A Molecular Test for Quantifying Functional Notch Signaling Pathway Activity in Human Cancer

Patient-Representative Cell Line Models in a Heterogeneous Disease: Comparison of Signaling Transduction Pathway Activity Between Ovarian Cancer Cell Lines and Ovarian Cancer

Background/Objectives: Advances in treatment options have barely improved the prognosis of ovarian carcinoma (OC) in recent decades. The inherent heterogeneity of OC underlies challenges in treatment (development) and patient stratification. One hurdle for effective drug development is the lack of patient-representative disease models available for preclinical drug research. Based on quantitative measurement of signal transduction pathway (STP) activity in cell lines, we aimed to identify cell line models that better mirror the different clinical subtypes of OC. Methods: The activity of seven oncogenic STPs (signal transduction pathways) was determined by previously described STP technology using transcriptome data from untreated OC cell lines available in the GEO database. Hierarchal clustering of cell lines was performed based on STP profiles. Associations between cell line histology (original tumor), cluster, and STP profiles were analyzed. Subsequently, STP profiles of clinical OC tissue samples were matched with OC cell lines. Results: Cell line search resulted in 80 cell line transcriptome data from 23 GEO datasets, with 51 unique cell lines. These cell lines were derived from eight different histological OC subtypes (as determined for the primary tumor). Clustering revealed seven clusters with unique STP profiles. When borderline tumors (n = 6), high-grade serous (n = 51) and low-grade (n = 31) OC were matched with cell lines, twelve different cell lines were identified as potentially patient-representative OC cell line models. Conclusions: Based on STP activity, we identified twelve different cell lines that were the most representative of the common subtypes of OC. These findings are important to improve drug development for OC.

Quantitative characterization of immune cells by measuring cellular signal transduction pathway activity

For many diseases, including cancer, infections, and auto-immune diseases, the immune response is a major determinant of disease progression, response to therapy, and clinical outcome. Innate and adaptive immune responses are controlled by coordinated activity of different immune cell types. The functional activity state of immune cells is determined by Signal Transduction Pathways (STPs). A recently developed technology (Simultaneous Transcriptome-based Activity Profiling of Signal Transduction Pathways, STAP-STP) enables simultaneous and quantitative activity measurement of relevant STPs in immune cells based on mRNA-analysis. STAP-STP technology was used to analyze public transcriptome data of a variety of immune cell types in resting and activated functional state. In addition, a clinical study on rheumatoid arthritis (RA) was analyzed to illustrate utility of the technology. Per sample, activity of androgen and estrogen receptor, PI3K, MAPK, TGFβ, Notch, NFκB, JAK-STAT1/2, and JAK-STAT3 STPs was calculated, generating an STP activity profile (SAP) consisting of 9 activity scores. Each analyzed immune cell type, i.e. naive/resting and immune-activated CD4 + and CD8 + T cells, T helper cells, B cells, NK cells, monocytes, macrophages, and dendritic cells, had a reproducible and characteristic SAP, reflecting both cell type and its activity state. Analysis of clinical RA samples revealed increased TGFβ STP activity in whole blood samples. In conclusion, STAP-STP technology enables quantitative measurement of the functional activity state of immune cells of the innate and adaptive immune system. Aside from diagnostic applications, utility lies in unravelling abnormal immune function in disease and immunomodulatory drug development.

Increased H19/miR-675 Expression in Adult T-Cell Leukemia Is Associated with a Unique Notch Signature Pathway

The Notch pathway is a key cancer driver and is important in tumor progression. Early research suggested that Notch activity was highly dependent on the expression of the intracellular cleaved domain of Notch-1 (NICD). However, recent insights into Notch signaling reveal the presence of Notch pathway signatures, which may vary depending on different cancer types and tumor microenvironments. Herein, we perform a comprehensive investigation of the Notch signaling pathway in adult T-cell leukemia (ATL) primary patient samples. Using gene arrays, we demonstrate that the Notch pathway is constitutively activated in ATL patient samples. Furthermore, the activation of Notch in ATL cells remains elevated irrespective of the presence of activating mutations in Notch itself or its repressor, FBXW7, and that ATL cells are dependent upon Notch-1 expression for proliferation and survival. We demonstrate that ATL cells exhibit the expression of pivotal Notch-related genes, including notch-1, hes1, c-myc, H19, and hes4, thereby defining a critical Notch signature associated with ATL disease. Finally, we demonstrate that lncRNA H19 is highly expressed in ATL patient samples and ATL cells and contributes to Notch signaling activation. Collectively, our results shed further light on the Notch pathway in ATL leukemia and reveal new therapeutic approaches to inhibit Notch activation in ATL cells.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Machine learning developed a CD8+ exhausted T cells signature for predicting prognosis, immune infiltration and drug sensitivity in ovarian cancer

CD8+ exhausted T cells (CD8+ Tex) played a vital role in the progression and therapeutic response of cancer. However, few studies have fully clarified the characters of CD8+ Tex related genes in ovarian cancer (OC). The CD8+ Tex related prognostic signature (TRPS) was constructed with integrative machine learning procedure including 10 methods using TCGA, GSE14764, GSE26193, GSE26712, GSE63885 and GSE140082 dataset. Several immunotherapy benefits indicators, including Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore (IPS), TMB score and tumor escape score, were used to explore performance of TRPS in predicting immunotherapy benefits of OC. The TRPS constructed by Enet (alpha = 0.3) method acted as an independent risk factor for OC and showed stable and powerful performance in predicting clinical outcome of patients. The C-index of the TRPS was higher than that of tumor grade, clinical stage, and many developed signatures. Low TRPS score indicated a higher level of CD8+ T cell, B cell, macrophage M1, and NK cells, representing a relative immunoactivated ecosystem in OC. OC patients with low risk score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score and lower tumor escape score, suggesting a better immunotherapy response. Moreover, higher TRPS score indicated a higher score of cancer-related hallmarks, including angiogenesis, EMT, hypoxia, glycolysis, and notch signaling. Vitro experiment showed that ARL6IP5 was downregulated in OC tissues and inhibited tumor cell proliferation. The current study constructed a novel TRPS for OC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy benefits for OC patients.

Phenotype-guided targeted therapy based on functional signal transduction pathway activity in recurrent ovarian cancer patients: The STAPOVER study protocol

Objective

Ovarian cancer is the fifth cause of cancer-related death among women. The benefit of targeted therapy for ovarian cancer patients is limited even if treatment is stratified by molecular signature. There remains a high unmet need for alternative diagnostics that better predict targeted therapy, as current diagnostics are generally inaccurate predictors. Quantitative assessment of functional signal transduction pathway (STP) activity from mRNA measurements of target genes is an alternative approach. Therefore, we aim to identify aberrantly activated STPs in tumour tissue of patients with recurrent ovarian cancer and start phenotype-guided targeted therapy to improve survival without compromising quality of life.

Study design

Patients with recurrent ovarian cancer and either 1) have platinum-resistant disease, 2) refrain from standard therapy or 3) are asymptomatic and not yet eligible for standard therapy will be included in this multi-centre prospective cohort study with multiple stepwise executed treatment arms. Targeted therapy will be available for patients with aberrantly high functional activity of the oestrogen receptor, androgen receptor, phosphoinositide 3-kinase or Hedgehog STP. The primary endpoint of this study is the progression-free survival (PFS) ratio (PFS2/PFS1 ratio) according to RECIST 1.1 determined by the PFS on matched targeted therapy (PFS2) compared to PFS on prior therapy (PFS1). Secondary endpoints include among others best overall response, overall survival, side effects, health-related quality of life and cost-effectiveness.

Conclusion

The results of this study will show the clinical applicability of STP activity in selecting recurrent ovarian cancer patients for effective therapies.

Study Deciphering the Crucial Involvement of Notch Signaling Pathway in Human Cancers

In recent years, dysregulation of the notch pathway has been associated with the development and progression of various cancers. Notch signaling is involved in several cellular processes, such as proliferation, differentiation, apoptosis, and angiogenesis, and its abnormal activation can lead to uncontrolled cell growth and tumorigenesis. In various human cancers, the Notch pathway has been shown to have both tumor-promoting and tumor-suppressive effects, depending on the context and stage of cancer development. Notch signaling has been implicated in tumor initiation, cancer cell proliferation, cell migration and maintenance of cancer stem cells in several human cancers, including leukemia, breast, pancreatic and lung cancer. Understanding the role of the Notch pathway in cancer development and progression may provide new opportunities for the development of potent targeted therapies for cancer treatment. Several drugs targeting the Notch pathway are currently in preclinical or clinical development and may hold promise for anticancer therapy in the future.

Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer

Background

Ovarian cancer (OC) is the leading cause of gynecological cancer death and the fifth most common cause of cancer-related death in women in America. Programmed cell death played a vital role in tumor progression and immunotherapy response in cancer.

Methods

The prognostic cell death signature (CDS) was constructed with an integrative machine learning procedure, including 10 methods, using TCGA, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082 datasets. Several methods and single-cell analysis were used to explore the correlation between CDS and the ecosystem and therapy response of OC patients.

Results

The prognostic CDS constructed by the combination of StepCox (n = both) + Enet (alpha = 0.2) acted as an independent risk factor for the overall survival (OS) of OC patients and showed stable and powerful performance in predicting the OS rate of OC patients. Compared with tumor grade, clinical stage, and many developed signatures, the CDS had a higher C-index. OC patients with low CDS score had a higher level of CD8+ cytotoxic T, B cell, and M1-like macrophage, representing a related immunoactivated ecosystem. A low CDS score indicated a higher PD1 and CTLA4 immunophenoscore, higher tumor mutation burden score, lower tumor immune dysfunction and exclusion score, and lower tumor escape score in OC, demonstrating a better immunotherapy response. OC patients with high CDS score had a higher gene set score of cancer-related hallmarks, including angiogenesis, epithelial-mesenchymal transition, hypoxia, glycolysis, and notch signaling.

Conclusion

The current study constructed a novel CDS for OC, which could serve as an indicator for predicting the prognosis, ecosystem, and immunotherapy benefits of OC patients.

Improved diagnosis of inflammatory bowel disease and prediction and monitoring of response to anti-TNF alpha treatment based on measurement of signal transduction pathway activity

Objective: Ulcerative colitis (UC) and Crohn’s disease (CD) are two subtypes of chronic inflammatory bowel disease (IBD). Differential diagnosis remains a challenge. Anti-TNFα treatment is an important treatment for IBD, yet resistance frequently occurs and cannot be predicted. Consequently, many patients receive ineffective therapy with potentially adverse effects. Novel assays are needed to improve diagnosis, and predict and monitor response to anti-TNF-α compounds.

Design: Signal transduction pathway (STP) technology was used to quantify activity of STPs (androgen and estrogen receptor, PI3K, MAPK, TGFβ, Notch, Hedgehog, Wnt, NFκB, JAK-STAT1/2, and JAK-STAT3 pathways) in colon mucosa samples of CD and UC patients, based on transcriptome analysis. Previously described STP assay technology is based on computational inference of STP activity from mRNA levels of target genes of the STP transcription factor.

Results: Results show that NFκB, JAK-STAT3, Wnt, MAPK, and androgen receptor pathways were abnormally active in CD and UC. Colon and ileum-localized CD differed with respect to STP activity, the JAK-STAT1/2 pathway being abnormally active in ileal CD. High activity of NFκB, JAK-STAT3, and TGFβ pathways was associated with resistance to anti-TNFα treatment in UC and colon-located CD, but not in ileal CD. Abnormal STP activity decreased with successful treatment.

Conclusion: We believe that measuring mucosal STP activity provides clinically relevant information to improve differential diagnosis of IBD and prediction of resistance to anti-TNFα treatment in patients with colon-localized IBD, and provides new targets for treatment and overcoming anti-TNFα resistance.

Measurement of activity of developmental signal transduction pathways to quantify stem cell pluripotency and phenotypically characterize differentiated cells

Important challenges in stem cell research and regenerative medicine are reliable assessment of pluripotency state and purity of differentiated cell populations. Pluripotency and differentiation are regulated and determined by activity of developmental signal transduction pathways (STPs). To date activity of these STPs could not be directly measured on a cell sample. Here we validate a novel assay platform for measurement of activity of developmental STPs (STP) for use in stem cells and stem cell derivatives. In addition to previously developed STP assays, we report development of an additional STP assay for the MAPK-AP1 pathway. Subsequently, activity of Notch, Hedgehog, TGFβ, Wnt, PI3K, MAPK-AP1, and NFκB signaling pathways was calculated from Affymetrix transcriptome data of human pluripotent embryonic (hES) and iPS cell lines under different culture conditions, organ-derived multipotent stem cells, and differentiated cell types, to generate quantitative STP activity profiles. Results show that the STP assay technology enables reliable and quantitative measurement of multiple STP activities simultaneously on any individual cell sample. Using the technology, we found that culture conditions dominantly influence the pluripotent stem cell STP activity profile, while the origin of the stem cell line was a minor variable. A pluripotency STP activity profile (Pluripotency qPAP) was defined (active PI3K, MAPK, Hedgehog, Notch, TGFβ, and NFκB pathway, inactive Wnt pathway). Differentiation of hES cells to intestinal progenitor cells resulted in an STP activity profile characterized by active PI3K, Wnt and Notch pathways, comparable to the STP activity profile measured on primary intestinal crypt stem cells. Quantitative STP activity measurement is expected to improve experimental reproducibility and standardization of pluripotent and multipotent stem cell culture/differentiation, and enable controlled manipulation of pluripotency/differentiation state using pathway targeting compounds.

A Microfluidic Cancer-on-Chip Platform Predicts Drug Response Using Organotypic Tumor Slice Culture

Optimal treatment of cancer requires diagnostic methods to facilitate therapy choice and prevent ineffective treatments. Direct assessment of therapy response in viable tumor specimens could fill this diagnostic gap. Therefore, we designed a microfluidic platform for assessment of patient treatment response using tumor tissue slices under precisely controlled growth conditions. The optimized Cancer-on-Chip (CoC) platform maintained viability and sustained proliferation of breast and prostate tumor slices for 7 days. No major changes in tissue morphology or gene expression patterns were observed within this time frame, suggesting that the CoC system provides a reliable and effective way to probe intrinsic chemotherapeutic sensitivity of tumors. The customized CoC platform accurately predicted cisplatin and apalutamide treatment response in breast and prostate tumor xenograft models, respectively. The culture period for breast cancer could be extended up to 14 days without major changes in tissue morphology and viability. These culture characteristics enable assessment of treatment outcomes and open possibilities for detailed mechanistic studies. SIGNIFICANCE: The Cancer-on-Chip platform with a 6-well plate design incorporating silicon-based microfluidics can enable optimal patient-specific treatment strategies through parallel culture of multiple tumor slices and diagnostic assays using primary tumor material.

Characterization of Immunoactive and Immunotolerant CD4+ T Cells in Breast Cancer by Measuring Activity of Signaling Pathways That Determine Immune Cell Function

Simple Summary

Immunotherapy enhances the immune response against cancer and is potentially curative. Unfortunately, few patients with breast cancer benefit from this therapy. It is not possible to predict which patients will benefit. A blood cell, called CD4+ T-cell, plays a role in the immune response and in resistance to immunotherapy. Its function is determined by activity of biochemical processes, called signal transduction pathways (STPs). We developed a new technology to measure activity of these STPs, which was used to investigate whether CD4+ T cells function abnormally in breast cancer patients. We show that in CD4+ T-cells from most of the investigated breast cancer patients a number of these STPs are overactive. The abnormal activity of a few notable STPs (Notch and TGFβ) suggests that CD4+ T-cells have changed into regulatory T-cells, which inhibit the immune response against cancer and have been associated with resistance to immunotherapy. We also provide evidence that this change in the CD4+ T- cells is caused by a factor produced by breast cancer cells. We conclude that this new technology can be used to measure STP activity in blood of patients with cancer and has the potential to better identify patients who will benefit from immunotherapy.

Abstract

Cancer immunotolerance may be reversed by checkpoint inhibitor immunotherapy; however, only a subset of patients responds to immunotherapy. The prediction of clinical response in the individual patient remains a challenge. CD4+ T cells play a role in activating adaptive immune responses against cancer, while the conversion to immunosuppression is mainly caused by CD4+ regulatory T cell (Treg) cells. Signal transduction pathways (STPs) control the main functions of immune cells. A novel previously described assay technology enables the quantitative measurement of activity of multiple STPs in individual cell and tissue samples. The activities of the TGFβ, NFκB, PI3K-FOXO, JAK-STAT1/2, JAK-STAT3, and Notch STPs were measured in CD4+ T cell subsets and used to investigate cellular mechanisms underlying breast cancer-induced immunotolerance. Methods: STP activity scores were measured on Affymetrix expression microarray data of the following: (1) resting and immune-activated CD4+ T cells; (2) CD4+ T-helper 1 (Th1) and T-helper 2 (Th2) cells; (3) CD4+ Treg cells; (4) immune-activated CD4+ T cells incubated with breast cancer tissue supernatants; and (5) CD4+ T cells from blood, lymph nodes, and cancer tissue of 10 primary breast cancer patients. Results: CD4+ T cell activation induced PI3K, NFκB, JAK-STAT1/2, and JAK-STAT3 STP activities. Th1, Th2, and Treg cells each showed a typical pathway activity profile. The incubation of activated CD4+ T cells with cancer supernatants reduced the PI3K, NFκB, and JAK-STAT3 pathway activities and increased the TGFβ pathway activity, characteristic of an immunotolerant state. Immunosuppressive Treg cells were characterized by high NFκB, JAK-STAT3, TGFβ, and Notch pathway activity scores. An immunotolerant pathway activity profile was identified in CD4+ T cells from tumor infiltrate and blood of a subset of primary breast cancer patients, which was most similar to the pathway activity profile in immunosuppressive Treg cells. Conclusion: Signaling pathway assays can be used to quantitatively measure the functional immune response state of lymphocyte subsets in vitro and in vivo. Clinical results suggest that, in primary breast cancer, the adaptive immune response of CD4+ T cells may be frequently replaced by immunosuppressive Treg cells, potentially causing resistance to checkpoint inhibition. In vitro study results suggest that this is mediated by soluble factors from cancer tissue. Signaling pathway activity analysis on TIL and/or blood samples may improve response prediction and monitoring response to checkpoint inhibitors and may provide new therapeutic targets (e.g., the Notch pathway) to reduce resistance to immunotherapy.

Shooting at Moving and Hidden Targets-Tumour Cell Plasticity and the Notch Signalling Pathway in Head and Neck Squamous Cell Carcinomas

Head and Neck Squamous Cell Carcinoma (HNSCC) is often aggressive, with poor response to current therapies in approximately 40-50% of the patients. Current therapies are restricted to operation and irradiation, often combined with a small number of standard-of-care chemotherapeutic drugs, preferentially for advanced tumour patients. Only very recently, newer targeted therapies have entered the clinics, including Cetuximab, which targets the EGF receptor (EGFR), and several immune checkpoint inhibitors targeting the immune receptor PD-1 and its ligand PD-L1. HNSCC tumour tissues are characterized by a high degree of intra-tumour heterogeneity (ITH), and non-genetic alterations that may affect both non-transformed cells, such as cancer-associated fibroblasts (CAFs), and transformed carcinoma cells. This very high degree of heterogeneity likely contributes to acquired drug resistance, tumour dormancy, relapse, and distant or lymph node metastasis. ITH, in turn, is likely promoted by pronounced tumour cell plasticity, which manifests in highly dynamic and reversible phenomena such as of partial or hybrid forms of epithelial-to-mesenchymal transition (EMT), and enhanced tumour stemness. Stemness and tumour cell plasticity are strongly promoted by Notch signalling, which remains poorly understood especially in HNSCC. Here, we aim to elucidate how Notch signal may act both as a tumour suppressor and proto-oncogenic, probably during different stages of tumour cell initiation and progression. Notch signalling also interacts with numerous other signalling pathways, that may also have a decisive impact on tumour cell plasticity, acquired radio/chemoresistance, and metastatic progression of HNSCC. We outline the current stage of research related to Notch signalling, and how this pathway may be intricately interconnected with other, druggable targets and signalling mechanisms in HNSCC.

Androgen Receptor Pathway Activity Assay for Sepsis Diagnosis and Prediction of Favorable Prognosis

Introduction: Sepsis is a life-threatening complication of a bacterial infection. It is hard to predict which patients with a bacterial infection will develop sepsis, and accurate and timely diagnosis as well as assessment of prognosis is difficult. Aside from antibiotics-based treatment of the causative infection and supportive measures, treatment options have remained limited. Better understanding of the immuno-pathophysiology of sepsis is expected to lead to improved diagnostic and therapeutic solutions. Functional activity of the innate (inflammatory) and adaptive immune response is controlled by a dedicated set of cellular signal transduction pathways, that are active in the various immune cell types. To develop an immune response-based diagnostic assay for sepsis and provide novel therapeutic targets, signal transduction pathway activities have been analyzed in whole blood samples from patients with sepsis. Methods: A validated and previously published set of signal transduction pathway (STP) assays, enabling determination of immune cell function, was used to analyze public Affymetrix expression microarray data from clinical studies containing data from pediatric and adult patients with sepsis. STP assays enable quantitative measurement of STP activity on individual patient sample data, and were used to calculate activity of androgen receptor (AR), estrogen receptor (ER), JAK-STAT1/2, JAK-STAT3, Notch, Hedgehog, TGFβ, FOXO-PI3K, MAPK-AP1, and NFκB signal transduction pathways. Results: Activity of AR and TGFβ pathways was increased in children and adults with sepsis. Using the mean plus two standard deviations of normal pathway activity (in healthy individuals) as threshold for abnormal STP activity, diagnostic assay parameters were determined. For diagnosis of pediatric sepsis, the AR pathway assay showed high sensitivity (77%) and specificity (97%), with a positive prediction value (PPV) of 99% and negative prediction value (NPV) of 50%. For prediction of favorable prognosis (survival), PPV was 95%, NPV was 21%. The TGFβ pathway activity assay performed slightly less for diagnosing sepsis, with a sensitivity of 64% and specificity of 98% (PPV 99%, NPV 39%). Conclusion: The AR and TGFβ pathways have an immunosuppressive role, suggesting a causal relation between increased pathway activity and sepsis immunopathology. STP assays have been converted to qPCR assays for further evaluation of clinical utility for sepsis diagnosis and prediction of prognosis, as well as for prediction of risk at developing sepsis in patients with a bacterial infection. STPs may present novel therapeutic targets in sepsis.

Insights into the Role of Oxidative Stress in Ovarian Cancer

Oxidative stress (OS) arises when the body is subjected to harmful endogenous or exogenous factors that overwhelm the antioxidant system. There is increasing evidence that OS is involved in a number of diseases, including ovarian cancer (OC). OC is the most lethal gynecological malignancy, and risk factors include genetic factors, age, infertility, nulliparity, microbial infections, obesity, smoking, etc. OS can promote the proliferation, metastasis, and therapy resistance of OC, while high levels of OS have cytotoxic effects and induce apoptosis in OC cells. This review focuses on the relationship between OS and the development of OC from four aspects: genetic alterations, signaling pathways, transcription factors, and the tumor microenvironment. Furthermore, strategies to target aberrant OS in OC are summarized and discussed, with a view to providing new ideas for clinical treatment.

Predictive and Prognostic Biomarker Identification in a Large Cohort of Androgen Receptor-Positive Salivary Duct Carcinoma Patients Scheduled for Combined Androgen Blockade

Patients suffering from recurrent or metastatic (R/M) salivary duct carcinoma (SDC) are often treated with combined androgen blockade (CAB). However, CAB frequently fails, resulting in a worse prognosis. Therefore, biomarkers that can predict treatment failure are urgently needed. mRNA from 76 R/M androgen receptor (AR)-positive SDC patients treated with leuprorelin acetate combined with bicalutamide was extracted from pre-treatment tumor specimens. AR, Notch, MAPK, TGFβ, estrogen receptor (ER), Hedgehog (HH), and PI3K signaling pathway activity scores (PAS) were determined based on the expression levels of target genes. Additionally, 5-alpha reductase type 1 (SRD5A1) expression was determined. These markers were related to clinical benefit (complete/partial response or stable disease ≥6 months) and progression-free and overall survival (PFS/OS). SRD5A1 expression had the highest general predictive value for clinical benefit and positive predictive value (PPV: 85.7%). AR PAS had the highest negative predictive value (NPV: 93.3%). The fitting of a multivariable model led to the identification of SRD5A1, TGFβ, and Notch PAS as the most predictive combination. High AR, high Notch, high ER, low HH PAS, and high SRD5A1 expression were also of prognostic importance regarding PFS and SRD5A1 expression levels for OS. AR, Notch PAS, and SRD5A1 expression have the potential to predict the clinical benefit of CAB treatment in SDC patients. SRD5A1 expression can identify patients that will and AR PAS patients that will not experience clinical benefit (85.7% and 93.3% for PPV and NPV, respectively). The predictive potential of SRD5A1 expression forms a rational basis for including SRD5A1-inhibitors in SDC patients' treatment.

RNA Based Approaches to Profile Oncogenic Pathways From Low Quantity Samples to Drive Precision Oncology Strategies

Precision treatment of cancer requires knowledge on active tumor driving signal transduction pathways to select the optimal effective targeted treatment. Currently only a subset of patients derive clinical benefit from mutation based targeted treatment, due to intrinsic and acquired drug resistance mechanisms. Phenotypic assays to identify the tumor driving pathway based on protein analysis are difficult to multiplex on routine pathology samples. In contrast, the transcriptome contains information on signaling pathway activity and can complement genomic analyses. Here we present the validation and clinical application of a new knowledge-based mRNA-based diagnostic assay platform (OncoSignal) for measuring activity of relevant signaling pathways simultaneously and quantitatively with high resolution in tissue samples and circulating tumor cells, specifically with very small specimen quantities. The approach uses mRNA levels of a pathway's direct target genes, selected based on literature for multiple proof points, and used as evidence that a pathway is functionally activated. Using these validated target genes, a Bayesian network model has been built and calibrated on mRNA measurements of samples with known pathway status, which is used next to calculate a pathway activity score on individual test samples. Translation to RT-qPCR assays enables broad clinical diagnostic applications, including small analytes. A large number of cancer samples have been analyzed across a variety of cancer histologies and benchmarked across normal controls. Assays have been used to characterize cell types in the cancer cell microenvironment, including immune cells in which activated and immunotolerant states can be distinguished. Results support the expectation that the assays provide information on cancer driving signaling pathways which is difficult to derive from next generation DNA sequencing analysis. Current clinical oncology applications have been complementary to genomic mutation analysis to improve precision medicine: (1) prediction of response and resistance to various therapies, especially targeted therapy and immunotherapy; (2) assessment and monitoring of therapy efficacy; (3) prediction of invasive cancer cell behavior and prognosis; (4) measurement of circulating tumor cells. Preclinical oncology applications lie in a better understanding of cancer behavior across cancer types, and in development of a pathophysiology-based cancer classification for development of novel therapies and precision medicine.