Enhancing decision-making about adjuvant chemotherapy in early breast cancer following EndoPredict testing

Objective Chemotherapy side‐effects can be substantial. There is increasing recognition that some oestrogen receptor positive (ER +ve), human epidermal growth factor receptor 2 negative (HER2 −ve) patients with breast cancer derive no benefit from chemotherapy and experience only iatrogenic harm. Gene expression profiling tests help refine recurrence risk and likely chemotherapy benefit. EndoPredict® is one such test, which classifies risks of distant recurrence as low or high in patients treated with surgery and adjuvant endocrine therapy alone. We compared treatment decisions pre‐test and post‐test results, patients' anxiety, decisional conflict, and oncologists' confidence about the decisions made. Methods Fourteen oncologists in 7 UK hospitals saw 149 pts judged to have equivocal indications for chemotherapy. Provisional treatment decisions were recorded then reconsidered when EPClin results were available. Pre‐test and post‐test results, patients completed State/Trait Anxiety Inventories (STAI), and the decisional conflict scale. Oncologists also recorded basic clinical details, their agreement with, and confidence about treatment decisions. Results Sixty‐seven percent patients initially prescribed endocrine alone with high risk result upgraded to endocrine+chemotherapy (E + C); 83% prescribed E + C and had low risk scores, downgraded to E. None of 46 patients initially favouring E alone, who were low risk, changed decisions. Oncologists' confidence about decisions was significantly increased following the results (P = 0.002). Patients with downgraded treatment decisions had significantly lower anxiety scores (P = 0.045); those upgraded had increased scores (P = 0.001). Overall decisional conflict and uncertainty fell significantly post‐test (P < 0.022). Conclusions EndoPredict scores increased oncologists' and patients' decision‐making confidence, generally improving the matching of risk with therapy decisions

The cost-effectiveness of EndoPredict to inform adjuvant chemotherapy decisions in early breast cancer

Background Adjuvant chemotherapy in breast cancer patients post resection has been estimated to reduce mortality rates by up to 30%. However, the heterogeneous nature of the disease and patients implies that not all patients should receive the treatment. Many existing prognostic tools, may not definitively estimate the most effective treatment strategy, resulting in an indeterminate risk classification. In such cases gene expression profiling tests can aid the treatment decision. Methods This study evaluated the cost-effectiveness of EndoPredict in patients with indeterminate risk classification. A mathematical model was constructed to determine how the change in treatment decisions impacted the long term health of the population, and the future cost implications to the NHS. Results EndoPredict was found to lead to 36.9% of patients having a change in treatment decision. As a result its use was found to result in an increase in population health but also in total costs, resulting in an incremental cost-effectiveness ratio of £26,836 per quality adjusted life year. This was subject to significant parametric and structural uncertainty. Conclusion While EndoPredict was found to be more expensive overall, its ability to affect a more optimal allocation of chemotherapy, resulted in long term health gains, however, they were insufficient to justify the high cost of EndoPredict

Proliferation and estrogen signaling can distinguish patients at risk for early versus late relapse among estrogen receptor positive breast cancers

Introduction: We examined if a combination of proliferation markers and estrogen receptor (ER) activity could predict early versus late relapses in ER-positive breast cancer and inform the choice and length of adjuvant endocrine therapy. Methods: Baseline affymetrix gene-expression profiles from ER-positive patients who received no systemic therapy (n = 559), adjuvant tamoxifen for 5 years (cohort-1: n = 683, cohort-2: n = 282) and from 58 patients treated with neoadjuvant letrozole for 3 months (gene-expression available at baseline, 14 and 90 days) were analyzed. A proliferation score based on the expression of mitotic kinases (MKS) and an ER-related score (ERS) adopted from Oncotype DX® were calculated. The same analysis was performed using the Genomic Grade Index as proliferation marker and the luminal gene score from the PAM50 classifier as measure of estrogen-related genes. Median values were used to define low and high marker groups and four combinations were created. Relapses were grouped into time cohorts of 0-2.5, 0-5, 5-10 years. Results: In the overall 10 years period, the proportional hazards assumption was violated for several biomarker groups indicating time-dependent effects. In tamoxifen-treated patients Low-MKS/Low-ERS cancers had continuously increasing risk of relapse that was higher after 5 years than Low-MKS/High-ERS cancers [0 to 10 year, HR 3.36; p = 0.013]. High-MKS/High-ERS cancers had low risk of early relapse [0-2.5 years HR 0.13; p = 0.0006], but high risk of late relapse which was higher than in the High-MKS/Low-ERS group [after 5 years HR 3.86; p = 0.007]. The High-MKS/Low-ERS subset had most of the early relapses [0 to 2.5 years, HR 6.53; p < 0.0001] especially in node negative tumors and showed minimal response to neoadjuvant letrozole. These findings were qualitatively confirmed in a smaller independent cohort of tamoxifen-treated patients. Using different biomarkers provided similar results. Conclusions: Early relapses are highest in highly proliferative/low-ERS cancers, in particular in node negative tumors. Relapses occurring after 5 years of adjuvant tamoxifen are highest among the highly-proliferative/high-ERS tumors although their risk of recurrence is modest in the first 5 years on tamoxifen. These tumors could be the best candidates for extended endocrine therapy

Comparison of Technetium-99m-MIBI imaging with MRI for detection of spine involvement in patients with multiple myeloma

BACKGROUND: Recently, radiopharmaceutical scanning with Tc-99m-MIBI was reported to depict areas with active bone disease in multiple myeloma (MM) with both high sensitivity and specificity. This observation was explained by the uptake of Tc-99m-MIBI by neoplastic cells. The present investigation evaluates whether Tc-99m-MIBI imaging and magnetic resonance imaging (MRI) perform equally well in detecting myelomatous bone marrow lesions. METHODS: In 21 patients with MM, MRIs of the vertebral region TH12 to S1 and whole body scans with Tc-99m-MIBI were done. RESULTS: Tc-99m-MIBI scanning missed bone marrow infiltration in 43 of 87 vertebrae (50.5%) in which MRI showed neoplastic bone marrow involvement. In patients with disease stage I+II, Tc-99m-MIBI scanning was negative in all of 24 vertebrae infiltrated according to MRI. In patients with disease stage III, Tc-99m-MIBI scanning detected 44 of 63 (70%) vertebrae involved by neoplastic disease. CONCLUSION: Tc-99m-MIBI scanning underestimated the extent of myelomatous bone marrow infiltration in the spine, especially in patients with low disease stage

Markers for the identification of late breast cancer recurrence

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited

HER2 and ESR1 mRNA expression levels and response to neoadjuvant trastuzumab plus chemotherapy in patients with primary breast cancer

Introduction: Recent data suggest that benefit from trastuzumab and chemotherapy might be related to expression of HER2 and estrogen receptor (ESR1). Therefore, we investigated HER2 and ESR1 mRNA levels in core biopsies of HER2-positive breast carcinomas from patients treated within the neoadjuvant GeparQuattro trial. Methods: HER2 levels were centrally analyzed by immunohistochemistry (IHC), silver in-situ hybridization (SISH) and qRT-PCR in 217 pretherapeutic formalin-fixed, paraffin-embedded (FFPE) core biopsies. All tumors had been HER2-positive by local pathology and had been treated with neoadjuvant trastuzumab/ chemotherapy in GeparQuattro. Results: Only 73% of the tumors (158 of 217) were centrally HER2-positive (cHER2-positive) by IHC/SISH, with cHER2-positive tumors showing a significantly higher pCR rate (46.8% vs. 20.3%, p<0.0005). HER2 status by qRT-PCR showed a concordance of 88.5% with the central IHC/SISH status, with a low pCR rate in those tumors that were HER2-negative by mRNA analysis (21.1% vs. 49.6%, p<0.0005). The level of HER2 mRNA expression was linked to response rate in ESR1-positive tumors, but not in ESR1-negative tumors. HER2 mRNA expression was significantly associated with pCR in the HER2-positive/ESR1-positive tumors (p=0.004), but not in HER2-positive/ESR1-negative tumors. Conclusions: Only patients with cHER2-positive tumors - irrespective of the method used - have an increased pCR rate with trastuzumab plus chemotherapy. In patients with cHER2-negative tumors the pCR rate is comparable to the pCR rate in the non-trastuzumab treated HER-negative population. Response to trastuzumab is correlated to HER2 mRNA levels only in ESR1-positive tumors. This study adds further evidence to the different biology of both subsets within the HER2-positive group

Programmed death ligand 1 expression and tumor-infiltrating lymphocytes in glioblastoma

Background Immune checkpoint inhibitors targeting programmed cell death 1 (PD1) or its ligand (PD-L1) showed activity in several cancer types. Methods We performed immunohistochemistry for CD3, CD8, CD20, HLA-DR, phosphatase and tensin homolog (PTEN), PD-1, and PD-L1 and pyrosequencing for assessment of the O6-methylguanine-methyltransferase (MGMT) promoter methylation status in 135 glioblastoma specimens (117 initial resection, 18 first local recurrence). PD-L1 gene expression was analyzed in 446 cases from The Cancer Genome Atlas. Results Diffuse/fibrillary PD-L1 expression of variable extent, with or without interspersed epithelioid tumor cells with membranous PD-L1 expression, was observed in 103 of 117 (88.0%) newly diagnosed and 13 of 18 (72.2%) recurrent glioblastoma specimens. Sparse-to-moderate density of tumor-infiltrating lymphocytes (TILs) was found in 85 of 117 (72.6%) specimens (CD3+ 78/117, 66.7%; CD8+ 52/117, 44.4%; CD20+ 27/117, 23.1%; PD1+ 34/117, 29.1%). PD1+ TIL density correlated positively with CD3+ (P < .001), CD8+ (P < .001), CD20+ TIL density (P < .001), and PTEN expression (P = .035). Enrichment of specimens with low PD-L1 gene expression levels was observed in the proneural and G-CIMP glioblastoma subtypes and in specimens with high PD-L1 gene expression in the mesenchymal subtype (P = 5.966e-10). No significant differences in PD-L1 expression or TIL density between initial and recurrent glioblastoma specimens or correlation of PD-L1 expression or TIL density with patient age or outcome were evident. Conclusion TILs and PD-L1 expression are detectable in the majority of glioblastoma samples but are not related to outcome. Because the target is present, a clinical study with specific immune checkpoint inhibitors seems to be warranted in glioblastom

Third CECOG consensus on the systemic treatment of non-small-cell lung cancer

The current third consensus on the systemic treatment of non-small-cell lung cancer (NSCLC) builds upon and updates similar publications on the subject by the Central European Cooperative Oncology Group (CECOG), which has published such consensus statements in the years 2002 and 2005 (Zielinski CC, Beinert T, Crawford J et al. Consensus on medical treatment of non-small-cell lung cancer—update 2004. Lung Cancer 2005; 50: 129-137). The principle of all CECOG consensus is such that evidence-based recommendations for state-of-the-art treatment are given upon which all participants and authors of the manuscript have to agree (Beslija S, Bonneterre J, Burstein HJ et al. Third consensus on medical treatment of metastatic breast cancer. Ann Oncol 2009; 20 (11): 1771-1785). This is of particular importance in diseases in which treatment options depend on very particular clinical and biologic variables (Zielinski CC, Beinert T, Crawford J et al. Consensus on medical treatment of non-small-cell lung cancer—update 2004. Lung Cancer 2005; 50: 129-137; Beslija S, Bonneterre J, Burstein HJ et al. Third consensus on medical treatment of metastatic breast cancer. Ann Oncol 2009; 20 (11): 1771-1785). Since the publication of the last CECOG consensus on the medical treatment of NSCLC, a series of diagnostic tools for the characterization of biomarkers for personalized therapy for NSCLC as well as therapeutic options including adjuvant treatment, targeted therapy, and maintenance treatment have emerged and strongly influenced the field. Thus, the present third consensus was generated that not only readdresses previous disease-related issues but also expands toward recent developments in the management of NSCLC. It is the aim of the present consensus to summarize minimal quality-oriented requirements for individual patients with NSCLC in its various stages based upon levels of evidence in the light of a rapidly expanding array of individual therapeutic option

Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table