ersistent HPV infection, in episomal or integrated form, is necessary but not sufficient for the development of cervical cancer 1. HPV-16 and HPV-18 are detected in at least 70% of affected individuals 2. HPV-16 (clade A9) is common in both squamous cell carcinomas and adenocarcinomas, while HPV-18 (clade A7) is associated with adenocarcinomas 2 and inferior survival 3-5. Cervical cancer prevention strategies include vaccination and screening for HPV and treatment of high-grade precancer. Although effective 6 , vaccine use remains low in low-and middle-income countries 7 where HIV is prevalent. Resource constraints similarly complicate screening, surgery 8 and radiotherapy 9 , such that a 50% increase in cervical cancer mortality by 2040 is predicted 10. Genomic cervical cancer studies, primarily conducted in non-African individuals 11,12 , identified APOBEC mutational signatures, copy number amplifications of CD274 (PD-L1) and PDCD1LG2 (PD-L2), somatic alterations affecting the PI(3)K-MAPK and TGFβR2 pathways 11,12 and mutations in chromatin modifier genes 11-13. Studies in HPV-infected individuals with head and neck squamous cell carcinomas linked HPV integration to changes in histone modification 14 and DNA methylation 15 , suggesting the potential for similar findings in cervical cancer. As part of the National Cancer Institute's (NCI's) HIV+ Tumor Molecular Characterization Project (HTMCP), we characterized the genomic, transcriptomic and epigenomic landscapes of cervical cancers from Ugandan patients. We identified previously uncharacterized differences in the epigenomes and transcriptomes of cervical tumors from individuals infected by different HPV clades and note that these clades appear relevant to prognosis. Results Patient samples and clinical data. Our cohort of 212 patients with cervical cancer received treatment at the Uganda Cancer Institute in Kampala. Of these, 118 made up our discovery cohort and 89 made up our extension cohort (Supplementary Tables 1 and 2, and Methods). HIV + patients (72/118, 61%) were 10 years younger, on average, than HIV-negative (HIV-) patients (mean, 42.9 versus 52.4 years).

Autophagy is an evolutionarily conserved cellular recycling process in cell homeostasis and stress adaptation. It confers protection and promotes survival in response to metabolic/environmental stress, and is upregulated in response to nutrient deprivation, hypoxia, and chemotherapies. Autophagy is also known to sustain malignant cell growth and contributes to cancer stem cell survival when challenged by cytotoxic and/or targeted therapies, a potential mechanism of disease persistence and drug resistance that has gathered momentum. However, different types of human leukemia utilize autophagy in complex, context-specific manners, and the molecular and cellular mechanisms underlying this process involve multiple protein networks that will be discussed in this review. There is mounting preclinical evidence that targeting autophagy can enhance the efficacy of cancer therapies. Chloroquine and other lysosomal inhibitors have spurred initiation of clinical trials and demonstrated that inhibition of autophagy restores chemosensitivity of anticancer drugs, but with limited autophagy-dependent effects. Intriguingly, several autophagy-specific inhibitors, with better therapeutic indexes and lower toxicity, have been developed. Promising preclinical studies with novel combination approaches as well as potential challenges to effectively eradicate drug-resistant cells, particularly cancer stem cells, in human leukemia are also detailed in this review.

BackgroundMany gram-negative bacteria use type III secretion systems (T3SSs) to translocate effector proteins into host cells. T3SS effectors can give some bacteria a competitive edge over others within the same environment and can help bacteria to invade the host cells and allow them to multiply rapidly within the host. Therefore, developing efficient methods to identify effectors scattered in bacterial genomes can lead to a better understanding of host-pathogen interactions and ultimately to important medical and biotechnological applications.ResultsWe used 21 genomic and proteomic attributes to create a precise and reliable T3SS effector prediction method called Genome Search for Effectors Tool (GenSET). Five machine learning algorithms were trained on effectors selected from different organisms and a trained (voting) algorithm was then applied to identify other effectors present in the genome testing sets from the same (GenSET Phase 1) or different (GenSET Phase 2) organism. Although a select group of attributes that included the codon adaptation index, probability of expression in inclusion bodies, N-terminal disorder, and G + C content (filtered) were better at discriminating between positive and negative sets, algorithm performance was better when all 21 attributes (unfiltered) were used. Performance scores (sensitivity, specificity and area under the curve) from GenSET Phase 1 were better than those reported for six published methods. More importantly, GenSET Phase 1 ranked more known effectors (70.3%) in the top 40 ranked proteins and predicted 10–80% more effectors than three available programs in three of the four organisms tested. GenSET Phase 2 predicted 43.8% effectors in the top 40 ranked proteins when tested on four related or unrelated organisms. The lower prediction rates from GenSET Phase 2 may be due to the presence of different translocation signals in effectors from different T3SS families.ConclusionsThe species-specific GenSET Phase 1 method offers an alternative approach to T3SS effector prediction that can be used with other published programs to improve effector predictions. Additionally, our approach can be applied to predict effectors of other secretion systems as long as these effectors have translocation signals embedded in their sequences.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3363-1) contains supplementary material, which is available to authorized users.