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Precision oncology for acute myeloid leukemia using a knowledge bank approach

Abstract

Underpinning the vision of precision medicine is the concept that causative mutations in a patient's cancer drive its biology and, by extension, its clinical features and treatment response. However, considerable between-patient heterogeneity in driver mutations complicates evidence-based personalization of cancer care. Here, by reanalyzing data from 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, which were validated using data from independent patients in The Cancer Genome Atlas. Comparison of long-term survival probabilities under different treatments enables therapeutic decision support, which is available in exploratory form online. Personally tailored management decisions could reduce the number of hematopoietic cell transplants in patients with AML by 20–25% while maintaining overall survival rates. Power calculations show that databases require information from thousands of patients for accurate decision support. Knowledge banks facilitate personally tailored therapeutic decisions but require sustainable updating, inclusive cohorts and large sample sizes.

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Figure 1: Systematic comparison of models.
Figure 2: Multistage modeling of patient fate.
Figure 3: Multistage outcome predictions for 1,024 patients.
Figure 4: Individualized risk exemplified for two patients.
Figure 5: Benefit of allograft in CR1 versus allograft after relapse.
Figure 6: Extrapolations and power calculations.

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Acknowledgements

We thank C. Holmes for stimulating discussions. We gratefully acknowledge D. Weber for clinical data managing, V. Teleanu for assistance in cytogenetics data classification and S. Kayser for assistance in morphological evaluation. This work was supported by grants from the Wellcome Trust (077012/Z/05/Z; P.J.C.), the Bloodwise charity (P.J.C.), the Leukemia and Lymphoma Society (P.J.C.) and the Deutsche Krebshilfe (109675; K.D.), in part by grants from the German Bundesministerium für Bildung und Forschung (BMBF) (01GI9981 (H.D.) and 01KG0605 (R.F.S. and H.D.)), by a Wellcome Trust Senior Clinical Research Fellowship (WT088340MA; P.J.C.), by an EHA early career fellowship (E.P.), and by the Deutsche Forschungsgemeinschaft (project B3, Sonderforschungsbereich (SFB) 1074; K.D. and L.B.); H.D. is coordinating investigator. L.B. is a Heisenberg Professor of the DFG (BU 1339/3-1). We are grateful to all members of the German–Austrian AML Study Group (AMLSG) for their participation in this study and for providing patient samples; a list of participating institutions and investigators appears in the Appendix of Papaemmanuil et al.5. AMLSG treatment trials were in part supported by Amgen and DKH grant 109675.

Author information

Authors and Affiliations

Authors

Contributions

M.G. developed the statistical methods, analyzed data and wrote the manuscript and supporting information, with input from E.P. and P.J.C. E.P. prepared and curated the genetic and clinical data. I.M. analyzed TCGA data. R.F.S., H.D., K.D., L.B., V.I.G., P.P., M.H., F.T. and A.G., along with all of the institutions contributing to the study group (AMLSG), recruited patients in this study, and collated and contributed clinical data. N.B., P.G. and U.M. provided input into analyses and interpretation of results. E.P., K.D., H.D., R.F.S. and P.J.C. initiated the study. P.J.C. and H.D. wrote the manuscript and are joint corresponding authors.

Corresponding authors

Correspondence to Hartmut Döhner or Peter J Campbell.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Constellations of risk factors for overall survival.

(a) Predicted log-hazard for overall survival versus patient outcome (color) of 1,540 patients. The black curve denotes the censored fraction, averaged over 50 adjacent patients. (b) Radar plots showing the distribution of aggregated risk in seven categories for five selected patients. Color indicates the observed survival, with solid lines around each symbol denoting an observed death and censored outcome otherwise. NK, normal karyotype; HR, hazard ratio. (c) Constellations of risk factors for 1,024 randomly selected patients, arranged by hierarchical clustering according to their risk profiles and laid out in 2D according to a Hilbert curve to preserve the proximity of the clustering. Related to Figure 2.

Supplementary Figure 2 Predicted effects of allograft in CR1 or after relapse.

(ad) Predicted outcome for four patients, under a therapy regimen with standard chemotherapy in CR1 and allograft after relapse (left) or with allograft in CR1 (right). The height of each colored segment corresponds to the probability of being alive in remission (purple), alive after relapse (yellow), dead after relapse (green) or dead without relapse (blue). The bars at the bottom illustrate the actual outcome and treatment the patient received. Predictions are based on a model where the given patient was excluded. Related to Figure 3.

Supplementary Figure 3 Effects of allogeneic transplants.


(ae) Kaplan–Meier curves for patients from different ELN risk groups. In each individual panel, patients were stratified by their predicted benefit from allografts in CR1 and by whether they had received allogeneic transplant in CR1.
There was a clear benefit to allogeneic transplants over chemotherapy in CR1 in the 12% of patients we predicted to have >10 percentage points improvement in absolute survival (HR = 0.5, 95% CI = 0.3–0.81, P = 0.003, ELN-stratified Cox proportional-hazards model). In contrast, for the other 88% of patients, the stratified hazard ratio was found to be HR = 0.74 with 95% CI = 0.55–0.89 (P = 0.01), matching our predictions of lower benefit from transplant of these patients in CR1. Related to Figure 4c.

Supplementary Figure 4 Predicted population-level survival gains for different estimates of overall benefit of early allograft.


(a) Scenario with low benefit of allografts in CR1 (5% quantile of all predictions). This scenario is based on the high toxicity of allografts, especially in elderly patients. Overall, only about one-third of patients (in CR, younger than 60 years) would benefit from an early allograft, as indicated by the decline of the red curve, beyond 75% total allografts (CR1 + relapse combined). The added survival of the knowledge bank would be about 1%, and the same survival could be achieved using much fewer allografts. (b) Scenario with intermediate benefit (50% quantile of all predictions), similar to Figure 4d. (c) Scenario with high benefit (95% quantile of all predictions). In this limit case, all patients are predicted to benefit from early allografts and there is no decline of the curves. Related to Figure 5.

Supplementary Figure 5 Screenshot of the multistage calculator.

The calculator is available online at http://cancer.sanger.ac.uk/aml-multistage. A docker image can be installed locally from https://hub.docker.com/r/mg14/aml-multistage. All source code can be found at http://github.com/mg14/aml-multistage.

Supplementary Figure 6 Implications of the number of genes sequenced.


(a) Subsampling of genes shows that the predicted variation in risk increases linearly with the average number of mutations per patient. This allows extrapolation from this cohort (111 genes sequenced; average of 2.3 driver mutations per patient) to an exome study (TCGA: ~20,000 genes sequenced; ~3.7 driver mutations predicted per patient.
(b) Algorithmic imputation of missing genes based on the multistage model, as implemented in the web portal. Shown is the concordance if only information on a subset of genes is present. With seven genes, the concordance is about 72% and plateaus at about 30 genes entered into the portal.

Supplementary Figure 7 Extrapolated power for detecting novel associations.

(a) Power calculations showing the expected ability to detect significant genomic features as a function of effect and mutation frequency for cohort sizes of 100, 1,540 and 10,000 patients. Shown are selected terms and the distribution of effect sizes in the random-effects model. (b) Distribution of P values (y axis) in comprehensive simulations using 100 (+), 1,000 (Δ) and 10,000 (o) simulated patients, respectively. P values are shown as a function of the product of N (sample size), ψ (uncensored fraction), p (mutation frequency) and β2 (squared log-hazard), as indicated on the x axis. The solid line indicates an analytical approximation by Schoenfeld, described in Schmoor (2000).

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Gerstung, M., Papaemmanuil, E., Martincorena, I. et al. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet 49, 332–340 (2017). https://doi.org/10.1038/ng.3756

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