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Endometrial cancer is the most common gynecologic malignancy, and its incidence and associated mortality are increasing. Despite the immediate need to detect these cancers at an earlier stage, there is no effective screening methodology or protocol for endometrial cancer. The comprehensive, genomics-based analysis of endometrial cancer by The Cancer Genome Atlas (TCGA) revealed many of the molecular defects that define this cancer. Based on these cancer genome results, and in a prospective study, we hypothesized that the use of ultra-deep, targeted gene sequencing could detect somatic mutations in uterine lavage fluid obtained from women undergoing hysteroscopy as a means of molecular screening and diagnosis. Uterine lavage and paired blood samples were collected and analyzed from 107 consecutive patients who were undergoing hysteroscopy and curettage for diagnostic evaluation from this single-institution study. The lavage fluid was separated into cellular and acellular fractions by centrifugation. Cellular and cell-free DNA (cfDNA) were isolated from each lavage. Two targeted next-generation sequencing (NGS) gene panels, one composed of 56 genes and the other of 12 genes, were used for ultra-deep sequencing. To rule out potential NGS-based errors, orthogonal mutation validation was performed using digital PCR and Sanger sequencing. Seven patients were diagnosed with endometrial cancer based on classic histopathologic analysis. Six of these patients had stage IA cancer, and one of these cancers was only detectable as a microscopic focus within a polyp. All seven patients were found to have significant cancer-associated gene mutations in both cell pellet and cfDNA fractions. In the four patients in whom adequate tumor sample was available, all tumor mutations above a specific allele fraction were present in the uterine lavage DNA samples. Mutations originally only detected in lavage fluid fractions were later confirmed to be present in tumor but at allele fractions significantly less than 1%. Of the remaining 95 patients diagnosed with benign or non-cancer pathology, 44 had no significant cancer mutations detected. Intriguingly, 51 patients without histopathologic evidence of cancer had relatively high allele fraction (1.0%-30.4%), cancer-associated mutations. Participants with detected driver and potential driver mutations were significantly older (mean age mutated = 57.96, 95% confidence interval [CI]: 3.30-∞, mean age no mutations = 50.35; p-value = 0.002; Benjamini-Hochberg [BH] adjusted p-value = 0.015) and more likely to be post-menopausal (p-value = 0.004; BH-adjusted p-value = 0.015) than those without these mutations. No associations were detected between mutation status and race/ethnicity, body mass index, diabetes, parity, and smoking status. Long-term follow-up was not presently available in this prospective study for those women without histopathologic evidence of cancer. Using ultra-deep NGS, we identified somatic mutations in DNA extracted both from cell pellets and a never previously reported cfDNA fraction from the uterine lavage. Using our targeted sequencing approach, endometrial driver mutations were identified in all seven women who received a cancer diagnosis based on classic histopathology of tissue curettage obtained at the time of hysteroscopy. In addition, relatively high allele fraction driver mutations were identified in the lavage fluid of approximately half of the women without a cancer diagnosis. Increasing age and post-menopausal status were associated with the presence of these cancer-associated mutations, suggesting the prevalent existence of a premalignant landscape in women without clinical evidence of cancer. Given that a uterine lavage can be easily and quickly performed even outside of the operating room and in a physician's office-based setting, our findings suggest the future possibility of this approach for screening women for the earliest stages of endometrial cancer. However, our findings suggest that further insight into development of cancer or its interruption are needed before translation to the clinic.

The aim of this study was to demonstrate the role of advanced fabrication technology across a broad spectrum of head and neck surgical procedures, including applications in endoscopic sinus surgery, skull base surgery, and maxillofacial reconstruction. The initial case studies demonstrated three applications of rapid prototyping technology are in head and neck surgery: i) a mono-material paranasal sinus phantom for endoscopy training ii) a multi-material skull base simulator and iii) 3D patient-specific mandible templates. Digital processing of these phantoms is based on real patient or cadaveric 3D images such as CT or MRI data. Three endoscopic sinus surgeons examined the realism of the endoscopist training phantom. One experienced endoscopic skull base surgeon conducted advanced sinus procedures on the high-fidelity multi-material skull base simulator. Ten patients participated in a prospective clinical study examining patient-specific modeling for mandibular reconstructive surgery. Qualitative feedback to assess the realism of the endoscopy training phantom and high-fidelity multi-material phantom was acquired. Conformance comparisons using assessments from the blinded reconstructive surgeons measured the geometric performance between intra-operative and pre-operative reconstruction mandible plates. Both the endoscopy training phantom and the high-fidelity multi-material phantom received positive feedback on the realistic structure of the phantom models. Results suggested further improvement on the soft tissue structure of the phantom models is necessary. In the patient-specific mandible template study, the pre-operative plates were judged by two blinded surgeons as providing optimal conformance in 7 out of 10 cases. No statistical differences were found in plate fabrication time and conformance, with pre-operative plating providing the advantage of reducing time spent in the operation room. The applicability of common model design and fabrication techniques across a variety of otolaryngological sub-specialties suggests an emerging role for rapid prototyping technology in surgical education, procedure simulation, and clinical practice.

Anti-PD-1 therapy yields objective clinical responses in 30–40% of advanced melanoma patients. Since most patients do not respond, predictive biomarkers to guide treatment selection are needed. We hypothesize that MHC-I/II expression is required for tumour antigen presentation and may predict anti-PD-1 therapy response. In this study, across 60 melanoma cell lines, we find bimodal expression patterns of MHC-II, while MHC-I expression was ubiquitous. A unique subset of melanomas are capable of expressing MHC-II under basal or IFNγ-stimulated conditions. Using pathway analysis, we show that MHC-II(+) cell lines demonstrate signatures of ‘PD-1 signalling’, ‘allograft rejection’ and ‘T-cell receptor signalling’, among others. In two independent cohorts of anti-PD-1-treated melanoma patients, MHC-II positivity on tumour cells is associated with therapeutic response, progression-free and overall survival, as well as CD4 + and CD8 + tumour infiltrate. MHC-II + tumours can be identified by melanoma-specific immunohistochemistry using commercially available antibodies for HLA-DR to improve anti-PD-1 patient selection. Immunotherapy is used to treat melanoma, however patient responses vary widely highlighting the need for factors that can predict therapeutic success. Here, the authors show that MHC-II molecules expressed by tumour cells are positively correlated with a good response to therapy and overall patient survival.

To mitigate the effects of coronavirus disease 2019 (COVID-19), jurisdictions worldwide ramped down nonemergent surgeries, creating a global surgical backlog. We sought to estimate the size of the nonemergent surgical backlog during COVID-19 in Ontario, Canada, and the time and resources required to clear the backlog. We used 6 Ontario or Canadian population administrative sources to obtain data covering part or all of the period between Jan. 1, 2017, and June 13, 2020, on historical volumes and operating room throughput distributions by surgery type and region, and lengths of stay in ward and intensive care unit (ICU) beds. We used time series forecasting, queuing models and probabilistic sensitivity analysis to estimate the size of the backlog and clearance time for a +10% (+1 day per week at 50% capacity) surge scenario. Between Mar. 15 and June 13, 2020, the estimated backlog in Ontario was 148 364 surgeries (95% prediction interval 124 508–174 589), an average weekly increase of 11 413 surgeries. Estimated backlog clearance time is 84 weeks (95% confidence interval [CI] 46–145), with an estimated weekly throughput of 717 patients (95% CI 326–1367) requiring 719 operating room hours (95% CI 431–1038), 265 ward beds (95% CI 87–678) and 9 ICU beds (95% CI 4–20) per week. The magnitude of the surgical backlog from COVID-19 raises serious implications for the recovery phase in Ontario. Our framework for modelling surgical backlog recovery can be adapted to other jurisdictions, using local data to assist with planning.

Marker enrichment modeling (MEM) provides an objective metric for characterizing cell populations from high-content single-cell analysis. The MEM score outperforms standard metrics and provides a machine-readeable label for cell subsets. Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.

We set out to determine the accuracy of 3D-navigated mandibular and maxillary osteotomies with the ultimate aim to integrate virtual cutting guides and 3D-navigation into ablative and reconstructive head and neck surgery. Four surgeons (two attending, two clinical fellows) completed 224 unnavigated and 224 3D-navigated osteotomies on anatomical models according to preoperative 3D plans. The osteotomized bones were scanned and analyzed. Median distance from the virtual plan was 2.1 mm unnavigated (IQR 2.6 mm, ≥3 mm in 33%) and 1.2 mm 3D-navigated (IQR 1.1 mm, ≥3 mm in 6%) (P<0.0001); median pitch was 4.5° unnavigated (IQR 7.1°) and 3.5° 3D-navigated (IQR 4.0°) (P<0.0001); median roll was 7.4° unnavigated (IQR 8.5°) and 2.6° 3D-navigated (IQR 3.8°) (P<0.0001). 3D-rendering enables osteotomy navigation. 3 mm is an appropriate planning distance. The next steps are translating virtual cutting guides to free bone flap reconstruction and clinical use.

An integrated augmented reality (AR) surgical navigation system that potentially improves intra-operative visualization of concealed anatomical structures. Integration of real-time tracking technology with a laser pico-projector allows the surgical surface to be augmented by projecting virtual images of lesions and critical structures created by multimodality imaging. We aim to quantitatively and qualitatively evaluate the performance of a prototype interactive AR surgical navigation system through a series of pre-clinical studies. Four pre-clinical animal studies using xenograft mouse models were conducted to investigate system performance. A combination of CT, PET, SPECT, and MRI images were used to augment the mouse body during image-guided procedures to assess feasibility. A phantom with machined features was employed to quantitatively estimate the system accuracy. All the image-guided procedures were successfully performed. The tracked pico-projector correctly and reliably depicted virtual images on the animal body, highlighting the location of tumour and anatomical structures. The phantom study demonstrates the system was accurate to 0.55 ± 0.33mm. This paper presents a prototype real-time tracking AR surgical navigation system that improves visualization of underlying critical structures by overlaying virtual images onto the surgical site. This proof-of-concept pre-clinical study demonstrated both the clinical applicability and high precision of the system which was noted to be accurate to <1mm.

With the declaration of the global pandemic, surgical slowdowns were instituted to conserve health care resources for anticipated surges in patients with COVID-19. The long-term implications on survival of these slowdowns for patients with cancer in Canada is unknown. We constructed a microsimulation model based on real-world population data on cancer care from Ontario, Canada, from 2019 and 2020. Our model estimated wait times for cancer surgery over a 6-month period during the pandemic by simulating a slowdown in operating room capacity (60% operating room resources in month 1, 70% in month 2, 85% in months 3–6), as compared with simulated prepandemic conditions with 100% resources. We used incremental differences in simulated wait times to model survival using per-day hazard ratios for risk of death. Primary outcomes included life-years lost per patient and per cancer population. We conducted scenario analyses to evaluate alternative, hypothetical scenarios of different levels of surgical slowdowns on risk of death. The simulated model population comprised 22 799 patients waiting for cancer surgery before the pandemic and 20 177 patients during the pandemic. Mean wait time to surgery prepandemic was 25 days and during the pandemic was 32 days. Excess wait time led to 0.01–0.07 life-years lost per patient across cancer sites, translating to 843 (95% credible interval 646–950) life-years lost among patients with cancer in Ontario. Pandemic-related slowdowns of cancer surgeries were projected to result in decreased long-term survival for many patients with cancer. Measures to preserve surgical resources and health care capacity for affected patients are critical to mitigate unintended consequences.

To evaluate CF800, a novel lipid-based liposomal nanoparticle that co-encapsulates indocyanine green (ICG) and iohexol, for CT imaging of pulmonary vasculature in minimally-invasive thoracic surgery planning. CF800 was intravenously administered to 7 healthy rabbits. In vivo CT imaging was performed 15 min post-injection, with a subset of animals imaged at 24h, 48h, and 72h post injection. Signal-to-background ratios (SBR) were calculated at the inferior vena cava and compared across time-points. A similar protocol was applied to 2 healthy pigs to evaluate the feasibility and efficacy in a large animal model. To evaluate the feasibility of clinical application, a survey was completed by 7 surgical trainees to assess pre- and post-injection CT images of rabbits and pigs. Responses on the discernibility of pulmonary vasculature sub-branches and comfort level to use the images for pre-operative planning were collected and analyzed. CF800 injection improved visualization of pulmonary vessels in both rabbit and pig models. The SBR of rabbit pulmonary vasculature was significantly higher after CF800 injection (range 3.7-4.4) compared to pre-injection (range 3.3-3.8, n = 7; p<0.05). SBR remained significantly different up to 24 hours after injection (range 3.7-4.3, n = 4; p<0.05). Trainees' evaluation found the post-injection CT images had significantly higher discernibility at the second vessel branch generation in both rabbit and pig models. Trainees identified smaller vasculature branch generations in the post-injection images compared to the pre-treatment images in both rabbit (mean 6.7±1.8 vs 5.4±2.1; p<0.05) and pig (mean 6.7±1.8 vs 5.4±2.1; p<0.05). Trainees were significantly more comfortable using post-injection images for surgical planning compared to the pre-injection images (rabbit: 8.1±1.1 vs. 4.7±2.1; pig: 7.6±2.1 vs. 4.9±2.2; p<0.05). CF800 provides SBR and contrast enhancement of pulmonary vasculature which may assist in pre-surgical CT planning of minimally invasive thoracic surgery.

Lung adenocarcinoma (ADC) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate whether CyTOF identifies cellular and molecular predictors of tumor behavior. We developed and validated a CyTOF panel of 34 antibodies in four ADC cell lines and PBMC. We tested our panel in a set of 10 ADCs, classified into long- (LPS) (n = 4) and short-predicted survival (SPS) (n = 6) based on radiomics features. We identified cellular subpopulations of epithelial cancer cells (ECC) and their microenvironment and validated our results by multiplex immunofluorescence (mIF) applied to a tissue microarray (TMA) of LPS and SPS ADCs. The antibody panel captured the phenotypical differences in ADC cell lines and PBMC. LPS ADCs had a higher proportion of immune cells. ECC clusters (ECCc) were identified and uncovered two ADC groups. ECCc with high HLA-DR expression were correlated with CD4+ and CD8+ T cells, with LPS samples being enriched for those clusters. We confirmed a positive correlation between HLA-DR expression on ECC and T cell number by mIF staining on TMA slides. Spatial analysis demonstrated shorter distances from T cells to the nearest ECC in LPS. Our results demonstrate a distinctive cellular profile of ECC and their microenvironment in ADC. We showed that HLA-DR expression in ECC is correlated with T cell infiltration, and that a set of ADCs with high abundance of HLA-DR+ ECCc and T cells is enriched in LPS samples. This suggests new insights into the role of antigen presenting tumor cells in tumorigenesis.