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BackgroundBiopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods.MethodsQuantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR.ResultsThe label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively.ConclusionsThis proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-based diagnostic model. It costs less than 10 dollars per test.

Sexually transmitted infection (STI) of the upper reproductive tract can result in inflammation and infertility. A biomarker of STI-induced upper tract inflammation would be significant as many women are asymptomatic and delayed treatment increases risk of sequelae. Blood mRNA from 111 women from three cohorts was profiled using microarray. Unsupervised analysis revealed a transcriptional profile that distinguished 9 cases of STI-induced endometritis from 18 with cervical STI or uninfected controls. Using a hybrid feature selection algorithm we identified 21 genes that yielded maximal classification accuracy within our training dataset. Predictive accuracy was evaluated using an independent testing dataset of 5 cases and 10 controls. Sensitivity was evaluated in a separate test set of 12 women with asymptomatic STI-induced endometritis in whom cervical burden was determined by PCR; and specificity in an additional test set of 15 uninfected women with pelvic pain due to unknown cause. Disease module preservation was assessed in 42 women with a clinical diagnosis of pelvic inflammatory disease (PID). We also tested the ability of the biomarker to discriminate STI-induced endometritis from other diseases. The biomarker was 86.7% (13/15) accurate in correctly distinguishing cases from controls in the testing dataset. Sensitivity was 83.3% (5/6) in women with high cervical Chlamydia trachomatis burden and asymptomatic endometritis, but 0% (0/6) in women with low burden. Specificity in patients with non-STI-induced pelvic pain was 86.7% (13/15). Disease modules were preserved in all 8 biomarker predicted cases. The 21-gene biomarker was highly discriminatory for systemic infections, lupus, and appendicitis, but wrongly predicted tuberculosis as STI-induced endometritis in 52.4%. A 21-gene biomarker can identify asymptomatic women with STI-induced endometritis that places them at risk for chronic disease development and discriminate STI-induced endometritis from non-STI pelvic pain and other diseases.

Objective: We explored mechanisms of PD-L1 upregulation in ovarian cancer cells and tested in vivo the efficacy of intraperitoneal anti-PD-L1 therapy in two different ovarian cancer mouse models. Methods: We have derived several novel murine ovarian cancer cell lines from orthotopic ovarian tumors of triple transgenic mice. 129x SvJ and C57Bl/6 tumor-bearing mice were treated with 200 μg anti-PD-L1 antibody (or rat IgG as control) every two weeks for three doses. Immune gene profiling of splenocytes collected at necropsy was performed using 561 Nanostring probes. Results: Cell cycle analysis of in vitro cultured tumor cells demonstrates that PD-L1 expression increases preferentially on cells in G2/M cells. Exposure to cisplatin modifies PD-L1 levels, although the effect varies according to tumors’ baseline susceptibility. In vivo, we observed murine PD-L1 upregulation on ascites resident tumor cells, an effect reproduced ex vivo upon exposure of tumor cells to ascites fluid. PD-L1 blockade triggers increase in survival in both preclinical models with immune gene signatures pointing to cytotoxic anti-tumor immune response. Conclusion: PD-L1 expression increases in response to inflammatory stimuli from cell-free ascites and shows preference for dividing cells. PD-L1 upregulation varies according to tumor cells’ susceptibility to cisplatin. PD-L1 blockade increases systemic T cell responses and intratumoral T cell accumulation suggesting therapeutic potential in ovarian cancer.

Endometriosis is a largely benign, chronic inflammatory disease that may predispose to some of the ovarian tumors histotypes, collectively called endometriosis-associated ovarian cancers (EAOC). Although it is widely accepted that chronic inflammation drives cancer, the immune deregulation in chronic precursors of ovarian cancer has not been systematically studied. We performed here the first comprehensive gene profile of the tissue immune microenvironment in normal endometrium, benign endometriosis and EAOC (n=133), using Nanostring and the nCounter® GX Human Immunology Kit comprising probes for 511 immune genes. We found that the complement pathway was most prominently upregulated, suggesting its involvement in the transition from chronic endometriosis to atypical (premalignant) endometriosis and to EAOC. Complement protein expression was confirmed via immunohistochemistry, which identified upregulation of complement in epithelial cells. To explore the roles of complement in early EAOC carcinogenesis, we derived several novel ovarian cell lines with conditional mutations that can be turned on and off, using the Cre-loxP system. Using this in vitro model we demonstrate that genetic instability triggers upregulation of complement genes in epithelial cells, but without an advantage on cell death. These findings reveal an early link between epithelium and innate immune environment and further support a paradigm shift on complement roles in supporting cancer growth.