The Trypanosoma cruzi Virulence Factor Oligopeptidase B (OPBTc) Assembles into an Active and Stable Dimer

Oligopeptidase B, a processing enzyme of the prolyl oligopeptidase family, is considered as an important virulence factor in trypanosomiasis. Trypanosoma cruzi oligopeptidase B (OPBTc) is involved in host cell invasion by generating a Ca2+-agonist necessary for recruitment and fusion of host lysosomes at the site of parasite attachment. The underlying mechanism remains unknown and further structural and functional characterization of OPBTc may help clarify its physiological function and lead to the development of new therapeutic molecules to treat Chagas disease. In the present work, size exclusion chromatography and analytical ultracentrifugation experiments demonstrate that OPBTc is a dimer in solution, an association salt and pH-resistant and independent of intermolecular disulfide bonds. The enzyme retains its dimeric structure and is fully active up to 42°C. OPBTc is inactivated and its tertiary, but not secondary, structure is disrupted at higher temperatures, as monitored by circular dichroism and fluorescence spectroscopy. It has a highly stable secondary structure over a broad range of pH, undergoes subtle tertiary structure changes at low pH and is less stable under moderate ionic strength conditions. These results bring new insights into the structural properties of OPBTc, contributing to future studies on the rational design of OPBTc inhibitors as a promising strategy for Chagas disease chemotherapy

Personalized detection of circulating tumor DNA antedates breast cancer metastatic recurrence

Purpose: Up to 30% of patients with breast cancer relapse after primary treatment. There are no sensitive and reliable tests to monitor these patients and detect distant metastases before overt recurrence. Here, we demonstrate the use of personalized circulating tumor DNA (ctDNA) profiling for detection of recurrence in breast cancer. Experimental Design: Forty-nine primary patients with breast cancer were recruited following surgery and adjuvant therapy. Plasma samples (n = 208) were collected every 6 months for up to 4 years. Personalized assays targeting 16 variants selected from primary tumor whole-exome data were tested in serial plasma for the presence of ctDNA by ultradeep sequencing (average >100,000X). Results: Plasma ctDNA was detected ahead of clinical or radiologic relapse in 16 of the 18 relapsed patients (sensitivity of 89%); metastatic relapse was predicted with a lead time of up to 2 years (median, 8.9 months; range, 0.5–24.0 months). None of the 31 nonrelapsing patients were ctDNA-positive at any time point across 156 plasma samples (specificity of 100%). Of the two relapsed patients who were not detected in the study, the first had only a local recurrence, whereas the second patient had bone recurrence and had completed chemotherapy just 13 days prior to blood sampling. Conclusions: This study demonstrates that patient-specific ctDNA analysis can be a sensitive and specific approach for disease surveillance for patients with breast cancer. More importantly, earlier detection of up to 2 years provides a possible window for therapeutic intervention

Personalized detection of circulating tumor DNA antedates breast cancer metastatic recurrence.

PURPOSE: Up to 30% of breast cancer patients relapse after primary treatment. There are no sensitive and reliable tests to monitor these patients and detect distant metastases before overt recurrence. Here we demonstrate the use of personalized ctDNA profiling for detection of recurrence in breast cancer. METHODS: Forty-nine primary breast cancer patients were recruited following surgery and adjuvant therapy. Plasma samples (n=208) were collected every 6 months for up to 4 years. Personalized assays targeting 16 variants selected from primary tumor whole exome data were tested in serial plasma for the presence of ctDNA by ultra-deep sequencing (average >100,000X). RESULTS: Plasma ctDNA was detected ahead of clinical or radiological relapse in 16 of the 18 relapsed patients (sensitivity of 89%); metastatic relapse was predicted with a lead time of up to 2 years (median=8.9 months; range: 0.5-24.0 months). None of the 31 non-relapsing patients were ctDNA-positive at any time point across 156 plasma samples (specificity of 100%). Of the two relapsed patients who were not detected in the study, the first had only a local recurrence, while the second patient had bone recurrence and had completed chemotherapy just 13 days prior to blood sampling. CONCLUSIONS: This study demonstrates that patient-specific ctDNA analysis can be a sensitive and specific approach for disease surveillance for breast cancer patients. More importantly, earlier detection of up to two years provides a possible window for therapeutic intervention

Adult body mass index and risk of ovarian cancer by subtype: A Mendelian randomization study

BACKGROUND: Observational studies have reported a positive association between body mass index (BMI) and ovarian cancer risk. However, questions remain as to whether this represents a causal effect, or holds for all histological subtypes. The lack of association observed for serous cancers may, for instance, be due to disease-associated weight loss. Mendelian randomization (MR) uses genetic markers as proxies for risk factors to overcome limitations of observational studies. We used MR to elucidate the relationship between BMI and ovarian cancer, hypothesizing that genetically predicted BMI would be associated with increased risk of non-high grade serous ovarian cancers (non-HGSC) but not HGSC. METHODS: We pooled data from 39 studies (14 047 cases, 23 003 controls) in the Ovarian Cancer Association Consortium. We constructed a weighted genetic risk score (GRS, partial F-statistic = 172), summing alleles at 87 single nucleotide polymorphisms previously associated with BMI, weighting by their published strength of association with BMI. Applying two-stage predictor-substitution MR, we used logistic regression to estimate study-specific odds ratios (OR) and 95% confidence intervals (CI) for the association between genetically predicted BMI and risk, and pooled these using random-effects meta-analysis. RESULTS: Higher genetically predicted BMI was associated with increased risk of non-HGSC (pooled OR = 1.29, 95% CI 1.03-1.61 per 5 units BMI) but not HGSC (pooled OR = 1.06, 95% CI 0.88-1.27). Secondary analyses stratified by behaviour/subtype suggested that, consistent with observational data, the association was strongest for low-grade/borderline serous cancers (OR = 1.93, 95% CI 1.33-2.81). CONCLUSIONS: Our data suggest that higher BMI increases risk of non-HGSC, but not the more common and aggressive HGSC subtype, confirming the observational evidence