Publication highlights update - November 2024
Proteomics in clinical trials – biological insights to accelerate drug development
Escalating costs and increasing demands from regulatory authorities for a more precision medicine-based approach to drug development are substantial challenges for the pharmaceutical industry. Olink’s PEA technology is increasingly used to analyze samples from clinical trials to stratify patient populations, predict responses, and provide insights into the biology of disease and drug modes of action. This blog post presents four recent examples of high-impact publications illustrating this increased application of proteomics in the clinical trial setting.
How will proteomics drive future drug development?
Before you dive into the four articles below, Dr. Chris Whelan, Director of Neuroscience Data Science at Johnson & Johnson, recently shared his vision for population proteomics during his keynote presentation at the Olink Proteomics World event. In his talk, he explains how population-based, clinical, and translational proteomics will advance precision medicine. You can still watch this presentation on-demand, along with all other event sessions.
Validation of drug mode of action in the EMPEROR clinical trial
The SGLT2 Inhibitor empagliflozin was developed as a blood glucose-lowering drug to treat type 2 diabetes but was subsequently found to be highly protective against heart failure (HF). The EMPEROR clinical trial used Olink® Explore 1536 for a proteomics-based investigation of the underlying mode of action (MOA). In a new validation study, these findings were replicated and expanded using Olink® Explore 3072 to investigate many more proteins, using samples taken from >1,000 patients who were not used in the original study.
Key highlights:
- The large majority of drug-responsive marker from the original study were replicated in the validation cohort and many new significant proteins were identified.
- The significant proteins showed marked enrichment for autophagy and other cellular QC functions, enhanced mitochondrial health and ATP production, and cellular iron mobilization or erythropoiesis, thereby replicating the findings of the original study in terms of drug MOA.
- These findings in patients are highly consistent with the published effects of SGLT2 inhibitors demonstrated in multiple experimental studies, providing clinical corroboration of these translational investigations.
The robust replication of these mechanistic signatures across the discovery and validation cohorts is closely aligned with the demonstrated cellular and organ effects of SGLT2 inhibitors.
Mechanistic insights into weight loss in HF patients treated with dapagliflozin
The Olink Explore 3072 platform was used to gain mechanistic insights in a clinical trial where patients with heart failure with preserved ejection fraction (HFpEF) were treated with the SGLT2 inhibitor, dapagliflozin. This drug, originally developed for diabetes and later found to be protective against HF, had been observed to produce long-term weight loss in obese patients, but the underlying mechanisms were unclear. Here, plasma proteomics were measured in patients given dapagliflozin or placebo over the course of 24 weeks.
Key highlights:
- The protein PYY (peptide YY) was significantly upregulated in response to dapagliflozin, with a mean fold-change of +1.74.
- These changes were confirmed using a PYY ELISA, with excellent correlation with the Olink data.
- The increase in PPY in response to dapagliflozin was mirrored by an average placebo-corrected weight loss of 3.4 kg.
- Increased plasma PPY correlated with greater weight loss in these patients.
- PPY is a hormone released by the intestine in response to feeding that promotes a feeling of satiety – providing a clear mechanistic link between dapagliflozin mode of action and the observed weight loss.
The present data connecting fat reduction and hemodynamic benefits with PYY also support future efforts at developing PYY agonists as novel treatments for patients with HFpEF, particularly those with obesity.
Protein-based prediction of disease progression during chemotherapy
FOLFIRINOX is a quadruple chemotherapy combination used for pancreatic ductal adenocarcinoma (PDAC) that shows high toxicity rates and variable efficacy. In an effort to identify non-invasive markers to rapidly identify patients who do not respond to this therapy and show disease progression, the Olink Explore 384 Inflammation panel was used to monitor serum proteomics (in parallel with transcriptomics) in patients enrolled in a FOLFIRINOX clinical trial.
Key highlights:
- Machine learning development of predictive models for disease progression during FOLFIRINOX therapy showed convincingly that proteins exhibited superior predictive accuracy than genes.
- In a combined RNA/protein analysis, no differentially expressed genes were among the top 60 performers.
- A 6-protein model (AMN, BANK1, IL1RL2, ITGB6, MYO9B, and PRSS) predicted PDAC progression with high accuracy (AUC = 0.89).
- The standard tumor load marker for PDAC, CA19-9 had a predictive power for progression of just AUC = 0.54 and actually reduced the accuracy of the protein model when used in combination.
- The 6-protein model also showed predictive accuracy across all disease stages, removing this as a confounding factor for disease progression assessment.
Our six-protein FFX-IPEP signature holds solid potential as a liquid biomarker for the early prediction of PDAC progression during toxic FOLFIRINOX chemotherapy.
Analysis of a revisited clinical trial gives insights into long-term therapeutic benefits
The phase III Lung-MAP S1400I clinical trial for combinational immunotherapy in metastatic lung squamous cell carcinoma (SqNSCLC) failed to show improved clinical outcomes. After improved progression-free survival (PFS) and overall survival (OS) were noted in follow-up analysis, however, samples from the trial were reexamined in a multiomic study including targeted transcriptomics, whole-exome sequencing and serum proteomics with the Olink Target 96 Immuno-oncology panel. Overall, the data indicated that higher immune scores were associated with treatment response and improved survival, while a “cold immune landscape” with increased regulatory T cells and higher chromosomal copy number variation burden associated with worse overall survival. The proteomics analysis provided several interesting observations that led the authors to highlight the potential of biomarker-based strategies to select patients for immune checkpoint inhibitor regimens and dynamically monitor their responses.
Key highlights:
- Several serum chemokines were consistently elevated in response to both mono- and combination therapy, indicating the anticipated immune-regulating effects of these drugs.
- While markers of immune activation and priming were upregulated in responders at baseline, stromal proteins and those associated with hyperinflammation were upregulated in non-responders.
- Joint modeling of survival with longitudinal Olink data showed that upregulation of serum CXCL13, MMP12, CSF-1, and IL-8 were associated with worse survival.
- This association of serum proteins with poorer prognosis was also seen in independent Kaplan-Meier analyses based on median protein levels at baseline.
In the era of immune-oncology, the Olink soluble protein detection platform has emerged as a promising tool to assess and monitor host immune response.
Publication highlights for proteomics in clinical trials
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