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 dependencies 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.

Castration-resistant prostate cancer (CRPC) is an incurable form of prostate cancer (PCa), with DNMT1 and G9a being reported as overexpressed, rendering them highly attractive targets for precision medicine. CM-272 is a dual inhibitor of both methyltransferases' activity. Herein, we assessed the response of different PCa cell lines to CM-272, in both 2D and 3D models, and explored the molecular mechanisms underlying CM-272 inhibitory effects.CRPC tissues displayed significantly higher DNMT1, G9a and H3K9me2 expression than localized PCa. In vitro, CM-272 caused a significant decrease in PCa cell viability and proliferation alongside with increased apoptotic levels. We disclose that, under the evaluated dose, CM-272 led to G9a activity inhibition, while not significantly affecting DNMT1 activity. Upon G9a knockdown, DU145 and PC3 showed decreased cell viability. Remarkably, DU145 cells treated with CM-272 or with G9a knockdown displayed no differences in viability, suggesting a SET dependent mechanism. Contrarily, PC3 cell viability impact was higher in G9a knockdown, compared with CM 272 treatment, suggesting an additional G9a function. Moreover, DU145 cells overexpressing catalytically functional G9a disclosed higher resistance to CM-272 treatment, reinforcing that the drug mechanism of action is dependent on G9a catalytic function.Importantly, we successfully assembled spheroids from several prostate cell lines. Our results showed that CM 272 retained its anti-tumoral effects in 3D PCa models, leading to a clear reduction in cancer cell survival. We concluded that inhibition of G9a methyltransferase activity by CM-272 has anti-tumor effect in PCa cells, holding therapeutic potential against CRPC.

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.

Simple Summary Multiple Myeloma (MM) is considered an incurable chronic disease, which prognosis depends on the presence of different genomic alterations. To accomplish a complete molecular diagnosis in a single essay, we have designed and validated a capture-based NGS approach to reliably identify pathogenic mutations (SNVs and indels), genomic alterations (CNVs and chromosomic translocations), and IGH rearrangements. We have observed a good correlation of the results obtained using our capture panel with data obtained by both FISH and WES techniques. In this study, the molecular classification performed using our approach was significantly associated with the stratification and outcome of MM patients. Additionally, this panel has been proven to detect specific IGH rearrangements that could be used as biomarkers in patient follow-ups through minimal residual disease (MRD) assays. In conclusion, we think that MM patients could benefit from the use of this capture-based NGS approach with a more accurate, single-essay molecular diagnosis. Next-generation sequencing (NGS) has greatly improved our ability to detect the genomic aberrations occurring in multiple myeloma (MM); however, its transfer to routine clinical labs and its validation in clinical trials remains to be established. We designed a capture-based NGS targeted panel to identify, in a single assay, known genetic alterations for the prognostic stratification of MM. The NGS panel was designed for the simultaneous study of single nucleotide and copy number variations, insertions and deletions, chromosomal translocations and V(D)J rearrangements. The panel was validated using a cohort of 149 MM patients enrolled in the GEM2012MENOS65 clinical trial. The results showed great overall accuracy, with positive and negative predictive values close to 90% when compared with available data from fluorescence in situ hybridization and whole-exome sequencing. While the treatments used in the clinical trial showed high efficacy, patients defined as high-risk by the panel had shorter progression-free survival (p = 0.0015). As expected, the mutational status of TP53 was significant in predicting patient outcomes (p = 0.021). The NGS panel also efficiently detected clonal IGH rearrangements in 81% of patients. In conclusion, molecular karyotyping using a targeted NGS panel can identify relevant prognostic chromosomal abnormalities and translocations for the clinical management of MM patients.

Acute myeloid leukemia (AML) in the elderly remains a clinical challenge, with a five-year overall survival rate below 10%. The current ELN 2017 genetic risk classification considers cytogenetic and mutational characteristics to stratify fit AML patients into different prognostic groups. However, this classification is not validated for elderly patients treated with a non-intensive approach, and its performance may be suboptimal in this context. Indeed, the transcriptomic landscape of AML in the elderly has been less explored and it might help stratify this group of patients. In the current study, we analyzed the transcriptome of 224 AML patients > 65 years-old at diagnosis treated in the Spanish PETHEMA-FLUGAZA clinical trial in order to identify new prognostic biomarkers in this population. We identified a specific transcriptomic signature for high-risk patients with mutated TP53 or complex karyotype, revealing that low expression of B7H3 gene with high expression of BANP gene identifies a subset of high-risk AML patients surviving more than 12 months. This result was further validated in the BEAT AML cohort. This unique signature highlights the potential of transcriptomics to identify prognostic biomarkers in in elderly AML.

The study of nutrigenetics and nutrigenomics requires an identification of the genetic background of an organism to determine how this affects nutrient metabolism and the effects that the nutrients have on it. Advances in sequencing technologies have caused a shift from gene-based to genome-wide analyses that have provided information previously unsuspected and unavailable. Although these techniques are commonly used, they are still evolving and susceptible to improvement: therefore, some potential biases need to be considered regarding their performance and use. In this chapter, we describe the most common techniques used to study nutrigenetics and nutrigenomics, focusing on key steps to be followed to obtain reliable results.