On December 14, 2021 Cellworks Group, Inc., a world leader in Personalized Medicine in the key therapeutic areas of Oncology and Immunology, reported the results from two clinical studies using the Cellworks Biosimulation Platform and Computational Omics Biology Model (CBM) to predict therapy response for individual MDS patients were featured in two poster presentations at the 63rd American Society of Hematology (ASH) (Free ASH Whitepaper) Annual Meeting and Exposition held December 11-14, 2021 in Atlanta, Georgia (Press release, Cellworks, DEC 14, 2021, View Source [SID1234597117]). The complete results from these clinical studies are available online in the ASH (Free ASH Whitepaper) Meeting Library as Abstract 2615 and Abstract 3690.
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In the ASH (Free ASH Whitepaper) Abstract 2615 study, the Cellworks Biosimulation Platform and CBM identified genomic and molecular markers for decitabine (DAC) plus valproic-acid (VPA) treatment response in patients with Myelodysplastic Syndromes (MDS). In the ASH (Free ASH Whitepaper) Abstract 3690 study, the Cellworks Biosimulation Platform and CBM identified immune modulation as a key pathway for predicting azacitidine (AZA) response in MDS.
"There is a need for a predictive clinical approach that can stratify MDS patients according to their chance of a favorable outcome from current therapies, while also identifying and predicting their responses to new and emerging treatment options," said Dr. Michael Castro, MD, Chief Medical Officer at Cellworks. "Ideally, patients predicted to be non-responders could be offered to participate in a clinical trial for a new therapy or combination treatment where they were predicted to have a higher likelihood of response based on their genetic biomarkers. By using Cellworks MDS biomarker identifications and therapy response predictions in advance of participation in a clinical trial, pharmaceutical companies can increase the success rate of trials and accelerate the approval timeframe for new treatments."
The Cellworks Biosimulation Platform simulates how a patient’s personalized genomic disease model will respond to therapies prior to treatment and identifies novel drug combinations for treatment-refractory patients. The platform is powered by the groundbreaking Cellworks Computational Omics Biology Model (CBM), a network of 4,000+ human genes, 30,000+ molecular species and 100+ signaling pathways. By reliably predicting an individual patient’s therapy response prior to receiving the treatment, the Cellworks Platform can guide selection of the optimal treatment, help patients avoid ineffective therapies and improve patient outcomes.
Clinical Study: ASH (Free ASH Whitepaper) Abstract 2615
Biosimulation using the Cellworks Computational Omics Biology Model (CBM) identifies genomic and molecular markers for decitabine (DAC) plus valproic-acid (VPA) treatment response in patients with Myelodysplastic Syndromes (MDS).
Background
DNA methyltransferase inhibition (DNMTi) with hypomethylating agents (HMA), azacitidine (AZA) or decitabine (DAC), remains the mainstay of therapy for most high-risk MDS patients. However, only 40-50% of MDS patients achieve clinical improvement with DNMTi. This study explored the molecular basis of observed clinical response in a group of patients treated with DAC and valproic-acid (VPA). Biosimulations were conducted on each patient-specific disease model to measure the effect of DAC + VPA according to a cell growth score.
Results
In the biosimulation, VPA is a relatively weak HDAC inhibitor, but it also inhibits GSK3B and in turn increases beta-catenin (CTNNB1) levels. Additionally, monosomy 7 associated with loss of CAV1, HIPK2 and TRRAP also caused high CTNNB1, thereby further contributing to drug resistance. Biosimulation correctly identified that 7 of 8 patients with these genomic findings were clinical non-responders to VPA, indicating that CTNNB1 status is likely to predict treatment failure from the VPA + HMA combination in this disease. By contrast, high levels of c-MYC predict response to VPA + HMA combination.
Conclusions
Cellworks Biosimulation Platform found that signaling pathway consequences related to CTNNB1 and c-MYC modulation predict response to DAC + VPA. Although HMA plus HDAC inhibition can be generally beneficial for MDS, variable mechanisms of action among various HDAC inhibitors and unique patient disease characteristics should be considered for optimal treatment selection. Also, CTNNB1 emerged from the Cellworks biosimulations as a therapeutically relevant target in MDS that determines whether VPA synergizes or antagonizes the effect of other agents in this challenging subtype of MDS.
Clinical Study: ASH (Free ASH Whitepaper) Abstract 3690
Biosimulation using the Cellworks Computational Omics Biology Model (CBM) identifies immune modulation as a key pathway for predicting azacitidine (AZA) response in MDS.
Background
Only 40-50% of MDS patients achieve clinical improvement with DNMTi, the mainstay of therapy for the majority of high-risk MDS patients. Recently, a discovery of immune modulation by HMA has emerged. Although the PD-L1/PD1 blockade plus HMA has been recognized as a beneficial combination, there are no established markers to guide decision-making. This study analyzed the utility of immunomic profiling of chromosome 9 copy number status as a significant mechanism of immune evasion and HMA resistance.
Results
Although AZA treatment increased tumor associated antigens and interferon signaling, it also increased PD-L1 expression to inactivate cytotoxic CD8(+) T cells. Copy number alternations of the chromosome 9p region were found to significantly drive PD-L1 expression with multiple genes such as CD274, IFNA1, JAK2, PDCD1LG and KDM4C playing a role in PD-L1 regulation further increasing immune suppression.
Conclusion
Based on the results from the Cellworks Biosimulation Platform and Computational Biology Model (CBM), copy number variants of chromosome 9p and 16 can be used as biomarkers for selecting patients that may achieve high clinical benefit from addition of immune checkpoint inhibitors to HMA regimen.