What the trial was testing
The STORM enrolled 202 patients with multiple myeloma. The study was sponsored by Karyopharm Therapeutics Inc and tracked outcomes across the full group of patients who matched the trial's eligibility profile.
It was initial testing (phase 2). Trials at this stage are designed to produce evidence regulators and physicians can act on — not just observations to follow up later.
What the results showed
26% of patients with heavily treated myeloma responded to selinexor plus dexamethasone.
The New England journal of medicine · 2019 · NCT02336815
These findings — that about 1 in 4 patients saw their myeloma shrink with this treatment — were published in the The New England journal of medicine and represent the headline result of the study.
Researchers tracked outcomes across 202 patients enrolled in the trial. The result was consistent enough across the group that the team felt confident reporting it.
What this means for patients
For patients with multiple myeloma, this result changes the calculus on what to ask their care team about. Whether it changes day-to-day care depends on factors like disease subtype, prior treatments, and where the patient is in their care journey.
What you can do now
Selinexor (Xpovio) is FDA-approved for multiple myeloma that hasn't responded to other treatments. This combination can cause side effects including fatigue, nausea, and low platelet counts. Talk to your oncologist about whether this option fits your treatment history and current health status.
Eligibility for the treatments mentioned above depends on specific test results and clinical history. Bring this summary, the trial name, and your most recent labs or pathology report to your next visit.
Open multiple myeloma trials
Predicting Progression of Developing Myeloma in a High-Risk Screened Population (PROMISE)
The PROMISE Study aims to establish a prospective cohort of individuals with precursor conditions to multiple myeloma, such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). We will study these patients as a means to identify risk factors for progression to symptomatic multiple myeloma.
Randomised Controlled Trial of Artificial Intelligence-assisted Health Education
With the rapid advancement of biopharmaceutical technology, clinical trials have become the crucial bridge connecting new drugs from the laboratory to clinical application. Despite the increasing number of clinical trial projects being conducted, nearly all such projects face the common challenge of recruitment difficulties. Subject recruitment constitutes a pivotal stage in clinical trials; the ability to recruit a sufficient number of subjects meeting the trial requirements significantly impacts trial quality and also serves as a key factor influencing trial progress. Hematologic cancers constitute a highly heterogeneous group of malignant diseases originating in the haematopoietic organs and primarily affecting the haematopoietic system. They encompass acute and chronic leukaemias, malignant lymphomas, multiple myeloma, myelodysplastic syndromes, and related disorders. For patients facing treatment decisions, clinical trials represent not only a vital avenue for accessing cutting-edge therapies but also impose heightened demands on their capacity for informed decision-making. Conversational artificial intelligence (AI) based on large language models is rapidly advancing in health education and public health communication. Medical chatbots offer scalable and personalised advantages in delivering health information, promoting behavioural change, and enhancing patient engagement, providing a viable pathway for improving trial literacy and decision support. Accordingly, this study proposes to conduct a clinical trial literacy intervention using AI-powered chatbots among haematological malignancy patients. Through a randomised controlled trial (RCT), it aims to evaluate the impact of AI-assisted health education on patients' understanding of clinical trials and intention to participate. This research seeks to validate the application value of AI technology in health education and explore scalable AI-assisted health education intervention models.