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Circulating Tumor Cell Distribution and PD-L1 Expression Across Cancer Types; Insights from 5,935 Patients 

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Presented by Jayant Khandare, PhD | Actorius Innovations and Research | Publication Only: Developmental Therapeutics:- Immunotherapy 

The Largest CTC Study of Its Kind in the Indian Population 

India’s diverse genetic landscape and unique cancer incidence patterns make it one of the most important and most underrepresented populations in global oncology research. Common cancers in India span a wide spectrum: head and neck, lung, breast, colorectal, prostate, ovarian, and gastrointestinal cancers; and the clinical behaviour of these cancers in the Indian population is shaped by genetic, environmental, and demographic factors that are not fully captured by Western datasets. 

This retrospective study: one of the largest CTC analyses ever conducted in the Indian cancer patient population, sought to change that, analysing CTC distribution and PD-L1 expression across 5,935 patients to generate real-world insights that can directly inform personalised cancer management strategies. 

How We Did It 

All blood samples were processed using the CDSCO-approved OncoDiscover platform: analysing just 1.5 mL of peripheral blood per patient using a multifunctional magnetonanosystem mediated by anti-EpCAM antibody. CTCs were confirmed using a stringent four-marker validation protocol: EpCAM+ve, CK18+ve, DAPI+ve, and CD45-ve, ensuring only true epithelial-origin tumour cells were counted. 

PD-L1 expression on captured CTCs was evaluated using linear fluorescence intensity gradients and automated image acquisition on a Zeiss Microscope enabling precise, objective quantification of immune checkpoint marker expression at the single-cell level. 

An advanced computational model was also developed to evaluate CTC frequency, mean distribution, regression analysis, and normal probability plots, enabling predictive insights into CTC numbers across cancer types, patient age, disease stage, and gender. 

What the Data Revealed 

The findings from this landmark dataset carry significant clinical implications: 

  • CTC distribution ranged from 1–10 cells, with a mean CTC count of 1.12 across the full cohort 
  • A striking 69.87% of CTC-positive patients (n=2,854) showed PD-L1 expression on their CTCs;a finding with direct implications for immunotherapy eligibility and response prediction 
  • The 51–60 age group showed the highest proportion of both CTC-positive patients (19.16%) and PD-L1-positive CTCs (19.71%) identifying a critical surveillance window.  
  • Pancreatic cancer patients exhibited the highest mean CTC count (1.4), while laryngeal cancer had the lowest (0.78), reflecting tumour-type-specific patterns of haematogenous spread 
  • CTC clusters were most commonly identified in breast, colorectal, and endometrial cancers: consistent with the aggressive metastatic biology of these tumour types 
  • Higher CTC counts correlated strongly with advanced disease stages, particularly in cancers with high propensity for blood-borne spread, including breast, lung, and prostate cancers 
  • The computational model demonstrated strong correlation between blood-based CTC outcomes and normal probability scores, validating its utility as a predictive tool for clinical risk stratification 

Why This Matters 

This study makes a compelling case for the routine integration of CTC profiling from baseline into personalised cancer management. The detection of CTCs — particularly PD-L1-positive CTCs — provides oncologists with critical real-time intelligence about disease burden, therapy response, minimal residual disease, and early recurrence risk that conventional imaging and standard blood markers simply cannot deliver. 

For Indian patients specifically, this dataset establishes a vital, population-specific reference framework for CTC-based diagnostics — one that reflects the true genetic and clinical diversity of one of the world’s largest cancer patient populations. 

This is the power of liquid biopsy at scale — and it is at the heart of everything we build at 1Cell.ai. 

Published at the ASCO Annual Meeting 2025 | DOI: 10.1200/JCO.2025.43.16_suppl.e14540 

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