Precision and Personalized Medicine in Immuno-Oncology


  • Addressing disease complexity and interpatient variability necessitates integrating precision and personalized medicine approach into cancer research and treatment.
  • About 44% of all newly diagnosed patients with cancer are eligible for immuno-oncology testing and therapy.
  • Single marker for example Standard of care PD-L1 incorrectly identifies many patients as potential responders to immune checkpoint inhibitors and misses certain other patients who may respond.
  • Immuno-therapy can cost up to $300,000 per patient. Accurately identifying the patients that are most likely to respond will reduce expensive drug costs for those that are unlikely to benefit.
  • 30% of patients experience a serious adverse event as a result of immune checkpoint inhibitor treatment. Avoid introducing potential adverse outcomes to patients for whom treatment is unlikely to be effective.


Immunotherapy, a rapidly emerging treatment for cancer utilizes the body’s immune system to prevent and eliminate its occurrence. In a majority of cancers, the tumor microenvironment leverages receptors and pathways to evade host defense mechanisms for survival. With promising outcomes in many cancer types such as melanoma and liver cancer, immunotherapy can function in several ways such as cancer prevention, slowing cancer spread/progression and stopping cancer growth, etc. Like any other treatment, immunotherapy also suffers from patient heterogeneity where different patients respond to treatment differently. Unlike other drugs, immunotherapy renders an expensive burden on treatment cost at around $300,000 per patient [1], which significantly reduces opportunities of multiple treatment trials. On the other hand, only limited standard of cancer tests like immunohistochemistry (IHC) based PD-L1 testing, CTLA4 testing are currently available for patient stratification to undergo immunotherapy [2]. However, cancer immunotherapy initiation upon single test outcomes like PD-L1/CTLA4, renders more than a half of patients non-responsive to immune checkpoint inhibitors. In addition, 1 in 6 patients who might respond to immunotherapy are missed with just single test-based treatment decisions [2]. As per studies, about 44% of all newly diagnosed cancer patients are eligible for immuno-oncology testing as well as immunotherapy [3]. With such high responsive rates, there is a need to implement more comprehensive and integrative approaches of utilizing genomics, transcriptomics, proteomics to explore deeper the events of tumor and its microenvironment in individual patients. With these combination strategies, it is thus possible to effectively stratify patients to identify best responders as well as avoid cost and adverse effects on ineffective treatments which constitutes ~30% of current immune checkpoint treatments [3, 4].


Integrated Correlation Engine (iCE)

iCE, a proprietary algorithm developed at iNDX.Ai, combines and investigates multiomics data such as genomics, transcriptomics, etc., to interpret the physiology and molecular events of tumor and its micro-environment. An easy-to-use intuitive interface, robust and reproducible analytical tool that complements biomarker discovery team and breaks down traditional barriers between computational and scientific persona looking at these datasets. Using this integrative approach, iCE aims to effectively stratify patients and predict treatment responses driven by multi-omics markers.


Benefits of iCE approach to cancer immunotherapies

  • Effective patient stratification (separating actual responders from mis-classification)
  • Improved accuracy and reliability with multiple confirmatory biomarkers
  • Reduction of treatment cost by minimizing treatment trials
  • Reduction of opportunities for potential adverse events among predicted non-responders



  1. Institute for Clinical and Economic Review. (2016) ICER Releases CORRECTED Evidence Report on Treatments for Non-Small Cell Lung Cancer.
  2. Khagi et al. (2017) Next generation predictive biomarkers for immune checkpoint inhibition. Cancer Metastasis Rev 36:179.
  3. 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.
  4. Jing, Y., Liu, J., Ye, Y., Pan, L., Deng, H., Wang, Y., Yang, Y., Diao, L., Lin, S.H., Mills, G.B. and Zhuang, G., 2020. Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy. Nature communications, 11(1), pp.1-7.
  5. Building the Foundation for Personalized Medicine – Trends Issue 182 – June 2018, Health Care accessed on 01/08/21


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