Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.

With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism.

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, CBF beta-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.

Simple Summary This work shows that the predictions of lethal dependencies (LEDs) between genes can be dramatically improved by incorporating the "HUb effect in Genetic Essentiality" (HUGE) of gene alterations. In three genome-wide loss-of-function screens-Project Score, CERES score and DEMETER score-LEDs are identified with 75 times larger statistical power than using state-of-the-art methods. In AML, we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal de-pendencies of either NRAS or PTPN11 depending on the NRAS mutational status. Recent functional genomic screens-such as CRISPR-Cas9 or RNAi screening-have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem is related to a large number of gene knockouts limiting the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the "HUb effect in Genetic Essentiality" (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens-Project Score, CERES score and DEMETER score-identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. Using acute myeloid leukemia, breast cancer, lung adenocarcinoma and colon adenocarcinoma as disease models, we validate that our predictions are enriched in a recent harmonized knowledge base of clinical interpretations of somatic genomic variants in cancer (AUROC > 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer. All the graphs showing lethal dependencies for the 19 tumor types analyzed can be visualized in an interactive tool.

Immunotherapy has improved the treatment of malignant skin cancer of the melanoma type, yet overall clinical response rates remain low. Combination therapies could be key to meet this cogent medical need. Because epigenetic hallmarks represent promising combination therapy targets, we studied the immunogenic potential of a dual inhibitor of histone methyltransferase G9a and DNA methyltransferases (DNMTs) in the preclinical B16-OVA melanoma model. Making use of tumor transcriptomic and functional analyses, methylation-targeted epigenetic reprogramming was shown to induce tumor cell cycle arrest and apoptosis in vitro coinciding with transient tumor growth delay and an IFN-I response in immune-competent mice. In consideration of a potential impact on immune cells, the drug was shown not to interfere with dendritic cell maturation or T-cell activation in vitro. Notably, the drug promoted dendritic cell and, to a lesser extent, T-cell infiltration in vivo, yet failed to sensitize tumor cells to programmed cell death-1 inhibition. Instead, it increased therapeutic efficacy of TCR-redirected T cell and dendritic cell vaccination, jointly increasing overall survival of B16-OVA tumor-bearing mice. The reported data confirm the prospect of methylation-targeted epigenetic reprogramming in melanoma and sustain dual G9a and DNMT inhibition as a strategy to tip the cancer-immune set-point towards responsiveness to active and adoptive vaccination against melanoma.

Concomitant inhibition of key epigenetic pathways involved in silencing tumor suppressor genes has been recognized as a promising strategy for cancer therapy. Herein, we report a first-in-class series of quinoline-based analogues that simultaneously inhibit histone deacetylases (from a low nanomolar range) and DNA methyltransferase-1 (from a mid-nanomolar range, IC50 < 200 nM). Additionally, lysine methyltransferase G9a inhibitory activity is achieved (from a low nanomolar range) by introduction of a key lysine mimic group at the 7-position of the quinoline ring. The corresponding epigenetic functional cellular responses are observed: histone-3 acetylation, DNA hypomethylation, and decreased histone-3 methylation at lysine-9. These chemical probes, multitarget epigenetic inhibitors, were validated against the multiple myeloma cell line MM1.S, demonstrating promising in vitro activity of 12a (CM-444) with GI(50) of 32 nM, an adequate therapeutic window (>1 log unit), and a suitable pharmacokinetic profile. In vivo, 12a achieved significant antitumor efficacy in a xenograft mouse model of human multiple myeloma.

Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on bone marrow mesenchymal stromal cells (MSCs). MM-MSCs exhibit abnormal transcriptomes, suggesting the involvement of epigenetic mechanisms governing their tumor-promoting functions and prolonged osteoblast suppression. Here, we identify widespread DNA methylation alterations of bone marrow-isolated MSCs from distinct MM stages, particularly in Homeobox genes involved in osteogenic differentiation that associate with their aberrant expression. Moreover, these DNA methylation changes are recapitulated in vitro by exposing MSCs from healthy individuals to MM cells. Pharmacological targeting of DNMTs and G9a with dual inhibitor CM-272 reverts the expression of hypermethylated osteogenic regulators and promotes osteoblast differentiation of myeloma MSCs. Most importantly, CM-272 treatment prevents tumor-associated bone loss and reduces tumor burden in a murine myeloma model. Our results demonstrate that epigenetic aberrancies mediate the impairment of bone formation in MM, and its targeting by CM-272 is able to reverse MBD. Mesenchymal stromal cells (MSCs) have been shown to support multiple myeloma (MM) development. Here, MSCs isolated from the bone marrow of MM patients are shown to have altered DNA methylation patterns and a methyltransferase inhibitor reverts MM-associated bone loss and reduces tumour burden in MM murine models.

Multiple myeloma (MM) is a plasma cell neoplasm associated with a broad variety of genetic lesions. In spite of this genetic heterogeneity, MMs share a characteristic malignant phenotype whose underlying molecular basis remains poorly characterized. In the present study, we examined plasma cells from MM using a multi-epigenomics approach and demonstrated that, when compared to normal B cells, malignant plasma cells showed an extensive activation of regulatory elements, in part affecting coregulated adjacent genes. Among target genes up-regulated by this process, we found members of the NOTCH, NF-kB, MTOR signaling, and TP53 signaling pathways. Other activated genes included sets involved in osteoblast differentiation and response to oxidative stress, all of which have been shown to be associated with the MM phenotype and clinical behavior. We functionally characterized MM-specific active distant enhancers controlling the expression of thioredoxin (TXN), a major regulator of cellular redox status and, in addition, identified PRDM5 as a novel essential gene for MM. Collectively, our data indicate that aberrant chromatin activation is a unifying feature underlying the malignant plasma cell phenotype.

Granulocytic myeloid-derived suppressor cells (G-MDSCs) promote tumor growth and immunosuppression in multiple myeloma (MM). However, their phenotype is not well established for accurate monitoring or clinical translation. We aimed to provide the phenotypic profile of G-MDSCs based on their prognostic significance in MM, immunosuppressive potential, and molecular program. The preestablished phenotype of G-MDSCs was evaluated in bone marrow samples from controls and MM patients using multidimensional flow cytometry; surprisingly, we found that CD11b+CD14-CD15+CD33+HLADR- cells overlapped with common eosinophils and neutrophils, which were not expanded in MM patients. Therefore, we relied on automated clustering to unbiasedly identify all granulocytic subsets in the tumor microenvironment: basophils, eosinophils, and immature, intermediate, and mature neutrophils. In a series of 267 newly diagnosed MM patients (GEM2012MENOS65 trial), only the frequency of mature neutrophils at diagnosis was significantly associated with patient outcome, and a high mature neutrophil/T-cell ratio resulted in inferior progression-free survival (P < .001). Upon fluorescence-activated cell sorting of each neutrophil subset, T-cell proliferation decreased in the presence of mature neutrophils (0.5-fold; P = .016), and the cytotoxic potential of T cells engaged by a BCMA×CD3-bispecific antibody increased notably with the depletion of mature neutrophils (fourfold; P = .0007). Most interestingly, RNA sequencing of the 3 subsets revealed that G-MDSC-related genes were specifically upregulated in mature neutrophils from MM patients vs controls because of differential chromatin accessibility. Taken together, our results establish a correlation between the clinical significance, immunosuppressive potential, and transcriptional network of well-defined neutrophil subsets, providing for the first time a set of optimal markers (CD11b/CD13/CD16) for accurate monitoring of G-MDSCs in MM.

Epigenetic modifications such as DNA and histone methylation functionally cooperate in fostering tumor growth, including that of hepatocellular carcinoma (HCC). Pharmacological targeting of these mechanisms may open new therapeutic avenues. We aimed to determine the therapeutic efficacy and potential mechanism of action of our dual G9a histone-methyltransferase and DNA-methyltransferase 1 (DNMT1) inhibitor in human HCC cells and their crosstalk with fibrogenic cells. The expression of G9a and DNMT1, along with that of their molecular adaptor ubiquitin-like with PHD and RING finger domains-1 (UHRF1), was measured in human HCCs (n = 268), peritumoral tissues (n = 154), and HCC cell lines (n = 32). We evaluated the effect of individual and combined inhibition of G9a and DNMT1 on HCC cell growth by pharmacological and genetic approaches. The activity of our lead compound, CM-272, was examined in HCC cells under normoxia and hypoxia, human hepatic stellate cells and LX2 cells, and xenograft tumors formed by HCC or combined HCC+LX2 cells. We found a significant and correlative overexpression of G9a, DNMT1, and UHRF1 in HCCs in association with poor prognosis. Independent G9a and DNMT1 pharmacological targeting synergistically inhibited HCC cell growth. CM-272 potently reduced HCC and LX2 cells proliferation and quelled tumor growth, particularly in HCC+LX2 xenografts. Mechanistically, CM-272 inhibited the metabolic adaptation of HCC cells to hypoxia and induced a differentiated phenotype in HCC and fibrogenic cells. The expression of the metabolic tumor suppressor gene fructose-1,6-bisphosphatase (FBP1), epigenetically repressed in HCC, was restored by CM-272. Conclusion: Combined targeting of G9a/DNMT1 with compounds such as CM-272 is a promising strategy for HCC treatment. Our findings also underscore the potential of differentiation therapy in HCC.