last line of systemic therapy before TV treatment. As a result, these biopsies may not fully represent the tumor or immune landscape immediately before treatment with TV. However, the distribution of global gene expression across biopsy samples collected before and after the last systemic therapy was comparable, and there was no discernible difference in TF mRNA expression or immune signatures of interest between the biopsies collected before and after last systemic therapy, suggesting that biopsy timing alone was not a significant factor in the signature associations with outcomes observed in this dataset.

Consistent with these findings, enrichment of spatial features linked to ADCP and ADCC, as identified using an artificial intelligence–based digital pathology approach, was found to be associated with clinical response in the innovaTV 207 trial in head and neck squamous cell carcinoma samples (19). Similar studies examining these spatial patterns in patients with cervical cancer would further confirm the results observed in this study.

Using in vitro coculture systems, we demonstrated that TV can induce ADCP and ADCC across tumor cell lines exhibiting variable levels of TF expression. Interestingly, some cell lines were primarily sensitive to ADCP, others to ADCC, and some to both. This is indicative of intrinsic variability in susceptibility to these mechanisms of immune cell killing among tumor cells. Because this variability was observed across a wide range of TF expression levels, it is likely influenced by additional biological factors, such as differences in effector cell engagement or compensatory resistance mechanisms. Notably, ADCC activity was also observed in an expanded panel of cervical cancer cell lines with TF^lo and TF^mid expression levels, supporting the relevance of these findings in the disease setting investigated in innovaTV 204. Inclusion of a broader multitumor cell line panel enabled evaluation of ADCP and ADCC activity across a wider dynamic range of TF expression, and the observation that TV induces ADCP and ADCC across cell lines derived from multiple tumor types suggests that these effector mechanisms may not be indication specific and may represent a general aspect of TV’s mechanism of action.

Together, these results provide a reverse translational framework integrating clinical biomarker analyses with in vitro functional assays to explore mechanisms of TV activity beyond direct cytotoxicity. Our findings are consistent with a potential role for Fc-mediated effector functions in TV activity. However, the biomarker analyses are exploratory, and the in vitro studies do not establish that ADCC or ADCP is required for TV efficacy in patients. The extent to which these mechanisms contribute to clinical benefit therefore remains to be determined.

Supplementary Material

Supplementary Data 1

Supplemental Figures

Supplementary Data 2

Supplemental Tables

Data Availability

Deidentified individual participant data collected during the trial are not publicly available and are subject to restrictions related to participant privacy, informed consent, contractual obligations, and applicable regulatory requirements. Aggregated clinical trial information is available through publicly accessible study registries/databases as required by law. Requests for additional information about data access may be directed to clinicaltrials@genmab.com and will be evaluated by Genmab in accordance with applicable policies and requirements.

Authors’ Disclosures

S. Sridhar reports employment with Genmab US, Inc. and ownership of stock in the company. M.G. Roukens reports employment with Genmab BV. and ownership of stock in the company. C.Y. Yu reports employment with Genmab US, Inc. and ownership of stock in the company. H. Si reports other support from Genmab US, Inc. during the conduct of the study. S. Xu reports ownership of stock in Genmab US, Inc. L.K. Brady reports other support from Genmab US, Inc. during the conduct of the study. M. Bieda reports personal fees from Pfizer during the conduct of the study, as well as personal fees from Pfizer outside the submitted work. K.N. Shah reports other support from Pfizer during the conduct of the study. R. Aquino reports employment with Genmab US, Inc. and ownership of stock in the company. D. Gallagher reports employment with Genmab US, Inc. and ownership of stock in the company. C.J. Thalhauser reports other support from Genmab US, Inc. outside the submitted work. B.W. Higgs reports other support from Genmab US, Inc. during the conduct of the study, as well as other support from Genmab US, Inc. outside the submitted work. M. Jure-Kunkel reports ownership of stock in Genmab US, Inc. J.R. Harris reports employment with Genmab US, Inc. and ownership of stock in the company. No disclosures were reported by the other authors.

Authors’ Contributions

S. Sridhar: Conceptualization, data curation, software, formal analysis, supervision, validation, visualization, methodology, writing–original draft, writing–review and editing. M.G. Roukens: Formal analysis, visualization, methodology, writing–original draft, writing–review and editing. C.Y. Yu: Software, formal analysis, visualization, writing–review and editing. T. Dielen: Formal analysis, writing–review and editing. B. van Kessel-Welmers: Formal analysis, writing–review and editing. H. Si: Data curation, software, formal analysis, visualization, writing–review and editing. S. Xu: Data curation, software, formal analysis, visualization, writing–review and editing. L.K. Brady: Formal analysis, writing–review and editing. M. Belete: Formal analysis, visualization, writing–review and editing. M. Bieda: Formal analysis, writing–review and editing. K.N. Shah: Writing–review and editing. R. Aquino: Data curation, writing–review and editing. D. Gallagher: Writing–review and editing. C.J. Thalhauser: Supervision, writing–review and editing. B.W. Higgs: Supervision, writing–review and editing. M. Fereshteh: Supervision, writing–review and editing. M. Jure-Kunkel: Conceptualization, methodology, writing–review and editing. J.R. Harris: Conceptualization, resources, formal analysis, supervision, investigation, methodology, writing–original draft, project administration, writing–review and editing.