Background Peritoneal dissemination of abdominal malignancy (carcinomatosis) has a clinical course marked by bowel obstruction and death; it traditionally does not respond well to systemic therapy and has been approached with nihilism. To treat carcinomatosis, we utilize cytoreductive surgery (CS) with hyperthermic intraperitoneal chemotherapy (HIPEC). Methods A prospective database of patients has been maintained since 1992. Patients with biopsy proven peritoneal surface disease (PSD) were uniformly evaluated for, and treated with, CS and HIPEC. Patient demographics, performance status (ECOG), resection status (R), PSD was classified according to primary site. Univariate and multivariate analysis were performed. The experience was divided into quintiles and compared with outcomes. Results Between 1991 and 2013, 1,000 patients underwent 1,097 HIPEC procedures. Average age was 52.9 years and 53.1% were female. Primary tumor sites were: appendix 472(47.2%), colorectal 248(24.8%), mesothelioma 72(7.2%), ovary 69(6.9%), gastric 46(4.6%), others 97(9.7%). Thirty day mortality rate was 3.8% and median hospital stay was 8 days. Median overall survival (OS) was 29.4 months, with a 5 year survival of 32.5%. Factors correlating with improved survival on univariate and multivariate analysis (p≤.0001 for each) were preoperative performance status, primary tumor type, resection status, and experience quintile (p=.04). Over the 5 quintiles, the 1 and 5 year survival, as well as the complete cytoreduction score (R0,R1,R2a) have increased, while transfusions, stoma creations, and complications have all significantly decreased (p<.001 for all). Conclusions This largest reported single center experience with CS and HIPEC demonstrates that prognostic factors include primary site, performance status, completeness of resection, and institutional experience. The data shows that outcomes have improved over time with more complete cytoreduction and fewer serious complications transfusions and stomas. This was due to both better patient selection, and increased operative experience. CS with HIPEC represents a substantial improvement in outcomes compared to historical series, and shows that meaningful long term survival is possible for selected carcinomatosis patients. Multi-institutional cooperative trials are needed to further refine the utility of CS and HIPEC.

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The current drug development pipeline takes approximately fifteen years and $2.6 billion to get a new drug to market. Typically, drugs are tested on two-dimensional (2D) cell cultures and animal models to estimate their efficacy before reaching human trials. However, these models are often not representative of the human body. The 2D culture changes the morphology and physiology of cells, and animal models often have a vastly different anatomy and physiology than humans. The use of bioengineered human cell-based organoids may increase the probability of success during human trials by providing human-specific preclinical data. They could also be deployed for personalized medicine diagnostics to optimize therapies in diseases such as cancer. However, one limitation in employing organoids in drug screening has been the difficulty in creating large numbers of homogeneous organoids in form factors compatible with high-throughput screening (e.g., 96-and 384-well plates). Bioprinting can be used to scale up deposition of such organoids and tissue constructs. Unfortunately, it has been challenging to 3D print hydrogel bioinks into small-sized wells due to well-bioink interactions that can result in bioinks spreading out and wetting the well surface instead of maintaining a spherical form. Here, we demonstrate an immersion printing technique to bioprint tissue organoids in 96-well plates to increase the throughput of 3D drug screening. A hydrogel bioink comprised of hyaluronic acid and collagen is bioprinted into a viscous gelatin bath, which blocks the bioink from interacting with the well walls and provides support to maintain a spherical form. This method was validated using several cancerous cell lines, and then applied to patient-derived glioblastoma (GBM) and sarcoma biospecimens for drug screening.

Variability in patient response to anti-cancer drugs is currently addressed by relating genetic mutations to chemotherapy through precision medicine. However, practical benefits of precision medicine to therapy design are less clear. Even after identification of mutations, oncologists are often left with several drug options, and for some patients there is no definitive treatment solution. There is a need for model systems to help predict personalized responses to chemotherapeutics. We have microengineered 3D tumor organoids directly from fresh tumor biopsies to provide patient-specific models with which treatment optimization can be performed before initiation of therapy. We demonstrate the initial implementation of this platform using tumor biospecimens surgically removed from two mesothelioma patients. First, we show the ability to biofabricate and maintain viable 3D tumor constructs within a tumor-on-a-chip microfluidic device. Second, we demonstrate that results of on-chip chemotherapy screening mimic those observed in subjects themselves. Finally, we demonstrate mutation-specific drug testing by considering the results of precision medicine genetic screening and confirming the effectiveness of the non-standard compound 3-deazaneplanocin A for an identified mutation. This patient-derived tumor organoid strategy is adaptable to a wide variety of cancers and may provide a framework with which to improve efforts in precision medicine oncology.

IMPORTANCE Adrenocortical carcinoma (ACC) is a rare but aggressive endocrine tumor, and the prognostic factors associated with long-term outcomes after surgical resection remain poorly defined. OBJECTIVES To define clinicopathological variables associated with recurrence-free survival (RFS) and overall survival (OS) after curative surgical resection of ACC and to propose nomograms for individual risk prediction. DESIGN, SETTING, AND PARTICIPANTS Nomograms to predict RFS and OS after surgical resection of ACC were proposed using a multi-institutional cohort of patients who underwent curative-intent surgery for ACC at 13 major institutions in the United States between March 17, 1994, and December 22, 2014. The dates of our study analysis were April 15, 2015, to May 12, 2015. MAIN OUTCOMES AND MEASURES The discriminative ability and calibration of the nomograms to predict RFS and OS were tested using C statistics, calibration plots, and Kaplan-Meier curves. RESULTS In total, 148 patients who underwent surgery for ACC were included in the study. The median patient age was 53 years, and 65.5% (97 of 148) of the patients were female. One-third of the patients (35.1% [52 of 148]) had a functional tumor, and the median tumor size was 11.2 cm. Most patients (77.7% [115 of 148]) underwent R0 resection, and 8.8% (13 of 148) of the patients had N1 disease. Using backward stepwise selection of clinically important variables with the Akaike information criterion, the following variables were incorporated in the prediction of RFS: tumor size of at least 12 cm (hazard ratio [HR], 3.00; 95% CI, 1.63–5.70; P < .001), positive nodal status (HR, 4.78; 95% CI, 1.47–15.50; P = .01), stage III/IV (HR, 1.80; 95% CI, 0.95–3.39; P = .07), cortisol-secreting tumor (HR, 2.38; 95% CI, 1.27–4.48; P = .01), and capsular invasion (HR, 1.96; 95% CI, 1.02–3.74; P = .04). Factors selected as predicting OS were tumor size of at least 12 cm (HR, 1.78; 95% CI, 1.00–3.17; P = .05), positive nodal status (HR, 5.89; 95% CI, 2.05–16.87; P = .001), and R1 margin (HR, 2.83; 95% CI, 1.51–5.30; P = .001). The discriminative ability and calibration of the nomograms revealed good predictive ability as indicated by the C statistics (0.74 for RFS and 0.70 for OS). CONCLUSIONS AND RELEVANCE Independent predictors of survival and recurrence risk after curative-intent surgery for ACC were selected to create nomograms predicting RFS and OS. The nomograms were able to stratify patients into prognostic groups and performed well on internal validation.

When assessed as a categorical variable, LNR was the most powerful manner to stratify patients on the basis of LN status. LODDS was a better predicator of survival when LN status was modeled as a continuous variable, especially among those patients with either very low or high LNR.

Survival estimates following surgical resection of gastric adenocarcinoma is dynamic; the probability of survival increases with time already survived. Patients with worse prognostic features at the time of surgery had the greatest increases in CS over time. Conditional survival estimates provide important information about the changing probability of survival over time and should be used among patients with resected gastric adenocarcinoma to guide subsequent follow-up strategies.