This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require the application of clinical bioinformatics. These developments have triggered multiple national initiatives in precision medicine—a data-driven approach centering on the individual. However, clinical implementation of precision medicine poses numerous challenges. Foremost, precision medicine needs to be contrasted with the powerful and widely used practice of evidence-based medicine, which is informed by meta-analyses or group-centered studies from which mean recommendations are derived. This “one size fits all” approach can provide inadequate solutions for outliers. Such outliers, which are far from an oddity as all of us fall into this category for some traits, can be better managed using precision medicine. Here, we argue that it is necessary and possible to bridge between precision medicine and evidence-based medicine. This will require worldwide and responsible data sharing, as well as regularly updated training programs. We also discuss the challenges and opportunities for achieving clinical utility in precision medicine. We project that, through collection, analyses and sharing of standardized medically relevant data globally, evidence-based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.

/pdf/reconciling-evidence-based-medicine-and-precision-medicine-4k86yns3nd.pdf

Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities

https://doc.rero.ch/record/330253/files/Rossi_2020_blood_b_international.pdf

International prognostic score for asymptomatic early-stage chronic lymphocytic leukemia

Patient-reported outcomes in hematology: is it time to focus more on them in clinical trials and hematology practice?

We report a customized gene panel assay based on multiplex long-PCR followed by third generation sequencing on nanopore technology (MinION), designed to analyze five frequently mutated genes in chronic lymphocytic leukemia (CLL): TP53, NOTCH1, BIRC3, SF3B1 and MYD88. For this purpose, 12 patients were selected according to specific cytogenetic and molecular features significantly associated with their mutational status. In addition, simultaneous analysis of the targets genes was performed by molecular assays or Sanger Sequencing. Data analysis included mapping to the GRCh37 human reference genome, variant calling and annotation, and average sequencing depth/error rate analysis. The sequencing depth resulted on average higher for smaller amplicons, and the final breadth of coverage of the panel was 94.1%. The error rate was about 6% and 2% for insertions/deletions and single nucleotide variants, respectively. Our gene panel allows analysis of the prognostically relevant genes in CLL, with two PCRs per patient. This strategy offers an easy and affordable workflow, although further advances are required to improve the accuracy of the technology and its use in the clinical field. Nevertheless, the rapid and constant development of nanopore technology, in terms of chemistry advances, more accurate basecallers and analysis software, offers promise for a wide use of MinION in the future.

/pdf/design-and-minion-testing-of-a-nanopore-targeted-gene-1ahb8sh27x.pdf

Design and MinION testing of a nanopore targeted gene sequencing panel for chronic lymphocytic leukemia.

Because chronic lymphocytic leukemia is a heterogeneous disease, there are considerable efforts underway to develop increasingly accurate and precise analytics with which to estimate the probability of future events such as the need for and probability of response to therapy, progression-free survival, and survival. These analytics typically are constructed from clinical and laboratory variables. These variables often are combined into scores or staging systems, some of which are prognostic (therapy-independent), whereas others are predictive (therapy-dependent). Predictive variables differ with different therapies. Because response to therapy is a necessary condition for the improvement of survival, predictive biomarkers are extremely important. However, despite some progress to identify new predictive biomarkers, del(17p)/TP53 mutation remains the only widely accepted variable used to guide therapy. New laboratory techniques and analytical tools may contribute to improvements in the precision and accuracy of outcome indicators. However, there are inherent limitations when applying cohort-based estimates to individuals within the cohort. The accuracy and precision of prediction also are limited by measurement error and chance. Ultimately, estimating outcomes requires a careful balance between clinical experience, imperfect prediction, and uncertainty.

https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cncr.32353

Predicting the outcome of patients with chronic lymphocytic leukemia: Progress and uncertainty.

Present and future of personalized medicine in CLL

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Present and future of personalized medicine in CLL