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Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

Background: WHO has developed a global HIV-drug resistance surveillance strategy, including assessment of pretreatment HIV-drug resistance. We aimed to do a nationally representative survey of pretreatment HIV-drug resistance in Mexico using WHO-recommended methods., Methods: Among 161 Ministry of Health antiretroviral therapy (ART) clinics in Mexico, the largest, including 90% of ART initiators within the Ministry of Health (66 in total), were eligible for the survey. We used a probability-proportional-to-size design method to sample 25 clinics throughout the country. Consecutive ART-naive patients with HIV about to initiate treatment were invited to participate in the survey; individuals with previous exposure to ART were excluded. We assessed pretreatment HIV-drug resistance by Sanger sequencing and next-generation sequencing of viruses from plasma specimens from eligible participants with Stanford University HIV Drug Resistance Database methods. We obtained follow-up data for a median of 9·4 months (range 6-12) after enrolment. We investigated possible relations between demographic variables and pretreatment drug resistance with univariate and multivariate logistic regression., Findings: Between Feb 3 and July 30, 2015, we screened 288 patients in 25 clinics, from whom 264 provided successfully sequenced viruses with no evidence of current exposure to antiretroviral drugs. With the Sanger method, of these 264 participants, 41 (15·5%, 95% CI 11·4-20·5) had pretreatment resistance to any antiretroviral drug and 28 (10·6%, 7·2-15·0) had pretreatment resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs). At least low-level pretreatment resistance (Stanford penalty score ≥15) was noted in 13 (4 · 9%) of participants to efavirenz and in 23 (8·7%) to the combination tenofovir plus emtricitabine plus efavirenz. With next-generation sequencing, of 264 participants, 38 (14·4%, 95% CI 10·4-19·2) had pretreatment resistance to any antiretroviral drug and 26 (9·8%, 6·5-14·1) had pretreatment resistance to NNRTIs. After median follow-up of 8 months (IQR 6·5-9·4, range 5-11) after ART initiation, 97 (72%) of 135 NNRTI initiators achieved viral suppression (<50 copies per mL) compared with ten (40%) of 25 individuals who started with protease inhibitor-based regimens (p=0·0045). After multivariate regression considering pretreatment resistance and initial ART regimen as composite variables, people starting NNRTIs with pretreatment drug resistance achieved significantly lower viral suppression (odds ratio 0·24, 95% CI 0·07-0·74; p=0·014) than patients without NNRTI resistance., Interpretation: High levels of pretreatment drug resistance were noted in Mexico, and NNRTI pretreatment drug resistance significantly reduced the effectiveness of first-line ART regimens based on these drugs. Baseline HIV-drug resistance testing for initial ART follow-up and decision making should be considered., Funding: The Mexican Government and Consejo Nacional de Ciencia y Tecnología., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Killer cell immunoglobulin-like receptors (KIRs) represent the most polymorphic genes responsible for natural killer cell function, while human leukocyte antigen (HLA) class I molecules define and restrict cytotoxic T lymphocyte responses. Specific KIR, HLA, or KIR-HLA combinations have been implicated in the outcome of human immunodeficiency virus (HIV) disease. The remarkable polymorphism of KIR and HLA genes warrants descriptive gene frequency studies in different populations, as well as their impact on HIV disease progression in different immunogenetic contexts. We report KIR and HLA class I gene profiles of 511 unrelated HIV-infected Mexican Mestizo individuals from 18 states for whom genetic ancestry proportions were assessed. KIR and HLA gene profiles were compared between individuals from the north and central-south regions of the country and between individuals with higher European (EUR) or Amerindian (AMI) genetic ancestry component. A total of 65 KIR genotypes were observed, 11 harboring novel KIR gene combinations. A total of 164 HLA alleles were observed: 43 HLA-A, 87 HLA-B, and 34 HLA-C. Differences in the distribution of 12 HLA alleles were observed between individuals with higher AMI or EUR ancestry components (p < 0.05, q < 0.2). After correcting for genetic ancestry, only individual HLA alleles were associated with HIV disease progression, including a novel association with A*02:06, an Amerindian HLA allele associated with lower CD4+ T cell counts. No KIR effects were significant. Our results highlight the advantages of considering a detailed genetic stratification within populations when studying genetic profiles that could be implicated in disease-association studies.