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.

Fluorescence Lifetime Imaging in Stargardt Disease: Potential Marker for Disease Progression.

PURPOSE The purpose of this study was to describe autofluorescence lifetime characteristics in Stargardt disease (STGD) using fluorescence lifetime imaging ophthalmoscopy (FLIO) and to investigate potential prognostic markers for disease activity and progression. METHODS Fluorescence lifetime data of 16 patients with STGD (mean age, 40 years; range, 22-56 years) and 15 age-matched controls were acquired using a fluorescence lifetime imaging ophthalmoscope based on a Heidelberg Engineering Spectralis system. Autofluorescence was excited with a 473-nm laser, and decay times were measured in a short (498-560 nm) and long (560-720 nm) spectral channel. Clinical features, autofluorescence lifetimes and intensity, and corresponding optical coherence tomography images were analyzed. One-year follow-up examination was performed in eight STGD patients. Acquired data were correlated with in vitro measured decay times of all-trans retinal and N-retinylidene-N-retinylethanolamine. RESULTS Patients with STGD displayed characteristic autofluorescence lifetimes within yellow flecks (446 ps) compared with 297 ps in unaffected areas. In 15% of the STGD eyes, some flecks showed very short fluorescence lifetimes (242 ps). Atrophic areas were characterized by long lifetimes (474 ps), with some remaining areas of normal to short lifetimes (322 ps) toward the macular center. CONCLUSIONS Patients with recent disease onset showed flecks with very short autofluorescence lifetimes, which is possible evidence of accumulation of retinoids deriving from the visual cycle. During the study period, many of these flecks changed to longer lifetimes, possibly due to accumulation of lipofuscin. Therefore, FLIO might serve as a useful tool for monitoring of disease progression. (ClinicalTrials.gov number, NCT01981148.).

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.