Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials.

BACKGROUND: The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases. METHODS AND FINDINGS: Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5-29, 49-69 versus 70-82 months, χ2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI -1 to 31, 92-120 versus 113-129 months, χ2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8+ T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6-36, 49-73 versus 74-90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies. CONCLUSIONS: In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies.

Payer decision making for next-generation sequencing-based genetic tests: insights from cell-free DNA prenatal screening.

PURPOSE: Cell-free DNA (cfDNA) prenatal screening tests have been rapidly adopted into clinical practice, due in part to positive insurance coverage. We evaluated the framework payers used in making coverage decisions to describe a process that should be informative for other sequencing tests. METHODS: We analyzed coverage policies from the 19 largest US private payers with publicly available policies through February 2016, building from the University of California San Francisco TRANSPERS Payer Coverage Policy Registry. RESULTS: All payers studied cover cfDNA screening for detection of trisomies 21, 18, and 13 in high-risk, singleton pregnancies, based on robust clinical validity (CV) studies and modeled evidence of clinical utility (CU). Payers typically evaluated the evidence for each chromosomal abnormality separately, although results are offered as part of a panel. Starting in August 2015, 8 of the 19 payers also began covering cfDNA screening in average-risk pregnancies, citing recent CV studies and updated professional guidelines. Most payers attempted, but were unable, to independently assess analytic validity (AV). CONCLUSION: Payers utilized the standard evidentiary framework (AV/CV/CU) when evaluating cfDNA screening but varied in their interpretation of the sufficiency of the evidence. Professional guidelines, large CV studies, and decision analytic models regarding health outcomes appeared highly influential in coverage decisions.Genet Med advance online publication 22 September 2016.

Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma.

Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.

The path from bench to bedside: considerations before starting the journey.

Our understanding of human biology has increased tremendously for the last several decades, yet the pace at which these discoveries have translated into new therapies for patients has been frustratingly stagnant. Universities and academic health centers, as the major recipients of public investment in biomedical science, have an obligation to translate new knowledge into applications that confer human benefit. However, translating fundamental discoveries into practical applications is expensive and involves highly regulated steps with which few academic scientists have experience. Challenges in engaging universities and academic health centers in translational research include building the appropriate infrastructures for human investigation, training and stabilizing the careers of young scientists and physicians interested in the requisite work, educating academic investigators about the regulatory requirements inherent in successful therapeutic discovery and development, and finding more efficient ways to match good ideas with adequate funding resources. The purpose of this article is to examine the early-stage drug development process and evaluate the role that academia could play in it. Because interest in early-stage drug development grows among academic investigators, the need for more integrated partnerships among academia, government, and industry has become increasingly apparent.