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.

Prognostic Relevance of Palliative Primary Tumor Removal in 37,793 Metastatic Colorectal Cancer Patients: A Population-Based, Propensity Score-Adjusted Trend Analysis

OBJECTIVE To assess whether palliative primary tumor resection in colorectal cancer patients with incurable stage IV disease is associated with improved survival. BACKGROUND There is a heated debate regarding whether or not an asymptomatic primary tumor should be removed in patients with incurable stage IV colorectal disease. METHODS Stage IV colorectal cancer patients were identified in the Surveillance, Epidemiology, and End Results database between 1998 and 2009. Patients undergoing surgery to metastatic sites were excluded. Overall survival and cancer-specific survival were compared between patients with and without palliative primary tumor resection using risk-adjusted Cox proportional hazard regression models and stratified propensity score methods. RESULTS Overall, 37,793 stage IV colorectal cancer patients were identified. Of those, 23,004 (60.9%) underwent palliative primary tumor resection. The rate of patients undergoing palliative primary cancer resection decreased from 68.4% in 1998 to 50.7% in 2009 (P < 0.001). In Cox regression analysis after propensity score matching primary cancer resection was associated with a significantly improved overall survival [hazard ratio (HR) of death = 0.40, 95% confidence interval (CI) = 0.39-0.42, P < 0.001] and cancer-specific survival (HR of death = 0.39, 95% CI = 0.38-0.40, P < 0.001). The benefit of palliative primary cancer resection persisted during the time period 1998 to 2009 with HRs equal to or less than 0.47 for both overall and cancer-specific survival. CONCLUSIONS On the basis of this population-based cohort of stage IV colorectal cancer patients, palliative primary tumor resection was associated with improved overall and cancer-specific survival. Therefore, the dogma that an asymptomatic primary tumor never should be resected in patients with unresectable colorectal cancer metastases must be questioned.

NLCHECK: Stata module to check linearity assumption after model estimation

nlcheck is a simple diagnostic tool that can be used after fitting a model to quickly check the linearity assumption for a given predictor. nlcheck categorizes the predictor into bins, refits the model including dummy variables for the bins, and then performs a joint Wald test for the added parameters. Alternative, nlcheck uses linear splines for the adaptive model. Support for discrete variables is also provided. Optionally, nlcheck also displays a graph of the adjusted linear predictions from the original model and the adaptive model

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.