Associations of blood lipids and LDL cholesterol lowering drug-targets with colorectal cancer risk: a Mendelian randomisation study.

Background: Whether blood lipids are causally associated with colorectal cancer (CRC) risk remains unclear.

Methods: Using two-sample Mendelian randomisation (MR), our study examined the associations of genetically-predicted blood concentrations of lipids and lipoproteins (primary: LDL-C, HDL-C, triglycerides, and total cholesterol), and genetically-proxied inhibition of HMGCR, NPC1L1, and PCSK9 (which mimic therapeutic effects of LDL-lowering drugs), with risks of CRC and its subsites. Genetic associations with lipids were obtained from the Global Lipids Genetics Consortium (n = 1,320,016), while genetic associations with CRC were obtained from the largest existing CRC consortium (n = 58,221 cases and 67,694 controls).

Ataxia telangiectasia-mutated rs56009889 and risk of common cancers.

A polymorphic variant in the ataxia telangiectasia-mutated (ATM) gene, rs56009889, was recently associated with an increased risk of lung cancer. We studied the role of this variant in the etiology of other cancers. Data from three population-based case-control studies of colon, breast, and lung cancer were used.

Differences in tumor-associated T-cell receptor repertoires between early-onset and average-onset colorectal cancer.

The incidence of colorectal cancer (CRC) among individuals younger than age 50 (early-onset CRC [EOCRC]) has substantially increased, and yet the etiology and molecular mechanisms underlying this alarming rise remain unclear. We compared tumor-associated T-cell repertoires between EOCRC and average-onset CRC (AOCRC) to uncover potentially unique immune microenvironment-related features by age of onset. Our discovery cohort included 242 patients who underwent surgical resection at Cleveland Clinic from 2000 to 2020.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes.

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk.

Germline genetic regulation of the colorectal tumor immune microenvironment.

Objective: To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC).

Methods: Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA).

Large-scale alternative polyadenylation (APA)-wide association studies to identify putative susceptibility genes in human common cancers.

Alternative polyadenylation (APA) modulates mRNA processing in the 3' untranslated regions (3'UTR), which affect mRNA stability and translation efficiency. Here, we build genetic models to predict APA levels in multiple tissues using sequencing data of 1,337 samples from the Genotype-Tissue Expression, and apply these models to assess associations between genetically predicted APA levels and cancer risk with data from large genome-wide association studies of six common cancers, including breast, ovary, prostate, colorectum, lung, and pancreas among European-ancestry populations. At a Bonferroni-corrected □<□0.

Real-world clinical effectiveness of sorafenib among patients with unresectable hepatocellular carcinoma at two centers in the United States.

Background: In the United States, sorafenib monotherapy was approved in 2007 for first-line (1L) treatment of patients with unresectable hepatocellular carcinoma (uHCC). As other therapies have been approved in recent years for hepatocellular carcinoma treatment in later lines, it is essential to assess clinical effectiveness of older therapies in actual clinical practice to inform healthcare practitioners' decisions for better patient care.

Aim: To assess patient characteristics/clinical effectiveness of 1L sorafenib in uHCC patients treated in United States academic and community practice settings.

Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer.

Objective: Reduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host's ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC).

Methods: We imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC).

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation.

Clinical Network Systems Biology: Traversing the Cancer Multiverse.

In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance.