Cancer Immunotherapy and Beyond

Turning Computational Biology and New Data into Clinical Success

Our mechanistic approach aims to reveal ALL patient’s tumor-immune receptor-ligand inhibitory pairs (“brakes”) at every cancer indication, and predict response with minimal samples – aiming to enable successful trials, precise patient enrichment, and faster wins for immuno oncology.

THE PROBLEM

Immune Checkpoint Inhibitors 2025

The Backbone of Immunotherapy

• Cure is possible!
• Approved in ~20 cancers

• Global Market: $50bn (‘24). ‘Keytruda’ Alone: $29bn

• No efficient biomarkers → hard to match drug & patient

• 70-80% of patients don’t respond:

• 96% of clinical trials fail

Tumor complexity exceeds current technologies - a new approach is required

WHY IT HAPPENS

Immune Checkpoint Inhibitors (ICI)

Let the immune system win without toxic chemicals

ICI success depends on decoding these interactions →
to match the right drug to the right patient in the right indication

What’s missing:

Full understanding of ALL actual receptor-ligand interactions (“brakes”) across immune, tumor, and stroma.

Current correlative biomarkers (PD L1, TMB) and scRNA-Seq cannot see this.

THIS IS EXACTLY WHAT WE DO

For each patient, we map and score all receptor-ligand inhibitory interactions → Revealing which ‘brakes’ are active - and need blocking

ICI and Targeted Therapy* treatments
We analyze 30+ cell types, with unmatched depth and accuracy

* mAbs & Small Molecules

'NetwoRx Score' - The Patient Level

Scoring Interactions to Predict Response

Illustration of receptor - ligand pair e.g. PD-1/PD-L1

Patient 1

Weak communication → Low ‘NetwoRx Score’ → Lower therapeutic potential for this pair

NetwoRx Score: 4

Patient 2

Intensive communication → High ‘NetwoRx Score’ → Higher therapeutic potential for this pair

NetwoRx Score: 12

We Uniquely Explore

the Immuno-Tumor Interactions

01

We integrate unique data of over 30 immune subsets, stroma, and tumor cells – an exceptionally reproducible data, including cell frequencies and deep transcriptomes of all physically sorted immune, tumor and stroma cells

02

We employ unique computational tools to map and score communication networks formed between these cells

03

We generate ‘NetwoRx Map Score’ per each molecular interaction (e.g. the PD1-PDL1) per each patient which provides unprecedented depth into understanding the immune communication within tumors

04

Initial data show a strong correlation between the ‘NetwoRx Map Score’ and ICI drug effectiveness, suggesting it can predict responses to specific drugs and combinations — both for individual patients and cancer types.

We Turn correlation into causality

Replace scRNA-Seq's inaccuracy with accurate insights and successful decisions.

• Mechanistic TME mapping: We quantify and score all receptor-ligand pairs that govern response to ICIs and targeted immuno therapies.

• Deep, high-resolution, reproducible transcriptomics

• Minimal patient cohorts, maximal signal: Predictive power with very few patients/samples.

• All checkpoints: Works across tumor types and drug classes, much beyond PD-1/PD-L1.

• Pan cancer, indication-agnostic biomarkers

• Detects rare but critical cells (<1 in 10,000)

• Independent analytics from fresh samples

WHAT PARTNERS GET

The Value of Our Platform for Drug Development

Indications
Real-world (failed) trials marked in red.
Graph includes the following indications:
Melanoma, Gastric, GBM, HCC, Colon, Bladder & RCC

Why netwoRx is Different

• Mechanistic, causal approach – not black-box “feature-to-outcome” correlations.
• Indication-agnostic biomarkers
• All checkpoint coverage (beyond PD-(L)1)
• Deep, high-resolution, reproducible transcriptomics
• Detection of rare but critical cells (<1/10,000)
• Prospective, proprietary data – generated by NetwoRx
• Translatable biomarkers – designed for IHC/Flow-based assays
• Outperforming single-cell RNA-seq in gene coverage, data consistency, accuracy, and detection of rare but critical cells.

Early clinical signal: Our initial clinical results show we are in the right direction. We will be happy to share them in a meeting.

Leading Team

Ori Choshen

Chairman, co-founder,

board member

CEO of VLX Ventures – founded 10+ life science companies

Ilan Volovitz, PH.D.

Chief Scientific Officer

Head of Cancer Immunotherapy lab, The Neurosurgery dep., Tel-Aviv Sourasky MC

Prof. Ravit Geva, M.D.

Chief Medical Officer

Head of the oncology clinical research unit, Deputy director – Oncology div., Tel-Aviv Sourasky MC

Kelly Lipczyc

Head of Lab

Experienced in tissue processing and cell sorting; manages lab and data generation

Efrat Elis, PH.D.

Head of Computation

Experienced in computational biology and data science; leads model development

Board Members

Dr. Anat Cohen-Dayag, PH.D.

Board member

Compugen President & CEO
(NSDQ: CGEN)