Refined cytogenetic-risk categorization for overall and leukemia-free survival in primary myelofibrosis: a single center study of 433 patients

We have previously identified sole +9, 13q- or 20q-, as ‘favorable' and sole +8 or complex karyotype as ‘unfavorable' cytogenetic abnormalities in primary myelofibrosis (PMF). In this study of 433 PMF patients, we describe additional sole abnormalities with favorable (chromosome 1 translocations/duplications) or unfavorable (−7/7q-) prognosis and also show that other sole or two abnormalities that do not include i(17q), −5/5q-, 12p-, inv(3) or 11q23 rearrangement are prognostically aligned with normal karyotype, which is prognostically favorable. These findings were incorporated into a refined two-tired cytogenetic-risk stratification: unfavorable and favorable karyotype. The respective 5-year survival rates were 8 and 51% (hazard ratio (HR): 3.1, 95% confidence interval (CI): 2.2–4.3; P<0.0001). Multivariable analysis confirmed the International Prognostic Scoring System (IPSS)-independent prognostic value of cytogenetic-risk categorization and also identified thrombocytopenia (platelets <100 × 109/l) as another independent predictor of inferior survival (P<0.0001). A similar multivariable analysis showed that karyotype (P=0.001) and platelet count (P=0.04), but not IPSS (P=0.27), predicted leukemia-free survival; the 5-year leukemic transformation rates for unfavorable versus favorable karyotype were 46 and 7% (HR: 5.5, 95% CI: 2.5–12.0; P<0.0001). This study provides the rationale and necessary details for incorporating cytogenetic findings and platelet count in future prognostic models for PMF

Identification of Functional Networks of Estrogen- and c-Myc-Responsive Genes and Their Relationship to Response to Tamoxifen Therapy in Breast Cancer

BACKGROUND: Estrogen is a pivotal regulator of cell proliferation in the normal breast and breast cancer. Endocrine therapies targeting the estrogen receptor are effective in breast cancer, but their success is limited by intrinsic and acquired resistance. METHODOLOGY/PRINCIPAL FINDINGS: With the goal of gaining mechanistic insights into estrogen action and endocrine resistance, we classified estrogen-regulated genes by function, and determined the relationship between functionally-related genesets and the response to tamoxifen in breast cancer patients. Estrogen-responsive genes were identified by transcript profiling of MCF-7 breast cancer cells. Pathway analysis based on functional annotation of these estrogen-regulated genes identified gene signatures with known or predicted roles in cell cycle control, cell growth (i.e. ribosome biogenesis and protein synthesis), cell death/survival signaling and transcriptional regulation. Since inducible expression of c-Myc in antiestrogen-arrested cells can recapitulate many of the effects of estrogen on molecular endpoints related to cell cycle progression, the estrogen-regulated genes that were also targets of c-Myc were identified using cells inducibly expressing c-Myc. Selected genes classified as estrogen and c-Myc targets displayed similar levels of regulation by estrogen and c-Myc and were not estrogen-regulated in the presence of siMyc. Genes regulated by c-Myc accounted for 50% of all acutely estrogen-regulated genes but comprised 85% (110/129 genes) in the cell growth signature. siRNA-mediated inhibition of c-Myc induction impaired estrogen regulation of ribosome biogenesis and protein synthesis, consistent with the prediction that estrogen regulates cell growth principally via c-Myc. The 'cell cycle', 'cell growth' and 'cell death' gene signatures each identified patients with an attenuated response in a cohort of 246 tamoxifen-treated patients. In multivariate analysis the cell death signature was predictive independent of the cell cycle and cell growth signatures. CONCLUSIONS/SIGNIFICANCE: These functionally-based gene signatures can stratify patients treated with tamoxifen into groups with differing outcome, and potentially identify distinct mechanisms of tamoxifen resistance

Quality of life data as prognostic indicators of survival in cancer patients: an overview of the literature from 1982 to 2008

<p>Abstract</p> <p>Background</p> <p>Health-related quality of life and survival are two important outcome measures in cancer research and practice. The aim of this paper is to examine the relationship between quality of life data and survival time in cancer patients.</p> <p>Methods</p> <p>A review was undertaken of all the full publications in the English language biomedical journals between 1982 and 2008. The search was limited to cancer, and included the combination of keywords 'quality of life', 'patient reported-outcomes' 'prognostic', 'predictor', 'predictive' and 'survival' that appeared in the titles of the publications. In addition, each study was examined to ensure that it used multivariate analysis. Purely psychological studies were excluded. A manual search was also performed to include additional papers of potential interest.</p> <p>Results</p> <p>A total of 451 citations were identified in this rapid and systematic review of the literature. Of these, 104 citations on the relationship between quality of life and survival were found to be relevant and were further examined. The findings are summarized under different headings: heterogeneous samples of cancer patients, lung cancer, breast cancer, gastro-oesophageal cancers, colorectal cancer, head and neck cancer, melanoma and other cancers. With few exceptions, the findings showed that quality of life data or some aspects of quality of life measures were significant independent predictors of survival duration. Global quality of life, functioning domains and symptom scores - such as appetite loss, fatigue and pain - were the most important indicators, individually or in combination, for predicting survival times in cancer patients after adjusting for one or more demographic and known clinical prognostic factors.</p> <p>Conclusion</p> <p>This review provides evidence for a positive relationship between quality of life data or some quality of life measures and the survival duration of cancer patients. Pre-treatment (baseline) quality of life data appeared to provide the most reliable information for helping clinicians to establish prognostic criteria for treating their cancer patients. It is recommended that future studies should use valid instruments, apply sound methodological approaches and adequate multivariate statistical analyses adjusted for socio-demographic characteristics and known clinical prognostic factors with a satisfactory validation strategy. This strategy is likely to yield more accurate and specific quality of life-related prognostic variables for specific cancers.</p

Need for Global Partnership in Cancer Care: Perceptions of Cancer Care Researchers Attending the 2010 Australia and Asia Pacific Clinical Oncology Research Development Workshop

Cancer research globally faces significant barriers, many of which are magnified in the developing country setting