Real-time Cancer Profiling

PredictLung
AI Biomarkers for Lung Cancer*

Detecting oncogenic driver mutations and biomarkers in NSCLC based on tumor tissue images

The Growing Need for Rapid Biomarker Profiling

Lung cancer is one of the most prevalent malignancies and the leading cause of cancer-related mortality worldwide.

> 73%

of lung cancer patients
succumb to the disease 1
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only < 36%

of  patients are getting adequate biomarker testing​ 2

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> 64%

of lung cancer patients fail to
receive the appropriate treatment 2
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Quick treatment is essential, but many patients don’t get the right initial care. Most cancer patients wait weeks for treatment,
or start chemotherapy, because genomic profiling, which helps decide the best therapy, takes too long – usually 3 to 5 weeks.

Specifically, Non-small cell lung cancer (NSCLC) has multiple molecular subtypes, each with distinct different treatment pathways.
The number of targeted therapies for NSCLC subtypes continues to grow, and this trend is expected to persist.

Biomarkers recommended by the NCCN today
EGFR
NTRK1/2/3 
ALK
KRAS
ERBB2 (HER2 
ROS1
BRAF
RET 
METex14

Identifying these actionable alterations is essential for providing patients with optimal first-line targeted therapy.
With existing and new targeted therapies, rapid biomarker screening 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 

PredictLungTM: Rapid AI-driven Biomarker Profiling for NSCLC

PredictLung enables clinicians with a fast, cost-effective solution that can inform patient therapy in minutes 3,4.
Why is it cost-effective? At the time of diagnosis, every patient undergoes a biopsy, which is then processed and stained with Hematoxylin and Eosin (H&E) so pathologists can evaluate the cancer, its aggressiveness, and isolate the tumor region for downstream genomic testing. With PredictLung, we use the digital image of the H&E slide and apply our proprietary AI to identify which biomarkers are expressed.
This information can then be shared with the clinician to inform decisions in minutes – without having to consume further scarce tumor tissue and at a fraction of the cost.

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PredictLung: reduce Biomarker 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

AI biomarker panels can be developed and applied to other indications and tumor types, too, including breast, colorectal, and prostate cancer.

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*AI Biomarkers are in development.

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