April 16, 2020| By Kathryn Vanderlaag, PhD: Senior Director, Biology and Assay Development and Kamran Ali, PhD*: Head, R&D Platform

What is precision medicine?

Precision medicine is matching the right drug to the right patient at the right time.

In the world of oncology, precision medicine has been synonymous with genomics. Once genetic abnormalities are identified in a patient through cytogenetics and sequencing, an appropriate therapy can be identified that exploits this abnormality thought to be driving cancer growth. One of the best success stories for oncology precision medi- cine has been the genetic abnormality, Bcr-Abl translocation, where pieces of chromosome 9 and 22 break off to form a fused Bcr-Abl gene. The detection of this abnormality, a common feature in chronic myelogenous leukemia (CML) patients, has revolutionized treatment for this patient population and has led to a 10-year overall survival rate of 83%.

Limitations of genomics-based precision medicine.

Although there are some success stories for precision medicine, overall, this has been a challenging area in oncology. One recent review predicted that genomics-based precision medicine may only benefit 1.5% of cancer patients. There are several reasons to account for this very low number. After genetic testing, depending on the abnormality identified, there is not always a therapeutic option. In the NCI-MATCH Trial, a 739 patient trial in which genetic screening was used to identify actionable mutations for placement on a matched treatment arm, only 2.5% of patients were placed on a treatment arm (Letai et al. 2017). In addition, even if a targeted drug is available, patients usually have multiple genetic abnormalities and therefore targeting only one of these pathways is often not sufficient for a durable therapeutic response. Along those lines, it is also not always obvious which driver mutation should be targeted if multiple pathways are identified. Cancer is a very complex disease, which has been slowly forming for years, sometimes decades, and also has the ability to evolve over time in response to a therapy, so it is likely that a multi-faceted approach is necessary to effectively treat this multifarious disease.

Blood cancers in particular are highly diverse, with more than 200 chromosomal translocations and mutations identified in AML patients. Moreover, up to 86% of AML patients carry two or more driver mutations, and there is a long tail of patients with rare mutations. Because of this, most AML patients die from disease progression following relapse due in part by resistance and clonal evolution. Clonal evolution occurs when a cell(s) from the original cancer population survives the initial therapeutic regimen and gains additional mutations, selecting for cells that are more resistant to the initial therapy and subsequent therapies. To minimize clonal evolution and prevent relapse, personalized therapies should ideally target multiple subclones in a patient. A primary challenge of the genomics-based precision medicine approach is that patients are not matched to treatments with a full understanding of the underlying relationships between the diversity of mutations within their cancer, their effects as drivers of cancer progression, and the specific drugs that counter those mechanisms. This knowledge gap could be mitigated with functional approaches capable of identifying appropriate treatment solutions without the need to fully understand the underlying interplay of diverse biological mechanisms a priori.

What are the advantages of Notable’s functional precision medicine platform?

Notable has developed a functional precision medicine platform to complement genetic screening in the search to find the right drug for the right patients with blood cancers. Notable’s platform allows for testing of large numbers of monotherapies or drug combinations in fresh patient samples, with a high-throughput flow cytometry readout that incorporates diverse phenotypes, including measures of apoptosis, proliferation, differentiation, and “stemness”, as well as immunotherapy targets.

Compared to solid tumors, blood cancers provide an excellent opportunity to use ex vivo systems to identify personalized therapies. First, the cancer cells are often abundant and accessible, so there is minimal need to expand and maintain cultures for an extended period of time. Serial sampling can also be accomplished to monitor

the activity of therapies and determine whether there is evolving acquired resistance in real time. Our platform minimally processes samples in an attempt to maintain conditions as close to the patient as possible. Each assay microenvironment is tuned to accurately reflect the sample’s specific disease (e.g., acute myelogenous leukemic, chronic lymphoid leukemia, etc.). In addition, microenviron- mental conditions can be tuned for each specific drug class (e.g. differentiation agents, immunotherapies, kinase inhibitors, etc.), as drug responses may be more or less pronounced under different specialized microenvironments. All assay parameters are optimized using this pipeline, including modified dosing concentrations and schedules.

Assay results are analyzed as a function of prospective clinical results and retrospective clinical data (samples with known patient outcomes, correlations with genomic signatures and correlations with clinical trial results). For an initial proof-of-concept study, Notable collaborated with Dr. Peter Greenberg of Stanford University in myelodysplastic syndrome (MDS). The study established a molecular tumor board to review individual patient data from a genomics plat- form and drug sensitive screening from Notable’s platform to identify potential therapeutic recommendations for MDS patients who had failed standard of care therapies. Fresh patient samples were screened for sensitivity to a list of up to 74 different available therapies using Notable’s platform. For patients who later received a subsequent therapy, and where the therapy was tested on the Notable platform, the clinical outcome was compared to the ex vivo drug sensitivity. The results demonstrated a positive predictive value (PPV) of 85% and a negative predictive value (NPV) of 83% (Aleshin et al. 2019). Additional collaborations are ongoing to build the predictive power, which allows the platform to adjust assay conditions to continuously “learn”, improving accuracy with each sample. These early, but promising results speak to the potential of this platform.

How can Notable’s platform apply to drug development?

Oncology drug development is a long, expensive, challenging process given that the majority of drug candidates fail for any number of reasons. One of the challenges is finding an appropriate model in the preclinical, early discovery period to build confidence in a novel molecule/pathway. Notable’s platform can allow for both phenotyping and drug sensitivity screening in primary patient samples to help understand whether there is a responsive patient population using a relevant model. The automated high throughput design facilitates testing drug candidates as both monotherapies and in combination with other drugs at scale across a range of primary patient samples to conduct virtual clinical trials. Compared to current in vitro cell culture and mouse xenograft models and PDX approaches, the speed, flexibility, and scalability of the Notable platform has the potential to speed preclinical decision-making and enable early patient selection strategies to streamline drug development in the clinic. Once in the clinic, the Notable platform has great potential to help get the right drug to the right patients – true precision medicine in practice.


  1. Letai A. Functional precision cancer medicine-moving beyond pure genomics. Nat Med. 2017;23(9):1028-1035.
  2. Aleshin A, Santaguida MT, Schaffert SA, Spinner MA, Sanders JN, Zehnder J, Patterson AS, Heiser D and PL Greenberg Ex Vivo Drug Sensitivity Profiling in Myelodysplastic Syndrome (MDS) Patients Defines Novel Drug Sensitivity Patterns for Predicting Clinical Therapeutic Outcomes. EHA Library. Aleshin A. 06/14/19; 267334; S133, 2019.