Sacituzumab govitecan: breakthrough targeted therapy for triple-negative breast cancer

Introduction: Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype associated with an increased risk of recurrence and cancer-related death. Unlike hormone receptor-positive or HER2-positive breast cancers, there are limited targeted therapies available to treat TNBC and cytotoxic chemotherapy remains the mainstay of treatment. Sacituzumab govitecan (IMMU-132) is an antibody-drug conjugate targeting Trop-2 expressing cells and selectively delivering SN-38, an active metabolite of irinotecan.

Areas covered: This review covers the mechanism of action, safety and efficacy of sacituzumab govitecan in patients with previously treated, metastatic TNBC. Additionally, efficacy data in other epithelial malignancies is included based on a PubMed search for ‘sacituzumab govitecan’ and ‘clinical trial’.

Expert opinion: Sacituzumab govitecan has promising anti-cancer activity in patients with metastatic TNBC previously treated with at least two prior lines of systemic therapy based on a single arm Phase I/II clinical trial. A confirmatory Phase III randomized clinical trial is ongoing. Sacituzumab govitecan has a manageable side effect profile, with the most common adverse events being nausea, neutropenia, and diarrhea. The activity of sacituzumab govitecan likely extends beyond TNBC with promising early efficacy data in many other epithelial cancers, including hormone receptor-positive breast cancer.     

Atypical Abnormal Pulmonary Vein Drainage with Atrial Septal Defect: Surgical Treatment

Sinus venosus atrial septal defect (SV‐ASD) usually coexists with partial anomalous pulmonary vein connection (PAPVC). It is a difficult diagnosis in transthoracic echocardiography (TTE) due to eccentric position of defects. We present a rare case of atypical anatomical variation in PAPVC, which was never described before. Two right pulmonary veins drained into superior vena cava, which overrode SV‐ASD and interatrial septum, a third pulmonary vein into the right atrium. Complete diagnosis could not be set after TTE, nor transesophageal echocardiography, whereas angio‐CT was finally conclusive. This diagnostic approach allowed the surgical planning.     

Germline gain-of-function MMP11 variant results in an aggressive form of colorectal cancer

Matrix metalloproteinase-11 (MMP11) is an enzyme with proteolytic activity against matrix and nonmatrix proteins. Although most MMPs are secreted as inactive proenzymes and are later activated extracellularly, MMP11 is activated intracellularly by furin within the constitutive secretory pathway. It is a key factor in physiological tissue remodeling and its alteration may play an important role in the progression of epithelial malignancies and other diseases. TCGA colon and colorectal adenocarcinoma data showed that upregulation of MMP11 expression correlates with tumorigenesis and malignancy. Here, we provide evidence that a germline variant in the MMP11 gene (NM_005940: c.232C>T; p.(Pro78Ser)), identified by whole exome sequencing, can increase the tumorigenic properties of colorectal cancer (CRC) cells. P78S is located in the prodomain region, which is responsible for blocking MMP11's protease activity. This variant was detected in the proband and all the cancer-affected family members analyzed, while it was not detected in healthy relatives. In silico analyses predict that P78S could have an impact on the activation of the enzyme. Furthermore, our in vitro analyses show that the expression of P78S in HCT116 cells increases tumor cell invasion and proliferation. In summary, our results show that this variant could modify the structure of the MMP11 prodomain, producing a premature or uncontrolled activation of the enzyme that may contribute to an early CRC onset in these patients. The study of this gene in other CRC cases will provide further information about its role in CRC development, which might improve patient treatment in the future.     

Stress Testing Versus CT Angiography in Patients With Diabetes and Suspected Coronary Artery Disease

Background

The optimal noninvasive test (NIT) for patients with diabetes and stable symptoms of coronary artery disease (CAD) is unknown.

Nonproportional hazards and unobserved heterogeneity in clustered survival data: When can we tell the difference?

Multivariate survival data are frequently encountered in biomedical applications in the form of clustered failures (or recurrent events data). A popular way of analyzing such data is by using shared frailty models, which assume that the proportional hazards assumption holds conditional on an unobserved cluster-specific random effect. Such models are often incorporated in more complicated joint models in survival analysis. If the random effect distribution has finite expectation, then the conditional proportional hazards assumption does not carry over to the marginal models. It has been shown that, for univariate data, this makes it impossible to distinguish between the presence of unobserved heterogeneity (eg, due to missing covariates) and marginal nonproportional hazards. We show that time-dependent covariate effects may falsely appear as evidence in favor of a frailty model also in the case of clustered failures or recurrent events data, when the cluster size or number of recurrent events is small. When true unobserved heterogeneity is present, the presence of nonproportional hazards leads to overestimating the frailty effect. We show that this phenomenon is somewhat mitigated as the cluster size grows. We carry out a simulation study to assess the behavior of test statistics and estimators for frailty models in such contexts. The gamma, inverse Gaussian, and positive stable shared frailty models are contrasted using a novel software implementation for estimating semiparametric shared frailty models. Two main questions are addressed in the contexts of clustered failures and recurrent events: whether covariates with a time-dependent effect may appear as indication of unobserved heterogeneity and whether the additional presence of unobserved heterogeneity can be detected in this case. Finally, the practical implications are illustrated in a real-world data analysis example.