DataSheet_2_Explainable artificial intelligence for precision medicine in acute myeloid leukemia.pdf

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of “black box” in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFβ-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.</p

DataSheet_1_Explainable artificial intelligence for precision medicine in acute myeloid leukemia.xlsx

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of “black box” in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFβ-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.</p

Maximum enrichment values on experimentally-validated targets for LDS dataset.

<p>The table shows the maximum enrichment values (point of minimum p-value) for the union of TaRBase and miRecords, for MCC dataset. N_E: is the number of experimentally-validated targets rescued in the point of minimum p-value and N_T: is the total number of predicted targets in that minimum. N_E⁵⁰⁰: is the amount of experimentally-validated targets in the first 500 predicted interactions.</p

KEGG pathway enrichment results for LDS dataset.

<p>Enrichment analysis on KEGG pathways of the 200 top-ranked genes. The figure shows the results for TaLasso, GenMiR++ and Pearson Correlation. In the figure, the x-axis indicates the number of mRNAs on each enriched pathway. The associated p-value is also shown. The list of genes on each enriched KEGG pathway is included in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030766#pone.0030766.s004" target="_blank">text S2</a>.</p

Maximum enrichment values on experimentally-validated targets for MCC dataset.

<p>The table shows the maximum enrichment values (point of minimum p-value) for the union of TaRBase and miRecords, for MCC dataset. N_E: is the number of experimentally-validated targets rescued in the point of minimum p-value and N_T: is the total number of predicted targets in that minimum. N_E⁵⁰⁰: is the amount of experimentally-validated targets in the first 500 predicted interactions.</p

Enrichment on experimentally-validated targets for LDS dataset.

<p>For each value of the tuning factor and different number of predicted interactions, the figure shows the probability of drawing the predicted number of experimentally-validated targets by using a hypergeometric test. The figure shows TaLasso enrichment results for different <i>κ^G</i> values (in blue), compared to the enrichment values of GenMiR++ (black crosses) and Pearson Correlation (black dashed).</p

Shared interactions among the different databases of human miRNA targets that have been used as initial set of putative interactions.

<p>The overlap among the different databases is small. With reference to databases with experimentally-validated targets, the union of miRecords and TarBase includes 623 interactions that are also cited in any of the computationally predicted databases. This number rises to 4372 in case miRWalk is also considered.</p

Predicted experimentally-validated targets and the cancer to which they have been related in the literature: results for LDS dataset.

<p>CLL: Chronic Lymphoblastic Leukaemia, ALL: Acute Lymphoblastic Leukaemia, AML: Acute Myeloid Leukaemia, IC: Immunce Cells, IR: Immune Response, HSC: Haematopoietic SC.</p><p>The experimentally-validated targets included in the top 500 targets predicted were selected and their literature references included on TaRBase, miRecords and miRWalk were analyzed in search of biological relevancy. In the table only those interactions with a literature reference related with LDS environment have been included. This was made for the predictions of TaLasso, GenMiR++ and Pearson Correlation.</p

MSE errors for Cross Validation analysis for <i>global tuning parameters</i>.

<p>In the figure the LOOCV mean squared errors for different κ values of the <i>global tuning parameter</i> for MCC and LDS datasets are shown.</p

Reversible dual inhibitor against G9a and DNMT1 improves human iPSC derivation enhancing MET and facilitating transcription factor engagement to the genome

<div><p>The combination of defined factors with small molecules targeting epigenetic factors is a strategy that has been shown to enhance optimal derivation of iPSCs and could be used for disease modelling, high throughput screenings and/or regenerative medicine applications. In this study, we showed that a new first-in-class reversible dual G9a/DNMT1 inhibitor compound (CM272) improves the efficiency of human cell reprogramming and iPSC generation from primary cells of healthy donors and patient samples, using both integrative and non-integrative methods. Moreover, CM272 facilitates the generation of human iPSC with only two factors allowing the removal of the most potent oncogenic factor cMYC. Furthermore, we demonstrated that mechanistically, treatment with CM272 induces heterochromatin relaxation, facilitates the engagement of OCT4 and SOX2 transcription factors to OSKM refractory binding regions that are required for iPSC establishment, and enhances mesenchymal to epithelial transition during the early phase of cell reprogramming. Thus, the use of this new G9a/DNMT reversible dual inhibitor compound may represent an interesting alternative for improving cell reprogramming and human iPSC derivation for many different applications while providing interesting insights into reprogramming mechanisms.</p></div