MOESM1 of Clinical and molecular characteristics of East Asian patients with von Hippel–Lindau syndrome

Additional file 1: Table S1. Genetic and clinical information about VHL disease in Asia

Substrate Stiffness Modulates the Maturation of Human Pluripotent Stem-Cell-Derived Hepatocytes

Obtaining functional hepatocytes from human pluripotent stem cells (hPSCs) holds great potential for applications in drug safety testing, as well in the field of regenerative medicine. However, developing functionally mature hPSC-derived hepatocytes (hPSC-Heps) remains a challenge. We hypothesized that the cellular microenvironment plays a vital role in the maturation of immature hepatocytes. In this study, we examined the role of mechanical stiffness, a key component of the cellular microenvironment, in the maturation of hPSC-Heps. We cultured hPSC-Heps on collagen-coated polyacrylamide hydrogels with varying elastic moduli. On softer substrates the hPSC-Heps formed compact colonies while on stiffer substrates they formed a diffuse monolayer. We observed an inverse correlation between albumin production and substrate stiffness. The expression of key cytochrome enzymes, which are expressed at higher levels in the adult liver compared to the fetal liver, also correlated inversely with substrate stiffness, whereas fetal markers such as Cyp3A7 and AFP showed no correlation with stiffness. Culture of hPSC-Heps on soft substrates for 12 days led to 10–30 fold increases in the expression of drug-metabolizing enzymes. These results demonstrate that substrate stiffness similar to that of the liver enables aspects of the maturation of hPSC-Heps

Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction

<div><p>Introduction</p><p>Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.</p><p>Methods</p><p>We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.</p><p>Results</p><p>During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm², 21-30cm², 31-40cm², 41-50cm², 51-60cm², and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.</p><p>Conclusion</p><p>Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.</p></div

Three receiver operating characteristic (ROC) curves for predicting breast cancer: vGail + BMI (black), vGail + BMI + mean breast dense area (red), vGail + BMI + mean breast dense area + GRS (green).

<p>For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) are 0.63, 0.66 and 0.68 respectively. The straight dashed line represents the ROC curve expected by chance only.</p

Survival curves.

<p>(A) Patients grouped according to the <i>ABCB1 3435C/T</i>, <i>1236C/T</i>, <i>2677G/T</i> haplotype; median PFS was 2.4 months for homozygous carriers of the <i>TTT</i> haplotype and 8.4 months for other cases (<i>P</i> = 0.001). (B) Patients grouped according to the <i>ABCB1 3435C/T</i>, <i>1236C/T</i>, <i>2677G/TA</i> haplotype; median OS was 4.6 months for homozygous carriers of the <i>TTT</i> haplotype and 19.6 months for other cases (<i>P</i> = 0.005).</p

Concordance probabilities of the three risk prediction models (absolute dense area).

<p>Concordance probabilities of the three risk prediction models (absolute dense area).</p

Polymorphisms genotyped and allele frequencies.

<p>^a Patients successfully genotyped.</p><p>^b Includes 34 <i>GT</i> and 10 <i>AG</i> individuals.</p><p>^c Includes 2 <i>AA</i>, 12 <i>AT</i> and 13 <i>TT</i> individuals.</p><p>^d A 2,903-bp deletion polymorphism in intron 2 of <i>BIM</i> previously associated with resistance to tyrosine kinase inhibitors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134102#pone.0134102.ref028" target="_blank">28</a>]. As we were unable to genotype formalin-fixed tissues with the current method, only 45 patients were typed.</p><p>^e Variant allele frequencies.</p><p>Polymorphisms genotyped and allele frequencies.</p

Association of <i>ABCB1</i> and <i>FLT3</i> Polymorphisms with Toxicities and Survival in Asian Patients Receiving Sunitinib for Renal Cell Carcinoma

<div><p>Sunitinib is a tyrosine kinase inhibitor used as first-line treatment for metastatic renal cell carcinoma (mRCC). Asian ethnicity has been previously associated with lower clearance and greater toxicities for sunitinib treatment, relative to Caucasian ethnicity. Research focusing on identifying corresponding biomarkers of efficacy and toxicity has been hitherto conducted in Caucasian populations, and few of the reported associations have been externally validated. Our work thus aims to investigate candidate biomarkers in Asian patients receiving sunitinib, comparing the observed genotype effects with those reported in Caucasian populations. Using data from 97 Asian mRCC patients treated with sunitinib, we correlated 7 polymorphisms in <i>FLT3</i>, <i>ABCB1</i>, <i>VEGFR2</i>, <i>ABCG2</i> and <i>BIM</i> with patient toxicities, response, and survival. We observed a stronger association of <i>FLT3 738T</i> genotype with leucopenia in our Asian dataset than that previously reported in Caucasian mRCC patients (odds ratio [OR]=8.0; <i>P</i>=0.03). We observed significant associations of <i>FLT3 738T</i> (OR=2.7), <i>ABCB1 1236T</i> (OR=0.3), <i>ABCB1 3435T</i> (OR=0.1), <i>ABCB1 2677T</i> (OR=0.4), <i>ABCG2 421A</i> (OR=0.3) alleles and <i>ABCB1 3435</i>, <i>1236</i>, <i>2677 TTT</i> haplotype (OR=0.1) on neutropenia. Primary resistance (OR=0.1, <i>P</i>=0.004) and inferior survival (progression-free: hazard ratio [HR]=5.5, <i>P</i>=0.001; overall: HR=5.0, <i>P</i>=0.005) were associated with the <i>ABCB1 3435</i>, <i>1236</i>, <i>2677 TTT</i> haplotype. In conclusion, <i>ABCB1</i> and <i>FLT3</i> polymorphisms may be helpful in predicting sunitinib toxicities, response and survival benefit in Asian mRCC patients. We have also validated the association between <i>FLT3 738T</i> and sunitinib-induced leucopenia previously reported in Caucasian populations, but have not validated other reported genetic associations.</p></div

Distribution of baseline characteristics in breast cancer patients (cases) and healthy individuals (non-cases).

<p>Distribution of baseline characteristics in breast cancer patients (cases) and healthy individuals (non-cases).</p

Distribution of predicted 10-year absolute risk for patients (red) and healthy individuals (black) using the three prediction models.

<p>As mean breast dense area and GRS are added to the model, the discrimination between cases and non-cases increases. Y-axis is the density which reflects the number of subjects.</p