RNA-Based Biomarker Platform

Predictive biomarkers historically worked on single-driver mutations yet only 10% of people with cancer have known driver mutations with available targeted therapies1,2. For the majority of people with cancer who have no known single-driver mutation, or oncogenic drivers for a small number of genes, biological processes within the tumor and its environment still drive the progression of their disease.

 

Oncologie is advancing a new paradigm of precision oncology with an RNA-based approach that identifies a larger number of genes associated with the dominant biology of the tumor microenvironment (TME) within cancer patients. With this understanding of tumor biology at the RNA level, Oncologie plans to match a patient’s dominant biology to novel, targeted therapies that addresses their specific biology to dramatically improve patient outcomes and accelerate the timeline of drug development.

Oncologie’s First RNA-based Biomarker Panel (TME Panel-1) 

Oncologie has developed a biomarker panel (TME Panel-1) that consists of unique, RNA-based signatures that describe the active, dominant biology of an individual patient’s TME, with the goal of identifying people with cancer who would benefit most from targeted therapies that modify the TME.  Based on collaborations through academic-industry consortiums, Oncologie has expanded and refined this panel using RNA sequencing from thousands of cancer patients in multiple solid tumors and machine-learning to develop robust algorithms that have been validated in clinical data sets and are being implemented in Oncologie’s clinical trials.

 

TME Panel-1 enables the assignment of a patient’s tumor sample into one of four TME dominant biological subtypes:

 

  • Immune Active (IA)
  • Immune Suppressed (IS)
  • Immune Desert (ID)
  • Angiogenesis (A)

These four subtypes, and how Oncologie hypothesizes they are ideally suited to predict benefit for relevant therapeutics, are depicted below (Figure 1):

Marquart et al.,  JAMA Oncol. 2018; 4:1093-1098.  Chakravarty et al., JCO Precis. Oncol. Jul; 2017.
Creation and validation of TME Panel-1:
Creation

Identifying dominant biology subgroups began in preclinical studies by using both:

 

  • A well-characterized mouse model of stromal activation.
  •  Preclinical patient-derived xenografts (PDX) gastric cancer models3.

RNA expression signatures within each subgroup were further refined and then applied to samples from people with primary gastric tumors from large-scale studies where outcome data (overall survival) were available.4 Upon analysis, Kaplan Meier curves revealed (Figure 2):

 

  • Subgroups were differentially correlated to patient survival, indicating they were prognostic
  • Cancer stage was not the driver of the differential survival benefits
3 Uhlik, et al, 2016 3 Asian Cancer Research Group (ACRG, Cristescu et al 2015), TCGA (Zhang et al 2014), and Singapore cohort (Lei et al 2013)

 

 

3 Collaboration with J. Lee from Samsung Medical Center 4Wilke et al, https://doi.org/10.1016/S1470-2045(14)70420-6

Validation

Oncologie used their TME-panel to generate predictive algorithms to test on two different gastric cancer studies3:

 

  • Study 1: RNA sequencing data was obtained from patients with advanced gastric cancer who were treated with pembrolizumab, a checkpoint inhibitor. The majority of responding patients fell into the IA TME subgroup, with a 100% response rate, and as theorized based on the mechanism of checkpoint inhibitors to improve T cell activity. Importantly, few IS patients and no A patients responded to treatment with single agent pembrolizumab.
    • This validates the panel to identify patients who would likely respond, or not, to targeted treatment, and supports our approach to match the right subgroups with the right targeted treatment. It also indicates that patients in the angiogenic (A) subgroup, for instance, may have better outcomes with anti-angiogenics rather than checkpoint inhibitors (Figure 3).
  • Study 2: RNA sequencing data was obtained from patients with advanced gastric cancer who were treated with ramucirumab, an anti-VEGFR2 antibody, in combination with paclitaxel as a second-line treatment. In this study, patients selected by Oncologie’s algorithm (i.e., biomarker positive patients) had nearly twice the overall response rate of the patients not selected (i.e., biomarker negative patients).
    •  This demonstrated that despite some background response contributed by the chemotherapy paclitaxel, a difference in response rate was still observed and based on identifying the VEGFR2-driven dominant biology (Figure 4).
Ongoing work and future plans:

A deep understanding of how to identify patients based on their dominant biology at the RNA-level has allowed Oncologie to search for and obtain clinical-stage therapies that address, or modify, these biologies.

 

  • Bavituximab:
    • An immune modulator that Oncologie is testing in combination with pembrolizumab as a second-line treatment in patients with advanced gastric or gastroesophageal cancer.
    • Oncologie is analyzing patient tissue samples with its TME Panel-1 from an ongoing Phase 2 trial with results expected to inform future clinical development for this program. Please visit here for more information on this clinical trial.
  • Navicixizumab:
    • A bispecific antibody targeting both VEGF and DLL4, a vascular specific Notch ligand and known resistance mechanism for anti-VEGF.
    • Results from a Phase 1a/1b trial resulted in the FDA granting Fast Track designation to navicixizumab for the treatment of high-grade ovarian, primaryperitoneal or fallopian tube cancer in patients who have received at least three prior therapies and/or prior treatment with bevacizumab.
    • Oncologie is analyzing patient tissue samples with its TME Panel-1 from navicixizumab trials with results expected to inform future clinical development with this program.

Collectively, these findings indicate that a genomics-based systems approach focused on RNA signatures from the TME can be used to discover putative predictive biomarkers of treatment response. Our validated approach using our proprietary RNA-based biomarker platform has the potential to be used more broadly in the anti-angiogenic and immunotherapy treatment landscape, thus offering an opportunity to improve patient stratification for Oncologie’s drug pipeline and those of potential collaborators.

To inquire about Oncologie’s TME Panel-1 and future panels, please contact us here.