DetermaIOTM is a proprietary gene expression test that assesses the tumor microenvironment. It is run in a CLIA/CAP-accredited laboratory and is available for use in immunotherapy biopharma trials.

Identifying responders to immunotherapies

As many as 44%

of all newly diagnosed patients with cancer are eligible for immuno-oncology testing and therapy.1

Unfortunately, with standard of care PD-L1 IHC testing, more than half of PD-L1 positive patients do not respond to immune checkpoint inhibitors, and

1 in 6

patients who will respond are missed.2

We need a

better way

to identify the patients that will best respond to immunotherapies and avoid the cost and potential adverse effects of ineffective treatment.

About

DetermaIO

DetermaIOTM is a 27-target multivariate gene expression test performed on FFPE biopsy specimens that measures the presence of subtypes of infiltrating inflammatory cells, and the presence or absence of a differentiated stromal microenvironment. DetermaIO’s proprietary algorithm combines mRNA gene expression data and interprets the physiology of both the tumor and its surrounding micro-environment in order to predict the response to immuno-oncology therapies. Data supporting a strong association with response to checkpoint inhibitors has been shown in non-small cell lung cancer (NSCLC).3 The assay is currently offered for pharma service research only as a CLIA-certified real time PCR LDT from our CAP-accredited laboratory, or as an in silico interpretation of whole transcriptome RNA data.

  • In patients with NSCLC treated with an immune checkpoint inhibitor, DetermaIO-identified responders (IM+) had significantly longer progression-free survival than DetermalIO-identified non-responders (IM-).3
  • In a study presented at the Society for Immunotherapy of Cancer (SITC) Annual Meeting, data suggested that DetermaIO outperformed PD-L1 in predicting immunotherapy responders as well as non-responders.3

See our

DetermaIO Performance

DetermaIO

for Research

Identify immune checkpoint inhibitor responders

Effective Results

Standard of care PD-L1 testing incorrectly identifies many patients as potential responders to immune checkpoint inhibitors, and misses certain other patients who may respond.2 Data from a recent study suggested that DetermaIO outperformed PD-L1 in identifying both responders and non-responders.3

Reduce Wasted Spend

Immuno-therapy can cost up to $300,000 per patient.4 Accurately identify the patients that are most likely to respond, reducing expensive drug costs for those that are unlikely to benefit.

Decrease Adverse Events

30% of patients experience a serious adverse event as a result of immune checkpoint inhibitor treatment.2 Avoid introducing potential adverse outcomes (including autoimmune disease) to patients for whom treatment is unlikely to be effective.

Nielsen TJ, Ring BZ, Seitz RS, Hout DR, Schweitzer BL. (2021) A novel immuno-oncology algorithm measuring tumor micrenvironment to predict response to immunotherapies. Heliyon Open 3:e06438 – View Article

Seitz RS, Nielsen TJ, Schweitzer BL, Gandara DR, Parikh M, Ross DT. (2021) Association with immune checkpoint inhibitor efficacy of a 27-gene classifier in renal cell cancer Poster presented at the American Society of Clinical Oncology Annual Meeting.  – View Abstract

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View Video of Explanation


Seitz RS, Ross DT, Nielsen TJ, Hout DR, Schweitzer BL. (2021) Validation of a 27-gene immuno-oncology algorithm in metastatic urothelial carcinoma treated with an immune checkpoint inhibitor.  Presented at the American Association for Cancer Research Annual Meeting.  – View Abstract

View Video of Presentation


Seitz RS, Nielsen TJ, Schweitzer BL, Hout DR, Ross DT. (2021) Pathway modeling to translate the 27-gene immuno-oncology algorithm into bladder cancer.  Poster presented at the American Association for Cancer Research Annual Meeting. – View Abstract

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View Video of Explanation


Nielsen, TJ, McMahon FB, Spille J, Hout DR, Dickenson K, et al. (2020)  Tissue Requirements of a Novel 27-Gene Immuno-Oncology Algorithm Measuring Tumor Microenvironment to Predict Response to Immunotherapies.  Poster Presented at the Association for Molecular Pathology Annual Meeting & Expo


Nielsen T, Seitz R, Ross D, Hout D, Schweitzer B. (2020, November) Mesenchymal features of a novel 27-gene algorithm associate with canonical tumor promoting signaling pathways which may identify therapeutic options for immunotherapy resistant patients. Poster presented at Society for Immunotherapy of Cancer (SITC) Annual Meeting. – View Abstract


Iwase T, Pusztai L, Blenman K, Li X, Seitz R, et al. (2020, May). Validation of an immunomodulatory gene signature algorithm to predict response to neoadjuvant immunochemotherapy in patients with primary triple-negative breast cancer. Journal of Clinical Oncology. 38 supplement: abstr 3117. – View Abstract

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Ranganath H, Jain A, Smith J, Ryder J, Chaudry A, et al. (2019). One-year progression-free survival in lung cancer patients treated with immune checkpoint inhibitors is significantly associated with a novel immunomodulatory signature but not PD-L1 staining. Poster presented at Society for Immunotherapy of Cancer (SITC) Annual Meeting, National Harbor, MD. – View Abstract

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References

1. Haslam and Prasad. (2019) Estimation of the percentage of US patients with cancer who are eligible for and respond to checkpoint inhibitor immunotherapy drugs. JAMA Network Open 2(5):e192535.
2. Khagi et al. (2017) Next generation predictive biomarkers for immune checkpoint inhibition. Cancer Metastasis Rev 36:179.
3. Hout et al. One-year progression-free survival in lung cancer patients treated with immune checkpoint inhibitors is significantly associated with a novel immunomodulatory signature but not PD-L1 staining. Poster presented at: Society for Immunotherapy of Cancer (SITC) Annual Meeting; Nov 6-10, 2019; National Harbor, Maryland.
4. Institute for Clinical and Economic Review. (2016) Treatment Options for Advanced Non-Small Cell Lung Cancer: Effectiveness, Value and Value-Based Price Benchmarks. Final Evidence Report and Meeting Summary. https://icer-review.org/wp-content/uploads/2016/10/MWCEPAC_NSCLC_Final_Evidence_Report_Meeting_Summary_110116.pdf Accessed October 2019.