Anti-PD-1 immunotherapy is the standard of care for treating many patients with non-small cell lung cancer (NSCLC), yet mechanisms of treatment failure are emerging. We present a case of NSCLC, who rapidly progressed during a trial (NCT02318771) combining palliative radiotherapy and pembrolizumab. Planned tumor biopsy demonstrated PD-1 expression by NSCLC cells. We validated this observation by detecting PD-1 transcript in lung cancer cells and by co-localizing PD-1 and lung cancer-specific markers in resected lung cancer tissues. We further investigated the biological role of cancer-intrinsic PD-1 in a mouse lung cancer cell line, M109. Knockout or antibody blockade of PD-1 enhanced M109 viability in-vitro, while PD-1 overexpression and exposure to recombinant PD-L1 diminished viability. PD-1 blockade accelerated growth of M109-xenograft tumors with increased proliferation and decreased apoptosis in immune-deficient mice. This represents a first-time report of NSCLC-intrinsic PD-1 expression and a potential mechanism by which PD-1 blockade may promote cancer growth.

BACKGROUND: This study was designed to determine whether a standardized recovery pathway could reduce post-pancreaticoduodenectomy hospital length of stay to 5 days without increasing complication or readmission rates. STUDY DESIGN: Pancreaticoduodenectomy patients (high-risk patients excluded) were enrolled in an IRBapproved, prospective, randomized controlled trial (NCT02517268) comparing a 5-day Whipple accelerated recovery pathway (WARP) with our traditional 7-day pathway (control). Whipple accelerated recovery pathway interventions included early discharge planning, shortened ICU stay, modified postoperative dietary and drain management algorithm, rigorous physical therapy with in-hospital gym visit, standardized rectal suppository administration, and close telehealth follow-up post discharge. The trial was powered to detect an increase in postoperative day 5 discharge from 10% to 30% (80% power, a ¼ 0.05, 2sided Fisher's exact test, target accrual: 142 patients). RESULTS: Seventy-six patients (37 WARP, 39 control) were randomized from June 2015 to September 2017. A planned interim analysis was conducted at 50% trial accrual resulting in mandatory early stoppage, as the predefined efficacy end point was met. Demographic variables between groups were similar. The WARP significantly increased the number of patients discharged to home by postoperative day 5 compared with controls (75.7% vs 12.8%; p < 0.001) without increasing readmission rates (8.1% vs 10.3%; p ¼ 1.0). Overall complication rates did not differ between groups (29.7% vs 43.6%; p ¼ 0.24), but the WARP significantly reduced the time from operation to adjuvant therapy initiation (51 days vs 66 days; p ¼ 0.005) and hospital cost ($26,563 vs $31,845; p ¼ 0.011). CONCLUSIONS: The WARP can safely reduce hospital length of stay, time to adjuvant therapy, and cost in selected pancreaticoduodenectomy patients without increasing readmission risk.

Purpose Radiation dose to specific cardiac substructures, such as the atria and ventricles, has been linked to post‐treatment toxicity and has shown to be more predictive of these toxicities than dose to the whole heart. A deep learning‐based algorithm for automatic generation of these contours is proposed to aid in either retrospective or prospective dosimetric studies to better understand the relationship between radiation dose and toxicities. Methods The proposed method uses a mask‐scoring regional convolutional neural network (RCNN) which consists of five major subnetworks: backbone, regional proposal network (RPN), RCNN head, mask head, and mask‐scoring head. Multiscale feature maps are learned from computed tomography (CT) via the backbone network. The RPN utilizes these feature maps to detect the location and region‐of‐interest (ROI) of all substructures, and the final three subnetworks work in series to extract structural information from these ROIs. The network is trained using 55 patient CT datasets, with 22 patients having contrast scans. Threefold cross validation (CV) is used for evaluation on 45 datasets, and a separate cohort of 10 patients are used for holdout evaluation. The proposed method is compared to a 3D UNet. Results The proposed method produces contours that are qualitatively similar to the ground truth contours. Quantitatively, the proposed method achieved average Dice score coefficients (DSCs) for the whole heart, chambers, great vessels, coronary arteries, the valves of the heart of 0.96, 0.94, 0.93, 0.66, and 0.77 respectively, outperforming the 3D UNet, which achieved DSCs of 0.92, 0.87, 0.88, 0.48, and 0.59 for the corresponding substructure groups. Mean surface distances (MSDs) between substructures segmented by the proposed method and the ground truth were <2 mm except for the left anterior descending coronary artery and the mitral and tricuspid valves, and <5 mm for all substructures. When dividing results into noncontrast and contrast datasets, the model performed statistically significantly better in terms of DSC, MSD, centroid mean distance (CMD), and volume difference for the chambers and whole heart with contrast. Notably, the presence of contrast did not statistically significantly affect coronary artery segmentation DSC or MSD. After network training, all substructures and the whole heart can be segmented on new datasets in less than 5 s. Conclusions A deep learning network was trained for automatic delineation of cardiac substructures based on CT alone. The proposed method can be used as a tool to investigate the relationship between cardiac substructure dose and treatment toxicities.

Concurrent chemoradiation (cCRT) with platinum-based chemotherapy is standard-of-care therapy for patients with stage III unresectable non-small cell lung cancer (NSCLC). Although cCRT is potentially curative, 5-year overall survival has hovered around 20%, despite extensive efforts to improve outcomes with increasing doses of conformal radiation and intensification of systemic therapy with either induction or consolidation chemotherapy. PD-1/PD-L1 immune checkpoint inhibitors have demonstrated unprecedented efficacy in patients with stage IV NSCLC. In addition, preclinical and early clinical evidence suggests that chemotherapy and radiation may work synergistically with anti-PD-1/PD-L1 therapy to promote antitumor immunity, which has led to the initiation of clinical trials testing these drugs in patients with stage III NSCLC. A preliminary report of a randomized phase III trial, the PACIFIC trial, demonstrated an impressive increase in median progression-free survival with consolidative durvalumab, a PD-L1 inhibitor, compared with observation after cCRT. Here, we discuss the clinical and translational implications of integrating PD-1/PD-L1 inhibitors in the management of patients with unresectable stage III NSCLC. .

Elevated BMI or diabetes may negatively impact both overall survival and local control in patients with brain metastases from breast cancer, highlighting the importance of the translational development of therapeutic metabolic interventions. Given its prognostic significance, BMI should be used as a stratification in future clinical trial design in this patient population.

Postoperative pancreatic fistula (PF) remains one of the most significant complications after pancreaticoduodenectomy (PD). Recently, studies have suggested that post-PD serum hyper-amylasemia (HA) may be a risk factor. In this study, we evaluate the relationship of pancreas texture and post-operative serum amylase levels in determining PF risk. This retrospective cohort study evaluated all patients who underwent PD at Thomas Jefferson University from 2009 to 2014. The highest postoperative serum amylase level from postoperative day (POD) 0 to POD 5 was obtained. Chi-square analyses and odds ratio (OR) evaluated the relationship between pancreas texture, serum amylase level, and the development of PF. Data from 524 consecutive patients were analyzed. Serum amylase threshold value of 165 IU/L yielded greatest accuracy from the receiver operating characteristic curve analysis (Sensitivity, 0.70; specificity, 0.72). Grade B or C PF were more common among HA patients (20 vs 3%; P < 0.001). HA was associated with increased rates of PD-associated complications. On multivariable analysis, early postoperative serum HA was more predictive of PF risk (OR, 4.87; P < 0.001) than either pancreatic duct size ≤3 mm (OR, 2.97; P = 0.01) or pancreas texture (OR,1.87; P = 0.05). Conclusion: The presence of HA on POD 0 or POD 1 is more predictive than soft pancreas texture or small pancreas duct size alone.

Purpose: Adaptive antitumor immunity following ablative radiotherapy (ART) is attenuated by host myeloid-derived suppressor cell (MDSC), tumor-associated macrophage (TAM), and regulatory T-cell (Treg) infiltrates. We hypothesized treatment with ART and a secondary mitochondrialderived activators of caspase (SMAC) mimetic could reverse the immunosuppressive lung cancer microenvironment to favor adaptive immunity.Experimental Design: To evaluate for synergy between ART and the SMAC mimetic Debio 1143 and the dependence upon CD8 þ T cells and TNFa, we used LLC-OVA syngeneic mouse model of lung cancer and treated them with Debio 1143 and/or ART (30 Gy) with or without anti-CD8, anti-TNFa, or anti-IFNg antibodies. Tumorinfiltrating OVA-specific CD8 þ T cells, Tc1 effector cells, MDSCs, TAMs, and Tregs, were quantified by flow cytometry. Tc1-promoting cytokines TNFa, IFNg, and IL1b and the immunosuppressive IL10 and Arg-1 within LLC-OVA tumor tissue or mouse serum were measured by RT-PCR and ELISA.Results: ART delayed tumor growth, and the addition of Debio 1143 greatly enhanced its efficacy, which included several complete responses. These complete responders rejected an LLC-OVA tumor rechallenge. ART and Debio 1143 synergistically induced a tumor-specific, Tc1 cellular and cytokine response while eliminating immunosuppressive cells and cytokines from the tumor microenvironment. Depletion of CD8 þ cells, TNFa, and IFNg with blocking antibody abrogated synergy between ART and Debio 1143 and partially restored tumor-infiltrating MDSCs.Conclusions: Debio 1143 augments the tumor-specific adaptive immunity induced by ART, while reversing host immunosuppressive cell infiltrates in the tumor microenvironment in a TNFa, IFNg, and CD8 þ T-cell-dependent manner. This provides a novel strategy to enhance the immunogenicity of ART. The combination of ART and Debio 1143 reduces immunosuppressive cell populations and produces an OVA-specific cytotoxic T-cell response in the tumor microenvironment. A, CD8 þ T lymphocytes primed with OVA-tetramer were quantified in LLC-OVA tumors using flow cytometry, and are expressed as a percentage of CD8 þ cells. IFNg (B) and TNFa (C) expression among OVA þ CD8 þ lymphocytes was determined by flow cytometry. These cells are expressed as a percentage of CD8 þ cells. D, Tumors were analyzed for the MDSC markers CD45/Gr1/CD11b using flow cytometry. CD11b þ Gr-1 þ cells are expressed as a percentage of CD45 þ cells. E, LLC-OVA tumors were assessed for the TAM markers CD45/F4/80/CD11b by flow cytometry. The percentage of TAMs is expressed as a percentage of CD45 þ cells. F, LLC-OVA tumors were assessed for the Treg markers CD4/FOXP3/CD25 by flow cytometry. Tregs are expressed as a percentage of CD4 þ cells. All plots show a representative sample (left) and are expressed as a mean with 5 plotted replicates (right). Statistical differences were assessed using the unpaired Student t test. P values are indicated as follows: Ã , P < 0.05; ÃÃ , P < 0.01; ÃÃÃ , P < 0.001.

Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. This may be due to time consuming process of manually segmenting up to 15 cardiac substructures that can have large anatomic variations from one patient to the other. In this work, a new deep learning (DL) -based mutual enhancing strategy is introduced for an accurate and automatic segmentation, especially of smaller substructures. Our proposed method is consisted of three subnetworks: retina U-net, classification module and segmentation module. Retina U-Net is used as a backbone network architecture that aims to learn deep features from the whole heart. Whole heart feature maps from retina U-net are then transferred to four different sets of classification modules to generate classification localization maps of coronary arteries, great vessels, chambers of the heart, and valves of the heart. Each classification module is in sync with its corresponding subsequent segmentation module in a bootstrapping manner, which allows them to share their encoding paths to generate mutual enhancing strategy. Segmentation accuracies were statistically compared through dice similarity coefficient (DSC), Jaccard, 95% Hausdorff distance (HD95), mean surface distance (MSD), root mean square distance (RMSD), center of mass distance (CMD), and volume difference (VD). The proposed method yielded good spatial consistency to the ground truth segmentations of cardiac substructures. Our method generated substructure segmentations with significantly (P < 0.05) better accuracy for small substructures, especially coronary arteries, compared to three competing methods, 3D U-Net, mask R-CNN and mask scoring R-CNN. Promising results of this work demonstrate potential of this framework to be used as a tool to rapidly generate substructure segmentations followed by physician’s review to improve clinical efficiency and treatment outcomes.