Leukemic blasts program bone marrow adipocytes to generate a protumoral microenvironment.

Despite currently available therapies, most patients diagnosed with acute myeloid leukemia (AML) die of their disease. Tumor-host interactions are critical for the survival and proliferation of cancer cells; accordingly, we hypothesize that specific targeting of the tumor microenvironment may constitute an alternative or additional strategy to conventional tumor-directed chemotherapy. Because adipocytes have been shown to promote breast and prostate cancer proliferation, and because the bone marrow adipose tissue accounts for up to 70% of bone marrow volume in adult humans, we examined the adipocyte-leukemia cell interactions to determine if they are essential for the growth and survival of AML. Using in vivo and in vitro models of AML, we show that bone marrow adipocytes from the tumor microenvironment support the survival and proliferation of malignant cells from patients with AML. We show that AML blasts alter metabolic processes in adipocytes to induce phosphorylation of hormone-sensitive lipase and consequently activate lipolysis, which then enables the transfer of fatty acids from adipocytes to AML blasts. In addition, we report that fatty acid binding protein-4 (FABP4) messenger RNA is upregulated in adipocytes and AML when in coculture. FABP4 inhibition using FABP4 short hairpin RNA knockdown or a small molecule inhibitor prevents AML proliferation on adipocytes. Moreover, knockdown of FABP4 increases survival in Hoxa9/Meis1-driven AML model. Finally, knockdown of carnitine palmitoyltransferase IA in an AML patient-derived xenograft model improves survival. Here, we report the first description of AML programming bone marrow adipocytes to generate a protumoral microenvironment.

Unemployment, public-sector health-care spending and breast cancer mortality in the European Union: 1990-2009.

BACKGROUND: The global economic crisis has been associated with increased unemployment, reduced health-care spending and adverse health outcomes. Insights into the impact of economic variations on cancer mortality, however, remain limited. METHODS: We used multivariate regression analysis to assess how changes in unemployment and public-sector expenditure on health care (PSEH) varied with female breast cancer mortality in the 27 European Union member states from 1990 to 2009. We then determined how the association with unemployment was modified by PSEH. Country-specific differences in infrastructure and demographic structure were controlled for, and 1-, 3-, 5- and 10-year lag analyses were conducted. Several robustness checks were also implemented. RESULTS: Unemployment was associated with an increase in breast cancer mortality [P < 0.0001, coefficient (R) = 0.1829, 95% confidence interval (CI) 0.0978-0.2680]. Lag analysis showed a continued increase in breast cancer mortality at 1, 3, 5 and 10 years after unemployment rises (P < 0.05). Controlling for PSEH removed this association (P = 0.063, R = 0.080, 95% CI -0.004 to 0.163). PSEH increases were associated with significant decreases in breast cancer mortality (P < 0.0001, R = -1.28, 95% CI -1.67 to -0.877). The association between unemployment and breast cancer mortality remained in all robustness checks. CONCLUSION: Rises in unemployment are associated with significant short- and long-term increases in breast cancer mortality, while increases in PSEH are associated with reductions in breast cancer mortality. Initiatives that bolster employment and maintain total health-care expenditure may help minimize increases in breast cancer mortality during economic crises.

Genomic scars as biomarkers of homologous recombination deficiency and drug response in breast and ovarian cancers.

Poly (ADP-ribose) polymerase (PARP) inhibitors and platinum-based chemotherapies have been found to be particularly effective in tumors that harbor deleterious germline or somatic mutations in the BRCA1 or BRCA2 genes, the products of which contribute to the conservative homologous recombination repair of DNA double-strand breaks. Nonetheless, several setbacks in clinical trial settings have highlighted some of the issues surrounding the investigation of PARP inhibitors, especially the identification of patients who stand to benefit from such drugs. One potential approach to finding this patient subpopulation is to examine the tumor DNA for evidence of a homologous recombination defect. However, although the genomes of many breast and ovarian cancers are replete with aberrations, the presence of numerous factors able to shape the genomic landscape means that only some of the observed DNA abnormalities are the outcome of a cancer cell's inability to faithfully repair DNA double-strand breaks. Consequently, recently developed methods for comprehensively capturing the diverse ways in which homologous recombination deficiencies may arise beyond BRCA1/2 mutation have used DNA microarray and sequencing data to account for potentially confounding features in the genome. Scores capturing telomeric allelic imbalance, loss of heterozygosity (LOH) and large scale transition score, as well as the total number of coding mutations are measures that summarize the total burden of certain forms of genomic abnormality. By contrast, other studies have comprehensively catalogued different types of mutational pattern and their relative contributions to a given tumor sample. Although at least one study to explore the use of the LOH scar in a prospective clinical trial of a PARP inhibitor in ovarian cancer is under way, limitations that result in a relatively low positive predictive value for these biomarkers remain. Tumors whose genome has undergone one or more events that restore high-fidelity homologous recombination are likely to be misclassified as double-strand break repair-deficient and thereby sensitive to PARP inhibitors and DNA damaging chemotherapies as a result of prior repair deficiency and its genomic scarring. Therefore, we propose that integration of a genomic scar-based biomarker with a marker of resistance in a high genomic scarring burden context may improve the performance of any companion diagnostic for PARP inhibitors.

Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms.

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms.