Plasma AMH assessments are superior to FSH in identifying women with reduced ovarian reserve. Anti-mullerian hormone assessment should be considered as a useful adjunct to FSH/oestradiol levels and antral follicle count when estimating ovarian reserve.

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Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (COAD) gene expression data across nine immune-checkpoints (PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). IL2RB was identified as the most common gene associated with immune-checkpoint genes in CRC. Using human/murine single-cell RNA-seq data, we demonstrated that IL2RB was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (P < 0.0001). Confirmatory IL2RB immunohistochemistry (IHC) analysis in a large MSI-H colon cancer tissue microarray (TMA; n = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (P < 0.0001). IL2RB mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (P < 0.0001). Our evolutionary algorithm has identified IL2RB to be extensively linked to immune-checkpoints in CRC; its expression should be investigated for clinical utility as a potential predictive biomarker for CRC patients receiving immune-checkpoint blockade.

PurposeBRAF mutation occurs in 8–15% of colon cancers (CC), and is associated with poor prognosis in metastatic disease. Compared to wild-type BRAF (BRAFWT) disease, stage II/III CC patients with BRAF mutant (BRAFMT) tumors have shorter overall survival after relapse; however, time-to-relapse is not significantly different. The aim of this investigation was to identify, and validate, novel predictors of relapse of stage II/III BRAFMT CC.Experimental designWe used gene expression data from a cohort of 460 patients (GSE39582) to perform a supervised classification analysis based on risk-of-relapse within BRAFMT stage II/III CC, to identify transcriptomic biomarkers associated with prognosis within this genotype. These findings were validated using immunohistochemistry in an independent population-based cohort of Stage II/III CC (n = 691), applying Cox proportional hazards analysis to determine associations with survival.ResultsHigh gene expression levels of Bcl-xL, a key regulator of apoptosis, were associated with increased risk of relapse, specifically in BRAFMT tumors (HR = 8.3, 95% CI 1.7–41.7), but not KRASMT/BRAFWT or KRASWT/BRAFWT tumors. High Bcl-xL protein expression in BRAFMT, untreated, stage II/III CC was confirmed to be associated with an increased risk of death in an independent cohort (HR = 12.13, 95% CI 2.49–59.13). Additionally, BRAFMT tumors with high levels of Bcl-xL protein expression appeared to benefit from adjuvant chemotherapy (P for interaction = 0.006), indicating the potential predictive value of Bcl-xL expression in this setting.ConclusionsThese findings provide evidence that Bcl-xL gene and/or protein expression identifies a poor prognostic subgroup of BRAFMT stage II/III CC patients, who may benefit from adjuvant chemotherapy.

OBJECTIVES: Because bone metastases can cause costly SREs, lifetime estimates of SREs prevented can help payers compare the effectiveness of treatment options. Denosumab was recently approved in the US for SRE prevention in patients with bone metastases from solid tumors. This study presents a model for SRE predictions based on phase III trials comparing denosumab and ZA in different tumors. METHODS: A three-state Markov model (On Treatment, Off Treatment, and Dead) was developed using constant SRE incidence rates for each tumor type and treatment. Results were compared between the model and trial for the 3-year trial duration and extrapolated to the patient lifetime. Lifetime SREs were estimated for the US population based on the estimated annual number of new patients with bone metastases. Mortality rates were identical between treatments and estimated using trial-based generalized gamma distributions. Lifetime treatment was assumed. RESULTS: The number of all SREs observed (rate per patient-year) for denosumab and ZA were 660 (0.488) and 853 (0.631) for breast cancer, 780 (0.746) and 943 (0.947) for prostate cancer, and 469 (0.588) and 535 (0.690) for other solid tumors. Comparison between trial results and model projections over the trial time horizon resulted in differences in SRE counts ranging from -1.5% to 2.0%. Over the expected patient lifetime, estimated SREs per patient were 1.80 and 2.32 (denosumab and ZA) for breast cancer, 1.65 and 2.08 for prostate cancer, and 1.36 and 1.60 for other solid tumors. In annual incident cohorts of patients with bone metastases, the model projects 43,765 and 56,408 (denosumab and ZA) lifetime SREs in breast cancer and 30,429 and 38,359 lifetime SREs in prostate cancer. CONCLUSIONS: The model output is consistent with the clinical trial evidence, and can be used to compare estimates of the predicted lifetime SREs for denosumab and ZA. OBJECTIVES:Faced with decreasing reimbursement costs, greater patient volumes, higher operating costs and pressure to adopt quality standards, community oncology practices and infusion centers operate in an increasingly challenging environment. This study sought to assess the practice efficiency techniques cur-A179