Expression of antibody-drug conjugate targets in post-mortem samples of breast cancer metastases and normal tissue

Domain: chemistry
Published: 2025-12-26
Editorial outcome: minor_revision

Abstract
While many Antibody Drug Conjugates (ADCs) are investigated in solid tumors, their target expression and heterogeneity is poorly documented in metastatic breast cancer. Here, we perform RNA sequencing to investigate expression of 72 ADC targets in 909 samples from 30 female patients from our post-mortem tissue donation program UPTIDER (NCT04531696). We report that the highest median expression in metastases is seen for FN1, MUC1, LAMP1, HER3 and SLC39A6. TROP2 and HER2 have the 9th and 27th highest expression. We observe reduced expression in metastases as compared to primary untreated tumors for 15 targets but observe only limited differences in expression between metastases of different subtypes. We show that TROP2, VTCN1, NECTIN4, HER2 and HER3 are interesting targets as they have higher mRNA expression in tumor as compared to normal tissues. This is confirmed at the protein level by explorative immunohistochemistry. Correlations between targets can help guide design of bispecific ADCs. Taken together, the results of this study can help inform the design and prioritization of drugs for the treatment of patients with metastatic breast cancer.

Introduction
Antibody-drug conjugates (ADCs) are a rapidly expanding class of drugs that fall under the category of targeted cancer therapy. The drug design typically consists of recombinant monoclonal antibodies chemically bound to cytotoxic agents, commonly referred to as payloads, via a cleavable or non-cleavable linker molecule1. Upon successful binding of the antibody to its cognate antigen at the tumor cell surface, the ADC is internalized, and tumor cell death is initiated. Depending on the chemistry and type of the linker with the payload (i.e., cleavable vs non-cleavable linker), the cytotoxic agents also facilitate (tumor) cell death in the surrounding tumor microenvironment (TME) with the so-called bystander effect1,2. An ideal ADC target should meet the following requirements: (i) be predominantly expressed in the tumor on the cancer cell surface with a low expression in normal tissue, (ii) exhibit minimal expression heterogeneity between different tumor sites within a patient, (iii) be stable in plasma with sufficient half-life, and (iv) have an optimal binding affinity for internalization1. However, the minimal target expression needed for effective response is unknown and might depend on the ADC and its target3–7. Furthermore, mechanisms independent from internalization have been described for certain ADCs, such as trastuzumab deruxtecan (T-DXd)8.

Currently, 14 ADCs are approved by the United States Food and Drug Administration (FDA), of whom 8 are available for the treatment of solid cancers9. Four of the eight approved ADCs are also approved for the treatment of metastatic breast cancer, namely sacituzumab govitecan (SG) and dapotamab deruxtecan (Dato-DXd) targeting the trophoblast cell surface antigen 2 (TROP2) and T-DXd and trastuzumab emtansine (T-DM1) targeting the human epidermal growth factor receptor 2 (HER2). Multiple other ADCs have received fast-track designation from the FDA, such as emiltatug ledadotin (XMT-1660) targeting VTCN1 with corresponding protein B7-H4 and bulumtatug fuvedotin (9MW2821) targeting NECTIN4, both for the treatment of triple negative breast cancer (TNBC). This fast-track designation was given based on results of preclinical data and phase 1/2 trials10,11.

For SG, a clinically meaningful benefit was not only seen in the treatment of breast cancer, but also in the treatment of other solid tumors12. The same is true for T-DXd13,14, which has received FDA approval for any HER2-positive solid cancer6,9. This means that ADCs already developed for other solid cancers have the potential to be successfully used in breast cancer if the target is expressed in breast cancer.

SG is composed of an anti-TROP2 antibody linked to the topoisomerase I inhibitor govitecan (SN38). Currently, no prior immunohistochemistry testing for TROP2 expression is needed to treat patients with SG. However, Coates and colleagues described a patient with TNBC treated with SG who had primary resistance to SG15. Analysis of all the tumor lesions collected during the rapid autopsy showed no TROP2 expression at the mRNA or protein level, suggesting that expression of TROP2 is needed for SG to work. Bardia and colleagues recently performed an exploratory analysis of the metastatic TNBC ASCENT trial (NCT02574455) and showed that although this ADC improved progression-free and overall survival compared with single-agent chemotherapy across all four quartiles of TROP2 protein expression levels as assessed on immunohistochemistry, the magnitude of benefit on overall survival seemed to increase with increasing TROP2 expression3. They also suggest that efficacy in tumors with low TROP2 levels might be due to the bystander effect. Of note, the ASCENT trial considered either archival samples from the primary tumor or a metastasis for TROP2 analysis.

T-DXd consists of an anti-HER2 antibody linked to the topoisomerase I inhibitor deruxtecan. As opposed to SG, a minimal expression of HER2 is needed for a patient with metastatic breast cancer to be eligible to receive T-DXd4. Recently, the DESTINY-Breast06 (NCT04494425) trial has shown that patients with HER2-ultralow tumors (defined as ≤10% of invasive cells showing faint/weak and incomplete membrane staining) could benefit from this ADC, which has led to FDA approval for the treatment of hormone receptor (HR)+/HER2-ultralow tumors with T-DXd16. Since this HER2-ultralow status had to be determined on a tissue sample taken from one metastatic lesion, it indirectly assumes that one sample is representative of the whole metastatic disease. While response to therapy does not seem to be correlated with immunohistochemical HER2 protein expression, more quantitative HER2 protein or mRNA expression levels have been reported to be correlated, although available data are limited and controversial17–19.

Restricted sample collection and the availability of adequate samples from non-accessible metastatic organ sites represent a logistical problem for investigating the expression of these ADC targets and especially their intra-patient heterogeneity6,7,20–22. In this context, research autopsy programs enable a comprehensive investigation of a very large number of metastases present in a patient23. Using metastatic samples from our post-mortem tissue donation program UPTIDER (NCT04531696)24, we recently described the intra-patient inter-metastatic heterogeneity of HER2 protein expression, suggesting that, at least for HER2, one metastatic biopsy might not be representative of the disease25.

Here, we investigate the mRNA expression and the inter- and intra-patient heterogeneity of ADC targets being clinically evaluated in solid cancer trials, leveraging the samples and data collected in UPTIDER. In addition to TROP2 and HER2, we identify multiple other interesting targets with high expression in but low heterogeneity across our metastatic samples, such as HER3 and VTCN1. We provide evidence for the differential expression of these targets across different organs. Furthermore, in view of the ongoing discussions about the concomitant or sequential administration of ADCs and the emergent development of ADCs with bi- or even multispecific antibodies, which can bind two or more different targets or epitopes of the same target26,27, we are able to demonstrate the co-expression of certain treatment targets. Next, although most ADCs are relatively well tolerated, all do have side-effects28. While most adverse events are caused by off-target/off-tumor toxicities, some can be explained by off-tumor/on-target toxicity. These are caused by normal tissues expressing the target of the ADC29,30. Our results highlight the expression patterns of ADC targets on normal tissue samples from a large variety of organs. Lastly, we perform a first preliminary validation of our findings in metastatic and normal samples at the protein level for targets of interest. With this study, we hope to address some knowledge gaps in the rapidly evolving field of ADCs.

Results
Clinical and pathological characteristics of the patient cohort
Thirty female patients with metastatic breast cancer underwent tissue donation via rapid autopsy between January 2021 and November 2023. In total, 23 patients (77%) had hormone receptor (HR)+/HER2− breast cancer, 6 (20%) TNBC, and 1 (3%) HR+/HER2+ primary breast cancer (Fig. 1a). Eighteen patients (60%) had primary invasive breast carcinoma of no special type (IBC-NST), 7 (23%) had invasive lobular carcinoma (ILC), 3 (10%) had mixed IBC-NST/ILC, 1 (3%) had mixed IBC-NST/metaplastic and 1 (3%) was initially diagnosed with IBC-NST and 14 years later developed a contralateral ILC tumor. The median age of primary invasive diagnosis was 49 years (range: 35–83), while the median age of first metastatic diagnosis was 56 years (range: 37–83). Seven patients (23%) had de novo metastatic disease. Four patients (13%) received an ADC as part of their treatment. Pt2001 was treated with SAR566658 targeting CA6, a sialoglycotope of MUC1. Pt2003 received T-DM1 targeting HER2, while Pt2024 and Pt2032 received SG targeting TROP2. Patient characteristics are reported in Supplementary Table 1.

The median age at death was 60 years (range: 36–91). The autopsies had a median post-mortem interval of 2.9 h per patient (range: 1.2–24.5). A total of 749 metastases with a median of 25 metastases per patient (range: 7–50) and 64 primary untreated breast cancer samples with a median of 1 untreated primary tumor sample per patient (range: 0–10) were collected. Metastatic lesions were acquired from a median of 6 organ sites per patient (range: 3–16) (Fig. 1b). Metastatic samples were collected from 32 distinct organ sites (Supplementary Fig. 1a). However, organ site representation was most consistent in patients with ER+ disease and in patients with a primary histology of IBC-NST (Supplementary Fig. 1b, c). Normal samples (n=96) were acquired from 25 distinct organ sites with a median of 4 samples per organ site (range: 1–10) (Fig. 1c).

List of ADC targets being clinically investigated in oncology
Our search strategy identified 623 clinical trials testing 166 different ADCs in solid tumors (Supplementary Table 2). 221 trials (35%) were pan-cancer trials, and 177 trials (28%) were specific to breast cancer patients, which makes breast cancer the most common tumor type in ADC trials. Of all trials, 204 (33%) were phase 1, 323 (52%) were phase 2, 75 (12%) were phase 3, and 6 (1%) were phase 4 (Fig. 2a). A combination of phase 1 and 2 was seen in 10 trials, and phase 2 and 3 in 3 trials. Two trials monitored compassionate use programs. In total, we assembled a list of 72 ADC targets currently under investigation (Supplementary Data 1 and 2). Ten targets have been used in breast cancer-specific trials (ALCAM, CD44, HER2, HER3, FOLR1, FUT2, GPNMB, PTK7, SLC39A6, TROP2). HER2 (n=32) and TROP2 (n=10) were the most investigated targets.

Expression of ADC targets in breast cancer metastases
The threshold level of target expression needed for ADC activity has not yet been determined for any of the existing ADCs. However, there is evidence suggesting that higher target mRNA and/or protein expression can enhance the efficacy of some ADCs, while the absence of expression could indicate resistance3,15,19,31,32. This highlights the potential relevance of quantifying expression levels of ADC targets across metastases. Here, we investigated the mRNA expression of the 72 ADC targets across metastatic samples using normalized gene expression data. FN1, MUC1, LAMP1, HER3 and SLC39A6 had the highest global median expression level among all targets (median range: 8.46–10.34) (Fig. 2b), while the targets from the currently clinically approved ADCs or ADCs with fast-track designation by the FDA in metastatic breast cancer TROP2, NECTIN4, HER2 and VTCN1 had the 9th, 18th, 27th and 33rd highest global median expression (median range: 4.83–7.73), respectively. Additionally, other targets used in breast cancer-specific trials also had high global median expression, for example, CD44, ALCAM, and GPNMB, which were ranked with the 7th, 8th and 14th highest global median expression among all targets. Targets with minimal expression were CFC1B, CLDN6, DLL3, DPEP3, and MSLN (median of 1.67).

In the 14 patients where repeated sampling was performed, gathering 141 samples24, to investigate effects of PMI on sample quality, target expression did not vary substantially with time, with 82% of targets remaining constant in cooled tumor samples. Genes not remaining constant were among the lowest expressed genes, or changes were too subtle to be further considered (Supplementary Fig. 2a–k, Supplementary Data 3).

As most ADC targets are, per definition, expected to be preferentially expressed by tumor cells, we investigated the correlation between their mRNA expression in the tumor tissue and tumor cellularity. Here, a weak positive or negative correlation (Spearman Rho ranging between −0.25 and 0.32) was observed for 39 and 33 targets, respectively (Supplementary Fig. 3). HER2, HER3, and TROP2 displayed a weak positive correlation with tumor cellularity (range: 0.20–0.28).

To investigate the intra-patient inter-metastasis heterogeneity in target expression, we evaluated the interquartile range (IQR) per target, with increasing ranges indicating an increasing degree of heterogeneity. The targets with the highest global median IQR were LRCC15, CEACAM6, CEACAM5, FN1, and MUC1 (median range: 1.40–1.61), while a high IQR was also seen for VTCN1 (1.36). Median IQR was relatively low for HER2, HER3, TROP2, and NECTIN4 (median range: 0.75–1.07, Fig. 2c), with most patients (range: 57–66%) having intra-patient median IQR below the global median IQR per target (Supplementary Fig. 4a).

We further explored the expression of ADC targets within subgroups of patients with primary HR+/HER2− breast cancer (n=556) and TNBC (n=178). In patients with HR+/HER2− breast cancer, the highest global median expression level was seen for HER3, LAMP1, MUC1, SLC39A6, and FN1 (median range: 8.68–10.40). TROP2, NECTIN4, HER2, and VTCN1 had the 9th, 19th, 27th, and 32nd highest global median expression (median range: 4.70–7.73). Similarly, for patients with TNBC, the highest global median expression was seen for HER3, CD44, MUC1, LAMP1, and FN1 (median range 8.15–9.49). TROP2, NECTIN4, HER2, and VTCN1 had the 7th, 13th, 22nd, and 33rd highest global median expression (median range: 4.71–7.75) (Fig. 3, Supplementary Fig. 4b, c).

Differences in target expression between the primary tumor and metastases
As tumors evolve from primary disease to metastatic disease, so do their molecular and TME profiles, leading to a potential increase or decrease in ADC target expression33,34. To investigate this, we compared target expression levels between primary untreated tumors and their corresponding metastases. Due to the relatively limited amount of primary untreated tumors in the cohort, we initially validated target expression in our primary untreated tumor samples (n=64) by comparing normalized target gene expression with primary untreated tumors from The Cancer Genome Atlas (TCGA) cohort (n=1111)35. We observed a very high correlation (R=0.92, p<0.001) between the two datasets, indicating that the target expression in our primary breast cancer samples can be considered representative of untreated primary breast cancer (Supplementary Fig. 5a).

We then compared target expression between metastases (n=749) and primary untreated tumors (n=64). The regression model revealed significantly reduced expression in metastases as compared to primary untreated tumors for 14 targets, including LRRC15, PD-L1, and CD276, with a trend for HER2 and GPNMB (Fig. 3, “Methods”). Significantly higher expression in metastases was seen for 4 targets (ADAM9, CDH6, NCAM1, and SEZ6). Of these 4, CDH6 had the highest increase in expression in metastases compared to primary untreated tumors (Estimate: 1.15, Fig. 3). Since the expression of CFC1B was minimal across all metastases (Fig. 2b), evidence of statistical relevance cannot be considered reliable (Fig. 3, Supplementary Data 4.1).

When comparing HR+/HER2− metastases (n=556) to their corresponding untreated primary tumors (n=56), statistical evidence of reduced expression in metastases was observed for 13 targets, including CD276 and VTCN1, with the greatest change being in LRRC15 (Estimate: −4.27, Supplementary Fig. 5b), while an increased expression was observed for 6 targets, including CDH6. Furthermore, in patients with TNBC disease (primary n=7, metastases n=178), the difference in target expression between metastases and untreated primary tumors was rather limited, with reduced expression for 6 targets, including PD-L1, HER2, and SLC39A6, while increased expression was observed in 3 targets, including ADAM9 (Supplementary Fig. 5b, Supplementary Data 4.1). Additionally, a trend for higher FGFR2 expression was observed in untreated primary tumors with TNBC disease.

Differences in target expression according to histological subtypes, molecular subtypes, and ER protein expression
We first looked at tumor histology and compared target expression between ILC (n=265) and IBC-NST (n=432) metastases across all patients. We observed no significant changes or trends in target expression (Fig. 3, Supplementary Data 4.2). Second, we compared target expression between metastases from patients with primary HR+/HER2− breast cancer (n=556) with metastases from patients with primary TNBC (n=178). Here, our models identified statistical evidence for higher expression of SLC39A6 in HR+/HER2− metastases with no evidence or trends in TNBC metastases (Fig. 3, Supplementary Data 4.3). It should, however, be noted that we observed higher expression of NECTIN4 and VTCN1 in TNBC, the two targets of the ADCs that got fast-track designation by the FDA for metastatic TNBC. However, this was not statistically significant after correcting for multiple testing. Third, we investigated the potential association between estrogen receptor (ER) protein loss and ADC target expression in patients with primary ER+ disease. Here, between ER+ (n=427) and ER− (n=63, 13% loss) metastases, evidence for significantly higher expression was observed for SLC39A6, IGF1R, and TPBG with trends for HER3 and SLITRK6 in ER+ metastases as compared to ER− metastases (Fig. 3, Supplementary Data 4.4).

Target expression at the organ level
Since the composition of the TME across various metastatic sites is largely organ-specific, it could be expected that ADC target expression is dependent on the local microenvironment. We therefore performed an exploratory analysis to investigate organ-specific expression of ADC targets. To this end, we considered metastases from 13 organ sites which contained at least 10 samples across the cohort (Supplementary Fig. 1a). When considering the most prevalent metastatic sites in breast cancer irrespective of tumor histology: bones (n=44), brain (n=13), liver (n=219) and lung (n=44) showed distinct patterns of site-specific target expression compared to global expression across all 32 metastatic sites (Fig. 4, Supplementary Data 5). Bone metastases had the largest number of targets (n=9) with increased organ-specific expression, including LRRC15, FGFR2, and FN1 (Estimate range: 0.62–1.62). Bone metastases also had lower expression of TROP2 and NECTIN4 (estimate range: −0.77 to −1.47). Brain metastases had the largest number of targets with reduced expression, including HER2, VTCN1, and FN1 (estimate range: −0.78 to 1.86). In contrast, brain and lung metastases presented with 6 (including NCAM1) and 3 targets (including CLDN18) that displayed the highest median increase in organ-specific expression across all metastases (estimate > 1.5). Additionally, lung metastases showed a mild increase in PD-L1 expression (estimate: 0.68) with reduced expression for FN1 (Estimate: −0.53). Furthermore, while liver metastases had site-specific expression of 6 targets, the change in median expression was subtle (estimate range: −0.28 to 0.62). Across the remaining 9 organs, most metastases presented with no evidence or just trends for changes in expression (Supplementary Fig. 6). These findings highlight the heterogeneity of ADC target expression among multiple metastatic sites, which could be influenced by the organ-specific TME in patients with metastatic breast cancer.

Co-expression and mutual exclusivity of ADC targets in breast cancer metastases
Multiple strategies are being investigated regarding the potential benefit of sequential or concomitant administration of ADCs and bispecific, or even multispecific ADCs1,6,7,27,36,37. We therefore investigated the level of co-expression among targets within metastatic samples using weighted Spearman correlation. Across metastases from all patients, we identified 29 different target combinations with a strong or very strong negative or positive correlation (R>0.6 or R<−0.6 and p<0.05) (Supplementary Data 6.1) of which 23 pairs were linked to positive co-expression, while 6 pairs were linked to mutually exclusive expression. Considering our targets of clinical interest (HER2, HER3, TROP2, CD276, FGFR2, FN1, MUC1, NECTIN4, VTCN1), we found 1 combination with positive co-expression HER2-HER3 and 1 pair with negative co-expression (FN1-MUC1). Additionally, PD-L1 as immunotherapy has been used in combination with ADCs; however, none of our targets of interest had a strong positive or negative correlation, with only HER2 having a moderate negative correlation with PD-L1 (R=−0.43, p=0.04) (Fig. 5, Supplementary Data 6.1). Within breast cancer subtypes, in patients with HR+/HER2− metastases, strong positive correlations were observed for 39 pairs, while strong negative correlations were apparent for 16 pairs (Supplementary Fig. 7a, Supplementary Data 6.2). Considering our targets of interest, positive co-expression was observed for HER2-HER3, and negative co-expression was observed for HER3-PD-L1 and FN1-MUC1. In patients with TNBC, strong positive correlations were observed in 50 pairs, while strong negative correlations were observed in 39 pairs. Regarding our targets of interest, negative co-expression was observed for HER2-FN1, FGFR2-VTCN1, and positive co-expression was observed for TROP2-NECTIN4; however, no evidence for co-expression with PD-L1 was observed (Supplementary Fig. 7b, Supplementary Data 6.3). Overall, these findings highlight the presence of both positively and negatively co-expressed ADC target pairs in metastases across the entire cohort, but also the heterogeneity of co-expression patterns across breast cancer subtypes.

Expression of ADC targets in normal samples
Although most toxicities in ADCs are off-target and driven by effects caused by the cytotoxic payload, some can be explained by on-target activity caused by the expression of targets on normal tissue38. Identifying ADC targets that have a considerably higher expression in metastases compared to normal tissue can improve drug response and avoid preventable drug toxicities. Here, we studied ADC target expression in 96 normal tissue samples obtained from 25 different organ sites from patients with metastatic breast cancer (Fig. 1c). FGFR2, SLC39A6, FN1, ALCAM, and MUC1 showed the highest expression in normal tissues: their median expression values ranging from 9.46 to 10.50 (Fig. 6a). TROP2, NECTIN4, HER2 and VTCN1 were ranked with 21st, 25th, 37th and 42nd highest global median expression (median range: 3.34–5.60). For 32 targets, the median gene expression (Methods) was higher in metastatic samples than in normal samples including HER2, HER3, TROP2, VTCN1, and NECTIN4 and for 31 the expression was higher in normal samples including PD-L1 (Fig. 6b, Supplementary Fig. 8). The highest difference in median gene expression in favor of metastases was seen for TROP2, followed by VTCN1. Figure 6b also highlights the degree of overlap in gene expression between normal tissues and metastases (“Methods,” Supplementary Data 7). Of the 32 targets exhibiting a higher median expression in metastases, the median overlap in gene expression with normal tissues was 88.89% (range: 2.25–100%). Interestingly, we identified TROP2 as the only target in metastases with an overlap under 25% (2.25%), while HER2, HER3, NECTIN4, and VTCN1 had an overlap of 68.87%, 47.76%, 36%, and 33.33%, respectively (Fig. 6b). PD-L1 had higher median expression in normal samples with a 56.79% degree of overlap in expression (Supplementary Fig. 8).

When studying the expression levels within all organ sites, we observed the highest relative target expression (Z-score > 3) for CA9 in bladder, CFC1B in bone marrow, DLL3 in the brain, LAMP1 in the liver, CLDN18 in lung, DPEP3 in thoracic lymph nodes, CLDN6 in pancreas, CD70 in the spinal cord and LY75 in the thyroid (Fig. 6c). Moderate levels of HER2 expression (Z-score: 1–1.5) were found across several normal tissues, including bladder, kidney and heart, but not in lung and liver. For TROP2, higher expression (Z-score: 1–2.64) was seen in bladder, kidney, lung, and pancreas, but not in bone marrow (Fig. 6c). No association was observed between age at death, PMI, and target expression in normal samples (Supplementary Data 3). Altogether, these findings highlight target mRNA expression in normal tissues and provide insights into differences in expression across organ sites.

Exploratory characterization of protein expression of selected targets
Eight targets were selected for immunohistochemical staining of metastatic and normal samples: (i) targets of ADCs being approved for metastatic breast cancer (HER2, TROP2) or having received fast-track designation (NECTIN4, VTCN1), (ii) target with clinical potential based on high mRNA expression in metastatic samples and low mRNA expression in normal samples (HER3) and (iii) targets with high levels of mRNA expression in metastases but also in normal tissues, likely indicative of high risk for toxicities (FN1, MUC1, FGFR2) (Figs. 7 and 8, Supplementary Fig. 9, Supplementary Data 8). The analysis of the immunohistochemistry data was mainly explorative, given the relatively limited number of samples. Our main goal was to compare the expression between metastases and normal tissue samples, to see whether the conclusions we made based on the transcriptomic data were still valid on the preliminary immunohistochemical data.

For the 4 targets from clinically approved ADCs or ADCs having received fast-track designation by the FDA (HER2, TROP2, NECTIN4 and VTCN1), we could confirm a significantly higher expression in metastases as compared to the normal samples (Fig. 7). It should be noted that for these 4 targets, the percentages of stained tumor cells ranged from 0 to 100%, highlighting the heterogeneity in expression (Fig. 8, Supplementary Fig. 9). While no membranous staining and no staining at all was detected for, respectively, HER2 and VTCN1 in the normal tissues, variable percentages of cells expressing TROP2 or NECTIN4 were detected in the normal tissues (Supplementary Fig. 9). Regarding HER3, higher levels of stained cells were observed in normal tissues as compared to the metastases, which showed very low levels of expression (Figs. 7 and 8, Supplementary Fig. 9). Regarding the last 3 targets, we could not detect any difference in expression between normal and metastatic samples for FN1 and FGFR2. However, MUC1 expression was significantly higher in metastases as compared to normal tissues, but with variable percentages of cells expressing MUC1 in normal tissue samples (Figs. 7 and 8, Supplementary Fig. 9).

At the mRNA level, we observed a lower expression of HER2 in brain and pleural metastases. At the protein level, we stained 6 brain metastases for HER2; half had detectable HER2 expression (Supplementary Data 8). This suggests that while we do observe a lower mRNA expression in brain metastases, protein expression might still be present. Of the 6 pleural metastases we examined at the protein level, only 2 had detectable HER2 expression.

While these results should be interpreted with caution since only a proportion of samples were stained, they do validate what we observed in our transcriptomic data for 6 out of the 8 markers regarding differences in expression in metastatic versus normal samples. The results also clearly demonstrate the wide range of protein expression observed for all markers in metastatic breast cancer.

Methods
Identification of ADC targets currently under clinical investigation in solid tumors
Bosi et al. identified a list of 54 ADC target genes based on a search on clinicaltrials.gov with the 26th of September 2023 as the latest publication date. This search was based on different keywords, including “antibody-drug conjugate.” Only trials testing ADCs in solid tumors were included58. We extended this search on clinicaltrials.gov for studies posted between September 26th, 2023, and March 4th, 2024, with the search term “antibody drug conjugate.” Five additional target proteins were identified (CD274, CDH3, DLK1, EPHA5, CFC1B). We performed an additional search on clinicaltrials.gov with the search term “ADC” until March 4th, 2024. Another 2 targets were identified (NT5E, ST8SIA1). The study of Fang et al. included two targets not identifiable with the earlier mentioned search strategy (CD44 and CDH3), and these were also included59. Lastly, adcreview.com was searched for additional targets in solid tumors. CA9, EDNRB, EPHA2, LAMP1, LY6E, MUC16, NOTCH3, SEZ6, and STEAP1 were additionally identified. While some of these targets had a gene name different from their protein name, we named genes of commonly known protein markers to their corresponding protein names, such as ERBB2, ERBB3, NT5E, TACSTD2, and CD274 to HER2, HER3, CD73, TROP2, and PD-L1 for easier comprehension across analyses. Detailed information can be found in Supplementary Table 2 and Supplementary Data 1 and 2.

Patient enrollment and sample collection
UPTIDER (UZ/KU Leuven Program for Post-mortem Tissue Donation to Enhance Research) is a post-mortem tissue donation program that enrolls male and female patients with metastatic breast cancer (NCT04531696)24. Until now, only female patients have been included. After inclusion, archived tissue samples from primary tumors and/or metastatic biopsies were retrieved from the pathology department. Additional information regarding the study design and protocols is available in our original publication24. At autopsy, samples from macroscopically identified metastases as well as from normal tissues were collected. Location of sampling was registered using ICD-O-3 organ codes60. As indicated by the organ codes in ICD-O-3, the pleura and lung are classified as distinct anatomical sites. Post-mortem interval was defined as the time duration between patient death and the start of the autopsy. Samples of selected tumor tissues were repeatedly (at 1.5-h time intervals) sampled during the tissue donation procedure to assess the impact of ssPMI on sample quality. Two series of samples were obtained: (i) samples kept at room temperature; (ii) samples kept between 4 °C and 10 °C. All samples are conserved for further analysis. These samples may be shared with other researchers upon request, subject to appropriate agreements, including a Material Transfer Agreement, and ethical approvals.

Samples and data from the first 30 patients who underwent tissue donation between January 2021 and November 2023 are considered here. For these 30 patients, all available primary and metastatic samples (i.e., both historical tumor samples collected during life and samples obtained from the tissue donation program after death) were analyzed. Only untreated primary tumor samples were included in the analysis. A primary tumor was classified as untreated if the patient had not received any systemic anti-cancer therapy in the 5 years before the breast cancer diagnosis. Normal samples were selected based on the absence of oncological and non-oncological organ disease, which was confirmed on histology, across multiple patients. For example, if a patient had pneumonia at the time of death, collected lung samples were not considered as normal samples.

Histopathological characterization
Histopathological characterization was performed on all pre-mortem and post-mortem formalin-fixed paraffin-embedded (FFPE) samples to define the tumor cellularity and histological tumor subtype according to the WHO classification61. Tumor cellularity was defined as the relative percentage of viable tumor cells in the tissue. Samples with ≧10% tumor cellularity were selected for immunohistochemical staining with the ER antibody (clone EP1, DAKO, RTU, CE-IVD). ER and progesterone receptor (PR) were scored following ASCO/CAP 2020 guidelines with a cut-off for positivity at 1%62. HER2 was assessed according to the ASCO/CAP guidelines25,63. Patients were considered as HR positive/HER2 negative (HR+/HER2−) if they had either ER+ or PR+ and HER2− disease at primary diagnosis.

Eight targets were selected for immunohistochemical staining of metastatic and normal samples: FGFR2 (ab289968, EPR24075-418, 1:100, Abcam, RUO), FN1 (A0245, RTU, Dako), HER2 (A0485, Agilent, RTU, CE-IVD), HER3 (D22C5, 1:250, Cell Signaling Technology, RUO), NECTIN4 (EPR15613-68, 1:100, Abcam, RUO), MUC1 (M0613, clone E29, 1:1000, Dako), TROP2 (ab227689, clone SP293, 1:150, Abcam, RUO), VTCN1 (B7-H4 (D1M8I), 1:400, Cell Signaling Technology, RUO). Sample selection was performed to consider a range of mRNA target expression across the 6 most common organ sites. A larger number of samples were stained for HER2, TROP2, and HER3, as these were already considered for a separate project on metastatic TNBC. Regarding the normal tissue, 10 samples were taken across the liver, lung, and axillary lymph nodes. In metastatic samples, only staining in tumor cells was considered, while in normal samples, staining in every type of cell was taken into account. Immunohistochemical assessment of HER2 was done according to the ASCO/CAP guidelines, as mentioned above25,63. HER3 was scored according to HER2 ASCO/CAP guidelines in the metastatic samples64. TROP2 was scored using the H-score, but for our analysis, only the total percentage of cells displaying membranous or cytoplasmic staining was taken into account. Due to a lack of guidelines, protein expression of the other 5 targets in metastatic samples was scored as a continuous variable, % of tumor cells, and in normal samples, the other 5 targets and HER3 were assessed as the % of all normal cells that showed any level of staining, regardless of intensity and cytological localization.

RNA extraction and sequencing
RNA extraction was performed on fresh frozen (FF) and FFPE samples using the established protocol from our original study24. To summarize, RNA from the tissue specimen was isolated using the Invitrogen™ TRIzol™ reagent, after which chloroform was added, followed by a centrifugation step. The aqueous phase containing the RNA was transferred, after which Genelute—LPA (Sigma Aldrich—56575) and isopropanol were added, and a new centrifugation step was performed. The RNA pellet was washed with ethanol, air dried, and solubilized in nuclease-free water. Extracted RNA was quantified using the NanoDrop™ One spectrophotometer. The extracted RNA was subsequently sequenced using the Lexogen protocol (3’ mRNA FWD Quantseq) for Illumina NovaSeq 600065. The FASTQ files obtained were converted to reads after mapping to the GRCh38 reference genome (primary assembly, dated 08.06.2022) using the STAR aligner (v2.7.10a)66, and the transcripts were annotated using gencode (v40 for GRCh38.p13). Gene counts were calculated using the subread package (v2.0.3)67 and normalized using the variance stabilizing transformation (VST) method from the DESeq2 package (v1.34.0)68. No bias in the number of reads between FF and FFPE samples was observed. This conclusion was drawn using a mixed quantile regression model, with assigned reads as the outcome and patient ID as a random effect (estimate for FF vs. FFPE=−77616.99, 95% CI: −542500.19 to 387266.2, p value=0.74). Consequently, downstream analysis was not adjusted for sample conditioning.

TCGA data
Bulk mRNA sequencing data from 1111 primary breast cancer samples of TCGA were downloaded from the GDC portal of the National Cancer Institute (https://portal.gdc.cancer.gov, January 29, 2024). Data was queried from the TCGA-BRCA project that contained 1231 samples for which bulk transcriptomic profiling was performed, and for whom gene counts (in TPKM) from the STAR workflow were available to download as a TSV file. The gene expression of ADC targets was compared with our cohort after normalizing the dataset with log2(x+1) and rescaling the gene expression range to our cohort.

Statistics and reproducibility
As the study was intended to generate future hypotheses, no statistical analyses were performed to estimate effect/sample sizes. No data were excluded from the analyses. The experiments were not randomized. Patients with de novo metastatic disease were included for the estimation of the median age of first metastatic diagnosis in the patient cohort. Raw gene counts were normalized using VST and were reported as a continuous parameter. All analyses were performed in R (v4.3). Boxplots were used to report the intra-patient heterogeneity of target expression using the ggplot2 package (v3.4.3). Global median target expression in metastases across patients was reported using boxplots by calculating the median expression of each target across all metastases within each patient. Global IQR was also calculated in a similar manner. Weighted Spearman correlation on median gene expression was used to identify the presence of co-expressed/mutually exclusive targets after accounting for sample size effect from each patient using the wCorr package (v.1.9.8). The Spearman correlation coefficient (Rho) threshold for strong correlation between ADC target pairs was set at 0.6069. Since wCorr does not calculate evidence of statistical significance for Rho, permutation tests with 10,000 iterations were applied to assess this evidence. Regular Spearman rank correlation was performed to report the degree of correlation (i) between target gene expression and tumor cellularity and (ii) between normalized gene expression in primary untreated tumors in TCGA and UPTIDER cohorts. This was done using the base R stats package (v4.3.0). To accurately report ER% for each unique metastasis linked to multiple metastatic lesions collected from the site of metastasis, we applied a recently published methodology70 that used a weighted average to attribute the ER% evaluated on multiple tumor samples to each unique metastasis. We then assigned these scores to all linked metastatic lesion samples that underwent bulk mRNA sequencing. Associations between gene expression and independent covariate (sample status: metastases vs primary untreated tumors, metastases of interest vs all metastases, ER+ vs ER− metastases, ILC vs invasive breast carcinoma of no special type (IBC-NST) metastases) were assessed by linear mixed quantile regression with random effect on patient ID using the lqmm package (v.1.5.8)71. p values were derived from Wald’s test, and a correction for multiple testing was applied using the Benjamini & Hochberg procedure to control for false discovery rate (FDR). To expand the scope of hypothesis-based target detection in the cohort, evidence of statistical significance was reported for adjusted p values (FDR) < 0.10, and a trend was reported between 0.10 and 0.15. Global median target expression in normal samples across organ sites was reported using boxplots by calculating the median expression of each target at each organ site. All metastases and normal tissues, irrespective of organ site, were evaluated to compute median expression change to identify targets that had higher median levels in either metastases or normal tissues. Following this, the percentage of gene expression overlap between metastases and normal tissues was calculated by x: [Min(Q3met, Q3normal) − Max(Q1met, Q1normal)] / [(Q3normal − Q1normal)] × 100; with Q3=3rd quartile of gene expression, Q1=1st quartile of gene expression. No formal association test was performed between metastatic and normal samples. Z-scores were calculated using mean absolute deviation on median gene expression values per normal tissue to identify tissues having preferential target expression/targets displaying preferential expression across tissues. Statistical analysis of the repeated sampling experiment was performed as described in our earlier paper24. In brief, the ssPMI was defined as the time between the death of the patient and the start of sample fixation in hours. Associations between gene expression and ssPMI were assessed by linear regressions for longitudinal data, with gene expression scores as the dependent variable, time as the independent variable, and accounting for the clustering of the data by patient and organ using the generalized estimating equation method using the glmtoolbox R package (v0.1.4). Three nested models—with constant, linear, and non-linear relationships—were compared using an ANOVA testing strategy, and the best model was retained. Non-linearity was rendered by a restricted cubic spline with three knots. Mann–Whitney U-test was used to report evidence of statistical difference in % of positively stained cells across 8 targets of interest, between metastases and normal tissue. As only female patients were included and data on gender were not collected, no analysis based on sex/gender was performed. All reported values have been rounded to the nearest 2 digits after the decimal.

Ethical considerations
Written informed consent was provided by all patients. The study was approved by the ethics committee/institutional review board of University Hospital Leuven, Belgium (November 30th, 2020, S64410) and conducted in accordance with the Declaration of Helsinki.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors investigate expression of antibody-drug conjugate targets in breast cancer metastases and normal tissue. They use RNA sequencing to investigate the expression of 72 ADC targets, which are clinically approved or 37 under investigation, in 916 samples (64 primary untreated, 753 metastatic, 99 normal) from 30 patients enrolled in a post-mortem tissue donation program.\n\nThe introduction is somewhat lengthy, the detailed characterisations of SG and T-DXd are probably not needed.\nFrom a methodological point of view the use of a large number of different samples from various tumor manifestations and normal tissues is a strength enabled by the postmortem biobank. However, how stable are mRNA expression analyses in post-mortem samples. This should at least be critically discussed and explained. Another problem is the use of only one methodology. While the authors point out in the discussion that correlation with a complementary methods such as immunohistochemistry in the entire cohort would be very resource consuming, IHC validation for select ADC targets of interest would strengthen the manuscript, especially as some results do not seem to reflect prior results, e.g.: HER2 and HER3 are described to be decreased in expression in brain metastases as compared to extra-cerebral tumor manifestations in this manuscript. That seems to contradict prior reports on these molecules. It that a methodological problem? Or how can this be explained?\n\nMinor point: The discussion of T-DXd for brain metastases of HER2-positive breast cancer omits the pivotal trials (done before DESTINYBreast12) DEBBRAH and TUXEDO-1.\n\nOverall, the study is descriptive and relies on only one methodology, which limits the robustness of the results. The presentation and discussion of the data is diffuse and does not achieve to deliver a clear message - can the authors draw any more concrete conclusions than just saying their data may help to inform drug development? Selection of top priority ADC targets and vaidation of those with a complementary method that can also facilitate clinical use (e.g. IHC) may help to make the work more relevant.", "reviewer_2": "This manuscript presents an extensive analysis of RNA expression levels for 72 ADC targets across a large cohort of metastatic breast cancer samples, primary untreated tumors, and normal tissues. Leveraging post-mortem tissue donation, the authors provide insights into inter- and intra-patient heterogeneity, organ-specific expression patterns, and co-expression relationships between ADC targets. This study fills an important gap in our understanding of ADC target prevalence in metastatic breast cancer and contributes valuable data that could inform future ADC development.\n\nRegarding the strengths of the manuscript, the study analyzes 916 breast cancer-relevant samples collected from 30 patients, including a large number of metastatic lesions from diverse organ sites, which enhances the generalizability of the findings. The inclusion of normal tissues allows for an assessment of potential on-target off-tumor toxicities, a critical factor in ADC development. The updated clinical trials search methodology ensures that the study reflects the most current ADC targets under investigation. The authors use modern and validated techniques for RNA extraction, sequencing, and normalization, with validation through external datasets (TCGA), strengthening the reliability of findings. There is definite potential for clinical impact: results provide useful insights into which ADC targets are most promising for breast cancer metastases, an exploration of organ-specific trends in ADC target expression, as well as the feasibility of bispecific ADCs or ADC combinations.\n\nWe have a few suggestions to further improve the manuscript\n\nMajor Comments\n1.Biological and Clinical Relevance of RNA Expression Levels\n- While mRNA expression provides valuable insights, protein expression levels are a more accurate representation of ADC target availability. The manuscript acknowledges this limitation but does not provide a clear rationale for why RNA-based analysis alone is sufficient to draw conclusions about target suitability.\n- Since HER2 protein expression often differs from mRNA levels, can the authors provide any validation for RNA-protein correlation beyond the Bosi et al. reference? Even targeted immunohistochemistry (IHC) or proteomic data for a subset of samples would strengthen the conclusions.\n- Based on the study results, HER2 does not appear as an ideal target. However, empiric experience shows that HER2 represents one of the most successful ADC targets in oncology. Could the authors try to address this apparent contradiction in the discussion?\n2.Cohort Characteristics and Potential Biases\n- The cohort is heavily skewed toward HR+/HER2- disease (77%), with only 20% TNBC and 3% HER2+ cases. This imbalance raises concerns about how generalizable the findings are, particularly regarding HER2 and TROP2, which are critical in TNBC and HER2+ subtypes.\n- The study does not account for potential prior treatment effects on target expression. Although only 4 patients received prior ADCs, other systemic therapies could have influenced target expression, particularly for immune-related targets (e.g., PD-L1). Were there any differences in expression patterns between patients who received prior ADCs and those who did not? What about prior immune therapy in the metastatic setting?\n3.Statistical Considerations\n- The manuscript states RNA was extracted from some fresh frozen and some FFPE samples – was there correction for this batch effect?\n- The choice of p-value threshold (adjusted p < 0.10, trend p < 0.15) is relatively lenient compared to conventional thresholds. Given the large number of comparisons made, false positives could be a concern. Did the authors explore stricter thresholds or alternative statistical approaches to reduce false discovery rates?\n- The weighted Spearman correlation analysis is a useful approach, but the lack of statistical significance values in the wCorr package is a limitation. Did the authors validate the results using a secondary method (e.g., permutation tests)?\n4.Interpretation of Co-Expression Findings\n- The observed co-expression and mutual exclusivity of ADC targets in metastases provide useful insights for ADC combinations, but the correlation coefficients are moderate (mostly R 0.5–0.6). While the authors suggest that certain targets could be considered for bispecific ADCs, is there any supporting evidence (e.g., known protein-protein interactions or prior co-expression studies) that would justify prioritizing these combinations?\n5.Besides target expression, key features of an optimal ADC target include surface localization and adequate internalization. This aspect could be better highlighted in the article.\n\nMinor Comments\n1.Figures\n- Figure 6 (normal vs. metastatic tissue expression) is important for understanding potential ADC toxicities but does not clearly indicate statistical significance in overlap calculations. Adding p-values or confidence intervals for overlap scores would improve interpretability.\n2.Manuscript text\n- The manuscript text should be updated to include the FDA approval of datopotamab deruxtecan for metastatic breast cancer (such as in the introduction and discussion).\n- The discussion suggests that FN1 and MUC1 are poor ADC targets due to high intra-patient heterogeneity and overlap with normal tissue expression. However, high intra-patient heterogeneity could suggest a need for biomarker stratification rather than complete dismissal of these targets.\n- Some terminology should be more precise. For example, \"significantly increased expression\" in metastases compared to normal tissue is sometimes used without specifying whether the difference is statistically significant or just numerically higher.\n3.Statistics\n- The use of Spearman correlation thresholds (R > 0.6) to define strong correlation is reasonable, but a sensitivity analysis showing how results change with different cutoffs (e.g., R > 0.5 or R > 0.7) would be informative.\n- The regression models used to compare metastatic and primary tumor expression do not explicitly state how confounding variables (e.g., subtype, prior treatment) were handled. If possible, including adjusted effect sizes would strengthen the analysis.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to the reviewers\nFor the manuscript by Kris3en Borremans, Anirudh Pabba, Gi:e Zels and collaborators\nen3tled: “Expression of an3body-drug conjugate targets in metasta3c breast cancer” with\nreference NCOMMS-24-85586.\nResponse to Reviewer #1\nThe introduc-on is somewhat lengthy, the detailed characterisa-ons of SG and T-DXd are\nprobably not needed.\nRE: We thank the reviewer for this sugges-on. The paragraphs concerning SG and T-DXd have\nbeen shortened considerably. We did not remove them completely as they illustrate the\nimportance of target expression. Of note, we have also added now in the introduc-on the 2\nADCs recently given fast track designa-on by the FDA for pa-ents with metasta-c TNBC.\nFrom a methodological point of view the use of a large number of different samples from\nvarious tumor manifesta-ons and normal -ssues is a strength enabled by the postmortem\nbiobank. However, how stable are mRNA expression analyses in post-mortem samples. This\nshould at least be cri-cally discussed and explained.\nRE: We have performed a -mepoint analysis to evaluate changes in mRNA expression on\nsamples from 14 pa-ents taken from the same normal or metasta-c sample at 1.5h intervals\nduring the post-mortem -ssue dona-on. The methodology has been described in our\nmethods (line 146-149). Sta-s-cal analysis was performed as described in our earlier work\n(Geukens, De Schepper, Van Den Bogaert, et al. NPJ Breast Cancer. 2024, PMID: 38658604). In\nthese samples, the expression of the targets we inves-gated in this manuscript did not vary\nsubstan-ally with -me (Supplementary figure 2A-K, Supplementary Table 5). While these\nresults did not uncover changes with increasing post-mortem interval, we have addressed the\nlimita-on of only assessing metasta-c samples at death within this study in the discussion.\nAnother problem is the use of only one methodology. While the authors point out in the\ndiscussion that correla-on with a complementary methods such as immunohistochemistry in\nthe en-re cohort would be very resource consuming, IHC valida-on for select ADC targets of\ninterest would strengthen the manuscript, especially as some results do not seem to reflect\nprior results, e.g.: HER2 and HER3 are described to be decreased in expression in brain\nmetastases as compared to extra-cerebral tumor manifesta-ons in this manuscript. That\nseems to contradict prior reports on these molecules. It that a methodological problem? Or\nhow can this be explained?\nRE: We understand the concern of the reviewer and have performed addi-onal exploratory\nimmunohistochemistry analyses to expand on our transcriptomic findings. This methodology\nhas now been added to the methods sec-ons regarding histopathological characteriza-on.\nEight targets of interest were selected for immunohistochemical staining of metasta-c and\nnormal samples: (i) targets of ADCs being approved for metasta-c BC (HER2, TROP2) or having\nreceived fast track designa-on (NECTIN4 and VTCN1), (ii) target with clinical poten-al in breast\ncancer based on high mRNA expression in metasta-c samples and low mRNA expression in\nnormal samples (HER3) and (iii) targets with high levels of mRNA expression in metastases but\nalso in normal -ssues, likely indica-ve of high risk for toxici-es (FN1, MUC1, FGFR2).\nmost common organ sites. A larger number of samples were stained for HER2, TROP2 and\nHER3, as these were already considered for a separate project on metasta-c TNBC. Regarding\nthe normal -ssue, 10 samples were taken across liver, lung and axillary lymph nodes. In\nmetasta-c samples only staining in tumor cells was considered, while in normal samples\nstaining in every type of cell was considered. Immunohistochemical assessment of HER2 and\nHER3 was done according to the ASCO/CAP guidelines for HER2 (PMID: 37284804). TROP2\nwas scored using the H-score, but for our analysis, only the total percentage of cells displaying\nmembranous and/or cytoplasmic staining was considered. Due to lack of guidelines, protein\nexpression of the other 5 targets in metasta-c samples was scored as a con-nuous variable,\n% of tumor cells, and in normal samples the other 5 targets and HER3 were assessed as the %\nof all normal cells that showed any level of staining regardless of intensity and cytological\nlocaliza-on.\nThe analysis of the immunohistochemistry data was mainly explora-ve given the rela-vely\nlimited number of samples. Our main goal was to compare the expression between\nmetastases and normal -ssue samples, to see whether the conclusions we made based on the\ntranscriptomic data were s-ll valid based on the preliminary immunohistochemical data. For\nthe 4 targets from clinically approved ADCs or ADCs having received fast-track designa-on by\nthe FDA (HER2, TROP2, NECTIN4 and VTCN1), we could confirm a significantly higher\nexpression in metastases as compared to the normal samples (Figure 7A). It should be noted\nthat for these 4 targets, the percentages of stained tumor cells ranged from 0 to 100%,\nhighligh-ng the heterogeneity in expression. While no staining was detected for HER2 and\nVTCN1 in the normal -ssues, variable percentages of cells expressing TROP2 or NECTIN4 were\ndetected in the normal -ssues. Regarding HER3, higher levels of stained cells were observed\nin normal -ssues as compared to the metastases, which showed very low levels of expression.\nRegarding the last 3 targets, we could not detect any difference in expression between normal\nand metasta-c samples for FN1 and FGFR2. MUC1 was more highly expressed in metastases\nas compared to normal -ssues but with variable percentages of cells expressing MUC1 in\nnormal -ssue samples.\nWhile these results should be interpreted with cau-on since only a propor-on of samples\nwere stained, they do validate what we observed in our transcriptomic data for 6 out of the 8\nmarkers regarding differences in expression in metasta-c versus normal samples. The results\nalso clearly demonstrate the wide range of protein expression observed for all markers in\nmetasta-c BC.\nConcerning the ques-on regarding the brain metastases, of the 13 brain metastases in the\ncohort, 6 were part of the staining cohort. Three metastases were scored as HER2-undetected,\n2 as HER2-low (HER2-2+, FISH nega-ve) and 1 as HER2-ultralow. This suggests that while we\ndo observe a lower mRNA expression in brain metastases, the level of HER2 expression might\ns-ll be sufficiently high at the protein level to achieve therapeu-c response with an--HER2\nADCs. Furthermore, although we have assembled a very large cohort of samples, conclusions\nbased on the mRNA inves-ga-on in 13 samples are s-ll exploratory and should be further\ninves-gated. Finally, aker re-analyses with our updated cohort (see sec-on “other” at the end\nof the rebunal), sta-s-cal evidence for lower expression of HER3 in brain metastases was no\n2\nlonger observed although the es-mate was s-ll favoring lower mRNA expression in brain\nmetastases.\nMinor point: The discussion of T-DXd for brain metastases of HER2-posi-ve breast cancer\nomits the pivotal trials (done before DESTINYBreast12) DEBBRAH and TUXEDO-1.\nRE: We thank Reviewer 1 for the sugges-on of adding these trials. We have added the details\nof the DEBBRAH and TUXEDO-1 trial to the discussion.\nOverall, the study is descrip-ve and relies on only one methodology, which limits the\nrobustness of the results. The presenta-on and discussion of the data is diffuse and does not\nachieve to deliver a clear message - can the authors draw any more concrete conclusions than\njust saying their data may help to inform drug development? Selec-on of top priority ADC\ntargets and valida-on of those with a complementary method that can also facilitate clinical\nuse (e.g. IHC) may help to make the work more relevant.\nRE: We thank Reviewer 1 for these relevant and cri-cal remarks. As men-oned above, we\nhave selected 8 targets including 5 top priority targets for further IHC tes-ng. The discussion\nwas thoroughly revised and restructured based on these addi-onal results.\nResponse to Reviewer #2 and #3\nMajor Comments\n1.Biological and Clinical Relevance of RNA Expression Levels\n- While mRNA expression provides valuable insights, protein expression levels are a more\naccurate representa-on of ADC target availability. The manuscript acknowledges this\nlimita-on but does not provide a clear ra-onale for why RNA-based analysis alone is sufficient\nto draw conclusions about target suitability.\n- Since HER2 protein expression oken differs from mRNA levels, can the authors provide any\nvalida-on for RNA-protein correla-on beyond the Bosi et al. reference? Even targeted\nimmunohistochemistry (IHC) or proteomic data for a subset of samples would strengthen the\nconclusions.\n- Based on the study results, HER2 does not appear as an ideal target. However, empiric\nexperience shows that HER2 represents one of the most successful ADC targets in oncology.\nCould the authors try to address this apparent contradic-on in the discussion?\nRE: We thank the reviewers for raising these concerns, which were similar to those raised by\nReviewer 1. In this regard we copy the answer provided to Reviewer 1 for your considera-on.\nTo answer the reviewers’ concerns, we have performed addi-onal exploratory\nimmunohistochemistry analyses to expand our transcriptomic findings. This methodology has\nnow been added to the methods sec-ons regarding histopathological characteriza-on. Eight\ntargets of interest were selected for immunohistochemical staining of metasta-c and normal\nsamples: (i) targets of ADCs being approved for metasta-c BC (HER2, TROP2) or having\nreceived fast track designa-on (NECTIN4 and VTCN1), (ii) target with clinical poten-al in breast\ncancer based on high mRNA expression in metasta-c samples and low mRNA expression in\nnormal samples (HER3) and (iii) targets with high levels of mRNA expression in metastases but\nalso in normal -ssues, likely indica-ve of high risk for toxici-es (FN1, MUC1, FGFR2).\nmost common organ sites. A larger number of samples were stained for HER2, TROP2 and\nHER3, as these were already considered for a separate project on metasta-c TNBC. Regarding\nthe normal -ssue, 10 samples were taken across liver, lung and axillary lymph nodes. In\nmetasta-c samples only staining in tumor cells was considered, while in normal samples\nstaining in every type of cell was considered. Immunohistochemical assessment of HER2 and\nHER3 was done according to the ASCO/CAP guidelines for HER2 (PMID: 37284804). TROP2\nwas scored using the H-score, but for our analysis, only the total percentage of cells displaying\nmembranous and/or cytoplasmic staining was considered. Due to lack of guidelines, protein\nexpression of the other 5 targets in metasta-c samples was scored as a con-nuous variable,\n% of tumor cells, and in normal samples the other 5 targets and HER3 were assessed as the %\nof all normal cells that showed any level of staining regardless of intensity and cytological\nlocaliza-on.\nThe analysis of the immunohistochemistry data was mainly explora-ve given the rela-vely\nlimited number of samples. Our main goal was to compare the expression between\nmetastases and normal -ssue samples, to see whether the conclusions we made based on the\ntranscriptomic data were s-ll valid based on the preliminary immunohistochemical data. For\nthe 4 targets from clinically approved ADCs or ADCs having received fast-track designa-on by\nthe FDA (HER2, TROP2, NECTIN4 and VTCN1), we could confirm a significantly higher\nexpression in metastases as compared to the normal samples (Figure 7A). It should be noted\nthat for these 4 targets, the percentages of stained tumor cells ranged from 0 to 100%,\nhighligh-ng the heterogeneity in expression. While no staining was detected for HER2 and\nVTCN1 in the normal -ssues, variable percentages of cells expressing TROP2 or NECTIN4 were\ndetected in the normal -ssues. Regarding HER3, higher levels of stained cells were observed\nin normal -ssues as compared to the metastases, which showed very low levels of expression.\nRegarding the last 3 targets, we could not detect any difference in expression between normal\nand metasta-c samples for FN1 and FGFR2. MUC1 was more highly expressed in metastases\nas compared to normal -ssues but with variable percentages of cells expressing MUC1 in\nnormal -ssue samples.\nWhile these results should be interpreted with cau-on since only a propor-on of samples\nwere stained, they do validate what we observed in our transcriptomic data for 6 out of the 8\nmarkers regarding differences in expression in metasta-c versus normal samples. The results\nalso clearly demonstrate the wide range of protein expression observed for all markers in\nmetasta-c BC.\nConcerning the ques-on regarding the brain metastases, of the 13 brain metastases in the\ncohort, 6 were part of the staining cohort. Three metastases were scored as HER2-undetected,\n2 as HER2-low (HER2-2+, FISH nega-ve) and 1 as HER2-ultralow. This suggests that while we\ndo observe a lower mRNA expression in brain metastases, the level of HER2 expression might\ns-ll be sufficiently high at the protein level to achieve therapeu-c response with an--HER2\nADCs. Furthermore, although we have assembled a very large cohort of samples, conclusions\nbased on the mRNA inves-ga-on in 13 samples are s-ll exploratory and should be further\ninves-gated. Finally, aker re-analyses with our updated cohort (see sec-on “other” at the end\nof the rebunal), sta-s-cal evidence for lower expression of HER3 in brain metastases was no\n4\nlonger observed although the es-mate was s-ll favoring lower mRNA expression in brain\nmetastases.\n2.Cohort Characteris-cs and Poten-al Biases\n- The cohort is heavily skewed toward HR+/HER2- disease (77%), with only 20% TNBC and 3%\nHER2+ cases. This imbalance raises concerns about how generalizable the findings are,\npar-cularly regarding HER2 and TROP2, which are cri-cal in TNBC and HER2+ subtypes.\nRE: We thank the reviewers for this remark. However, as the samples are from a post-mortem\n-ssue dona-on program, we are limited in the selec-on of pa-ents. Nonetheless, as an\nexploratory measure, we thoroughly compared the expression of ADC targets between the\nsubgroup with metastases from HR+/HER2- breast cancer and with metastases from triple\nnega-ve breast cancer and saw only very limited differences in target expression (Figure 3).\nBased on this, we have now added a cau-onary remark to the discussion about the poten-al\nlimita-ons on generalizability for target expression in pa-ents with TNBC.\n- The study does not account for poten-al prior treatment effects on target expression.\nAlthough only 4 pa-ents received prior ADCs, other systemic therapies could have influenced\ntarget expression, par-cularly for immune-related targets (e.g., PD-L1). Were there any\ndifferences in expression panerns between pa-ents who received prior ADCs and those who\ndid not? What about prior immune therapy in the metasta-c serng?\nRE: We agree with the reviewers that prior treatment effect could have a poten-al impact on\ntarget expression. As an exploratory measure, we compared the gene expression of targets in\npa-ents that received prior ADC and/or immunotherapy. The median expression of targets\nwas only higher in pa-ents who received prior an--HER2 or an--MUC1 ADC therapy\ncompared to pa-ents who did not receive ADC. However, since these pa-ents received\ntherapy in their early lines of treatment, we did not proceed with any formal tes-ng and have\ndecided not to report this is in the manuscript. We have added the suppor-ng violin plots as\na figure to this rebunal (Fig 1 rebunal).\nAs the number of pa-ents treated with T-DXd and SG is now increasing in Belgium since last\nsummer, we plan to repeat these analyses later in -me when we will have analyzed a higher\nnumber of pa-ents having received these ADCs.\npa-ent treated and untreated with an ADC specifically for this target. Boxplots represent the\nmedian expression of the target. Coloured dots represent the target expression per pa-ent as\nshown in the legend. Shapes represent samples belonging to primary untreated (circle) and\nmetastases (triangle).\n3.Sta-s-cal Considera-ons\n- The manuscript states RNA was extracted from some fresh frozen and some FFPE samples –\nwas there correc-on for this batch effect?\nRE: We thank the reviewer for this important point. While we do observe numerically higher\nreads assigned to FF-OCT samples compared to FFPE, this associa-on is not sta-s-cally\nsignificant as reported here by the regression model: Es-mate: -77616.99, 95%CI: -542500.18-\n387266.2, p-value=0.74. We therefore did not adjust for this variable in the downstream\nanalysis. The method sec-on now include these elements.\n- The choice of p-value threshold (adjusted p < 0.10, trend p < 0.15) is rela-vely lenient\ncompared to conven-onal thresholds. Given the large number of comparisons made, false\nposi-ves could be a concern. Did the authors explore stricter thresholds or alterna-ve\nsta-s-cal approaches to reduce false discovery rates?\nRE: We agree with the comment by the reviewers. We would like to confirm that to account\nfor mul-ple comparisons, we applied the Benjamini-Hochberg procedure to control the false\ndiscovery rate (FDR). This has now been clarified in the methods. As such, since the nature of\nour work is exploratory, we believe an adjusted p-value of <0.1 is an acceptable threshold to\nhelp suggest targets for future hypotheses and valida-on. This threshold is also based on gene\nset enrichment analysis tools such as DESeq2, GSEA, GSVA which recommend FDR <0.1 for the\n6\nmost op-mal results to detect gene and gene set enrichment in hypothesis-genera-ng studies\n(PMID: 23323831, 16199517, 25516281). Addi-onal studies in breast cancer have also\nexperimented with FDR ranging between 0.05 and 0.2 based on the nature of their\ninves-ga-on. PMID: 39090342, 31665365, 36609566). For transparency, both raw and\nadjusted p-values are provided in Supplementary Table 9, enabling readers to apply custom\nsignificance thresholds as needed.\n- The weighted Spearman correla-on analysis is a useful approach, but the lack of sta-s-cal\nsignificance values in the wCorr package is a limita-on. Did the authors validate the results\nusing a secondary method (e.g., permuta-on tests)?\nRE: We agree with this comment. We have hence validated the correla-on values by\nperforming permuta-on tests with 10,000 itera-ons for each target pair and per subtype\nusing data described in Figure 5 and Supplementary Figure 6. The permuta-on reflected\nevidence of sta-s-cal significance for target pairs with correla-on coefficient >=0.6 (HER2-\nHER3, CEACAM5-CEACAM6, etc.) indica-ve of a true biological correla-on. These p-values\nwere also adjusted using the Benjamini-Hochberg procedure to reduce possibili-es for false\nposi-ves. This data has now been added to the manuscript and the figures and the\nsupplementary tables to complement the results.\n4.Interpreta-on of Co-Expression Findings\n- The observed co-expression and mutual exclusivity of ADC targets in metastases provide\nuseful insights for ADC combina-ons, but the correla-on coefficients are moderate (mostly R\n0.5–0.6). While the authors suggest that certain targets could be considered for bispecific\nADCs, is there any suppor-ng evidence (e.g., known protein-protein interac-ons or prior co-\nexpression studies) that would jus-fy priori-zing these combina-ons?\nRE: We thank the reviewers for this remark. We looked into combina-on with interes-ng\ntargets as iden-fied by our research. To compile this shortlist, we removed targets with no to\nminimal expression in metasta-c samples (10 targets). Next, we removed those targets with\nhigher median expression in normal samples as opposed to metasta-c samples which\nremoved 30 targets. PD-L1 was readded as is some-mes used as immune checkpoint inhibitor\nin combina-on with an ADC and not only in a bispecific ADC. Lastly, all targets with 50% or\nhigher overlap of expression between the 3rd and 1st quar-le between normal and metasta-c\nsamples were removed except for HER2. The 9 targets selected as “op-mal” were: CD276,\nCEACAM6, HER2, HER3, NECTIN4, PD-L1, PTK7, TROP2 and VTCN1. The combina-on HER2-\nHER3 was the only one with a strong correla-on (0.6). Zenocutuzumab is a HER2 x HER3\nbispecific an-body used in the treatment of pancrea-c adenocarcinomas and non-small-cell\ncell\nlung cancers (NSCLC) carrying NRG1 gene fusions. Zenocutuzumab in combina-on with\ntrastuzumab +- vinorelbine or with endocrine therapy has been explored in one breast cancer\nspecific trial (NCT NCT03321981). Only limited results, which do show poten-al, have been\nposted. Bispecific ADCs combining HER2 and HER3 are s-ll in the preclinical phase.\nIn our manuscript, we have now highlighted combina-ons with a strong nega-ve or posi-ve\ncorrela-on exis-ng of two targets based on our analysis of mRNA expression in metasta-c and\nnormal samples (CD276, HER2, HER3, NECTIN4, SLC39A6, TROP2, VTCN1). This includes the\ncombina-on of HER2-HER3 men-oned above. As our manuscript already has a considerable\nlength and the development of bispecific ADCs is dependent on more than only co-expression\n7\n(PMID: 38799638) and as such our data only exploratory (PMID: 38799638), we did not\nexpand our discussion.\n5.Besides target expression, key features of an op-mal ADC target include surface localiza-on\nand adequate internaliza-on. This aspect could be bener highlighted in the ar-cle.\nRE: We agreed with this sugges-on and have highlighted the importance of surface\nlocaliza-on and adequate internaliza-on to the introduc-on.\nMinor Comments\n1.Figures\n- Figure 6 (normal vs. metasta-c -ssue expression) is important for understanding poten-al\nADC toxici-es but does not clearly indicate sta-s-cal significance in overlap calcula-ons.\nAdding p-values or confidence intervals for overlap scores would improve interpretability.\nRE: We thank the reviewer for the comment. We have conducted permuta-on tests with\n10,000 itera-ons to determine the p-values for the overlaps. These p-values are now\npresented in Supplementary table 9, and the Methods sec-on has been updated to describe\nthe procedure in detail. Although none of the tests reached sta-s-cal significance given the\ndistribu-ons, the overlap scores provided a ra-onale for selec-ng poten-al targets for further\nprotein analysis.\n2.Manuscript text\n- The manuscript text should be updated to include the FDA approval of datopotamab\nderuxtecan for metasta-c breast cancer (such as in the introduc-on and discussion).\nRE: Dapotamab deruxtecan was now added to the manuscript, specifically to the introduc-on\nand discussion.\n- The discussion suggests that FN1 and MUC1 are poor ADC targets due to high intra-pa-ent\nheterogeneity and overlap with normal -ssue expression. However, high intra-pa-ent\nheterogeneity could suggest a need for biomarker stra-fica-on rather than complete\ndismissal of these targets.\nRE: We thank the reviewers for this comment. We rather considered these targets of less\ninterest given their high expression in normal -ssue. High levels of expression were also seen\nat the protein level in our new analyses for all normal samples for FN1 and part of the normal\nsamples for MUC1. High intra-pa-ent heterogeneity is indeed less of a problem as it might be\ncircumvented by combining the targe-ng of this target with another an-body as a bispecific\nADC.\n- Some terminology should be more precise. For example, \"significantly increased expression\"\nin metastases compared to normal -ssue is some-mes used without specifying whether the\ndifference is sta-s-cally significant or just numerically higher.\nRE: We thank the reviewers for this remark. We have adapted our manuscript to be more\nprecise.\n3.Sta-s-cs\n- The use of Spearman correla-on thresholds (R > 0.6) to define strong correla-on is\nreasonable, but a sensi-vity analysis showing how results change with different cutoffs (e.g.,\nR > 0.5 or R > 0.7) would be informa-ve.\nsegments from -1 to 1 to visually iden-fy pairs at a custom threshold, and added p-values to\nthe analysis using permuta-on tests. Addi-onally, supplementary table 8 provides detailed\nrho and p-values, allowing for explora-on at custom thresholds.\n- The regression models used to compare metasta-c and primary tumor expression do not\nexplicitly state how confounding variables (e.g., subtype, prior treatment) were handled.\nIf possible, including adjusted effect sizes would strengthen the analysis.\nRE: The regression models were designed to accommodate heterogeneity by accoun-ng for\nclustering per pa-ent. Since pa-ents received different lines of various treatments prior to\n-ssue dona-on, treatment could not be added as a confounding variable. Regarding the\nsubtype, we have filtered the dataset for pa-ent subtype prior to regression between\nmetastases and untreated primary tumor per subtype. This enabled us to independently\nreport the effects of subtype on target expression as shown in Supplementary Figure 4B.\nOther\nCo-first author\nTo be able to do the requested immunohistochemistry analysis, Gine Zels, another PhD\nstudent of the lab and pathologist in training, performed substan-al addi-onal work.\nShe was added as addi-onal co-first author instead of second author. She was already in the previous\nauthor list at the same place. This is why we did not ask all authors to sign an author form.\nExclusion of samples\nAker thorough cross checks, we had to exclude 8 of our samples (7 metasta-c and 1 normal)\nfrom the cohort due to incomplete metadata annota-on and sample duplica-on during\nprocessing. The cohort and the corresponding results have been updated to reflect these\nchanges.\n9"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my points of criticism adequately.", "reviewer_2": "I have reviewed the authors’ revisions and responses to the previous comments. All concerns have been adequately addressed, and the manuscript has been significantly improved. I am satisfied with the current version and congratulate with the authors for the interesting study.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter