Biologics and antibody drug conjugates operate at the cell's surface, yet many programs still guide patient selection using RNA expression or whole cell protein abundance. That practice can misclassify candidates because transcripts and total protein often do not correlate, due to post-transcriptional and post-translational mechanisms. Moreover, cellular localization and receptor density can’t be quantified by using whole cell bulk proteomics, therefore not guaranteeing that a receptor is exposed on the exterior membrane above drug relevant thresholds. In 2024, investigators profiled the surfaceome of 100 genetically diverse, primary human AML specimens and resolved antigen patterns on primitive and stem like cells with limited expression in essential normal tissues. The work demonstrated that surface level heterogeneity is real, clinically meaningful, and different from what one would infer from bulk RNA or whole cell proteomics alone. Cell+1
Calls to better map the surfaceome are growing. A 2024 Cancer Discovery commentary noted that only a few dozen cell surface targets currently anchor FDA or EMA approved therapies, despite the surface being the most accessible compartment for antibodies, CAR T cells, and radiopharmaceuticals. The authors argued for intensified efforts to chart the universe of surface proteins across cancers, which would accelerate target discovery and improve translational relevance for modalities that require true surface accessibility. AACR Journals+1
Modern reviews describe how advances in mass spectrometry, fractionation, and enrichment are making it feasible to survey the cancer surfaceome at scale. These approaches improve the identification of drug accessible proteins that whole cell proteomics can miss, because abundant intracellular proteins dominate unfractionated measurements and mask low abundance, surface localized receptors. The reviews also outline practical ways to mitigate technical challenges such as low copy number, hydrophobic domains, and contamination by intracellular compartments, for example through careful membrane preparation, parallel intracellular and whole cell fractions, and stringent enrichment and quality control thresholds. The program level implication is straightforward. When a therapy acts at the surface, the biomarker strategy should resolve surface localization directly rather than infer it from RNA or whole cell protein alone. PubMed+1
A second implication concerns internalization and trafficking. For ADCs and some bispecifics, internalization kinetics and routing to lysosomes affect payload delivery and potency. Surface resolved workflows can be coupled to orthogonal assays, for example flow cytometry with ligand induced internalization or live cell imaging, to determine whether a receptor is not only present at the surface but also behaves in a way that supports the intended mechanism. This behavioral dimension rarely appears in transcript based selection but influences both efficacy and safety.
Turning surface heterogeneity into clinical signal requires a sequence of disciplined steps. First, measure what matters. Use fractionated surface proteomics or validated surface specific immunohistochemistry and flow assays that distinguish the exterior membrane from total abundance. Second, define actionable thresholds that tie expression to benefit and that can be reproduced across sites. Educational content from the ASCO Educational Book emphasizes that quantitative cutoffs and standardized assays are central to patient selection for antibody drug conjugates, because target accessibility and abundance determine benefit, and because inconsistent thresholds erode the interpretability of early phase studies. Third, map prevalence by histology and by clinically relevant subgroups, since an attractive target with low prevalence may not support enrollment or may require a targeted site strategy. When these elements are present, enrichment reflects how the drug actually works and not a proxy signal. ASCO Publications+1
Surface level diversity can also sharpen indication strategy. If a receptor is highly enriched at the surface in a subset of colorectal cancer but not in pancreatic cancer, a program can prioritize the former for first in human evaluation, even if RNA levels are similar in both. This is particularly relevant where surface and whole cell abundances diverge. The AML study noted above showed precisely this phenomenon, where antigen exposure on stem like compartments could be quantified and connected to therapeutic concepts that require surface binding. Cell
Surface heterogeneity intersects with safety because many candidate antigens have some expression in normal tissues. A 2024 review in Trends in Pharmacological Sciences surveyed strategies that increase antibody selectivity in oncology, including superselectivity through avidity and multivalency, conditional or pH sensitive binding, dual targeting that requires co expression to achieve high affinity, and engineering designs that leverage tissue context. These concepts translate directly into earlier program decisions. Targets that can be paired with selective engineering approaches should score higher than those that would require unrealistic discrimination from a conventional binder. A transparent scoring framework, for example one that penalizes normal tissue expression in essential organs and rewards tumor specific co expression patterns, makes those choices auditable during governance reviews. Cell+1
The safety argument is not theoretical. ADCs can cause off tumor toxicities when a payload is delivered to normal tissues that express the target at modest levels. A biomarker plan that quantifies true surface exposure in disease and screens for surface exposure in a curated panel of normal tissues improves the chance of a workable therapeutic window. Reviews that focus on ADC biomarkers, together with trial design guidance, point in the same direction, namely that selection should be quantitative and assayable, not an exploratory cutpoint chosen after the fact. ASCO Publications
Evidence that surface resolved discovery translates into programs is accumulating. In late 2024, a Cancer Cell study used an integrative proteogenomic surfaceome approach to credential DLK1 as an immunotherapeutic target in neuroblastoma. The authors combined mass spectrometry based surface profiling with genomic and transcriptomic context to demonstrate surface exposure, tumor specificity, and functional relevance, then moved to validation experiments that supported drug development. This work illustrates how surface level datasets, anchored in clinical material, can identify and qualify targets that a transcript only screen might underrate. PMC+3Cell+3PubMed+3
Surfaceome maps are also becoming more accessible, which will help teams generalize findings across institutions. The AML dataset is public in GEO, enabling independent inspection of QC criteria, antigen lists, and statistical methods. Commentary from Cancer Discovery underscores the systemic opportunity, arguing that better cartography of the surfaceome across cancers is likely to grow the small set of currently actionable surface targets. Together, public data and field level commentary support a move from opportunistic target picking toward systematic, population informed discovery. NCBI+1
Programs that embrace surface resolved selection can operationalize three habits that improve downstream signal. First, connect discovery assays to clinical screening early. If discovery uses a fractionated surface proteomics threshold, define the clinical assay that will mirror that readout, for example an IHC score or a validated flow protocol, and harmonize cutoffs before first patient in. Second, pre specify enrichment rules in the protocol. Enrolling only the top percentiles of surface expressors may seem restrictive, but it increases the chance of a pharmacodynamic signal that validates the mechanism and informs dose expansion. Third, measure what happens when selection criteria are varied in sensitivity analyses, and share these details in publications and regulatory interactions. This transparency builds cumulative knowledge that benefits the field and informs the next iteration of thresholds.
There is also value in linking surface metrics to pharmacology. If a surface antigen is abundant but shows slow internalization, a payload with a bystander effect may be preferable to a payload that requires rapid lysosomal routing. If surface expression is heterogeneous at the lesion level, a radionuclide therapy that can exploit crossfire may offer advantages over a conventional ADC. These are not general rules, they are examples of how a surface resolved view can shape the choice of modality and payload in a way that reflects the physical constraints of the target.
Champions Oncology generates surface resolved datasets from low passage, clinically relevant tumor models and integrates them with deep multi omic profiles and in vivo pharmacology. This enables teams to quantify true surface positivity, to set and test actionable thresholds, and to understand prevalence by indication before committing to costly trials. The same datasets support orthogonal validation and method standardization so that discovery assays translate into clinical screening. The approach is designed to be non promotional and data first, the objective is to clarify risk and to help programs make better decisions earlier.