G-CSF Is a Novel Mediator of T-Cell Suppression and an Immunotherapeutic Target for Women with Colon Cancer.

PURPOSE: G-CSF enhances colon cancer development. This study defines the prevalence and effects of increased G-CSF signaling in human colon cancers and investigates G-CSF inhibition as an immunotherapeutic strategy against metastatic colon cancer. EXPERIMENTAL DESIGN: Patient samples were used to evaluate G-CSF and G-CSF receptor (G-CSFR) levels by IHC with sera used to measure G-CSF levels. Peripheral blood mononuclear cells were used to assess the rate of G-CSFR+ T cells and IFNγ responses to chronic ex vivo G-CSF. An immunocompetent mouse model of peritoneal metastasis (MC38 cells in C57Bl/6J) was used to determine the effects of G-CSF inhibition (αG-CSF) on survival and the tumor microenvironment (TME) with flow and mass cytometry. RESULTS: In human colon cancer samples, the levels of G-CSF and G-CSFR are higher compared to normal colon tissues from the same patient. High patient serum G-CSF is associated with increases in markers of poor prognosis, (e.g., VEGF, IL6). Circulating T cells from patients express G-CSFR at double the rate of T cells from controls. Prolonged G-CSF exposure decreases T cell IFNγ production. Treatment with αG-CSF shifts both the adaptive and innate compartments of the TME and increases survival (HR, 0.46; P = 0.0237) and tumor T-cell infiltration, activity, and IFNγ response with greater effects in female mice. There is a negative correlation between serum G-CSF levels and tumor-infiltrating T cells in patient samples from women. CONCLUSIONS: These findings support G-CSF as an immunotherapeutic target against colon cancer with greater potential benefit in women.

Development of a Risk Score to Predict Post-Discharge Rehabilitation Care After Liver Metastasectomy.

INTRODUCTION: Need for discharge to intermediate care (DCIC) can increase length of stay and be a source of stress to patients. Estimating risk of DCIC would allow earlier involvement of case managers, improve length of stay and patient satisfaction by setting realistic expectations. The aim was to use National Surgical Quality Improvement Program dataset to develop a prediction model for DCIC after undergoing liver metastasectomy. METHODS: Data were obtained from National Surgical Quality Improvement Program 2011-2018 covering liver metastasectomy. Recursive partitioning narrowed potential predictors and identified thresholds for categorization of continuous variables. Logistic regression identified a predictive model, internally validated by using 200 bootstrap samples with replacement. A risk score was derived using Framingham Study methodology by dividing all regression coefficients by the smallest model coefficient. Receiver operating characteristic analysis identified the score that maximized sensitivity/specificity, defining low/high risk. Finally, recursive partitioning identified categories low/medium/high. RESULTS: The most parsimonious model predicting DCIC area under the curve (, 0.722, 95%CI: 0.705-0.739) identified five independent predictors including age >60, procedure type, hypertension requiring medication, albumin <3.5 mg/dL and hematocrit <30%. Internal validation resulted in expected bias-corrected area under the curve of 0.717, 95% CI: 0.698-0.732. The maximum score was 17.9 and 5.8 maximized sensitivity (sn) and specificity (sp) [sn = 81%, sp = 51%) predicting DCIC. Stratified into three groups, a score ≥9.5 identified highest risk (12.8%), ≥4.3 medium (6.1%) and <4.3 lowest risk (1.5%). CONCLUSIONS: Determining risk of DCIC benefits shared decision making and patient care. This evidence may enhance discharge planning after liver metastasectomy expediting the process. Age >60 contributed the most weight to the score, but the use of additional variables in three groups allowed further discrimination between patients.

Obesity is an Independent Risk Factor for Mortality in Otherwise Healthy Patients After Hepatectomy.

BACKGROUND: Obesity is often associated with comorbidities that limit remnant liver recovery after hepatectomy. The extent to which obesity, in the absence of comorbidities, impacts surgical risk after hepatectomy is unknown. We hypothesized that an obese population without major comorbidities would not be at increased risk of adverse outcomes after hepatectomies. METHODS: We performed a retrospective analysis identifying patients who underwent hepatectomies from the American College of Surgeons National Surgical Quality Improvement Program data set 2005-2017. Outcomes of interest included the following: mortality, any morbidity, critical care complications, and failure to discharge home. Body mass index (BMI) was the primary variable of interest, grouped as ≥35 and <35 based on bivariate tests of associations with candidate cut-off points. In attempt to isolate the effect of obesity on outcomes among patients "without major comorbidities" (WOC), we included patients without diabetes, chronic obstructive pulmonary disease, renal insufficiency, and nonsmokers; remaining patients were grouped as "with major comorbidities" (WC). Multivariable logistic regression was used to test whether obesity is independently associated with the outcomes of interest after adjustment for other covariates. RESULTS: A total of 36,396 patients were included. There were 13,754 patients in the WOC group and 22,642 in the WC group. Among patients in the WOC group, the adjusted odds of mortality were 2.2 times higher for patients with a BMI ≥35 versus a BMI <35. Among the patients in the WC group, a BMI ≥35 was not a statistically significant predictor of mortality after adjustment for other covariates. Obese patients had increased odds of each outcome among the WOC group. CONCLUSIONS: Our hypothesis was refuted by these data. In fact, the adverse effect of obesity was more evident among healthy patients.