New AI Framework ‘OncoMark’ Decodes Cancer’s Molecular Signatures with Precision

For decades, oncology has relied heavily on staging methods such as the TNM system, which evaluates tumors based on their size (T), spread to lymph nodes (N), and metastasis (M).

A groundbreaking study has unveiled a powerful artificial intelligence framework that promises to transform the way scientists understand, classify, and potentially treat cancer. Developed by researchers at the S N Bose National Centre for Basic Sciences in collaboration with Ashoka University, this new AI system—named OncoMark—shifts the focus from traditional tumor staging to the underlying molecular programs that truly drive cancer progression.

For decades, oncology has relied heavily on staging methods such as the TNM system, which evaluates tumors based on their size (T), spread to lymph nodes (N), and metastasis (M). While useful, these systems often fail to capture the intricate biological forces fueling cancer. As a result, two patients with identical tumor stages can still face dramatically different outcomes. OncoMark aims to bridge this gap by reading cancer at a far deeper level—its molecular “personality.”

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Understanding Cancer Through Its Hallmarks: A New Diagnostic Lens

Cancer is propelled by a series of biological programs known as the hallmarks of cancer. These hallmarks describe how normal cells turn malignant—by gaining unchecked growth, evading the immune system, resisting therapy, spreading to distant organs, and more. Traditional clinical tools rarely measure hallmark activity directly.

With OncoMark, scientists have developed the first AI framework capable of decoding these hallmark-driven molecular patterns inside tumors. Instead of simply assessing what cancer looks like on scans or under a microscope, OncoMark analyzes the hidden biological signals that determine how the cancer behaves.

Dr. Shubhasis Haldar and Dr. Debayan Gupta, the study’s lead authors, explain that understanding hallmark activity offers clinicians a far more accurate picture of how dangerous a tumor is and how it is likely to respond to treatment.

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A Massive Dataset: 3.1 Million Cells, 14 Cancer Types, One Unified AI

Creating such an advanced AI model required an unprecedented amount of cellular data. The team fed OncoMark 3.1 million single cells from 14 different cancer types, allowing the neural network to learn the subtle molecular variations that define hallmark behaviors across cancers.

Using this rich dataset, the researchers generated synthetic “pseudo-biopsies”—digital constructions that mimic real tumor samples but represent specific hallmark-driven states. These virtual biopsies empowered OncoMark to understand how different biological programs interact:

• Immune evasion pathways

• Metastatic potential

• Genomic instability

• Resistance to therapy

• Uncontrolled proliferation

Over time, the AI learned to map these hallmark interactions with extraordinary precision.

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Accuracy Above 99%: Rigorous Testing Demonstrates Global Applicability

In internal evaluations, OncoMark exceeded all expectations, achieving over 99% accuracy in predicting hallmark patterns. The framework was then tested across five independent cohorts, maintaining above 96% accuracy—demonstrating remarkable robustness.

The researchers further validated the model using 20,000 real-world patient tumor samples sourced from eight global datasets. This cross-cohort validation confirms that OncoMark is not restricted to one type of cancer or dataset; it is potentially applicable across clinical scenarios worldwide.

For the first time, scientists could visually track how hallmark activity increases as cancer advances, offering unprecedented insight into disease progression.

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Transforming Cancer Diagnosis and Treatment Planning

The implications of OncoMark are profound. The framework offers clinicians:

1. Personalized Hallmark Profiling

Doctors can see which hallmarks are active in a patient’s tumor—revealing the biological processes that need to be targeted.

2. Better Drug Matching

Once hallmark activity is known, therapies can be chosen that specifically disrupt those malignant pathways.

3. Early Identification of Aggressive Cancers

OncoMark can detect tumors that appear low-risk in traditional staging but display high hallmark activity—allowing earlier, more proactive intervention.

4. Improved Clinical Decision-Making

The molecular information provided by OncoMark supports more accurate prognosis, treatment selection, and monitoring of therapeutic response.

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An AI-Powered Future for Precision Oncology

The study, published in Communications Biology (Nature Publishing Group), marks a major stride toward precision oncology, where treatment is driven not by broad classifications but by the unique biological blueprint of each tumor.

As AI-powered frameworks like OncoMark integrate with clinical workflows, oncologists anticipate a future where:

• Cancer diagnosis becomes more molecularly precise

• Therapies are uniquely tailored to individual patients

• Early detection of aggressive cancers improves survival

• Treatment resistance can be predicted before it occurs

By unveiling the molecular “mind” of cancer, OncoMark opens the door to a new era where AI does not simply assist medicine—it reshapes its very foundations.