Nutrition's checkpoint inhibition: The impact of nutrition on immunotherapy outcomes.

OBJECTIVES: To determine if nutritional status effects response to immunotherapy in women with gynecologic malignancies. METHODS: A retrospective chart review was conducted on gynecologic cancer patients who received immunotherapy at a single institution between 2015 and 2022. Immunotherapy included checkpoint inhibitors and tumor vaccines. The prognostic nutritional index (PNI) was calculated from serum albumin levels and total lymphocyte count. PNI values were determined at the beginning of treatment for each patient and assessed for their association with immunotherapy response. Disease control response (DCR) as an outcome of immunotherapy was defined as complete response, partial response, or stable disease. RESULTS: One hundred and ninety-eight patients received immunotherapy (IT) between 2015 and 2022. The gynecological cancers treated were uterine (38%), cervix (32%), ovarian (25%), and vulvar or vaginal (4%) cancers. The mean PNI for responders was higher than the non-responder group (p < 0.05). The AUC value for PNI as a predictor of response was 49. A PNI value of 49 was 43% sensitive and 85% specific for predicting a DCR. In Cox proportional hazards analysis, after adjusting for ECOG score and the number of prior chemotherapy lines, severe malnutrition was associated with progression-free survival (PFS) (HR = 1.85, p = 0.08) and overall survival (OS) (HR = 3.82, p < 0.001). Patients with PNI < 49 were at a higher risk of IT failure (HR = 2.24, p = 0.0001) and subsequent death (HR = 2.84, p = 9 × 10-5). CONCLUSIONS: PNI can be a prognostic marker to predict response rates of patients with gynecologic cancers treated with immunotherapy. Additional studies needed to understand the mechanistic role of malnutrition in immunotherapy response.

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.

Multiplex analysis of inflammatory proteins associated with risk of coronary artery disease in type-1 diabetes patients.

BACKGROUND: Chronic uncontrolled hyperglycemia, a precursor to chronic low-grade inflammation, is a leading cause of coronary artery disease (CAD) due to plaque buildup in type-1 diabetes (T1D) patients. We evaluated levels of 22 inflammatory markers in cross-sectional serum samples from 1222 subjects to evaluate their potential as risk factors for CAD in T1D patients. HYPOTHESIS: Circulating levels of markers of inflammation may be the risk factors for incident CAD. METHODS: The T1D subjects were divided into two groups: those without CAD (n = 1107) and with CAD (n = 115). Serum levels of proteins were assayed using multiplex immunoassays on a Luminex Platform. Differences between the two groups were made by univariate analysis. Multivariate logistic regression was used to ascertain the potential of proteins as risk factors for CAD. Influence of age, duration of diabetes, sex, hypertension, and dyslipidemia was determined in a stepwise manner. Serum levels of 22 proteins were combined into a composite score using Ridge regression for risk-based stratification. RESULTS: Mean levels of CRP, IGFBP1, IGFBP2, insulin-like growth factors binding protein-6 (IGFBP6), MMP1, SAA, sTNFRI, and sTNFRII were elevated in CAD patients (n = 115) compared to T1D patients without CAD (nCAD, n = 1107). After adjusting for age, duration of diabetes, sex, hypertension, and dyslipidemia, higher levels of sTNFRI (odds ratio [OR] = 2.18, 1.1 × 10-3 ), sTNFRII (OR = 1.52, 1 × 10-2 ), and IGFBP6 (OR = 3.62, 1.8 × 10-3 ) were significantly associated with CAD. The composite score based on Ridge regression, was able to stratify CAD patients into low, medium, and high-risk groups. CONCLUSIONS: The results show activation of the TNF pathway in CAD patients. Evaluating these markers in serum can be a potential tool for identifying high-risk T1D patients for intensive anti-inflammatory therapeutic interventions.

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.

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Development of a Single Molecule Counting Assay to Differentiate Chromophobe Renal Cancer and Oncocytoma in Clinics.

Malignant chromophobe renal cancer (chRCC) and benign oncocytoma (RO) are two renal tumor types difficult to differentiate using histology and immunohistochemistry-based methods because of their similarity in appearance. We previously developed a transcriptomics-based classification pipeline with "Chromophobe-Oncocytoma Gene Signature" (COGS) on a single-molecule counting platform. Renal cancer patients (n = 32, chRCC = 17, RO = 15) were recruited from Augusta University Medical Center (AUMC). Formalin-fixed paraffin-embedded (FFPE) blocks from their excised tumors were collected. We created a custom single-molecule counting code set for COGS to assay RNA from FFPE blocks. Utilizing hematoxylin-eosin stain, pathologists were able to correctly classify these tumor types (91.8%). Our unsupervised learning with UMAP (Uniform manifold approximation and projection, accuracy = 0.97) and hierarchical clustering (accuracy = 1.0) identified two clusters congruent with their histology. We next developed and compared four supervised models (random forest, support vector machine, generalized linear model with L2 regularization, and supervised UMAP). Supervised UMAP has shown to classify all the cases correctly (sensitivity = 1, specificity = 1, accuracy = 1) followed by random forest models (sensitivity = 0.84, specificity = 1, accuracy = 1). This pipeline can be used as a clinical tool by pathologists to differentiate chRCC from RO.

Use of a glycomics array to establish the anti-carbohydrate antibody repertoire in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease, characterized by the presence of autoantibodies to protein and non-protein antigens. Here we report the identification of specific anti-carbohydrate antibodies (ACAs) that are associated with pathogenesis and progression to T1D. We compare circulatory levels of ACAs against 202 glycans in a cross-sectional cohort of T1D patients (n = 278) and healthy controls (n = 298), as well as in a longitudinal cohort (n = 112). We identify 11 clusters of ACAs associated with glycan function class. Clusters enriched for aminoglycosides, blood group A and B antigens, glycolipids, ganglio-series, and O-linked glycans are associated with progression to T1D. ACAs against gentamicin and its related structures, G418 and sisomicin, are also associated with islet autoimmunity. ACAs improve discrimination of T1D status of individuals over a model with only clinical variables and are potential biomarkers for T1D.