Pharmacogenetics as a predictor chemotherapy induced peripheral neuropathy in gynecologic cancer patients treated with Taxane-based chemotherapy.

BACKGROUND: This study investigated whether there are pharmacogenomic markers predictive of chemotherapy induced peripheral neuropathy (CIPN) as a result of taxane-based chemotherapy. METHODS: Patients were enrolled from August 2020 to November 2020 in a prospective, case-control trial evaluating pharmacogenetic predictors of CIPN. All women were treated with at least 3 cycles of taxane-based chemotherapy for histologically confirmed gynecologic malignancies. Buccal saliva samples were used to test for 32 drug metabolism variations. All testing was performed by ⍺LPHA-GENOMIX laboratories. Fisher's Exact test was used to assess for event differences of categorical variables. RESULTS: Of 102 enrolled patients, 58%, 28%, and 14% had ovarian, endometrial, or cervical cancers, respectively. The median age was 67, 72% were Caucasian and 25% were African American. 16% of patients were treated with 3-4 cycles, 57% received 5-7 cycles, and 27% received 8 or more cycles of chemotherapy that included paclitaxel. Grade 2 CIPN was experienced by 51 patients. There was no difference in age, race, disease site, or number of chemotherapy cycles (p > 0.05) between those who did or did not develop CIPN. CYP2D6 genotype (p = 0.009) and CYP3A5 genotype (p = 0.023) had different frequencies among those with and without CIPN. Patients classified as having poor or intermediate function of CYP2D6 had increased risk of CIPN (OR 1.63, 95% CI 1.04-2.57, p = 0.026). There was no difference in CYP2D6 phenotype by race (p = 0.29). No patients with normal function of CYP3A5 developed CIPN. There were no Caucasians with normal function of CYP3A5, but 28% of African Americans had normal CYP3A5 function (p < 0.001). CONCLUSIONS: Pharmacogenomics appear associated with the development of CIPN and may be able to help personalize treatment decision making.

High serum levels of inflammatory markers are associated with early recurrence in patients with high-grade serous ovarian cancer after platinum therapy.

OBJECTIVE: To determine if inflammatory biomarkers can predict the long-term outcome of platinum therapy in patients with high-grade serous ovarian cancer. METHODS: Women diagnosed with high-grade serous epithelial ovarian cancer (n = 70) at a single institution were enrolled in a prospective serum collection study between 2005 and 2020. Seventeen markers of inflammation and oxidative stress were measured in serum samples on a chemistry analyzer. Association was tested for serum levels with progression-free survival (PFS), time to recurrence (TTR), overall survival (OS), and time to death (TTD) using Cox proportional hazards and Kaplan-Meier curves. Patient survival was censored at 10 years. RESULTS: Higher serum levels of LDH were associated with worse PFS (HR 2.57, p = 0.028). High serum levels of BAP (HR 0.38, p = 0.025), GSP (HR 0.40, p = 0.040), HDL-c (HR 0.27, p = 0.002), and MG (HR 0.36, p = 0.017) were associated with improved PFS. Higher expression of LDH was associated with worse OS (HR 2.16, p = 0.023). Higher levels of CK.nac (HR 0.39, p = 0.033) and HDL-c (HR 0.35, p = 0.029) were associated with improved OS. Similar outcomes were found with TTR and TTD analyses. CONCLUSION: General inflammatory biomarkers may serve as a guide for prognosis and treatment benefit. Future studies needed to further define their role in predicting prognosis or how these markers may affect response to therapy.

A novel monoclonal antibody associated with glucoside kills gastric adenocarcinoma AGS cells based on glycosylation target.

Glycosylation results in the production of glycans which are required for certain proteins to function. These glycans are also present on cell surfaces where they help maintain cell membrane integrity and are a key component of immune recognition. As such, cancer has been shown to alter glycosylation to promote tumour proliferation, invasion, angiogenesis, and immune envasion. Currently, there are few therapeutic monoclonal antibodies (mAb) which target glycosylation alterations in cancer. Here, we report a novel mAb associated with a glucoside, mAb 201E4, which is able induce cancer cell death and apoptosis based on a specific glycosylation target. This mAb evokes cancer cell death in vitro via caspase, fas, and mitochondrial associated apoptotic pathways. The efficacy of this mAb was further confirmed in vivo as treatment of mice with mAb 201E4 resulted in potent tumour shrinkage. Finally, the antibody was proven to be specific to glycosylation alterations in cancer and have no binding to normal tissues. This data indicates that mAb 201E4 successfully targets glycosylation alterations in neoplasms to induce cancer cell death, which may provide a new strategy for therapy in cancer.

Clinical calculator redefines prognosis for high-risk early-stage ovarian cancers and potential to guide treatment in the adjuvant setting.

OBJECTIVE: To determine the utility of a clinical calculator to redefine prognosis and need for chemotherapy among patients with early-stage high-risk epithelial ovarian cancer. METHODS: Data were abstracted for stage I-II, high-risk ovarian cancer from the National Cancer Database from years 2005 to 2015. Based on demographic, pathologic, surgical, and laboratory characteristics, a clinical score was developed using Cox regression. Propensity score weighting was used to adjust for differences between patients who did and did not receive chemotherapy. RESULTS: Of 8188 patients with early-stage high-risk ovarian cancer, 6915 (84%) did and 1273 (16%) did not receive chemotherapy. A clinical calculator was created utilizing age, stage, histology, grade, tumor size, number of pelvic and paraaortic lymph nodes examined, the presence of malignant ascites, and CA125. The calculator divided patients into low, moderate, and high-risk groups with 5-year OS (overall survival) of 92%, 82%, and 66%, and 10-year OS of 85%, 67%, and 44%, respectively. Chemotherapy improved 5-year OS and 10-year OS in the high-risk group (56% to 73%; p < 0.001, 34% to 48%; p < 0.001). The moderate risk group had improved 5-year OS (80% to 85%; p = 0.01) but not 10-year OS (66% to 66%; p = 0.13). Chemotherapy did not improve 5-year or 10-year OS in low-risk patients (93% to 92%, p = 1.0, 86% to 84%, p = 0.99). CONCLUSIONS: The prognosis among high-risk early-stage ovarian cancer patients is heterogeneous. This calculator may aid in patient-centered counseling regarding potential treatment benefits.

Are There Survival Differences Between Women with Equivalent Residual Disease After Interval Cytoreductive Surgery Compared with Primary Cytoreductive Surgery for Advanced Ovarian and Peritoneal Cancer?

OBJECTIVE: The aim of this study was to investigate survival differences between equivalent residual disease [complete gross resection (CGR), minimal residual disease (MRD), suboptimal] at the time of primary debulking surgery (PDS) and interval debulking surgery (IDS). METHODS: The National Cancer Database was used to identify patients from 2010 to 2015 with stage IIIC/IV primary peritoneal or ovarian cancer who had residual disease recorded. Propensity score matching (PSM) was used to correct for differences in characteristics between the PDS and IDS groups. RESULTS: Of 8683 patients with advanced ovarian cancer, 4493 (52%), 2546 (29%), and 1644 (19%) had CGR, MRD, or suboptimal resection, respectively. From 2010 to 2015, the number of patients undergoing IDS increased 27% (ptrend < 0.001), and there was an 18% increase in CGRs (ptrend = 0.005). The increased use of IDS from 2010 to 2015 was associated with increased CGRs (ptrend = 0.02) and decreased MRD (ptrend = 0.001), but not with decreased suboptimal resections (ptrend = 0.18). IDS, even after PSM, was associated with inferior overall survival [OS; hazard ratio (HR) 1.12, 95% confidence interval (CI) 1.03-1.22, p = 0.008]. A CGR at PDS had prolonged median OS compared with a CGR at IDS (51 vs. 44 months, p < 0.001). Additionally, MRD at PDS had worse median OS compared with a CGR at IDS (41 vs. 44 months, p = 0.03), but improved median OS compared with MRD at IDS (median OS 35 months, p = 0.05). CONCLUSION: The use of IDS continues to rise in the US, and is associated with improved surgical outcomes but not necessarily similar oncologic outcomes. There should be continued efforts to improve cytoreductive outcomes in women with advanced ovarian and peritoneal malignancies.

The elusive diagnosis and emergent management of a late-presenting ruptured interstitial pregnancy: a case report.

BACKGROUND: Interstitial pregnancies are rare and often difficult to diagnose given their proximal position to the uterine cavity, however most are identified by 12 weeks gestation. Delayed or missed diagnosis contributes to heightened incidence of poor outcomes including hemorrhage and death. CASE PRESENTATION: A 35-year-old woman at 15 weeks gestation with confirmed intrauterine pregnancy on first trimester ultrasound and prior negative MRI presented in hemorrhagic shock and was found to have a ruptured interstitial pregnancy. Exploratory laparotomy revealed the fetus to be in the abdomen as well as a large cornual defect and abnormal placentation that resulted in supracervical hysterectomy. CONCLUSIONS: Interstitial pregnancy should be considered in a patient presenting with symptoms consistent with ectopic rupture, especially in the setting of equivocal or suboptimal prior imaging. Earlier diagnosis may allow for fertility-sparing intervention and decreased risk of morbidity and mortality.

Long term survival outcomes of stage I mucinous ovarian cancer - A clinical calculator predictive of chemotherapy benefit.

OBJECTIVES: To determine the long-term potential benefit of adjuvant chemotherapy in subgroups of high-risk stage I mucinous ovarian cancer patients using a predictive scoring algorithm. METHODS: Data were collected from the National Cancer Database from 2004 to 2014. Based on demographic and surgical characteristics, a novel 10-year survival prognostic scoring system was developed using Cox regression. RESULTS: There were 2041 eligible patients with stage I mucinous ovarian cancer including 1362 (67%) with stage IA/IB disease, 598 (29%) with stage IC disease, and 81 (4%) with stage I disease not otherwise specified. Median age was 52 with a range of 13-90 years old. 737 (36%) patients were treated with adjuvant chemotherapy. Adjuvant chemotherapy was more common in patients with stage IC relative to stage IA/IB disease (69% vs. 21%, P < 0.001) or with poorly-differentiated relative to well-differentiated tumors (69% vs. 23%, P < 0.001). Unadjusted 10-year survival was 81% relative to 79% for patients treated with vs. without chemotherapy, respectively (P = 0.46). Patients were predicted to exhibit a low- or a high-risk of death using a multivariate Cox regression model with age, stage, grade, lymphovascular space invasion and ascites. Risk of death without vs. with adjuvant chemotherapy was similar in low-risk patients (88% vs. 84%; HR = 0.80, 95%CI = 0.56-1.15, P = 0.23) and worse in high-risk patients (51% vs. 74%; HR = 1.58, 95%CI: 1.05-2.38, P = 0.03) with stage I mucinous ovarian cancer. CONCLUSIONS: A predictive scoring algorithm may provide prognostic information on long-term survival and identify high-risk stage I mucinous ovarian cancer patients who might achieve a survival benefit from adjuvant chemotherapy.

A 73-gene proliferative transcriptomic signature predicts uterine serous carcinoma patient survival and response to primary therapy.

OBJECTIVES: To develop a transcriptomic signature capable of predicting overall survival (OS) for uterine serous carcinoma (USC). METHODS: RNAseq data for 58 USC patients were obtained from TCGA. Expression of 73 candidate genes was measured for 67 Augusta University (AU) samples using NanoString technology. RESULTS: Analysis of the TCGA RNAseq data identified 73 genes that individually predict prognosis for USC patients and an elastic net model with all 73 genes (USC73) distinguishes a good OS group with low USC73 score from a poor OS group with high USC73 score (5-year OS = 83.3% and 13.3% respectively, HR = 40.1; p = 3 × 10-8). This finding was validated in the independent AU cohort (HR = 4.3; p = 0.0004). The poor prognosis group with high USC73 score consists of 37.9% and 32.8% of patients in the TCGA and AU cohort respectively. USC73 score and pathologic stage independently contribute to OS and together provide the best prognostic value. Early stage, low USC73 patients have the best prognosis (5-year OS = 85.1% in the combined dataset), while advanced stage, high USC73 patients have the worst prognosis (5-year OS = 6.4%, HR = 30.5, p = 1.2 × 10-12). Consistent with the observed poor survival, primary cell cultures from high USC73 patients had higher proliferation rate and cell cycle progression; and high USC73 patients had lower rates of complete response to standard therapy. CONCLUSIONS: The USC73 transcriptomic signature and stage independently predict OS of USC patients and the best prediction is achieved using USC73 and stage. USC73 may also serve as a therapeutic biomarker to guide patient care.

Better survival is observed in cervical cancer patients positive for specific anti-glycan antibodies and receiving brachytherapy.

OBJECTIVE: To measure anti-glycan antibodies (AGA) in cervical cancer (CC) patient sera and assess their effect on therapeutic outcome. PATIENTS AND METHODS: Serum AGA was measured in 276 stage II and 292 stage III Peruvian CC patients using a high content and throughput Luminex multiplex glycan array (LMGA) containing 177 glycans. Association with disease-specific survival (DSS) and progression free survival (PFS) were analyzed using Cox regression. RESULTS: AGAs were detected against 50 (28.3%) of the 177 glycans assayed. Of the 568 patients, 84.5% received external beam radiation therapy (EBRT) plus brachytherapy (BT), while 15.5% only received EBRT. For stage-matched patients (Stage III), receiving EBRT alone was significantly associated with worse survival (HR 6.4, p < 0.001). Stage III patients have significantly worse survival than Stage II patients after matching for treatment (HR = 2.8 in EBRT+BT treatment group). Furthermore, better PFS and DSS were observed in patients positive for AGA against multiple glycans belonging to the blood group H, Lewis, Ganglio, Isoglobo, lacto and sialylated tetrarose antigens (best HR = 0.49, best p = 0.0008). CONCLUSIONS: Better PFS and DSS are observed in cervical cancer patients that are positive for specific antiglycan antibodies and received brachytherapy.

Impact of adjuvant treatment and prognostic factors in stage I uterine leiomyosarcoma patients treated in Commission on Cancer®-accredited facilities.

OBJECTIVES: Determine the impact of adjuvant chemotherapy (ACT) and prognostic factors in surgically managed patients with stage I uterine leiomyosarcoma (ULMS). METHODS: Women who underwent hysterectomy and were diagnosed with stage I ULMS between 2010 and 2014 in the National Cancer Database were eligible for this observation study. Inverse probability of treatment weighting based on propensity score was used to balance clinical characteristics between ACT and no ACT patients. Hazard ratio (HR) and 95% confidence interval (CI) were estimated from Cox modeling. RESULTS: There were 1059 eligible patients with stage I ULMS including 514 treated with ACT and 545 with no ACT. Patient characteristics and tumor features varied in patients treated with ACT vs. no ACT (P < .0001). Multivariate survival analysis demonstrated that patient age, comorbidity score, tumor size, lymphovascular space invasion (LVSI) and grade were independent prognostic factors. After propensity score weighting to control for imbalance of prognostic clinical factors, adjusted five-year survival was 61.7% vs. 61.3% and restricted mean survival time was 39.7 vs. 40.6 months for ACT vs. no ACT, respectively. Risk of death in a weighted Cox analysis of overall survival was similar (HR = 1.08, 95% CI = 0.85-1.37, P = .054) for ACT vs. no ACT patients. Subset analysis demonstrated that survival was similar in ACT vs. no ACT patients categorized by age, tumor size and LVSI or with high grade or ungraded tumors. In contrast, patients with low grade tumors had worse 5-year survival (82.3% vs. 91.5%) and an increased risk of death (HR = 3.79, 95% CI = 1.15-12.40, P = .028) following ACT vs. no ACT. CONCLUSIONS: ACT did not improve survival over no ACT in patients with stage I ULMS and was inferior in patients with low grade tumors.

A pan-cancer perspective of matrix metalloproteases (MMP) gene expression profile and their diagnostic/prognostic potential.

IMPLICATION: By understanding Matrix Metalloprotease (MMP) dysregulation from a pan-cancer perspective, this study sheds light on the diagnostic potentials of MMPs across multiple neoplasms. BACKGROUND: MMPs are intriguing genes related to cancer disease progression, functional promotion of angiogenesis, invasion, metastasis, and avoidance of immune surveillance. Many studies have noted these genes are frequently upregulated in cancer. However, expression patterns of all MMPs and their diagnostic and prognostic potential have not been investigated in a pan-cancer perspective. METHODS: The Cancer Genome Atlas (TCGA) data were used to evaluate diagnostic and prognostic potential of 24 MMPs in fifteen different cancer types. Gene expression measured by RNA-seq was analyzed by differential expression, hierarchical clustering, and ROC analysis for individual genes and in combination. RESULTS: MMP1, MMP9, MMP10, MMP11, and MMP13 were almost universally upregulated across all cancers, with significant (p < 0.05) fold change (FC > 2) in ten of fifteen cancers. MMP3, MMP7, MMP12 and MMP14) are significantly up-regulated in at least 10 cancer types. Interestingly, MMP2, MMP7, MMP23B, MMP27 and MMP28) are significantly down-regulated in seven to nine cancer types. Multiple MMPs possess AUC's > 0.9 in more than one cancer. However, survival analyses suggest that the prognostic value of MMPs is limited to clear cell renal carcinoma. CONCLUSIONS: Most MMPs have consistently increased gene expression across cancers, while several MMPs have consistently decreased expression in several cancer types. Many MMPs have diagnostic value individually or in combination, while the prognostic value of MMPs is restricted to one subtype of kidney cancer.

A combined score of clinical factors and serum proteins can predict time to recurrence in high grade serous ovarian cancer.

OBJECTIVE: To investigate the utility of a combined panel of protein biomarkers and clinical factors to predict recurrence in serous ovarian cancer patients. METHODS: Women at Augusta University diagnosed with ovarian cancer were enrolled between 2005 and 2015 (n = 71). Blood was drawn at enrollment and follow-up visits. Patient serum collected at remission was analyzed using the SOMAscan array (n = 35) to measure levels of 1129 proteins. The best 26 proteins were confirmed using Luminex assays in the same 35 patients and in an additional 36 patients (ntotal = 71) as orthogonal validation. The data from these 26 proteins was combined with clinical factors using an elastic net multivariate model to find an optimized combination predictive of progression-free survival (PFS). RESULTS: Of the 26 proteins, Brain Derived Neurotrophic Factor and Platelet Derived Growth Factor molecules were significant for predicting PFS on both univariate and multivariate analyses. All 26 proteins were combined with clinical factors using the elastic net algorithm. Ten components were determined to predict PFS (HR of 6.55, p-value 1.12 × 10-6, CI 2.57-16.71). This model was named the serous high grade ovarian cancer (SHOC) score. CONCLUSION: The SHOC score can predict patient prognosis in remission. This tool will hopefully lead to early intervention and consolidation therapy strategies in remission patients destined to recur.

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions.

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

Ovarian recurrence risk assessment using machine learning, clinical information, and serum protein levels to predict survival in high grade ovarian cancer.

In ovarian cancer, there is no current method to accurately predict recurrence after a complete response to chemotherapy. Here, we develop a machine learning risk score using serum proteomics for the prediction of early recurrence of ovarian cancer after initial treatment. The developed risk score was validated in an independent cohort with serum collected prospectively during the remission period. In the discovery cohort, patients scored as low-risk had a median time to recurrence (TTR) that was not reached at 10 years compared to 10.5 months (HR 4.66, p < 0.001) in high-risk patients. In the validation cohort, low-risk patients had a median TTR which was not reached compared to 4.7 months in high-risk patients (HR 4.67, p = 0.009). In advanced-stage patients with a CA125 < 10, low-risk patients had a median TTR of 68 months compared to 6 months in high-risk patients (HR 2.91, p = 0.02). The developed risk score was capable of distinguishing the duration of remission in ovarian cancer patients. This score may help guide maintenance therapy and develop innovative treatments in patients at risk at high-risk of recurrence.

Tumor-intrinsic and -extrinsic (immune) gene signatures robustly predict overall survival and treatment response in high grade serous ovarian cancer patients.

In the present study, we developed a transcriptomic signature capable of predicting prognosis and response to primary therapy in high grade serous ovarian cancer (HGSOC). Proportional hazard analysis was performed on individual genes in the TCGA RNAseq data set containing 229 HGSOC patients. Ridge regression analysis was performed to select genes and develop multigenic models. Survival analysis identified 120 genes whose expression levels were associated with overall survival (OS) (HR = 1.49-2.46 or HR = 0.48-0.63). Ridge regression modeling selected 38 of the 120 genes for development of the final Ridge regression models. The consensus model based on plurality voting by 68 individual Ridge regression models classified 102 (45%) as low, 23 (10%) as moderate and 104 patients (45%) as high risk. The median OS was 31 months (HR = 7.63, 95% CI = 4.85-12.0, P < 1.0-10) and 77 months (HR = ref) in the high and low risk groups, respectively. The gene signature had two components: intrinsic (proliferation, metastasis, autophagy) and extrinsic (immune evasion). Moderate/high risk patients had more partial and non-responses to primary therapy than low risk patients (odds ratio = 4.54, P < 0.001). We concluded that the overall survival and response to primary therapy in ovarian cancer is best assessed using a combination of gene signatures. A combination of genes which combines both tumor intrinsic and extrinsic functions has the best prediction. Validation studies are warranted in the future.

Applying Artificial Intelligence to Gynecologic Oncology: A Review.

IMPORTANCE: Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies. OBJECTIVE: The aim of this study was to review the role of AI in gynecologic oncology. EVIDENCE ACQUISITION: Artificial intelligence publications in gynecologic oncology were identified by searching "gynecologic oncology AND artificial intelligence" in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers. RESULTS: A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response. CONCLUSIONS AND RELEVANCE: Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology.

QuPath Automated Analysis of Optic Nerve Degeneration in Brown Norway Rats.

Purpose: A novel application of QuPath open-source digital analysis software is used to provide in-depth morphological analysis of progressive optic nerve (ON) degeneration in rats. Methods: QuPath software was adapted to assess axon and gliotic morphology in toluidine blue-stained, Brown Norway rat ON light micrographs. QuPath axon numbers, density, size distributions, and gliotic areas were obtained from test images and ON cross-sections separated by damage grade. QuPath results were compared with manual counting, AxonJ, and electron microscopy axon estimates. Results: QuPath-derived axon number, density, and diameter decreased with increasing ON damage. Axon density negatively correlated with gliotic areas in test images (R2 = 0.759; P < 0.0001; N = 40) and in ON cross-sections (R2 = 0.803; P < 0.0004; N = 10). Although axon losses occurred across most axon diameters, large axons were more susceptible to degeneration. The exception was swollen axons > 2 µm, which increased in moderately but not severely damaged images. QuPath axon counts correlated strongly with manual counts of test images (R2 = 0.956; P < 0.0001). QuPath outperformed AxonJ on test images and total ON axon counts. Compared to electron microscopy analysis, QuPath undercounted ON axons; however, correlation between the methods was robust (R2 = 0.797; P < 0.001; N = 10). Conclusions: QuPath analysis reliably identified axon loss, axon morphology changes, and gliotic expansion that occurred in degenerating ONs. Translational Relevance: QuPath is a valuable tool for rapid, automated, analysis of healthy and degenerating ONs. Reproducible preclinical studies for new glaucoma treatments depend on unbiased in-depth analysis of ON pathology. This was provided by the QuPath approach.

Age is nothing but a number: radical en bloc resection of colon adenocarcinoma with abdominal wall reconstruction in an 81 year old.

Colon adenocarcinoma is a common neoplasm, which rarely presents with full thickness invasion through the abdominal wall. Aggressive treatment is often reserved for younger patients, with many surgeons opting to consider elderly patients as non-operative candidates, especially in the setting of diffuse disease. We report a case of radical resection of a colon cancer, with full thickness abdominal wall invasion in an 81-year-old female. The patient presented with gradual abdominal swelling over multiple months. She had a CT scan revealing a mass eroding through her abdominal wall, up to the skin. Operative resection with adjuvant chemotherapy was chosen as therapy because the patient had no co-morbidities. The patient underwent en bloc resection of the abdominal wall with right hemicolectomy and resection of all structures attached to the mass. The patient has been disease free for 24 months and has had return to her baseline activities of daily living.

Multiplex glycan bead array for high throughput and high content analyses of glycan binding proteins.

Glycan-binding proteins (GBPs) play critical roles in diverse cellular functions such as cell adhesion, signal transduction and immune response. Studies of the interaction between GBPs and glycans have been hampered by the availability of high throughput and high-content technologies. Here we report multiplex glycan bead array (MGBA) that allows simultaneous analyses of 384 samples and up to 500 glycans in a single assay. The specificity, sensitivity and reproducibility of MGBA are evaluated using 39 plant lectins, 13 recombinant anti-glycan antibodies, and mammalian GBPs. We demonstrate the utility of this platform by the analyses of natural anti-glycan IgM and IgG antibodies in 961 human serum samples and the discovery of anti-glycan antibody biomarkers for ovarian cancer. Our data indicate that the MGBA platform is particularly suited for large population-based studies that require the analyses of large numbers of samples and glycans.

Streptococcus pyogenes quinolinate-salvage pathway-structural and functional studies of quinolinate phosphoribosyl transferase and NH3 -dependent NAD+ synthetase.

Streptococcus pyogenes, also known as Group A Strep (GAS), is an obligate human pathogen that is responsible for millions of infections and numerous deaths per year. Infection manifestations can range from simple, acute pharyngitis to more complex, necrotizing fasciitis. To date, most treatments for GAS infections involve the use of common antibiotics including tetracycline and clindamycin. Unfortunately, new strains have been identified that are resistant to these drugs, therefore, new targets must be identified to treat drug-resistant strains. This work is focused on the structural and functional characterization of three proteins: spNadC, spNadD, and spNadE. These enzymes are involved in the biosynthesis of nicotinamide adenine dinucleotide (NAD+ ). The structures of spNadC and spNadE were determined. SpNadC is suggested to play a role in GAS virulence, while spNadE, functions as an NAD synthetase and is considered to be a new drug target. Determination of the spNadE structure uncovered a putative, NH3 channel, which may provide insight into the mechanistic details of NH3 -dependent NAD+ synthetases in prokaryotes. ENZYMES: Quinolinate phosphoribosyltransferase: EC2.4.2.19 and NAD synthetase: EC6.3.1.5. DATABASE: Protein structures for spNadC, spNadCΔ69A , and spNadE are deposited into Protein Data Bank under the accession codes 5HUL, 5HUO & 5HUP, and 5HUH & 5HUJ, respectively.