A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

SIGNIFICANCE

Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

Treatment of Acute Promyelocytic Leukemia in Adults

The treatment of acute promyelocytic leukemia (APL) has evolved rapidly in the past two decades after the introduction of highly active drugs, including tretinoin (all- trans-retinoic acid) and arsenic trioxide. It is now possible to treat this disease without the use of traditional cytotoxic chemotherapy. Today’s clinical guidelines include multiple regimens, some of which continue to use cytotoxic chemotherapy. This leaves the practicing oncologist with multiple treatment options when faced with a new case of APL. In an effort to standardize our approach to the treatment of newly diagnosed APL, we sought to develop a set of treatment recommendations at our institution. We identified eight major controversial issues in the treatment of APL. These controversial issues include the optimal dose and schedule of both all- trans-retinoic acid and arsenic trioxide, the optimal regimen for high-risk APL, the need for intrathecal prophylaxis, the use of prophylactic corticosteroids, and the need for maintenance therapy after consolidation. We reviewed the relevant literature and used the Delphi method among the coauthors to reach consensus for recommendations on the basis of the best available data and our own clinical experience. In this clinical review, we present our consensus recommendations, the reasoning behind them, and the grading of the evidence that supports them.

Association of single nucleotide polymorphisms in pro-inflammatory cytokine and toll-like receptor genes with pediatric hematogenous osteomyelitis

Hematogenous osteomyelitis (HO) is a bone infection wherein bacteria penetrate to the bone through the blood stream. Several single nucleotide polymorphisms (SNPs) have been associated with susceptibility to infectious diseases. In this study, we investigated the contribution of SNPs in interleukin (IL)-1B1 (rs16944), IL1A (rs1800587), IL1B (rs1143634), toll-like receptor (TLR)-2 (rs3804099), TLR4 (rs4986790), TLR4 (rs4986791), IL1R (rs2234650), tumor necrosis factor (TNF)-α (rs1800629), TNF (rs361525), and IL1RN (rs315952) towards the development of HO in Saudi patients and compared to healthy controls. Fifty-two patients diagnosed with HO and 103 healthy individuals were genotyped. The frequencies of genotypes GG (rs16944) and AA (rs16944) were lower and higher in patients [odds ratio (OR) = 0.34, Pc = 0.05] and controls (OR = 1.33, Pc = 0.05), respectively, suggesting that SNPs at this locus could alter HO susceptibility. In addition, the patients and controls exhibited lower and higher frequencies of the alleles G (rs16944) (OR = 0.43, Pc = 0.007) and A (rs16944) (OR = 2.32, Pc = 0.007), respectively. The expression of alleles C (rs3804099) and T (rs3804099) were higher in patients (OR = 2.05, Pc = 0.04) and controls (OR = 0.49, Pc = 0.04), respectively. In conclusion, SNPs at rs16944 and rs3804099 were found to be associated with HO in the Saudi population.

Investigation of polymorphisms in anti-inflammatory cytokine genes in hematogenous osteomyelitis

Osteomyelitis is a progressive bone infection disease caused by destructive immunological inflammatory reactions following new bone formation. Anti-inflammatory cytokines are a series of immunoregulatory molecules that control the pro-inflammatory cytokine response. In this study, we investigated 9 single nucleotide polymorphisms in 5 different cytokine/cytokine receptor genes in hematogenous osteomyelitis (HO) patients, and compared their outcomes with normal healthy individuals. Sequence-specific forward and reverse primers and two TaqMan® MGB probes with dyes (VIC™ and FAM™) that specifically detect Allele 1 and Allele 2 of each SNP were utilized. The genotypes CC (P = 0.009) and CT (P = 0.041) of SNP rs2070874, and alleles A (P = 0.044) and G of SNP rs1800871 were significantly different between the patients and healthy controls. The expression of the CC genotype or C allele at rs2070874 was a risk factor for HO development, with higher frequencies of CT and T being found in the control samples. The expression of the A allele of rs1800871 was also significantly higher in patients than in controls, and was therefore considered a risk factor.