# Development of a lung cancer subtyping diagnostic

> **NIH NIH R44** · ORBIT GENOMICS, INC. · 2024 · $999,554

## Abstract

DEVELOPMENT OF A LUNG-CANCER SUBTYPING DIAGNOSTIC
Abstract
The overall goal of the proposed project will be to create a high sensitivity, high specificity subtyping test for lung
cancer. Lung cancer subtypes differ significantly in pathology, metastatic potential, therapy selection and
response. Lung cancer subtyping is critical for selecting the most effective treatment and thus reducing overall
mortality. This subtyping capability will be added to Orbit Genomics’ first lung cancer diagnostic test that is an
aid-to-diagnosis test for positive LDCTs with indeterminate pulmonary nodules. It distinguishes benign from
malignant pulmonary nodules with a prototype efficacy of 92% Positive Predictive Value (PPV) and 97%
Negative Predictive Value (NPV). Subtyping capability will markedly improve the value of the existing test which
is in validation trials now using samples from Drs. Yankelevitz and Henschke at the Icahn School of Medicine at
Mt. Sinai and is on a course to be launched as a Lab Developed Test (LDT) next year. Our first diagnostic test
will supplement LDCT imaging to achieve unprecedented overall diagnostic accuracies and should prevent
500,000 unnecessary biopsies in the US annually, saving $6 billion in procedural costs. The lung cancer
subtyping diagnostic test will expand the use of the test to all pulmonary nodules while providing
additional information needed for treatment, further reducing the need for biopsies and improving
patient outcomes while saving money. Clinical samples from Mt. Sinai with pathology determined lung cancer
subtypes were used to identify statistically significant subtype informative loci and subtype classification in
preliminary experiments. In previous publications we showed that an earlier version of our approach could
provide brain cancer subtyping information. In order to bring this novel lung cancer subtyping diagnostic test to
the market we will need to obtain subtype training samples (n=700, patient blood samples) from our existing and
expanded network of clinical collaborators (SA1); to upgrade microsatellite allelotyper/genotypers, our
informative loci selection and data mapping-to-image-space algorithms and train our new, novel AI Image-based
classifier on the exome sequences of samples with known subtypes (small cell lung carcinoma (SCLC), non-
small cell lung carcinoma (NSCLC) and its further subtypes (Squamous cell carcinoma; Adenocarcinoma; Large
cell carcinoma)) (SA2). While creating this new version of the test we will iteratively optimize SA1-2 performance
and then validate it (SA3) on independently obtained, pathologically well-characterized, clinically diagnosed
patient blood samples (n=200) analyzed in Orbit Genomics’ CLIA laboratory. Such a subtype capable diagnostic
test will be launched as an LDT, and will provide fast, accurate results to treating clinicians, ultimately saving
lives, patient suffering and money.

## Key facts

- **NIH application ID:** 11007304
- **Project number:** 1R44CA291262-01A1
- **Recipient organization:** ORBIT GENOMICS, INC.
- **Principal Investigator:** HAROLD R GARNER
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $999,554
- **Award type:** 1
- **Project period:** 2024-09-18 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11007304

## Citation

> US National Institutes of Health, RePORTER application 11007304, Development of a lung cancer subtyping diagnostic (1R44CA291262-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11007304. Licensed CC0.

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