Will antibody-drug conjugate and immune checkpoint inhibitor-based combination therapy close the gap between benefit and toxicity in the treatment of cancers? A systematic review and meta-analysis

doi:10.21203/rs.3.rs-3011927/v1

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Will antibody-drug conjugate and immune checkpoint inhibitor-based combination therapy close the gap between benefit and toxicity in the treatment of cancers? A systematic review and meta-analysis

https://doi.org/10.21203/rs.3.rs-3011927/v1

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Background

Although antibody-drug conjugate (ADC) fosters hope for the treatment of cancer, the effect of single drug treatment is limited. Therefore, to achieve wider therapeutic effects, the combination of ADC and Immune checkpoint inhibitors (ICI) therapy may hold potential.

Methods

After literature retrieval, screening and data extraction, quality assessment was done by two investigators. Then, overall response rate (ORR) and adverse effects (AEs) were evaluated to study its efficiency and safety. Publication bias was also calculated through Funnel plot, Begg's Test and Egger's test. Meanwhile, heterogeneity was investigated through subgroup and sensitivity analysis.

Results

11 eligible clinical studies with a total sample size of 551 were included. The pooled ORR was 59% (95% CI: 45% − 72%), especially in patients with relapsed classical Hodgkin lymphoma and patient treated with Brentuximab vedotin + Pembrolizumab. The most common AEs was peripheral neuropathy (38.2%) and AEs on skin (13.1% − 18.5%) and digestive system (8.7% − 28.1%) can not be overlooked.

Conclusion

ADC + ICI therapy may be recommended in cancer treatment, whereas strategies to manage toxicities warranted further exploration (PROSPERO CRD42023375601).

Antibody-drug conjugate

Immune checkpoint inhibitors

Cancer

Efficiency

Safety

Cancer, with dismal prognosis, was responsible for 19.3 million new cases and 10 million deaths globally in 2020 [1]. Every year, there is a substantial increase and the number is still growing. Recently, the emergence of antibody–drug conjugate (ADC) fosters hope for the treatment of multiple malignancies. From a structure- or function-based perspective, ADC is a targeted agent that connects a monoclonal antibody (mAb) with a cytotoxic drug via a linker. With this particular structure, ADC is able to specifically recognize a cellular surface antigen expressed on the cancer cells and deliver a toxic payload at the tumor site, thus inducing further apoptosis and reducing systemic exposure to healthy tissue [2, 3].

Since ADC is able to target cancer cells more precisely and selectively, it can be given to patients at higher doses than conventional chemotherapy [4] without increasing dispersion of cytotoxic materials around normal tissue and inducing more side effects. Meanwhile, ADC has been approved for successive indications in breast cancer (BC) [5], urothelial cancer (UC) [6, 7], gastric cancer (GC) [8], classical Hodgkin lymphoma (cHL) 29685160 et.al. However, overly complex preparation processes, limitations in the efficiency of drug penetration into tumor, premature release of active ingredients, and drug resistance have been constantly hindering ADC from achieving theoretically positive therapeutic effects in the clinical setting.

Immune checkpoint inhibitor (ICI), another widely used anti-tumor drug, can strengthen the immune attack and promote tumor killing by releasing the inhibitory brakes of T cells [9]. However, ICIs are always restricted by the mutation status of certain genes, and thereby a certain number of people are unable to achieve long-term sustained response. The response rate in some second-line studies is only 10%~20% [10, 11], and patients will even experience hyperprogressive disease (HPD) [12, 13] or secondary drug resistance following each subsequent line [14, 15].

It has been reported that specific payloads of several ADC could trigger direct activation and maturation of dendritic cells [16–18], leading to a growth of T cells. With the massive infiltration of T cells, the response of tumor cell to ICI will increase. Moreover, ADC could also stimulate both innate and adaptive immunity [19, 20] by inducing immunogenic cell death [21], that is also a key way for ADC to perform its killing effect. Simultaneously, this synergistic effect was also observed in a mice trials, which confirmed that ADC-induced antitumor immunity was facilitated by ICI [22]. Thus, it seems feasible that the combination of ADC and ICI can hold the promise to overcome the current limitations, and plenty trials of this combiniation therapy are under way based on rational results from previous clinical observations [23].

Overall, ADC and ICI are rapidly used in treatment landscape. To optimize their application, it’s necessary to comprehensively evaluate the clinical implications and irAEs of their combinations. However, there is no study summarizing efficacy and safety of ADC and ICI combinations to fill this gap. Therefore, this meta-analysis are carried out to figure out the efficacy and safety in patients treated with ADC and ICI combination therapy. Ulteriorly, this meta-analysis is reckoned to provide information on the high-quality treatment regimen for combinations.

2.1 Search strategy

Two investigators independently searched PubMed, Embase, Cochrane, etc. from inception to Dec. 20, 2022. All retrievals were implemented using Medical Subject Headings (MeSH) and free words, and the complete search string was introduced in Supplementary Method 1, 2, 3. Additional records identified through other sources including ClinicalTrials.gov, American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), books and the references of reviews were also checked to make sure that no eligible articles were omitted. The search term was ‘Neoplasms’ AND ‘Antibody-Drug Conjugates’ AND ‘Immune checkpoint inhibitors’ AND ‘Clinical trials’ with no restriction of countries, region, race, and language.

2.2 Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) clinical studies including randomized control trials and single-arm studies; (2) patients have received ADC + ICI. (3) studies including patients confirmed with tumor; (4) studies reporting overall response rate (ORR) with or without adverse events (AEs).

The exclusion criteria were as follows: (1) animal or in vitro test; (2) article type: letters, review, meta-analysis, comment, case report, conference abstract, editorials, expert opinions, etc. (3) studies report AEs without ORR; two investigators lay down the inclusion and exclusion criterion and a third reviewer assists in reaching consensus when necessary.

2.3 Quality assessment

With the risk of bias in non-randomized studies of interventions (ROBINS-I) tool, two investigators independently assessed the quality of the included studies. Each study was defined as ‘low risk’, ‘moderate risk’, ‘serious risk’, ‘critical risk’ or ‘no information’ respectively by considering the following characteristics covering bias due to confounding; bias in selection of study participants; bias in exposure measurement; bias due to misclassification of exposure during follow-up; bias due to missing data; bias in measurement of outcomes; and bias in selection of reported results.

2.4 Data extraction

Two investigators independently extracted data from included studies with PRISMA, and any inconsistencies were resolved by consensus with a third investigator. The following characteristic information of the included studies was recorded: (1) Study characteristics: first author, publication time, follow-up, cancer type, phase line, regimen, sample size, age range; (2) Study outcomes: ratio of ORR, partial response (PR), complete response (CR), stable disease (SD), progressive disease (PD), AEs; To make sure that no information was omitted, we also checked supplement materials of each clinical trial.

2.5 Statistical Analysis

Statistical analyses of study outcomes were performed by RevMan 5.3 and pooled as forest plots by STATA 15. The effect size of all pooled results was represented by 95% CI with an upper limit and a lower limit. Chi-square Q test and I² statistic was used to detect statistical heterogeneity. If heterogeneous was observed between studies (p < 0.10, I² > 50%), the random-effects model would be used for combined analysis. Otherwise, fixed-effects model was used for pooled results. Furthermore, subgroup analysis was implemented to identify the factors contributing risk of bias. We also conducted the sensitivity analysis (Supplementary Fig. 1) by sequential exclusion of included individual trial. Funnel plots, Begg’s and Egger’s tests were also used to examine potential publication bias.

3.1 Literature Search and Patients Characteristics

According to the proposed search strategy, 2152 publications were retrieved. After removing duplications, there were 1741 articles obtained. Then, by screening title and abstract only, 109 articles were assessed for eligibility. Finally, after assessing full text of the remaining articles, 11 studies were included in qualitative synthesis. The specific selection steps are summarized in Fig. 1.

Figure 1. Flowchart of literature search and selection of eligible studies

A total of 551 patients were enrolled in our study. The age of participants ranged from 18 to 90 years across all studies. The median follow-up period ranged from 1.80 to 49.90 months. Of the eligible 11 studies, four studies were randomized, three studies were non-randomized, three studies were single-armed, one study was retrospective case review. The regimens included sacituzumab govitecan (SG) + pembrolizumab (Pembro), brentuximab vedotin (BV) + nivolumab (Nivo),

Table 1

Characteristic of included studies
Study	Study type	Phase	Median follow-up time, months	Tumor types	Intervention	Sample size	Age, year
Grivas 2022[24]	Non-randomized	II	5.8	UC	SG + Pembro	41	67 (46–86)
Advani 2021[25]	Single-arm	I/II	34.3 (1.8, 49.9)	R/R cHL	BV + Nivo	91	38 (18,69)
Zinzani 2019[26]	Non-randomized	I/II	11.1	R/R NHL	BV + Nivo	30	35.5 (19–83)
Emens 2020[27]	Randomized	II	8.5 (6.1–16.5)	BC	T-DM1 + Atezo	132	54 (48–60)
Cheson 2020[28]	Single-arm	II	21.2 (15.6, 29.9)	cHL	BV + Nivo	46	71.5 (64–77)
Rottey 2022[29]	Randomized	I/II	NA	Advanced solid tumors	BMS-986148 + Nivo	30	62.5 (46–84)
Hoimes 2022[30]	Single-arm	Ib/II	24.9	UC	EV + Pembro	45	69 (51, 90)
Massaro 2022[31]	Retrospective case review	NA	16.5(2.4, 29.9)	R/R cHL	BV + Pembro	10	30.7 (20.6, 36.4)
Rosenberg 2022[32]	Randomized	Ib/II	14.8 (12.9, 17.3)	UC	EV + Pembro	76	NA
NCT03138499	Randomized	NA	NA	R/R cHL	BV + Nivo	12	37.5 (24.4, 50.6)
NCT04526691	Non-randomized	Ib	6.5	NSCLC	Dato-DXd + Pembro	38	NA
Abbreviations: OC, Ovarian Cancer; BC, Breast Cancer; RCC, Renal Cell Carcinoma; GC, Gastric Cancer; MPM, Malignant Pleural Mesothelioma; TDM-1, Trastuzumab Emtansine; NA, not available.

trastuzumab emtansine (T-DM1) + atezolizumab (Atezo), BMS-986148 + Nivo, enfortumab vedotin (EV) + Pembro, BV + Pembro and Datopotamab deruxtecan (Dato-DXd) + Pembro. There were six tumor types in our study, with three studies on urothelial cancer (UC), five studies on classical Hodgkin lymphoma (cHL), a study on non-Hodgkin lymphoma (NHL) as primary mediastinal B-cell lymphoma (PMBL), a study on breast cancer (BC), a study on Advanced solid tumors and a study on non small-cell lung cancer (NSCLC). The main characteristics and results are shown in Table 1.

3.2 Quality assessment

One included study (Rottey 2022) was assessed as serious based on the ROBINS-I tool (Supplementary Table 1). Five studies (Grivas 2022, Advani 2021, Cheson 2020, Hoimes 2022, NCT 03138499) was rated as moderate. The left five studies (Zinzani 2019, Emens 2020, Massaro 2022, Rosenberg 2022, NCT 04526691) was labelled as low.

3.3 Efficacy

All the 11 studies reported an ORR as the clinical activity outcome. The pooled ORRs was 59% (95% CI: 45%, 72%) (Fig. 2). As significant heterogeneity (I² = 91.8%, p < 0.01) was observed, the random-effects model was adopted.

Figure 2. Forest plot of the meta-analysis of objective response rate (ORR).

3.4 Subgroup analyses

3.4.1 Histotype

All studies provided ORR data according to histotype (Table 2 and Supplementary Fig. 2A). Three studies on UC had usable ORR data and the pooled ORR was 58% (95% CI: 36%, 79%) with significant heterogeneity (I² = 88.2%, p ≤ 0.001). Four studies on cHL provided ORR data. The pooled ORR was 76% (95% CI: 62%, 91%), and heterogeneity existed (I² = 64.3%, p ≤ 0.001). One study on NHL reported an ORR of 73% (95% CI: 58%, 89%) without significant heterogeneity (I² = 0.0%, p ≤ 0.001).The study on BC reported an ORR of 45% (95% CI: 37%, 54%) without significant heterogeneity (I² = 0.0%, p ≤ 0.001). Additionally, the study on advanced solid tumors reported an ORR of 20% (95% CI: 6%, 34%) without significant heterogeneity (I² = 0.0%, p ≤ 0.001). In the study on NSCLC, the pooled ORR was 37% (95% CI: 22%, 52%) without significant heterogeneity (I² = 0.0%, p ≤ 0.001).

Table 2

Selected subgroup analysis of objective response rate.
Variables		ORR
Variables		ES (95%CI)	p
Histotype	UC	0.58 (0.36, 0.79)	≤ 0.001
	cHL	0.76 (0.65, 0.87)	0.024
	NHL	0.73 (0.58, 0.89)	≤ 0.001
	BC	0.45 (0.37, 0.54)	≤ 0.001
	NSCLC	0.37 (0.21, 0.52)	≤ 0.001
	Advanced solid tumors	0.20 (0.06, 0.34)	≤ 0.001
Regimen	SG + Pembro	0.34 (0.20, 0.49)	≤ 0.001
	BV + Nivo	0.73 (0.60, 0.86)	0.021
	T-DM1 + Atezo	0.45 (0.37, 0.54)	≤ 0.001
	BMS-986148 + Nivo	0.20 (0.06, 0.34)	≤ 0.001
	EV + Pembro	0.68 (0.60, 0.77)	0.302
	BV + Pembro	0.90 (0.71, 1.09)	≤ 0.001
	Dato-DXd + Pembro	0.37 (0.22, 0.52)	≤ 0.001
Abbreviations: ORR, objective response rate; ES, effect size; UC, Urothelial cancer; NSCLC, Nonsmall-cell lung cancer; SG, Sacituzumab govitecan; Pembro, Pembrolizumab; R/R cHL, relapsed/refractory (r/r) classical Hodgkin lymphoma; NHL, non- Hodgkin lymphoma ; BV, Brentuximab vedotin; Nivo, Nivolumab; BC, Breast cancer; T-DM1, Trastuzumab emtansine; Atezo, Atezolizumab; NA, Not available; EV, Enfortumab vedotin; Dato-DXd, Datopotamab deruxtecan. Advanced solid tumors include mesothelioma, NSCLC, pan- creatic, ovarian, or gastric cancer

3.4.2 Regimen

All studies provided ORR data according to regimen (Table 2 and Supplementary Fig. 2B). SG combined with Pembro resulted in an ORR of 34% (95% CI: 20–49%) without significant heterogeneity (I² = 0.0%, p ≤ 0.001). BV combined with Nivo were given in four studies, and the ORR was 73% (95% CI: 60–86%) with heterogeneity (I² = 69.2%, p = 0.021). T-DM1 combined with Atezo subgroup showed a pooled ORR of 45% (95% CI: 37%-54%) without significant heterogeneity (I² = 0.0%, p ≤ 0.001). Additionally, the pooled ORR were 20% (95% CI: 6–34%) in BMS-986148 combined with Nivo subgroup, without significant heterogeneity (I² = 0.0%, p ≤ 0.001). Two studies on EV combined with Pembro reported an ORR of 68% (95% CI: 60–77%) without significant heterogeneity (I² = 6.2%, p = 0.302). Moreover, BV combined with Pembro resulted in an ORR of 90% (95% CI: 71–109%) and an ORR of 37% (95% CI: 22–52%) was reported in Dato-DXd combined with Pembro subgroup, without significant heterogeneity (I² = 0.0%, p ≤ 0.001)

3.5 Safety

As shown in Table 3, The most common AEs of ADC combined with ICI was peripheral neuropathy (38.2%). However, only 4.9% of the patients developed high-grade (≥ 3) peripheral neuropathy. Gastrointestinal toxicities included abdominal pain (8.7%), decreased appetite (24.9%), diarrhoea (20.1%) and nausea (28.1%). Despite high incidence rates of gastrointestinal AEs of all grade, the risks of grade 3 AEs or worse was just 0.9% − 4.3%. AEs on skin, such as pruritus, maculopapular rash and rash, developed in more than 18.5% of the patients and the risks of high-grade AEs (1.0% − 4.1%) on skin was lower than that of all-grade AEs. Moreover, 34.9% of the patients suffered from fatigue, 2.9% of which was high-grade. AST increased (15.8%) is another common AEs, but only 1.3% of the patients has suffered from high-grade AST increased.

Table 3

Treatment-related common adverse events in this meta-analysis.
Adverse events		Ratio (95% CI)
Adverse events		All Grade	Grade ≥ 3
Skin	Maculopapular rash	0.131 (0.029, 0.232)	0.041 (0.00, 0.081)
	Pruritus	0.155 (0.032, 0.278)	0.010 (-0.003, 0.024)
	Rash	0.185 (0.083, 0.288)	0.016 (-0.005, 0.036)
digestive system	Abdominal pain	0.087 (0.049, 0.125)	0.043 (-0.015, 0.102)
	Decreased appetite	0.249 (0.153, 0.344)	0.009 (-0.005, 0.023)
	Diarrhoea	0.201 (0.090, 0.313)	0.028 (0.008, 0.048)
	Nausea	0.281 (0.176, 0.386)	0.017 (-0.002,0.036)
Others	Peripheral neuropathy	0.382 (0.133, 0.632)	0.049 (0.004, 0.094)
	AST increased	0.158 (-0.026, 0.342)	0.013 (-0.007, 0.034)
	Fatigue	0.349 (0.266, 0.432)	0.029 (-0.004, 0.061)
Abbreviations: AST, Aspartate transaminase

3.6 Publication Bias and Sensitivity Analysis

Funnel plot, Begg's Test and Egger's test were carried out to access publication bias in this study. Slight asymmetric distribution was found in the funnel plots (Supplementary Fig. 3), indicating the existence of publication bias Therefore, we performed Begg's Test and Egger's test to further investigate publication bias. Begg’s test (p = 0.062) did not show significant publication bias among included studies (Supplementary Fig. 4). However, Egger’s test (p = 0.013) showed that the potential publication bias should not be ruled out across all studies (Supplementary Fig. 5).

To assess the impact of single study outcomes on the whole results, sensitivity analysis was performed as presented. Judging from the analysis, there was no statistically significant change of overall results after removing each trial, which confirmed the reliability and rationality of our meta-analysis.

To our knowledge, there has been so far no meta-analysis that evaluated the efficacy and safety of ADC plus ICI in cancer patients. Quhal [33] and Ulas [34] merely compared the efficacy of ICI-based therapy without referring to ADC, and another meta-analysis compared dual ICI with ICI monotherapy [35]. Besides, Zhang et al. presented the first meta-analysis that evaluated efficacy and safety of ADC [36]. But our current meta-analysis revealed satisfactory ORR and acceptable irAEs after statistical analysis of individual data of 551 patients from 11 clinical trials. The results showed that the cumulative prevalence of total ORR was up to 0.59 (95% CI 0.45–0.72) when treated with novel ADC plus ICI, and the AEs were mainly digestive and skin problems.

Subsequent clinical studies had been constantly carried out to evaluate the efficacy of ADC plus ICI, and most results validated some of the efficacy benefits of the product. However, some follow-up data from late-stage clinicals didn’t show effectiveness when compared with conventional chemotherapy drugs. For example, in the KATE-2 study, there is no significant PFS difference between T-DM1 and T-DM1 plus atezolizumab in HER2-positive BC patients according to intention to treat (ITT). Nevertheless, survival benefir was observed in PD-L1-positive subgroup with a median PFS of 8.5 vs 4·1 months (HR 0.60 95% CI, 0.32–1.11). Undeniably, patients failed to gain benefit from the combination therapy. It may be explained by lagging immune status and previous multiple lines of therapy. But on the other hand, the PD-L1 and HER2 positive patients showed a trend towards better survival benefits, which highlighted the importance of finding suitable ITT and treatment strategy based on gene detection. It is also worth noting that NCT0444886 and NCT04740918 were expected to kindle hope for precise treatment by offering more worthy evidence .

Further analysis suggested that the improvement in patients with combination therapy may show subgroup-level differences, which was correlate with information on histotype and regimens. It is clear that cHL benefited most from combinations, UC ranked second, followed by PMBL, BC, NSCLC and collection of several advanced solid tumors. Consequently, we inferred that it may be related to the drug target. Tumors with high expression of targeted antigen showed better effect when treated with ADC [37, 38] or ICI. CD30 and PD-L1 is consistently overexpressed in patients with HL [39] [40] and significant inflammatory cell infiltration was detected in tumor samples. Interestingly, PMBL, a rare but aggressive NHL, was also characterized by high expression of PD1 and CD30. From this we could speculate that Nivolumab and Pembrolizumab, anti–PD-1 immune checkpoint inhibitor, and brentuximab vedotin (BV), an anti-CD30 antibody–drug conjugate, may have synergistic activity. It has been proved that human epidermal growth factor 2 (HER2) was highly expressed in multiply solid tumors including BC, GC, UC and NSCLC [41, 42] (ordered based on the expression), with protein overexpression, gene amplification, mutation and etc. People also found that HER2-positive oncogenesis was more likely to be correlated to higher PD-L1 and immunogenicity [43], which suggested that HER2-high tumors may benefit more from ADC + ICI therapy. This may testified the ORR results of our subgroup based on histotype as well. As for why hematologic malignancy could better benefit from ADC + ICI, we supposed it may be related to the more complex tumor microenvironment of solid tumors [44]. However, we should also be cautious to make this inference for relevant researches are still in a relatively preliminary state, and HER2-low tumor is a field remaining to be explored for combinations [45].

As for the subgroup analysis based on regimens, since pairing ADC with ICI is a relatively new strategy, and many clinical trials exploring this dynamic field are still ongoing or in phase I/II stages, exaggerated efficacy evalution and data insufficience should be considered when comparing the different combinations. In our study, both BV + Nivo and BV + Pembro used for HM seemed to bring more ORR benefits than the rest arms, which could be explained by heterogeneity caused by tumor types mentioned above. When comparing the efficacy between BV + Nivo and BV + Pembro, we could not conclude which combination was better, given that both Pembro and Nivo were targeting PD-1 inhibitors whereas the totality of efficacy data were still not uniformly reported. Moreover, other ongoing clinical trails on combinations of BV + PD-1 inhibitor such as BV + Ipilimumab (NCT01896999) may also contribute to the selection and formulation of treatment plan for biomarker-based rationale drugs. Besides, we found EV (targeting Nectin-4) + Pembro could benefit UC patients more, compared with SG (targeting Trop-2) + Pembro. However, we should take a rational view of these data in case of other factors interfering with this results. For example, the differences in the baseline characteristics for patients in SG group experienced previous more than third-line therapy while patients in EV group didn't received therapy for locally advanced or metastatic UC. As for T-DM1 + Atezo therapy, the survival benefit of BC patient in the combination arm failed to show significant improvement when compared with the control arm. Therefore, further evaluation was still needed before the evidence was translated into reliable survival outcomes. As for other treatment schemes, the data of relevant studies are still needed to support clear comparative judgment. Anyway, as the selectivity and accessibility of ADC plus ICI demonstrated a biomarker-based rationale that was associated with efficacy of combinations, it is important to recognize that inter- and intra-tumor heterogeneity should be fully considered [46]. For more satisfactory risk-benefit profiles, new strategy of optimal combinations to maximize the advantages of them is something we require to work out.

As for safety analysis, emerging combinations are seen as a game-changer when the efficacy of monotherapy is limited, but it can act as a double-edged sword if the combinations induce more observed AEs. The irAEs identified in our study were mainly digestive and skin problems, and no severe AEs were spotted. In general, the toxicity of ADC therapy is mainly related to the cytotoxic drugs carried, including hematotoxicity, neurotoxicity, hepatotoxicity and so on [47], with regards to dosing, drug design, or off-target binding [48]. Besides, toxicity of ICI may have delayed onset and prolonged duration, from mild skin diseases to severe gastrointestinal tract reactions to life-threatening myocarditis, cheifly affecting skin, gastrointestinal, endocrine, lung and muscle tissue [49, 50]. Even worse, the superposition of this two toxicities may offset the potential therapeutic benefits. Fortunately, the early data from combination studies of ADC and ICI have not revealed significant additive toxicity so far, which were consistent with the results of our study. And in order to timely alleviate or improve the influence from this combinations, such as interruption or drug reduction, measures should be taken to relieve their symptoms. Additionally, Advanced assessment for patients’ physical condition is essential, especially for those at high risk. Once the AEs happened, clinical nursing, temporary dose interruption and/or dose reduction should be considered.

The following limitations merit consideration. For a lack of placebo arm, the efficacy of combinations may be exaggerated, and further control group are in demand to minimize the placebo effect. Furthermore, in our meta-analysis, the tumor type, disease progression and different drug mechanism may have biased the results. Although we have performed subgroup analysis to reduce this bias, some partial trials were still unable to be reassessed because of limited sample data. Last but not least, although the reasons for drug efficacy and specific benefit from specific caner type have been evaluated from antigen and target levels, our study still remained on the interpretation of phenomena, and further mechanism researches are needed.

In general, the combination of ADC and ICI therapeutics might be a promising treatment option. This single-arm meta-analysis suggested that both regimen and tumor type had great impact on treatment effect, indicating that selection of regimen and tumor type of patients are important. Meanwhile, potential toxicity on skin and digestive system were also observed in patients, suggesting management of adverse events are of vital importance.

ADC

Antibody-drug conjugate

AEs

Adverse effects

ASCO

American Society of Clinical Oncology

AST

Aspartate transaminase

Atezo

Atezolizumab

Breast cancer

Brentuximab vedotin

cHL

classical Hodgkin lymphoma

Complete response

Dato-DXd

Datopotamab deruxtecan

Effect siz

ESMO

European Society for Medical Oncology

Enfortumab vedotin

Gastric cancer

HER2

Human epidermal growth factor 2

HPD

Hyperprogressive disease

ICI

Immune checkpoint inhibitors

ITT

Intention to treat

mAb

monoclonal antibody

Not available

Nivo

Nivolumab

NHL

Non-Hodgkin lymphoma

NSCLC

Non small-cell lung cancer

ORR

Overall response rate

Progressive disease

Pembro

Pembrolizumab

Partial response

Stable disease

Sacituzumab govitecan

T-DM1

Trastuzumab emtansine

Urothelial cancer

7.1 Ethics approval and consent to participate

Not applicable.

7.2 Consent for publication

Not Applicable.

7.3 Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

7.4 Competing interests

The authors declare that they have no competing interests

7.5 Funding

This work was founded by the Young Elite Scientists Sponsorship Program by CACM (2021-QNRC2-B13); the National undergraduate innovation and entrepreneurship training program (202210344039); and the Research Project Funding of Zhejiang Chinese Medical University (2022FSYYZY07).

7.6 Author contributions

Leitao Sun contributed to the design and conception of the study. Leyin Zhang, Yixin Chen and Yici Yan carried out the collection and processing of data. Leyin Zhang, Yixin Chen and Jiamin Lu performed the data analysis, interpretation, and statistical analysis. Leyin Zhang, Yici Yan and Binbin Wang wrote the manuscript. Ning Ren, and Binbin Wang revised the manuscript, then Leitao Sun gave the final approval of manuscript. All authors contributed to the article and approved the submitted version.

7.7 Acknowledge

We appreciate the technical support from the Public Platform of Medical Research Center, Academy of Chinese Medical Science, Zhejiang Chinese Medical University.

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Will antibody-drug conjugate and immune checkpoint inhibitor-based combination therapy close the gap between benefit and toxicity in the treatment of cancers? A systematic review and meta-analysis

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Abstract

Background

Methods

Results

Conclusion

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1 Introduction

2 Methods

2.1 Search strategy

2.2 Inclusion and exclusion criteria

2.3 Quality assessment

2.4 Data extraction

2.5 Statistical Analysis

3 Results

3.1 Literature Search and Patients Characteristics

3.2 Quality assessment

3.3 Efficacy

3.4 Subgroup analyses

3.4.1 Histotype

3.4.2 Regimen

3.5 Safety

3.6 Publication Bias and Sensitivity Analysis

4 Discussion

5 Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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