A Theragnostic AI Platform for Precision NSCLC Treatment
K2 Think powers a scalable platform to identify tumor-specific molecular networks, assess inhibitor effects, and predict outcomes before treatment begins.
Demo Video Presentation
The Problem: Critical Gaps in NSCLC Care
Target Users & Scope
For oncologists, researchers, and developers in the fight against NSCLC, a disease fueled by complex genetic mutations and molecular networks.
Critical Pain Points
Care is hindered by blind spots in druggable mutations, tumor resistance to inhibitors like osimertinib, and toxicity from off-target effects.
Importance & Urgency
NSCLC claims 1.8M lives annually. Patients demand personalized therapies tailored to their unique tumor networks, as every delay costs lives.
Current Limitations
Today’s tools analyze single genes, missing the dynamic interplay of mutations and tumor biology that truly drives patient outcomes.
What You'll Build: The K2 Think Platform
Core Offering
- Integrates patient, sample, and molecular data to decode each tumor's landscape.
- Analyzes mutations, gene expression, and copy number variations (CNVs).
- Identifies driver genes (e.g., EGFR, KRAS) and simulates inhibitor interactions.
- Predicts drug response, resistance risks, and optimal therapy combinations.
- Generates onco-specific safety alerts
User Flow
- Input: Clinicians input patient records, tumor samples, and the corresponding molecular profile data.
- Process: he K2 Think AI runs its analysis, storing predictions and insights.
- Output: The app displays actionable insights, including primary targets, predicted drug efficacy, resistance risks, and suggested therapy combinations.
Tech Stack
- Native iOS & Android app powered by Firestore (NoSQL).
- Secure user management with Firebase Authentication.
- k2 Think API integration
- Backend automation using Node.js & Objective-C Cloud Functions.
- Interactive visualizations generated directly from cloud collections.
Workflow
Tap to expand
Name: John Doe
Age: 63
Sex: Male
Diagnosis: NSCLC
Subtype: Adenocarcinoma
Notes: High-risk patient
Type: Tumor
Tissue: Lung
Stage: IIIA
Grade: Moderately differentiated
Notes: RNA-seq prepared
Molecular Profile ID: MP001
TP53: R273H
KRAS: G12C
TP53: 1.0
KRAS: 1.8
TP53: Normal
KRAS: Normal
Predicted Response: Sensitive
Inhibitors: Erlotinib, Osimertinib
TP53: Tumor Suppressor, R273H
Predicted Response: Unknown
Best Candidate: EGFR
Confidence Score: 0.87
Explore the Data
Tap a chip to view the data
Why K2 Think
K2 Think’s ultra-fast reasoning (≈2,000 tokens/sec) enables real-time genomic interpretation and interactive molecular modeling. Its superior mathematical and analytical precision empowers the system to decode complex oncogenic networks, simulate molecular interactions, and recommend optimal drug strategies — capabilities essential for advancing personalized NSCLC treatment beyond current computational limits.
Demo (MVP)
1. Data Input
The patient's molecular profile data is fed into the native iOS/Android app and securely linked to our Firestore (NoSQL) database.
2. K2 Think Analysis
Node.js Cloud Functions trigger the K2 Think API to analyze the profile, simulate drug responses, and predict resistance risks.
3. Actionable Results
Actionable therapeutic recommendations are generated in real-time and presented in the app, ready for clinical decision-making.
Real-Time Voice Analysis
Talk with k2 Think. Ask complex questions in natural language and receive immediate, audible responses from the K2 Think engine. Analyze molecular profiles, simulate treatments, and explore hypotheses, all through a fluid, lively conversation.
Listening...
Our Motivations
Prior Diagnosis
By analyzing complex molecular signals, K2 Think helps identify lung cancer subtypes earlier and more accurately, opening a crucial window for timely and more effective intervention.
Increase Treatment Effectiveness
Our analyses aim to increase treatment success rates. By predicting a tumor's response to different therapies, we can help oncologists select the most effective treatment plan, improving patient outcomes.
Building a Knowledge Base
Each analysis contributes to an interconnected and growing database. This resource becomes increasingly powerful, uncovering new patterns and insights that can benefit the entire oncology community.
Accelerating Cancer Research
Our platform serves as a powerful research tool, allowing scientists to virtually test hypotheses and analyze molecular interactions in seconds, dramatically reducing the time and cost of discovering new therapeutic targets.
Our Team
Zhasmin
Clinical and Pharmacology Research Lead
Aibarly
K2 Logic and Verification Engineer, Researcher, and Team Leader
Luther
Project Manager, Developer, Data Integration & Backend Engineer
Axel
UI/UX Designer, and Demo Engineer