Nodality Showcases Novel, Proprietary Technology to Predict Response to Therapy and Relapse Risk in Cancer Patients
South San Francisco, CA, December 7, 2010Highlights of Presentations at ASH Meeting:
“Single Cell Network Profiling (SCNP) Demonstrates Success in Predicting Response to Therapy and Relapse Risk in Pediatric Patients with Acute Myeloid Leukemia (AML)”
“SCNP Signatures Independently Predict Response to Induction Therapy in Older Patients with Acute Myeloid Leukemia (AML)”
Nodality, Inc., developing highly predictive tests to enable biologically informed clinical treatment decisions, today announced the presentation of promising data related to the application of the company's novel, proprietary technology, Single Cell Network Profiling (SCNP) in hematologic malignancies. SCNP measures the activation levels of signaling networks at the single cell level by measuring the effects of multiple modulators, including drugs, on signaling pathways. This unique functional approach allows for the identification of the underlying heterogeneity in individual patient disease biology and enables efficient classification of patients into therapeutically relevant disease sub-populations by associating cell signaling with the pathogenesis of cancer. Data highlighting the clinical application of SCNP in patients with acute myeloid leukemia (AML) were presented today at the 52nd annual meeting of the American Society of Hematology (ASH) in Orlando, Florida.
“Today, physicians treat cancer patients based primarily on population data derived from clinical trials, not on individualized treatment regimens. Having knowledge regarding which drugs or drug combination will produce the best possible outcomes for each specific patient would mean greater effectiveness of their drug treatment,” said Norman J. Lacayo, M.D., Div. Hematology, Oncology and Stem Cell Transplantation, Dept. of Pediatrics, Stanford University School of Medicine and Stanford Cancer Center.
“This conference has been a great forum for us to introduce the value of this innovative technology in the management of hematologic malignancies. SCNP provides new insights into the biology of hematological cancers which is relevant to disease prognosis and response/resistance to therapy, and it establishes Nodality's leading presence in AML, a disease that remains a large area of unmet need,” stated David R. Parkinson, M.D., President and Chief Executive Officer of Nodality, Inc. “As such, the results presented here by our team are groundbreaking in nature with the potential to radically change how patients are cared for by matching individuals and specific therapies relevant to the biology of their disease.”
Data demonstrating the successful use of SCNP technology in predicting response to induction therapy and relapse risk in pediatric patients with AML was highlighted in an oral presentation at the ASH meeting. The presentation titled, “Single Cell Network Profiling (SCNP) Signatures Predict Response to Induction Therapy and Relapse Risk in Pediatric Patients with AML: COG study POG-9421” (abstract No. 954) was presented by Dr. Lacayo at 8:45am Eastern today.
Additional data demonstrating the potential for using the SCNP technology to independently predict response to induction therapy in older patients with AML was also presented by Dr. Elisabeth Paietta from the Eastern Cooperative Oncology Group, Principal Investigator of the study, in a poster presentation titled, “Single-Cell Network Profiling (SCNP) Signatures Independently Predict Response to Induction Therapy in Older Patients with Acute Myeloid Leukemia (AML)” (abstract No. 2695), presented December 5, 6:00pm – 8:00pm Eastern).
Validation studies currently are underway for tests to predict the success of induction chemotherapy in patients with AML (both elderly and pediatric patients). SCNP also is being studied in other hematologic malignancies and autoimmune diseases.
SCNP and Pediatric AML Study DesignSCNP assays were performed on 77 diagnostic bone marrow samples from pediatric AML patients enrolled in the Children's Oncology Group (COG) study, of which 67 were evaluable for analysis and were enriched for non-responders (NR) to anthracyline/cytarabine-based induction therapy. A total of 80 combinations of modulators and intra-cellular proteins (signaling nodes) were investigated, including signaling nodes involved in the phosphoinositide 3-kinase (PI3K), Janus Kinases (JAK), signal transducers and activators of transcription (STAT) and the DNA damage response and apoptosis pathways. Basal and modulated protein levels in leukemic blasts were measured and nodes were examined in univariate and multivariate analyses for their ability to discriminate between AML responsive (CR, n=46) and non-responsive (NR, n=21) to anthracyline/cytarabine-based induction therapy. Furthermore, nodes were examined for their ability to identify which patients were likely to be in complete continuous remission (CCR, n=23) or experience a relapse (CR-Rel, n=23) within 4 years from achieving CR.
SCNP and Pediatric AML Study ResultsUnivariate analysis revealed 19 nodes associated with disease response to conventional induction therapy and 9 associated with CR-Rel. Nodes involved in the apoptotic response to agents, inducing DNA damage, were higher in CR samples than in NR samples. In multivariate analysis, combinations of 2 to 8 nodes resulted in classifiers that predict response to induction therapy.
SCNP and Elderly AML Study DesignSCNP assays were performed on paired, bone marrow (BM) and peripheral blood (PB) samples from 44 AML patients greater than 60 years old, enrolled in ECOG trial E3999. A total of 38 combinations of modulators and intra-cellular proteins were investigated. Basal and modulated protein levels and the effect of modulation on protein levels in the leukemic blast cells were expressed using a variety of metrics. A total of 64 node/metric combinations were used to build multi-parametric classifiers using different modeling methodologies able to predict the likelihood of response to therapy. The performance characteristics of the classifiers built on the BM samples were then evaluated independently on the paired PB samples.
SCNP and Elderly AML Study ResultsWhen the predictive accuracy of the lead SCNP classifier was compared to that of a model based on traditional clinical/molecular predictors (i.e. the combination of age, therapy-related AML, and keryotype) the adjusted AUROC of the SCNP classifier far surpassed that of the clinical predictors (adjusted AUROC = 0.61 for clinical/molecular predictors vs. adjusted AUROC = 0.84 for the SCNP classifier). Additionally, when the nodes in the best BBLRS model developed on data from BM samples were used to model read outs from the paired PB samples, the adjusted AUROC of the resulting BBLRS model was comparable to that of the model fit to BM samples.
About NodalityNodality is a private, South San Francisco-based biotechnology company focused on improving the development and clinical use of therapeutics in cancer and autoimmune disease through the application of its proprietary Single Cell Network Profiling technology platform. Through its ability to functionally characterize cell signaling networks at the single cell level of resolution Nodality is committed to the biological characterization of individual patients to optimally match them with biologically-targeted treatments.
About Single Cell Network ProfilingSingle Cell Network Profiling (SCNP) is a proprietary technology licensed from Stanford University to characterize cell signalling networks in patients with cancer and autoimmune diseases. SCNP, by measuring functional signaling network behavior in single cells, has several advantages over other currently used molecular technologies. These include unprecedented insight into the presence and clinical meaning of cellular heterogeneity including the importance of rare cells such as drug-resistant and stem cells. As the technology has widespread application in both preclinical and clinical drug development, Nodality is also utilizing SCNP in collaboration with drug developers to decrease the time, cost, and risks of drug development.