Background Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM. Methods An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort ( n = 413) as training set and validated in an independent multi-center prospective cohort ( n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score. Results An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85–1.01) and 0.93 (95% CI: 0.87–0.99) in the retrospective and prospective urine cohorts respectively. Kaplan–Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing. Conclusions The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

Abstract Background There is an urgent need to accurately predict the risk of distant metastasis and metastatic castration-resistant prostate cancer (mCRPC) for treatment decision-making and reducing mortality. An artificial intelliegnce machine learning screening method in combination with liquid biopsy urine test was used to develop a novel gene expression-based algorithm for predicting prostate cancer distant metastasis and mCRPC in newly diagnosed patients. Methods Random forest machine learning algorithm screening was conducted to develop and validate a gene expression-based algorithm to predict the risk of metastasis and mCRPC using liquid biopsy urine samples from the patients with distant metastasis and mCRPC collected from multi-center retrospective (n = 505) and prospective (n = 243) studies with a median follow-up period of 8 and 6 years respectively. The prognostic performance of the algorithm test was assessed using univariate/multivariate Cox regression analyses, Kaplan-Meier disease-free survival plot, and univariate/multivariate discriminant analyses. Results A novel 18-Gene Algorithm urine test was developed and validated. The algorithm showed high accuracy to predict distant metastasis with an area under the curve (AUC) of 0.96 (95% CI 0.87–1.05) and 0.98 (95% CI 0.96–1.02) in the retrospective and prospective cohort respectively. In the prospective cohort, a hazard ratio (HR) to predict metastasis-free survival was 93.8 (95% CI 29.3-300.6) (p < 0.0001). In a prospective mCRPC cohort (n = 205), the algorithm predicted mCRPC-free survival with a HR of 154.4 (95% CI 36.8-647.5) (p < 0.0001) and predicted mCRPC with AUC of 0.98 (95% CI 0.95–1.01). In contrast, currently using clinicopathological parameters, such as Gleason grade and pre-operative PSA, had much lower prognostic power. Conclusions The novel 18-Gene Algorithm is the first highly accurate and non-invasive liquid biopsy urine test to predict distant metastasis and mCRPC in newly diagnosed prostate cancer patients with the potential to improve prostate cancer treatment decision-making and reduce mortality.

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan–Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94–0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3–41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7–101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96–0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4–79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.

Dear Editor, Currently no accurate prognostic test is available to predict prostate cancer (PCa) biochemical recurrence (BCR) after treatment or cancer metastasis.1-7 To address the unmet medical need, we developed a novel 23-Gene Classifier urine test as the first accurate and noninvasive tool for PCa prognosis with potential to improve cancer treatment. We used previously identified biomarkers with differential gene expression in PCa and benign prostate as candidates for BCR prediction and metastasis.8-10 Discriminant analysis was used to assess the ability of various combinations of mRNA expression quantities of the biomarker candidates in prostate tissue specimens collected before prostatectomy with BCR information during follow-up as classifiers to distinguish BCR and non-BCR patients. A 23-Gene Classifier consisting of PTEN, PIP5K1A, CDK1, TMPRSS2, ANXA3, HIF1A, FGFR1, BIRC5, AMACR, CRISP3, PMP22, GOLPH2, EZH2, GSTP1, PCA3, VEGFA, CST3, CCNA1, CCND1, FN1, MYO6, KLK3, and PSCA was found to predict BCR with the highest accuracy. We followed STARD guidelines for biomarker validation. Detailed patient cohorts and study methods are described in Supplementary Methods. The prostate epithelial cells are released into the urine so urine can be used as a noninvasive liquid biopsy source to detect prostate-specific biomarkers for PCa prognosis. The 23-Gene Classifier was developed as a urine test for BCR prognosis using urines collected without digital rectal examination (DRE). Using BCR Urine Prediction Algorithm, the mRNA levels of the 23 genes were used to generate a classification score to predict the patients as having BCR or Non-BCR (Supplementary Methods). A multicenter study was designed prospectively using retrospectively collected urine samples without DRE from 520 patients before prostatectomy or other treatments (IND-CHTN cohort). Forty-six patients developed BCR during the follow-up period averaging 8 years (Table 1). A total of 105 patients from the cohort were randomly selected as a training set to test the 23-Gene Classifier urine test for BCR prediction and the resulting area under the receiver operating characteristic curve (AUC) was 0.94 (95% CI 0.87-1.01). The prognostic performance of the 23-Gene Classifier urine test to predict BCR-free survival was validated in the remaining patients (n = 414). The patients were divided into two risk groups based on diagnosis by the 23-Gene Classifier and Kaplan-Meier survival analysis showed statistically significant association of the 23-Gene Classifier Negative group with shorter BCR-free survival (∼60% BCR-free survival at 48 months) as compared with the 23-Gene Classifier Positive group (100% BCR-free survival at 120 months) (Figure 1A) (log rank P = 0.000). In contrast, the two groups segregated by cancer stage or Gleason score had much smaller difference in BCR-free survival (Figures 1B and C). Univariate and multivariate Cox regression analysis was performed and the 23-Gene Classifier had a hazard ratio (HR) of 1730.90 (95% CI 4.52-6.63E+5) in the univariate analysis (Table 2), which indicated that the patients with a positive 23-Gene Classifier score was 1731 times more likely to have BCR than patients with a negative 23-Gene Classifier score and the BCR prediction was statistically significant (P = 0.014). Its predictive power remained large and significant in multivariate regression after adjusting for cancer stage and Gleason score with HR of 1795.01 (95% CI 4.30-7.49E+5) (P = 0.015). In contrast, cancer stage and Gleason score had much lower HR and were statistically insignificant (Table 2). In addition, univariate and multivariate logistic regression and discriminant analysis were performed to measure the predictive accuracy of the 23-Gene Classifier. The result showed high accuracy with sensitivity of 100% (95% CI 100-100%), specificity of 86.29% (95% CI 82.80-89.79%), and AUC of 0.93 (95% CI 0.90-0.96) (P < 0.0001) (Tables S1 and 3, Figure 1G). Cross-validation of the 23-Gene Classifier showed similarly high accuracy in BCR prediction (Table 3). In contrast, cancer stage and Gleason score had much lower specificity and AUC (Table 3, Figures 1H and I). 100% (100-100%) 7.28% (4.63-9.92%) 10.88% (7.77-13.99%) 100% (100-100%) 100% (100-100%) 2.43% (0.86-3.99%) 10.40% (7.42-13.37%) 100% (100-100%) 100% (100-100%) 86.29% (82.80-89.79%) 45.16% (35.05-55.28%) 100% (100-100%) 23G classifier Cross-validation 100% (100-100%) 86.17% (82.14-90.20%) 45.07% (33.50-56.64%) 100% (100-100%) 100% (100-100%) 88.11% (84.81-91.41%) 48.84% (38.27-59.40%) 100% (100-100%) 16.67% (4.49-28.84%) 99.04% (97.16-100.91%) 85.71% (59.79-111.64%) 77.44% (70.34-84.55%) 48.57% (32.01-65.13%) 96.12% (92.39-99.85%) 80.95% (64.16-97.75%) 84.62% (78.08-91.15%) 86.11% (74.81-97.41%) 100% (100-100%) 100% (100-100%) 95.41% (91.49-99.34%) 23G classifier Cross-validation 87.50% (64.58-110.42%) 100% (100-100%) 100% (100-100%) 96.97% (91.12-102.82%) 85.71% (74.12-97.31%) 100% (100-100%) 100% (100-100%) 95.37% (91.41-99.33%) In silico validation study was conducted to test if the 23-Gene Classifier can also be used in prostate tissue specimens for BCR prognosis using a tissue cohort MSKCC (Table 1). Its similarly high prognostic performance (Tables 2 and 3, Figures 1D-F, K-N) validated the results from the urine study and confirmed the 23-Gene Classifier as a more accurate prognostic tool for BCR prediction than cancer stage and Gleason score. Accurate prediction of cancer metastasis at diagnosis is important for patients to be treated early with effective therapies to prevent development of castration-resistant metastatic cancer and reduce mortality. We tested if the 23-Gene Classifier urine test could be used for metastatic cancer prediction. We tested its performance in the multicenter, retrospective IND-CHTN Cohort (n = 520), a multicenter, prospective 7-HOSPITALS Cohort (n = 207), and a combination cohort combining the patients (n = 727) (Table 1). mRNA expression quantities of the 23 genes were used to classifier each sample as metastatic or nonmetastatic cancer using MET Urine Prediction Algorithm and such classification was compared with the metastatic cancer diagnosis by the imaging measurements to calculate the predictive performance (Supplementary Methods). The result showed that the 23-Gene Classifier urine test had similarly high accuracy in predicting metastatic cancer in the retrospective, prospective and combination cohorts (AUC of 0.92 [95% CI 0.79-1.05] for the retrospective cohort, 0.89 [95% CI 0.83-0.95] for the prospective cohort, and 0.98 [95% CI 0.96-1.01] for the combination cohort) (P < 0.0001). In contrast, Gleason score had much lower specificity and AUC (Table S2 and Figure 2). Development of accurate and actionable prognostic tests is important and urgently needed for PCa treatment. None of the clinicopathological parameters, nomograms, or biomarker panels used in clinic or reported in publications was capable of accurately predicting BCR or cancer metastasis with HR above 20 or AUC above 0.9.1-7 The 23-Gene Classifier had HR above 40 and AUC above 0.9 in all cohorts assessed, suggesting its higher accuracy and more robust performance for PCa prognosis. In addition, the 23-Gene Classifier can be used with prostate tissue specimens. In this study, we developed and validated a novel 23-Gene Classifier that can be used as a highly accurate and noninvasive urine test for prediction of BCR and cancer metastasis with great potential to improve PCa treatment and reduce mortality in clinical practice. The authors would like to thank C. Yun for excellent technical support and S. Liao for skillful assistance in urine collection. The retrospective urine study was approved by IRB at San Francisco General Hospital (IRB #: 15–15816) to use archived urine sediment samples acquired from Cooperative Human Tissue Network Southern Division and Indivumed GmbH. These organizations obtained ethical approval and patient consent prior to collection of patient urine samples. The prospective urine study was approved by IRB at Shenzhen People's Hospital (Study Number: P2014-006) to use urine samples collected from patients treated at the collaborating hospitals in the study with prior consent. All authors have agreed to publish the manuscript. The data supporting this study are available from the corresponding authors upon reasonable request or are publicly available in GEO. Heather Johnson is an employee of Olympia Diagnostics, Inc., and inventor of a pending patent application of prostate cancer diagnostic and prognostic biomarkers. No conflict of interest or financial interest was declared by the other authors. This study was supported by grants from Sanming Project of Medicine in Shenzhen (SZSM201412014), The Science and Technology Foundation of Shenzhen (JCYJ20170307095620828), The Science and Technology Foundation of Shenzhen (JCYJ20160422145718224), and The Shenzhen Urology Minimally Invasive Engineering Center (GCZX2015043016165448) (to Jinan Guo, and Kefeng Xiao); funds from Olympia Diagnostics, Inc. (to Heather Johnson); the Swedish Cancer Society (CAN2017/381), The Swedish Children Foundation (TJ2015-0097), H2020-MSCA-ITN-2018 GlycoImaging (721279), The Swedish National Research Council, the Malmö Cancer Foundation, the Government Health Innovation Grant, the Medical Faculty, Lund University, Kempestiftelserna, Umeå University, Medical Faculty Grants, the Norland Fund for Cancer Forskning, Insamlings Stiftelsen, Umeå University, Bioteknik medel, the Medical Faculty, Umeå University, Medical Faculty Grants, Umeå University, and grant from Umeå University Center for Microbiology Research (UCMR) and Biofilm Center at Malmö University (to Jenny Persson). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. HJ, CZ, LC, KX, and JLP contributed to study concept and design. HZ, JG, XF, CZ, KX, AHBW, and LC participated in study coordination and supervision. JG, TX, FL, and WT contributed to sample collection. XZ, JG, HJ, HZ, and XF contributed to sample processing and analysis. HZ, HJ, AJ, AS, ND, and JLP contributed to data collection and processing, and statistical analysis. HJ, PA, ND, LK, AS, and JLP contributed to data interpretation. XZ, HJ, and JLP contributed to literature search. JG, HJ, HZ, JLP, and CZ contributed to manuscript writing. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups. Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892–0.942) in the retrospective cohort ( n = 520), AUC of 0.959 (95% CI 0.935–0.983) in the prospective cohort ( n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort ( n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1–3 cancer subgroups. Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.

Background Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies. Methods Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy. Results The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963–0.929) in the retrospective cohort ( n = 614), AUC of 0.901 (0.929–0.873) in the prospective cohort ( n = 396), and AUC of 0.936 (0.956–0.916) in the large combination cohort ( n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors ( p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980–0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947–0.909) in the combination cohort ( n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy. Conclusions The 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.

One of the major features of prostate cancer (PCa) is its heterogeneity, which often leads to uncertainty in cancer diagnostics and unnecessary biopsies as well as overtreatment of the disease. Novel non-invasive tests using multiple biomarkers that can identify clinically high-risk cancer patients for immediate treatment and monitor patients with low-risk cancer for active surveillance are urgently needed to improve treatment decision and cancer management. In this study, we identified 14 promising biomarkers associated with PCa and tested the performance of these biomarkers on tissue specimens and pre-biopsy urinary sediments. These biomarkers showed differential gene expression in higher- and lower-risk PCa. The 14-Gene Panel urine test (PMP22, GOLM1, LMTK2, EZH2, GSTP1, PCA3, VEGFA, CST3, PTEN, PIP5K1A, CDK1, TMPRSS2, ANXA3, and CCND1) was assessed in two independent prospective and retrospective urine study cohorts and showed high diagnostic accuracy to identify higher-risk PCa patients with the need for treatment and lower-risk patients for surveillance. The AUC was 0.897 (95% CI 0.939–0.855) in the prospective cohort (n = 202), and AUC was 0.899 (95% CI 0.964–0.834) in the retrospective cohort (n = 97). In contrast, serum PSA and Gleason score had much lower accuracy in the same 202 patient cohorts [AUC was 0.821 (95% CI 0.879–0.763) for PSA and 0.860 (95% CI 0.910–0.810) for Gleason score]. In addition, the 14-Gene Panel was more accurate at risk stratification in a subgroup of patients with Gleason scores 6 and 7 in the prospective cohort (n = 132) with AUC of 0.923 (95% CI 0.968–0.878) than PSA [AUC of 0.773 (95% CI 0.852–0.794)] and Gleason score [AUC of 0.776 (95% CI 0.854–0.698)]. Furthermore, the 14-Gene Panel was found to be able to accurately distinguish PCa from benign prostate with AUC of 0.854 (95% CI 0.892–0.816) in a prospective urine study cohort (n = 393), while PSA had lower accuracy with AUC of 0.652 (95% CI 0.706–0.598). Taken together, the 14-Gene Panel urine test represents a promising non-invasive tool for detection of higher-risk PCa to aid treatment decision and lower-risk PCa for active surveillance.

BACKGROUND/AIM:Prostate cancer (PCa) diagnosis using patient urine samples represents a non-invasive and more convenient method than the conventional biopsy and prostate-specific antigen (PSA) test. This study intended to identify a biomarker panel to distinguish PCa from benign prostate using urine samples.MATERIALS AND METHODS:We identified six biomarkers with differential gene expression in 154 PCa and benign prostate specimens. We then determined mRNA expression signature and the diagnostic performance of the 6-biomarker panel in 156 urine samples from patients with PCa and benign disease.RESULTS:The 6-biomarker panel distinguished PCa from benign prostate cases with sensitivity of 80.6%, specificity of 62.9% and area under the curve (AUC) of 0.803 (p<0.0001), whereas serum PSA at 4 ng/ml cutoff had sensitivity of 95.5%, specificity of 20.2% and AUC of 0.521 (p<0.0001).CONCLUSION:The 6-biomarker panel for use in urine samples was able to distinguish PCa from benign prostate with higher specificity and accuracy than PSA and may be useful in clinical settings.

Despite recent improvement in adjuvant therapies, triple-negative, and ER+ subtypes of breast cancer (BC) with metastatic potentials remain the leading cause of BC-related deaths. We investigated the role of phosphatidylinositol-4-phosphate 5-kinase alpha (PIP5Kα), a key upstream factor of PI3K/AKT, and the therapeutic effect of PIP5Kα inhibitor on subtypes of BC. The clinical importance of PIP5K1α and its association with survivals were analyzed using three BC cohorts from Nottingham (n = 913), KM plotter (n = 112) and TCGA (n = 817). Targeted overexpression or knockdown of PIP5K1α were introduced into BC cell lines. The effects of PIP5K1α and its inhibitor on growth and invasion of BC were confirmed by using in vitro assays including proliferation, migration, apoptosis and luciferase reporter assays and in vivo xenograft mouse models. All statistical tests were two-sided. PIP5K1α was associated with poor patient outcome in triple-negative BC (for PIP5K1α protein, p = 0.011 and for mRNA expression, p = 0.028, log-rank test). 29% of triple-negative BC had PIP5K1A gene amplification. Elevated level of PIP5K1α increased expression of pSer-473 AKT (p < 0.001) and invasiveness of triple-negative MDA-MB-231 cells (p < 0.001). Conversely, inhibition of PIP5K1α using its inhibitor ISA-2011B, or via knockdown suppressed growth and invasiveness of MDA-MB-231 xenografts (mean vehicle-treated controls = 2160 mm3, and mean ISA-2011B-treated = 600 mm3, p < 0.001). ISA-2011B-treatment reduced expression of pSer-473 AKT (p < 0.001) and its downstream effectors including cyclin D1, VEGF and its receptors, VEGFR1 and VEGFR2 (p < 0.001) in xenograft tumors. In ER+ cancer cells, PIP5K1α acted on pSer-473 AKT, and was in complexes with VEGFR2, serving as co-factor of ER-alpha to regulate activities of target genes including cyclin D1 and CDK1. Our study suggests that our developed PIP5K1α inhibitor has a great potential on refining targeted therapeutics for treatment of triple-negative and ER+ BC with abnormal PI3K/AKT pathways.

Objective: We aimed to determine whether IP-10 and RANTES plasma levels can be used in diagnosis and monitoring of pulmonary tuberculosis (PTB). Methods: Plasma levels of cytokines/chemokines were measured using a Bio-Plex' multiplex cytokine assay system in a cohort containing 457 clinically suspected PTB patients including a training set (n = 41)and two independent WA sets A (n = 242) and B (n = 174). Results: Plasma levels of IP-10 and RANTES were significantly higher in PTB patients than healthy controls' in both training and independent test sets (P < 0.05). Compared with other combinations, the combination of IP10 and RANTES had the best performance with an AUC of 1.0 in training set. The performance characteristic of this model was successfully validated in independent WA set A although this combination only resulted in a slightly improvement of AUC value in independent test set B. Plasma IP-10 and RANTES levels were weakly and positively correlated with blood glucose concentrations. Moreover, IP-10 levels were positively correlated with CRP and ESR in PTB patients. Furthermore, in response to therapy, both IP-10 and RANTES levels significantly decreased over the period of 6 months (P < 0.001). Conclusions: Taken together, combination of IP-10 and RANTES could be potentially used as diagnostic and monitoring biomarker in PTB management.

Prostate cancer (PCa) is the most common type of cancer in men and one of the leading causes of cancer-related death in Western world (Jemal et al., CA Cancer J Clin 57(1):43–66, 2007; Siegel et al., CA Cancer J Clin 63(1):11–30, 2013). Elevated levels of the male hormones, androgens, are known to contribute to development of PCa. As the growth of PCa at initial stage is dependent on hormones, hormone-deprivation therapies are therefore used as standard treatment to induce tumor regression in PCa patients (Litvinov et al., Proc Natl Acad Sci U S A 103(41):15085–15090, 2006). Despite hormone-deprivation treatment, most of treated PCa will resume the growth and become hormone-refractory, also termed castration-resistant PCa (CRPC) Aastration-resistant PCa (CRPC) RTKs Receptor tyrosine kinases (RTKs (Litvinov et al., Proc Natl Acad Sci U S A 103(41):15085–15090, 2006; Lindzey et al., Vitam Horm 49:383–432, 1994). CRPC is no longer responsive to most of the available therapies and is highly invasive with metastatic potentials to disseminate to distant organs including the lung, bone, and brain (Semenas et al., Curr Drug Targets 13(10):1308–1323, 2012). Thus, CRPC represents a major clinical challenge.

Currently, no ideal prostate cancer (PCa) diagnostic or prognostic test is available due to the lack of biomarkers with high sensitivity and specificity. There is an unmet medical need to develop combinations of multiple biomarkers which may have higher accuracy in detection of PCa and stratification of aggressive and indolent cancer patients. The aim of this study was to test two biomarker gene panels in distinguishing PCa from benign prostate and high-risk, aggressive PCa from low-risk, indolent PCa, respectively. We identified a five-gene panel that can be used to distinguish PCa from benign prostate. The messenger RNA (mRNA) expression signature of the five genes was determined in 144 PCa and benign prostate specimens from prostatectomy. We showed that the five-gene panel distinguished PCa from benign prostate with sensitivity of 96.59 %, specificity of 92.86 %, and area under the curve (AUC) of 0.992 ( p < 0.0001). The five-gene panel was further validated in a 137 specimen cohort and showed sensitivity of 84.62 %, specificity of 91.84 %, and AUC of 0.942 ( p < 0.0001). To define subtypes of PCa for treatment guidance, we examined mRNA expression signature of an eight-gene panel in 87 PCa specimens from prostatectomy. The signature of the eight-gene panel was able to distinguish aggressive PCa (Gleason score >6) from indolent PCa (Gleason score ≤6) with sensitivity of 90.28 %, specificity of 80.00 %, and AUC of 0.967 ( p < 0.0001). This panel was further validated in a 158 specimen cohort and showed significant difference between aggressive PCa and indolent PCa with sensitivity of 92.57 %, specificity of 70.00 %, and AUC of 0.962 ( p < 0.0001). Our findings in assessing multiple biomarkers in combination may provide new tools to detect PCa and distinguish aggressive and indolent PCa for precision and personalized treatment. The two biomarker panels may be used in clinical settings for accurate PCa diagnosis and patient risk stratification for biomarker-guided treatment.

Particle size has been demonstrated as a key parameter influencing the phagocytosis of drug-loaded PLGA microspheres (MS) by the target cells. However, the current preparative methods were either insufficient in controlling the homogeneity of the produced MS, or requires sophisticated and costly equipment. This study aimed to explore a simple and economical method for uniform PLGA MS preparation. Based on the heterogeneous emulsification of routine mechanical stirring, we designed an adjuvant strategy to enhance the homogeneity of MS. By using glass beads as adjutant, the dispersion produced during mechanical stirring was much more homogeneous in the solution. The particles produced were much smaller and the size distribution was much narrower as compared with those produced using the routine mechanical stirring method under the same condition. After enrichment by selective centrifugation, about 60% of the particles of similar size were obtained, providing further evidence for the efficiency of the novel method in controlling particle homogeneity. Further, the method was applied to prepare rifampicin-loaded PLGA MS of the optimized size for macrophage uptake. The functional evaluation showed that the prepared PLGA MS could efficiently deliver an antitubercular drug into macrophages and maintain a higher intracellular concentration by controlled release, suggesting the potential application of the method in PLGA MS-based drug delivery. Collectively, the study provided a simple and economical method for preparing uniform-sized PLGA MS with potential of widespread applications.

Rapid and accurate diagnosis of pulmonary tuberculosis (PTB) is an unresolved problem worldwide, especially for sputum smear− (S−) cases. In this study, five antigen genes including Rv3871, Rv3874, Rv3875, Rv3876, and Rv3879 were cloned from Mycobacterium tuberculosis (Mtb) RD1 and overexpressed to generate antigen fragments. These antigens and their combinations were investigated for PTB serodiagnosis. 298 serum samples were collected from active PTB patients, including 117 sputum smear+ (S+) and sputum culture+ (C+) cases, 101 S−/C+ cases, and 80 S−/C− cases. The serum IgG levels of the five antigens were measured by ELISA. Based on IgG levels, the sensitivity/specificity of Rv3871, Rv3874, Rv3875, Rv3876, and Rv3879 for PTB detection was 81.21%/74.74%, 63.09%/94.78%, 32.21%/87.37%, 62.42%/85.26%, and 83.56%/83.16%, respectively. Furthermore, the optimal result for PTB diagnosis was achieved by combining antigens Rv3871, Rv3876, and Rv3879. In addition, the IgG levels of Rv3871, Rv3876, and Rv3879 were found to be higher in S−/C+ PTB patients than in other PTB populations. More importantly, combination of the three antigens demonstrated superior diagnostic performance for both S−/C+ and S−/C− PTB. In conclusion, the combination of Rv3871, Rv3876, and Rv3879 induced higher IgG response in sputum S−/C+ PTB patients and represents a promising biomarker combination for diagnosing of PTB.

The present study aims to identify distinctive Raman spectrum metabolic peaks to predict hepatocellular carcinoma (HCC). We performed a label-free, non-invasive surface-enhanced Raman spectroscopy (SERS) test on 230 serum samples including 47 HCC, 60 normal controls (NC), 68 breast cancer (BC) and 55 lung cancer (LC) by mixing Au@AgNRs with serum directly. Based on the observed SERS spectra, discriminative metabolites including tryptophan, phenylalanine, and etc. were found in HCC, when compared with BC, LC, and NC (P<0.05 in all). Common metabolites-proline, valine, adenine and thymine were found in HCC, BC and LC with compared to NC group (P<0.05). Importantly, Raman spectra of HCC serum biomarker AFP were firstly detected to analyze the HCC prominent peak. Orthogonal partial least squares discriminant analysis was adopted to assess the diagnostic accuracy; area under curve value of HCC is 0.991. This study provides new insights into the HCC metabolites detection through Raman spectroscopy.

One mechanism of resistance of prostate cancer (PCa) to enzalutamide (MDV3100) treatment is the increased expression of AR variants lacking the ligand binding-domain, the best characterized of which is AR-V7. We have previously reported that Phosphatidylinositol-4-phosphate 5-kinase alpha (PIP5Kα), is a lipid kinase that links to CDK1 and AR pathways. The discovery of PIP5Kα inhibitor highlight the potential of PIP5K1α as a drug target in PCa. In this study, we show that AR-V7 expression positively correlates with PIP5K1α in tumor specimens from PCa patients. Overexpression of AR-V7 increases PIP5K1α, promotes rapid growth of PCa in xenograft mice, whereas inhibition of PIP5K1α by its inhibitor ISA-2011B suppresses the growth and invasiveness of xenograft tumors overexpressing AR-V7. PIP5K1α is a key co-factor for both AR-V7 and AR, which are present as protein-protein complexes predominantly in the nucleus of PCa cells. In addition, PIP5K1α and CDK1 influence AR-V7 expression also through AKT-associated mechanism dependent on PTEN-status. ISA-2011B disrupts protein stabilization of AR-V7 which is dependent on PIP5K1α, leading to suppression of invasive growth of AR-V7-high tumors in xenograft mice. Our study suggests that combination of enzalutamide and PIP5K1α may have a significant impact on refining therapeutic strategies to circumvent resistance to antiandrogen therapies.

Breast cancer is the most common cancer in women worldwide, identification of new biomarkers for early diagnosis and detection will improve the clinical outcome of breast cancer patients. In the present study, we determined serum levels of vitronectin (VN) in 93 breast cancer patients, 30 benign breast lesions, 9 precancerous lesions, and 30 healthy individuals by enzyme-linked immunosorbent assays. Serum VN level was significantly higher in patients with stage 0–I primary breast cancer than in healthy individuals, patients with benign breast lesion or precancerous lesions, as well as those with breast cancer of higher stages. Serum VN level was significantly and negatively correlated with tumor size, lymph node status, and clinical stage ( p < 0.05 in all cases). In addition, VN displayed higher area under curve (AUC) value (0.73, 95 % confidence interval (CI) [0.62–0.84]) than carcinoembryonic antigen (CEA) (0.64, 95 % CI [0.52–0.77]) and cancer antigen 15-3 (CA 15-3) (0.69, 95 % CI [0.58–0.81]) when used to distinguish stage 0–I cancer and normal control. Importantly, the combined use of three biomarkers yielded an improvement in receiver operating characteristic curve with an AUC of 0.83, 95 % CI [0.74–0.92]. Taken together, our current study showed for the first time that serum VN is a promising biomarker for early diagnosis of breast cancer when combined with CEA and CA15-3.