Yesterday I shared part of my conversation/interview with Dr. Ravi Vij, a multiple myeloma specialist with the University of Washington School of Medicine. The conversation centered on the University of Arkansas Medical Center’s intense research into classifying and treating high risk myeloma.
Here is a link to the American Society of Clinical Oncology (ASCO) Data Base, connecting you to some of the research studies which were presented at ASCO concerning high risk multiple myeloma. The featured abstract, Defining the prognostic variables in gene expression profiling (GEP)-defined high-risk multiple myeloma (MM): Distinguishing early failures (EF) from sustained control (SC), is the presentation Dr. Barlogie presented and I attended. The abstract isn’t very long, so let’s take a look:
Background: With the introduction of GEP analysis in Total Therapy 2 (TT2) and validation in Total Therapy 3 (TT3), GEP-defined high-risk, present in ~15% of patients, has emerged as the dominant adverse feature for both overall survival (OS) and event-free survival (EFS) and, despite achieving a similar rate of complete response (CR) as in low-risk MM, also for CR duration (CRD). Thus, median OS and EFS remain dismal not exceeding 3 and 2 years, respectively. Recognizing, however, that 10% to 20% of high-risk patients enjoy extended survival, we examined the presenting features and response characteristics that may distinguish this favorable subset. Methods: Our MM data base was scrutinized for TT2 and TT3 patients who had high-risk baseline GEP features. Modeling survival curves by an exponential function, 2 subsets could be defined comprising one with steep decline in OS and a second with a shallow slope/plateau. Baseline variables of these 2 groups were determined and logistic regression applied to distinguish their characteristics. Results: We identified, among the combined TT2 and TT3 populations, 123 subjects with GEP-defined high-risk features who, by modeling, could be segregated into 2 groups with early failure (EF, n=65) and sustained control (SC, n=58). According to logistic regression, the SC-distinguishing features included GEP risk score (OR=0.25; p=0.006) and delTP53 (OR=0.20; p=0.015), of which only delTP53 survived the multivariate model (OR=0.26; p=0.008). None of the other GEP-derived variables (molecular subgroup, amp1q21, del1p) or other standard variables (CA, B2M, CRP, albumin) were discriminatory, with a borderline effect of LDH ≥190U/L (OR=0.51; p=0.062). Conclusions: Among GEP-defined high- risk MM, ~50% could be distinguished as having SC which, in contrast to EF, was characterized by lower risk score and delTP53 frequency. Data will be presented on SC versus EF discriminating genes.
It’s scary when you realize how pivotal a patient’s genetic make up is to their prognosis. Scary because we don’t know enough about the how’s and why’s yet. Don’t you think researchers are on to something here? By studying how different patients with the same gene experession react to different treatments, hopefully scientists can begin to isolate which therapies will work best for a patient with a set of specific gene expressions.
Complicated stuff! Feel good and keep smiling! Pat