Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Quantifying the influence of mutation detection on tumour subclonal reconstruction.

In Nature communications ; h5-index 260.0

Whole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.

Liu Lydia Y, Bhandari Vinayak, Salcedo Adriana, Espiritu Shadrielle M G, Morris Quaid D, Kislinger Thomas, Boutros Paul C

2020-12-07

General General

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

In Nature communications ; h5-index 260.0

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

Kandel Mikhail E, He Yuchen R, Lee Young Jae, Chen Taylor Hsuan-Yu, Sullivan Kathryn Michele, Aydin Onur, Saif M Taher A, Kong Hyunjoon, Sobh Nahil, Popescu Gabriel

2020-12-07

Public Health Public Health

Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease.

In Nature communications ; h5-index 260.0

Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of functional annotations. AnnotBoost substantially increases the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying that Mendelian and common disease variants share similar properties. The boosted scores also produce improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores may improve fine-mapping and candidate gene discovery for common disease.

Kim Samuel S, Dey Kushal K, Weissbrod Omer, Márquez-Luna Carla, Gazal Steven, Price Alkes L

2020-12-07

Public Health Public Health

Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes.

In Diabetes care ; h5-index 125.0

OBJECTIVE : To identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features.

RESEARCH DESIGN AND METHODS : We used an interpretable machine learning framework to identify the type 2 diabetes-related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS-type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832).

RESULTS : The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23-1.33), 1.23 (1.13-1.34), and 1.12 (1.06-1.18) across three cohorts. The MRS was positively associated with future glucose increment (P < 0.05) and was correlated with a variety of gut microbiota-derived blood metabolites. Animal study further confirmed the MRS-type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome-type 2 diabetes relationship.

CONCLUSIONS : Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment.

Gou Wanglong, Ling Chu-Wen, He Yan, Jiang Zengliang, Fu Yuanqing, Xu Fengzhe, Miao Zelei, Sun Ting-Yu, Lin Jie-Sheng, Zhu Hui-Lian, Zhou Hongwei, Chen Yu-Ming, Zheng Ju-Sheng

2020-Dec-07

Public Health Public Health

Early-life and adult adiposity, adult height, and benign breast tissue composition.

In Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology

BACKGROUND : Early-life and adult anthropometrics are associated with breast density and breast cancer risk. However, little is known about whether these factors also influence breast tissue composition beyond what is captured by breast density among women with benign breast disease(BBD).

METHODS : This analysis included 788 controls from a nested case-control study of breast cancer within the Nurses' Health Study BBD subcohorts. Body fatness at ages 5 and 10 years was recalled using a 9-level pictogram. Weight at age 18, current weight, and height were reported via questionnaires. A deep-learning image analysis was used to quantify the percentages of epithelial, fibrous stromal, and adipose tissue areas within BBD slides. We performed linear mixed models to estimate beta coefficients(β) and 95% confidence intervals(CIs) for the relationships between anthropometrics and the log-transformed percentages of individual tissue type, adjusting for confounders.

RESULTS : Childhood body fatness(level≥4.5 vs. 1), BMI at age 18 (≥23 vs. <19 kg/m2), and current adult BMI(≥30 vs. <21 kg/m2) were associated with higher proportions of adipose tissue(β[95% CI]=0.34[0.03, 0.65], 0.19[-0.04, 0.42], 0.40[0.12, 0.68], respectively) and lower proportions of fibrous stromal tissue(-0.05[-0.10, 0.002], -0.03[-0.07, 0.003], -0.12[-0.16, -0.07], respectively) during adulthood(all p-trend<0.04). BMI at age 18 was also inversely associated with epithelial tissue(p-trend=0.03). Adult height was not associated with any of the individual tissue types.

CONCLUSIONS : Our data suggest that body fatness have long-term impacts on breast tissue composition.

IMPACT : This study contributes to our understanding of the link between body fatness and breast cancer risk.

Oh Hannah, Yaghjyan Lusine, Austin-Datta Rebecca J, Heng Yujing J, Baker Gabrielle M, Sirinukunwattana Korsuk, Vellal Adithya D, Collins Laura C, Murthy Divya, Eliassen A Heather, Rosner Bernard A, Tamimi Rulla M

2020-Dec-07

Public Health Public Health

Sex-specific associations between lipids and cognitive decline in the middle-aged and elderly: a cohort study of Chinese adults.

In Alzheimer's research & therapy ; h5-index 49.0

BACKGROUND : Studies regarding the lipid-cognition relationship have increasingly gained popularity but have generated much mixed results. To date, few studies have focused on the difference between sexes.

METHODS : This study included 6792 Chinese adults aged over 45 years (women, 48.56%; mean age, 57.28 years), who were free of severe conditions known to affect cognitive function at the baseline (2011). Blood concentrations of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), and triglycerides (TG) were assessed at baseline, and both continuous and categorical values were used in final analyses. Global cognitive functions were assessed by the word recall test and the mental status test in 2011, 2013, and 2015, respectively. We graded participants into three groups according to the cognitive change slopes: no decline (≥ 0), moderate decline (median to 0), and severe decline (< median). Sex-specific associations between blood lipids and cognitive decline were analyzed using ordinal logistic models, adjusting for sociodemographic information, lifestyle behaviors, and health status.

RESULTS : Higher baseline TC and LDL-C concentrations exhibited no significant association with 5-year cognitive decline in men but were significantly associated with greater 5-year cognitive decline in women [odds ratio (OR) 1.026, 95% confidence interval (CI) 1.003, 1.050; OR 1.026, CI 1.002, 1.051, respectively]. For higher serum HDL-c levels, a significantly protective effect on cognition was observed in men, but a slightly adverse effect was found in women (not significant after Bonferroni correction). TG presented almost no effect on later cognition in either sex.

CONCLUSION : Different associations between sexes were observed for the lipid-cognition relationship, and maintaining serum cholesterol levels at an appropriate range may have a positive effect on cognitive health.

Liu Lili, Zhang Chen, Lv Xiaozhen, Lai Xuefeng, Xu Lu, Feng Jingnan, Song Yongfeng, Wang Shengfeng, Zhan Siyan

2020-Dec-07

Cognitive decline, Lipids, Sex