Atherogenic dyslipidemia and residual cardiovascular risk in statin-treated patients

BACKGROUND AND PURPOSE Treatment with statins reduces the rate of cardiovascular events in high-risk patients, but residual risk persists. At least part of that risk may be attributable to atherogenic dyslipidemia characterized by low high-density lipoprotein cholesterol (≤40 mg/dL) and high triglycerides (triglycerides≥150 mg/dL). METHODS We studied subjects with stroke or transient ischemic attack in the Prevention of Cerebrovascular and Cardiovascular Events of Ischemic Origin With Terutroban in Patients With a History of Ischemic Stroke or Transient Ischemic Attack (PERFORM; n=19,100) and Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL; n=4731) trials who were treated with a statin and who had high-density lipoprotein cholesterol and triglycerides measurements 3 months after randomization (n=10,498 and 2900, respectively). The primary outcome measure for this exploratory analysis was the occurrence of major cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death). We also performed a time-varying analysis to account for all available high-density lipoprotein cholesterol and triglyceride measurements. RESULTS A total of 10% of subjects in PERFORM and 9% in SPARCL had atherogenic dyslipidemia after ≥3 months on start statin therapy. After a follow-up of 2.3 years (PERFORM) and 4.9 years (SPARCL), a major cardiovascular event occurred in 1123 and 485 patients in the 2 trials, respectively. The risk of major cardiovascular events was higher in subjects with versus those without atherogenic dyslipidemia in both PERFORM (hazard ratio, 1.36; 95% confidence interval, 1.14-1.63) and SPARCL (hazard ratio, 1.40; 95% confidence interval, 1.06-1.85). The association was attenuated after multivariable adjustment (hazard ratio, 1.23; 95% confidence interval, 1.03-1.48 in PERFORM and hazard ratio, 1.24; 95% confidence interval, 0.93-1.65 in SPARCL). Time-varying analysis confirmed these findings. CONCLUSIONS The presence of atherogenic dyslipidemia was associated with higher residual cardiovascular risk in PERFORM and SPARCL subjects with stroke or transient ischemic attack receiving statin therapy. Specific therapeutic interventions should now be trialed to address this residual risk.

Suprasellar chordoid neoplasm with expression of thyroid transcription factor 1: evidence that chordoid glioma of the third ventricle and pituicytoma may form part of a spectrum of lineage-related tumors of the basal forebrain.

Chordoid glioma of the third ventricle is a rare neuroepithelial tumor characterized by a unique histomorphology and exclusive association with the suprasellar/third ventricular compartment. Variously interpreted as either astrocytic- or ependymal-like, and speculatively ascribed to the lamina terminalis/subcommissural organ, its histogenesis remains, nevertheless, unsettled. Here, we report on a suprasellar chordoid glioma occurring in a 52-year-old man. Although displaying otherwise typical morphological features, the tumor was notable for expression of thyroid transcription factor 1, a marker of tumors of pituicytic origin in the context of the sellar region. We furthermore found overlapping immunoprofiles of this example of chordoid glioma and pituicytic tumors (pituicytoma and spindle cell oncocytoma), respectively. Specifically, phosphorylated ribosomal protein S6, a marker of mTOR pathway activation, was expressed in both groups. Based on these findings, we suggest that chordoid glioma and pituicytic tumors may form part of a spectrum of lineage-related neoplasms of the basal forebrain.

Minimal residual disease in cancer therapy - Small things make all the difference

Minimal residual disease (MRD) is a major hurdle in the eradication of malignant tumors. Despite the high sensitivity of various cancers to treatment, some residual cancer cells persist and lead to tumor recurrence and treatment failure. Obvious reasons for residual disease include mechanisms of secondary therapy resistance, such as the presence of mutant cells that are insensitive to the drugs, or the presence of cells that become drug resistant due to activation of survival pathways. In addition to such unambiguous resistance modalities, several patients with relapsing tumors do not show refractory disease and respond again when the initial therapy is repeated. These cases cannot be explained by the selection of mutant tumor cells, and the precise mechanisms underlying this clinical drug resistance are ill-defined. In the current review, we put special emphasis on cell-intrinsic and -extrinsic mechanisms that may explain mechanisms of MRD that are independent of secondary therapy resistance. In particular, we show that studying genetically engineered mouse models (GEMMs), which highly resemble the disease in humans, provides a complementary approach to understand MRD. In these animal models, specific mechanisms of secondary resistance can be excluded by targeted genetic modifications. This allows a clear distinction between the selection of cells with stable secondary resistance and mechanisms that result in the survival of residual cells but do not provoke secondary drug resistance. Mechanisms that may explain the latter feature include special biochemical defense properties of cancer stem cells, metabolic peculiarities such as the dependence on autophagy, drug-tolerant persisting cells, intratumoral heterogeneity, secreted factors from the microenvironment, tumor vascularization patterns and immunosurveillance-related factors. We propose in the current review that a common feature of these various mechanisms is cancer cell dormancy. Therefore, dormant cancer cells appear to be an important target in the attempt to eradicate residual cancer cells, and eventually cure patients who repeatedly respond to anticancer therapy but lack complete tumor eradication.

Tumor infiltrating lymphocytes in ovarian cancer.

Several improvements in ovarian cancer treatment have been achieved in recent years, both in surgery and in combination chemotherapy with targeting. However, ovarian tumors remain the women's cancers with highest mortality rates. In this scenario, a pivotal role has been endorsed to the immunological environment and to the immunological mechanisms involved in ovarian cancer behavior. Recent evidence suggests a loss of the critical balance between immune-activating and immune-suppressing mechanisms when oncogenesis and cancer progression occur. Ovarian cancer generates a mechanism to escape the immune system by producing a highly suppressive environment. Immune-activated tumor infiltrating lymphocytes (TILs) in ovarian tumor tissue testify that the immune system is the trigger in this neoplasm. The TIL mileau has been demonstrated to be associated with better prognosis, more chemosensitivity, and more cases of optimal residual tumor achieved during primary cytoreduction. Nowadays, scientists are focusing attention on new immunologically effective tumor biomarkers in order to optimize selection of patients for recruitment in clinical trials and to identify relationships of these biomarkers with responses to immunotherapeutics. Assessing this point of view, TILs might be considered as a potent predictive immunotherapy biomarker.

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

CPRPLOT2: Stata module to graph component-plus-residual plots for transformed regressors

cprplot2 is a variation of official Stata's cprplot and is used for graphing component-plus-residual plots (a.k.a. partial residual plots). Additional features (compared to cprplot): (1) cprplot2 can handle variables that enter the model repeatedly via different transformations (for example, polynomials). (2) cprplot2 can display component-plus-residual plots using the original units for transformed variables in the model. (3) A wrapper is provided to quickly display several component-plus-residual plots in a single image.