Precision oncology for acute myeloid leukemia using a knowledge bank approach

Abstract

Causative mutations in a patient’s cancer drive its biology and, by extension, its clinical features and treatment response, a concept underpinning the vision of precision medicine. However, considerable between-patient heterogeneity in driver mutations complicates evidence-based personalization of cancer care. Here, re-analysing 1,540 patients with acute myeloid leukemia (AML), we explore how large knowledge banks of matched genomic-clinical data can support clinical decision-making. Inclusive, multistage statistical models accurately predicted likelihoods of remission, relapse and mortality, validated on independent TCGA patients. Comparison of long-term survival probabilities under different treatments enables therapeutic decision support, available in exploratory form online. Personally tailored management decisions could reduce the number of hematopoietic cell transplants in AML by 20–25%, while maintaining overall survival rates. Power calculations show that databases require thousands of patients for accurate decision support. Knowledge banks facilitate personally tailored therapeutic decisions, but require sustainable updating, inclusive cohorts and large sample sizes.

Precision Oncology: The Road Ahead

“…Multiple tools now include “interpretations” or summaries of the driver mutations written by clinicians – including the Precision Medicine Knowledgebase (at Weill Cornell) and the Personalized Cancer Therapy knowledge base (at MD Anderson) – or by the “crowd” [111, 112] (see list of references in Table S2C). A related approach recently explored leveraging existing ‘omics datasets for the interpretation of variants in newly sequenced samples, in acute myeloid leukemia[113]. For example, one study recently demonstrated the use of this approach by building survival models that linked genomic and clinical data, and then using these models to choose treatment(s) and predict survival for new acute myeloid leukemia patients [113].…”

Emerging therapies for acute myeloid leukemia: translating biology into the clinic

“…Recently, clinical and genomic variables from multiple studies have been linked to provide an online algorithm that can predict survival and therapy needs based on an individual patient's clinical and molecular status (http://cancer.sanger. ac.uk/aml-multistage) (14). In the future, such resources may help tailor upfront and postremission therapy, including the need for allotransplant in first remission.…”

“…As of May 2016, 78 patients with IDH1-mutated hematological malignancy had been treated. The overall response rate was 38.5% (30 of 78) with 17.9% (14) achieving CR (65). The median duration on treatment was 3.2 months.…”

Big data and precision medicine: challenges and strategies with healthcare data

“…In particular, clinical outcome and follow-up data are often not accessible due to heterogeneity stemming from the many institutions involved. On the other side, it is advertised that integrating all data and analyzing these data using methods from data science, one might be able to improve health care in terms of precision medicine, predictive modeling, clinical decision support and also comparative effectiveness research [26,43,50,61]. This advancement is not impeded by data science, but rather by compliance issues.…”

“…In oncology patient, data from genome, epigenome and proteome analyses are already available on different scales ranging from single markers to whole-genome profiles. Nevertheless, this information is not sufficiently integrated into a structured database system [26,28,68]. Thus, even simple exploratory questions such as screenings for patients with a specific mutation are restricted by heterogeneous or disconnected data sources.…”