Indication Brief · NSCLC
NSCLCHigh-Resolution Data for IO Response & Resistance
Spatial single-cell and paired histopathology across adenocarcinoma and squamous subtypes, with linked clinical outcomes
Predicting checkpoint inhibitor response in NSCLC remains an open problem because the answer lives in the tumor microenvironment, not in bulk transcriptomics or single-marker IHC. Subtype-specific biology between adenocarcinoma and squamous cell carcinoma compounds the challenge. Enable Medicine's NSCLC cohort provides high-plex spatial protein data, paired H&E, and harmonized clinical metadata at the scale and resolution required to build stratification models and discover new response biomarkers.
Data at a Glance
20%
With Clinical
Follow-Up
Sample Composition
Tumor 716
Matched Normal 82
Metastasis (lymph node) 47
Tumor-Adjacent Tissue 37
Tissue Site & Outcomes
Lung 835
Lymph Node 47
Representative Paired Data — Lung Adenocarcinoma
DAPICD31CD4ECADASMACD8CD14CD45
NSCLC · Multimodal Data for Translational Development
Why the Data Layer Matters for NSCLC
IO Response Prediction
Checkpoint inhibitor benefit is concentrated in patient subsets that single-marker PD-L1 IHC cannot resolve. Spatial proximity, cell-state, and TME composition signatures derived from high-plex mIF have demonstrated stronger predictive power and a path to CDx development.
Adeno vs. Squamous Resolution
Subtype-specific TME architecture drives differential response to standard-of-care and emerging combinations. High-plex multimodal data across both subtypes, at cohort scale, supports parallel biomarker programs and indication-specific stratification.
Computational Pathology Ground Truth
Paired mIF and H&E from the same tissue section provides expert-annotated training labels at scale for virtual staining, cell phenotyping, and spatial prediction models. The foundation AI data teams need to build NSCLC-specific models.
Case Study · Predictive Biomarker Development in NSCLC
Challenge
Identify a spatial-protein biomarker predictive of immune checkpoint inhibitor response in NSCLC, with a clear path from RUO to CDx.
Approach
High-plex spatial proteomic profiling on an annotated NSCLC cohort with clinical response data. Computational analysis to define candidate ROC-maximizing signatures. Validation on an independent commercial cohort.
Outcome
Defined a predictive biomarker combination (PTEN + CD4 spatial co-localization) associated with ICI response; validated in a repeat NSCLC cohort. Framework supports milestone-based CDx development partnerships.
Reference
Monkman et al., 2023
Protein Biomarker Panel · NSCLC Context
Panel covers current and next-generation immunotherapy axes alongside tumor, stromal, and lineage markers relevant to NSCLC subtype resolution.
Tumor / Epithelial
PanCK · ECad · EpCAM
T Cells
CD3e · CD4 · CD8 · FoxP3 · Granzyme B
Myeloid
CD68 · CD163 · CD11c · CD14
Checkpoint
PD-1 · PD-L1 · LAG3
Stromal
aSMA · Vimentin · Collagen IV
Clinical Metadata Coverage · %
High coverage
Enriched subset
Flexible Licensing Models
Full NSCLC Cohort
Complete access across adeno, squamous, and other subtypes.
Subtype-Specific
Adeno-only, squamous-only, or treatment-stratified cohort.
Custom Sub-Cohort
Response-matched, stage-matched, or PD-L1-stratified for biomarker development.
Custom cohort assembly and bespoke data generation are also available: from tissue sourcing through profiling and AI-ready data delivery. Available modalities include high-plex mIF, paired H&E, Visium, Xenium, and IHC.
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