Integrating Sequoia Zanubrutinib Evidence into Digital Oncology Workflows and Clinical Decision Support
There are an increasing number of targeted therapies for B-cell malignancies and evidence from clinical trials needs to be translated into oncology work flows and clinical decision support systems. Such tools for treatment planning, clinical decision support, information management in clinics and hospitals etc. support the health care provider in managing information about the patient’s disease, lab results, treatment history, cautions and contraindications and clinical trial evidence.
Another source of evidence for previously untreated patients with CLL/SLL is the SEQUOIA study. This study compares Zanubrutinib to bendamustine plus rituximab (BR) in a phase III study of treatment-naïve patients with CLL/SLL without del(17p). The results of the SEQUOIA study show that free survival (PFS) outcomes favored Zanubrutinib compared with BR in the treatment of patients with CLL/SLL without del(17p).
While it would be sufficient to make the evidence available to the clinician, it needs to be presented in such a way that the clinician is also able to understand the study in question. This includes information about the study population, the endpoints that have been measured, the follow-up period, and also the limitations of the study.
Understanding the SEQUOIA Evidence
The SEQUOIA study was a randomized, open-label, phase III study in previously untreated patients with CLL/SLL requiring treatment. The study consisted of two arms: patients without del(17p) were randomized to receive Zanubrutinib or bendamustine plus rituximab (BR), while patients with del(17p) were enrolled in a separate Zanubrutinib cohort.
The results of the initial analysis of the SEQUOIA study were released after a median follow-up of 26.2 months for the patients on study. Zanubrutinib had a hazard ratio for PFS of 0.42 compared with BR.
Data from the longer-term follow-up of patients from the SEQUOIA study demonstrated the durability of treatment effect of Zanubrutinib in previously untreated CLL/SLL patients. The estimated median PFS for patients receiving Zanubrutinib had not been reached, compared with 44.1 months (HR 0.29; 95% CI 0.16–0.51)) for patients receiving BR in the primary analysis. The estimated 60-month OS rates for Zanubrutinib-treated and BR-treated patients were 85.8% (95% CI 76.4–91.7) and 85.0% (95% CI 76.2–91.4), respectively. There was no new safety signals identified from this updated follow-up of patients. It should not be used to make a treatment decision for all patients with CLL/SLL.sss
Translating Trial Eligibility into Digital Patient Profiles
A major function of CDS systems is to compare information from a patient’s clinical record to that of the populations that took part in the clinical trials to assess relevance.
In digital workflows the SEQUOIA Zanubrutinib evidence from previously untreated patients with CLL/SLL can be referenced in clinical decision support systems.
Potential data elements include:
- Treatment-nave versus previously treated disease
- CLL versus SLL
- del(17p) status
- TP53 mutation status
- IGHV mutation status
- Age and performance status
- Comorbidities
- Baseline blood counts
- Renal function
- Previous or concomitant therapies
- Disease-related symptoms and tumor burden
The list of factors to represent can also help identify factors that may not be represented, and thus lead to incorrect representation of evidence for age and performance status
A further distinction should be made between the randomized SEQUOIA cohort and the separate del(17p) cohort of previously untreated patients. These patients should not be represented in CDS systems as if they were part of the randomized comparison of Zanubrutinib vs BR in the SEQUOIA study.
Incorporating Molecular Risk Information
Molecular characteristics are central to contemporary CLL treatment planning. For the del(17p) cohort of previously untreated patients treated with Zanubrutinib, a 5-year follow-up analysis demonstrated durable PFS in these patients regardless of their IGHV gene status (mutated vs unmutated).
This is why in the digital era of Clinical Decision Support (CDS) it is so important to differentiate between the various populations that were evaluated in a given study. The system would instead identify the genomic features of the patient and display the relevant study population and results of the treatment in terms of efficacy for the given patient.
Therefore all this information can be easily presented to the clinician in a digital CDS system in a clear and distinct manner, separating the information from the randomized evidence from the single-arm cohorts, observational studies, clinical guidelines and the drug’s regulatory labeling.
Presenting Efficacy Evidence Without Replacing Clinical Judgment
Oncology Clinical Decision Support Systems
Most value is placed on supporting clinical reasoning rather than trying to take over for the clinician. Practical use of information for treatment decisions is provided in this section. Here’s a possible structure for a clinical decision support system for presenting the relevant clinical trial information:
Patient characteristics → Relevant trial population → Treatment evidence → Efficacy outcomes → Safety considerations → Clinical discussion
For example, a system would take a treatment-naïve patient with CLL/SLL through a series of screens, identifying relevant information to help support a clinical decision. The system would recognize that the patient’s information aligns with the SEQUOIA study population and then present the relevant PFS information for the treatment in question.
Supporting Clinical Reasoning Rather Than Replacing It
A practical evidence display for clinical decision support of treatment of individual patients with CLL/SLL can support clinical decision making by considering all relevant factors for clinical review that have not been represented fully in trial evidence. The clinical-trial evidence establishes only evidence regarding outcomes in a particular study population (e.g. previously untreated patients with CLL/SLL with mutated IGHV genes).
Integrating Safety Data into the Workflow
A digital oncology workflow can include information on safety and treatment information for efficacy. Five-year follow-up data from the SEQUOIA trial has recently been reviewed for new safety signals. Zanubrutinib associated adverse events include atrial fibrillation (7.1% @ median follow-up of 61.2 months), bleeding, hypertension, infections and cytopenias.
Safety information for oncology treatments (such as CLL/SLL) should be displayed together with efficacy information for a clinical review by the treating clinician using the EHR/CDS platform.
Relevant factors for clinical review may include:
- Cardiovascular history
- History of atrial fibrillation or another arrhythmia
- Bleeding history
- Anticoagulant or antiplatelet therapy
- Baseline and ongoing blood counts
- Infection history
- Renal and hepatic function
- Concomitant medications
The alerts for clinical review only are enabled for this scenario and the system will not automatically determine if treatment is indicated for this scenario.
Monitoring Patients Longitudinally
Digital oncology systems are also used to organize the information for the treatment of individual patients over time. Digital monitoring of the treatment of cancer patients, for example Zanubrutinib, for treatment dates, lab values, adverse events, clinical assessment, imaging studies (if applicable) and response to treatment.
Show trends over time in data for repeated measurements for same patient over time (e.g. same blood tests). Typically lost when reporting results for each visit. Monitoring of patients in oncology, however, must be done in line with the relevant disease-specific treatment response criteria. Monitoring of the ZANU single agent treatment for CLL for example could be based on a single laboratory value. In addition, however, symptoms, clinical examination, lymph node dimensions and relevant blood counts have to be recorded.
Keeping Evidence Current
Clinical evidence for Digital Oncology changes over time, therefore maintenance of the evidence is important. In SEQUOIA, the initial publication reported PFS primary analysis after 26.2 months of follow-up while the later publication reported a 5-year follow-up.
An evidence library should therefore record:
- Study name
- Trial registration number
- Publication date
- Data-cut date
- Median follow-up
- Patient population
- Comparator
- Primary and secondary endpoints
- Latest available analysis
- Relevant safety findings
Without this information, a CDS system cannot indicate that older information exists and that more up-to-date information is available.
Avoiding Overinterpretation of SEQUOIA
SEQUOIA is a randomized study of Zanubrutinib versus bendamustine plus rituximab in treatment-naïve patients with CLL/SLL without del(17p). The findings of the SEQUOIA trial cannot be used for a comparison between all current targeted therapies for the treatment of CLL.
However, this limitation needs to remain visible when incorporating the SEQUOIA evidence into a Clinical Decision Support (CDS) system, ideally by presenting the trial population, comparator, endpoint(s), follow-up period and relevant study limitations for a given single efficacy statistic. Clinicians should be made aware that a single study cannot be used to establish a hierarchy between all of the different treatments for use in a particular disease.
Building an Evidence-Aware Digital Workflow
A structured CDS workflow incorporating sequoia zanubrutinib evidence could include the following steps:
- Identify the disease: Confirm CLL/SLL and relevant disease characteristics.
- Treatment Status: The patient is either treatment-naïve or previously treated for their CLL/SLL.
- Review molecular findings: The findings for the patient’s molecular characteristics (e.g. del(17p), TP53 mutations, IGHV status) can be reviewed and included in the CDS as appropriate.
- Match the evidence population: Determine which SEQUOIA cohort is relevant.
- Display efficacy data: The efficacy data from the clinical trial should be displayed, such as the PFS results in SEQUOIA. The follow-up time for the reported outcomes should be indicated as well.
- Display safety information: The adverse events and patient-specific risk factors for certain adverse events that have been identified in clinical trials should be highlighted for the clinician.
- Review current evidence: Include updated publications, regulatory information, and applicable professional guidance.
- Support clinician assessment: Provide evidence as decision support instead of automatically generating a treatment recommendation.
Incorporating the evidence from SEQUOIA into a digital oncology workflow for making treatment decisions for patients with CLL/SLL therefore allows for the incorporation of evidence from mature clinical trials, while all individual medical judgment and clinical considerations are considered.
Key Takeaways
The mature clinical-trial data from the randomized, Phase III study, SEQUOIA, of Zanubrutinib versus bendamustine plus rituximab in treatment-naïve CLL/SLL patients without del(17p) reported continued PFS outcomes through five years of follow-up.
Incorporating SEQUOIA evidence into healthcare professionals’ digital clinical decision support systems (CDS) might be valuable when used to provide context to trial findings, such as patient’s treatment history, safety issues, molecular risk factors and provenance of the evidence.
The evidence from clinical trials can be incorporated into the healthcare professional’s digital oncology workflow to support their decision on the appropriate treatment for individual patients. By using the information from clinical trials in the context in which it was generated and with provenance, digital oncology can support the healthcare professional’s clinical decision-making.
Disclaimer:
This article is intended for general informational and educational purposes only and should not be considered a substitute for professional medical advice, diagnosis, or treatment. Please consult a qualified healthcare provider for any health-related concerns or before making decisions about medications or treatment plans. Never disregard or delay seeking professional medical advice based on information found here. In case of a medical emergency, contact your local emergency services immediately.
