Enumerating the gene sets in breast cancer, a "direct" alternative to hierarchical clustering

<p>Abstract</p> <p>Background</p> <p>Two-way hierarchical clustering, with results visualized as heatmaps, has served as the method of choice for exploring structure in large matrices of expression data since the advent of microarrays. While it has delivered important insights, including a typology of breast cancer subtypes, it suffers from instability in the face of gene or sample selection, and an inability to detect small sets that may be dominated by larger sets such as the estrogen-related genes in breast cancer. The rank-based partitioning algorithm introduced in this paper addresses several of these limitations. It delivers results comparable to two-way hierarchical clustering, and much more. Applied systematically across a range of parameter settings, it enumerates all the partition-inducing gene sets in a matrix of expression values.</p> <p>Results</p> <p>Applied to four large breast cancer datasets, this alternative exploratory method detects more than thirty sets of co-regulated genes, many of which are conserved across experiments and across platforms. Many of these sets are readily identified in biological terms, e.g., "estrogen", "erbb2", and 8p11-12, and several are clinically significant as prognostic of either increased survival ("adipose", "stromal"...) or diminished survival ("proliferation", "immune/interferon", "histone",...). Of special interest are the sets that effectively factor "immune response" and "stromal signalling".</p> <p>Conclusion</p> <p>The gene sets induced by the enumeration include many of the sets reported in the literature. In this regard these inventories confirm and consolidate findings from microarray-based work on breast cancer over the last decade. But, the enumerations also identify gene sets that have not been studied as of yet, some of which are prognostic of survival. The sets induced are robust, biologically meaningful, and serve to reveal a finer structure in existing breast cancer microarrays.</p

Stromal Genes Add Prognostic Information to Proliferation and Histoclinical Markers: A Basis for the Next Generation of Breast Cancer Gene Signatures

BACKGROUND: First-generation gene signatures that identify breast cancer patients at risk of recurrence are confined to estrogen-positive cases and are driven by genes involved in the cell cycle and proliferation. Previously we induced sets of stromal genes that are prognostic for both estrogen-positive and estrogen-negative samples. Creating risk-management tools that incorporate these stromal signatures, along with existing proliferation-based signatures and established clinicopathological measures such as lymph node status and tumor size, should better identify women at greatest risk for metastasis and death. METHODOLOGY/PRINCIPAL FINDINGS: To investigate the strength and independence of the stromal and proliferation factors in estrogen-positive and estrogen-negative patients we constructed multivariate Cox proportional hazards models along with tree-based partitions of cancer cases for four breast cancer cohorts. Two sets of stromal genes, one consisting of DCN and FBLN1, and the other containing LAMA2, add substantial prognostic value to the proliferation signal and to clinical measures. For estrogen receptor-positive patients, the stromal-decorin set adds prognostic value independent of proliferation for three of the four datasets. For estrogen receptor-negative patients, the stromal-laminin set significantly adds prognostic value in two datasets, and marginally in a third. The stromal sets are most prognostic for the unselected population studies and may depend on the age distribution of the cohorts. CONCLUSION: The addition of stromal genes would measurably improve the performance of proliferation-based first-generation gene signatures, especially for older women. Incorporating indicators of the state of stromal cell types would mark a conceptual shift from epithelial-centric risk assessment to assessment based on the multiple cell types in the cancer-altered tissue

Practical logic of deterrence, Simulating the

"2302"--handwritten on coverSeries from publisher's list"Prepared for delivery at the annual convention of the International Studies Association, the Royal York Hotel, March 21-24, 1979."Includes bibliographical referencesSupported by N.S.F. 780670

Multivariate Cox proportional hazards model with stromal-decorin for UPPSALA estrogen receptor-positive samples.

<p>Multivariate Cox proportional hazards model with stromal-decorin for UPPSALA estrogen receptor-positive samples.</p

Uppsala samples partitioned by lymph node status and estrogen-status, ordered on stromal-laminin gene expression.

<p>Yellow signifies up-regulation; blue signifies down-regulation. Rows represent probe sets on the Affymetrix HG U133A platform. Black bars record Breast Cancer Specific Survival events censored at 2.5 years. Blue bars record BCSS events that occur between 2.5 and 5 years.</p

Age distribution for five datasets.

<p>Median age for UPPSALA, STOCKHOLM, SAN FRANCISCO, and TRANSBIG cohorts. Mean age for MAINZ. Percentage of samples 50 years of age or younger. Source for median ages = <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037646#pone.0037646-Ringner1" target="_blank">[57]</a>. Source for percentage samples less than 51 = <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037646#pone.0037646-Karn1" target="_blank">[58]</a>.</p

Cox proportional hazards models for the UPPSALA cohort.

<p>Column labels indicate subsets of samples and follow-up period, e.g., “ER+@5” stands for estrogen-receptor positive samples with events censored at five years. Rows specify the predictors in a Cox proportional hazards model. Table entries report the z value of the first predictor of the model in the corresponding row for the samples in the corresponding column. Entries with <i>p-</i>values less than 0.05 appear in bold.</p

Cox proportional hazards models for the SAN FRANCISCO cohort.

<p>Column labels indicate subsets of samples and follow-up period, e.g., “SanFrancisco74ER+@5” stands for estrogen-receptor positive samples with events censored at five years. Rows specify the predictors in a Cox proportional hazards model. Table entries report the z value of the first predictor of the model in the corresponding row for the samples in the corresponding column. Entries with <i>p-</i>values less than 0.05 appear in bold.</p

Cox proportional hazards models for UPPSALA estrogen receptor-positive older (>60) and younger (<60) women.

<p>Upper model: UPPSALA estrogen receptor-positive, older women (>60 years of age) n = 117, 19 Breast Cancer Specific Survival events censored @ 5 years.</p><p>Lower model: UPPSALA estrogen receptor-positive, younger women (<60 years of age) n = 83, 13 Breast Cancer Specific Survival events censored @ 5 years.</p

Cox proportional hazards models for the STOCKHOLM cohort.

<p>Column labels indicate subsets of samples and follow-up period, e.g., “Stockholm130ER+@5” stands for estrogen-receptor positive samples with events censored at five years. Rows specify the predictors in a Cox proportional hazards model. Table entries report the z value of the first predictor of the model in the corresponding row for the samples in the corresponding column. Entries with <i>p-</i>values less than 0.05 appear in bold.</p