AI News, Artificial intelligence in clinical and genomic diagnostics. artificial intelligence

ASH Annual Meeting

While the hypomethylating agents (HMAs) azacitidine (AZA) and decitabine (DAC) improve cytopenias and prolong survival in MDS patients (pts), response is not guaranteed.

We developed a clinical model to predict response or resistance to HMA after 90 days of initiating therapy based on changes in blood counts using time series analysis technology similar to the kind used in Apple’s Siri or Google Assistant.

To address the potential for bias due to a small sample size, an oversampling algorithm was used to cluster similar pts based on their CBC data, Revised International Prognostic Scoring System (IPSS-R) score, and % bone marrow blasts at the time of diagnosis.

CBC data from the first 90 days of treatment were fed into deep neural network (recurrent neural network) and decision tree algorithms, which were trained to predict whether pts would achieve a response (defined as complete remission (CR), partial remission (PR), or hematologic Improvement (HI)).

Feature extraction algorithms identified increases in MCV and RDW during weeks 2-8 of treatment, increased proportion of lymphocytes, decreased proportion of monocytes, and increased platelet counts during weeks 6-8 as factors favoring response to HMA.

Such a model can be used to develop novel trial designs wherein pts predicted to not respond after 90 days of HMA treatment could be assigned to an investigational agent.

The power and potential of integrated diagnostics in acute myeloid leukaemia

The detection of residual disease in haematological neoplasms has been improved in parallel to therapy optimisation.

Sensitivities of 1:20 (cytomorphology) (Schuurhuis et al., 2018), or 1:100 (FISH) (Ravandi et al., 2018) were never thought to be sufficient to reliably monitor diseases kinetics in AML.

Sensitivities of 10−4 and 10−6 are needed to assess residual disease, and this can be achieved by using state‐of‐the‐art molecular approaches or multiparameter flow cytometry (MFC) with 8–10 colours (Schuurhuis et al., 2018).

The DfN approach focuses on leukaemia‐ rather than on patient‐specific aberrant markers and allows flow cytometric MRD monitoring even if the leukaemic immunophenotype at diagnosis is unknown.

In digital PCR (dPCR), the compartmentalisation of the reaction volume permits a binary fluorescence signal read out (signal or no signal) after PCR and thus absolute quantification (Sykes et al., 1992;

Compared to qPCR it offers several advantages: in addition to an improved signal‐to‐noise ratio and the independence from standards, potentially present PCR inhibitors and PCR efficiency have a much smaller influence on the measurement (Huggett et al., 2015;

Based on its properties, dPCR is a suitable and feasible method for sensitive MRD monitoring (Cilloni et al., 2019) and is likely to prove its value in the clinical setting.

Moreover, molecular monitoring of patients with t(8;21)/RUNX1‐RUNX1T1 or with mutated NPM1 after induction and consolidation therapy identified those at high risk of relapse and thus beneficiaries of allo‐SCT (Zhu et al., 2013) and/or high‐dose cytarabine (Krönke et al., 2011;

Molecular MRD markers were identified by the authors at diagnosis using a panel of 54 genes associated with myeloid neoplasms (Jongen‐Lavrencic et al., 2018).

If this was to be validated in broader prospective studies, molecular MRD detection for almost every AML patient would be feasible, since 96% of patients carry at least one driver mutation (Papaemmanuil et al., 2016).

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