Rapid Genomic Profiling

AI Biomarkers for Lung Cancer

Detecting oncogenic driver mutations in NSCLC based on tumor tissue images

The Growing Need for Rapid Genomic Profiling

Lung cancer ranks among the most common cancers and is the leading cause of cancer death globally.

> 73%

of lung of of lung cancer patients are dying from this disease1
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> 36%

of lung cancer patients are getting adequate biomarker testing​2
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> 64%

of lung cancer patients fail to receive the appropriate treatment2
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Quick treatment is essential, but many cancer patients don’t get the right initial care.  

This is often because genomic profiling, which helps decide the best therapy, takes too long-usually 3 to 5 weeks. 

Non-small cell lung cancer (NSCLC) has many molecular subtypes, each needing different treatments. Recently, there has been a rapid increase in new targeted therapies for many of these subtypes, and this trend is expected to continue. 

Biomarkers recommended by the NCCN today
EGFR
NTRK1/2/3 
ALK
KRAS
ERBB2 (HER2 
ROS1
BRAF
RET 
METex14
skipping  
Emerging Biomarkers expected soon
STK11
BRAF non-V600
TP53
NRG1
KEAP1
BRAC1/2
TMB
ERBB2 ampl.
MAP2K1 
KRAS
(non-G12C)
PIK3CA
Pan-Ras
Identifying these targetable changes is essential for giving patients the best initial targeted therapy, if available. With existing and new targeted therapies, Rapid Genomic Profiling is urgently needed to guide patients to the right initial treatments3,4

Barriers to Comprehensive Genomic Profiling today

Long turnaround

Often takes 3-5 weeks, in which case results are not available for early  
treatment decisions.

Destructive 

Consumes extra tumor tissue, which is often scarce, especially in lung cancer. Leaves less material for further diagnostic testing

Costly

Expensive, limiting access  
for many patients  
and many  
healthcare systems 

Rapid AI-based Biomarker Profiling for NSCLC   

BioAI is developing a fast genomic screening solution using images of H&E-stained tumor tissue samples on microscope slides. These slides are standard in diagnostics and help pathologists determine if it’s cancer.  

If it is, quick action is needed. Pathologists then prioritize further tests to subtype the cancer, assess its aggressiveness, and analyze it molecularly through biomarkers.  

BioAI aims to enhance this final step of molecular subtyping. Using one digital H&E slide, BioAI can potentially read an entire biomarker panel, eliminating the need for extra tumor tissue. The whole process, from scanning to biomarker report, takes just a few hours.

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AI Biomarkers: reduce Genomic Profiling from Weeks to Hours

AI Biomarker Benefits

  • Rapid results

available to potentially inform early decisions

  • No additional tumor tissue required

image taken from standard tissue slide after biopsy

  • Lower cost per patient

providing easier access

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References 

  1. State of Lung Cancer 2023 report. American Lung Association.  
https://www.lung.org/getmedia/186786b6-18c3-46a9-a7e7-810f3ce4deda/SOLC-2023-Print-Report.pdf 
  2. Impact of Clinical Practice Gaps on the Implementation of Personalized Medicine in Advanced Non-Small-Cell Lung Cancer. JCO Precis Oncol. 2022 Oct 6: https://pubmed.ncbi.nlm.nih.gov/36315914/  
  3. Oncogenic alterations in advanced NSCLC: a molecular super-highway
    https://doi.org/10.1186/s40364-024-00566-0  
  4. Future perspective for the application of predictive biomarker testing in advanced stage non-small cell lung cancer  
https://doi.org/10.1016/j.lanepe.2024.100839 
  5. NCCN Clinical Practice Guidelines in NSCLC Oncology 
https://jnccn.org/view/journals/jnccn/22/4/article-p249.xml 
  6. An Ensemble AI Model for RET Alteration Detection Using H&E Images as a Putative Screening Tool for More Efficient Genomic Alteration Detection 
https://doi.org/10.1089/aipo.2024.0015