Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers

Impact of depth of response on survival in patients treated with cobimetinib ± vemurafenib: pooled analysis of BRIM-2, BRIM-3, BRIM-7 and coBRIM.

BackgroundThis pooled analysis investigated the prognostic value of depth of response in two cohorts of patients with BRAFV600-mutated metastatic melanoma treated with vemurafenib or cobimetinib plus vemurafenib.MethodsThe data were pooled from BRIM-2, BRIM-3, BRIM-7 and coBRIM. Association of depth of response with survival was estimated by Cox proportional hazards regression, adjusted for clinically relevant covariates. Depth of response was analysed in previously identified prognostic subgroups based on disease characteristics and gene signatures.ResultsGreater tumour reduction and longer time to maximal response were significantly associated with longer progression-free survival (PFS) and overall survival (OS) when evaluated as continuous variables. Patients with the deepest responses had long-lasting survival outcomes (median PFS: 14 months; OS: 32 months with vemurafenib; not estimable with cobimetinib plus vemurafenib). Cobimetinib plus vemurafenib improved depth of response versus vemurafenib monotherapy regardless of other prognostic factors, including gene signatures.ConclusionsGreater depth of response was associated with improved survival, supporting its utility as a measure of treatment efficacy in melanoma and further evaluation of its incorporation into existing prognostic models. Cobimetinib plus vemurafenib improved outcomes across quartiles of response regardless of prognostic factors or gene signatures and provided durable survival benefits in patients with deep responses

Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are only suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discrimatory power of a prediction rule. Specifically, we propose a component-wise boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures.Comment: revised manuscript - added simulation study, additional result

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

Comparison of prognostic gene expression signatures for breast cancer

<p>Abstract</p> <p>Background</p> <p>During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms.</p> <p>Results</p> <p>We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters.</p> <p>Conclusion</p> <p>Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.</p

Development and Validation of a 28-gene Hypoxia-related Prognostic Signature for Localized Prostate Cancer.

BACKGROUND: Hypoxia is associated with a poor prognosis in prostate cancer. This work aimed to derive and validate a hypoxia-related mRNA signature for localized prostate cancer. METHOD: Hypoxia genes were identified in vitro via RNA-sequencing and combined with in vivo gene co-expression analysis to generate a signature. The signature was independently validated in eleven prostate cancer cohorts and a bladder cancer phase III randomized trial of radiotherapy alone or with carbogen and nicotinamide (CON). RESULTS: A 28-gene signature was derived. Patients with high signature scores had poorer biochemical recurrence free survivals in six of eight independent cohorts of prostatectomy-treated patients (Log rank test P \u3c .05), with borderline significances achieved in the other two (P \u3c .1). The signature also predicted biochemical recurrence in patients receiving post-prostatectomy radiotherapy (n = 130, P = .007) or definitive radiotherapy alone (n = 248, P = .035). Lastly, the signature predicted metastasis events in a pooled cohort (n = 631, P = .002). Prognostic significance remained after adjusting for clinic-pathological factors and commercially available prognostic signatures. The signature predicted benefit from hypoxia-modifying therapy in bladder cancer patients (intervention-by-signature interaction test P = .0026), where carbogen and nicotinamide was associated with improved survival only in hypoxic tumours. CONCLUSION: A 28-gene hypoxia signature has strong and independent prognostic value for prostate cancer patients

Biomarker analyses of clinical outcomes in patients with advanced hepatocellular carcinoma treated with Sorafenib with or without Erlotinib in the SEARCH Trial

Purpose: Sorafenib is the current standard therapy for advanced HCC, but validated biomarkers predicting clinical outcomes are lacking. This study aimed to identify biomarkers predicting prognosis and/or response to sorafenib, with or without erlotinib, in HCC patients from the phase 3 SEARCH trial. Experimental Design: 720 patients were randomized to receive oral sorafenib 400 mg BID plus erlotinib 150 mg QD or placebo. Fifteen growth factors relevant to the treatment regimen and/or to HCC were measured in baseline plasma samples. Results: Baseline plasma biomarkers were measured in 494 (69%) patients (sorafenib plus erlotinib, n=243; sorafenib plus placebo, n=251). Treatment arm–independent analyses showed that elevated HGF (HR, 1.687 [high vs low expression]; endpoint multiplicity adjusted [e-adj] P=0.0001) and elevated plasma VEGF-A (HR, 1.386; e-adj P=0..0377) were significantly associated with poor OS in multivariate analyses, and low plasma KIT (HR, 0.75 [high vs low]; P=0.0233; e-adj P=0.2793) tended to correlate with poorer OS. High plasma VEGF-C independently correlated with longer TTP (HR, 0.633; e-adj P=0.0010) and trended toward associating with improved disease control rate (univariate:OR, 2.047; P=0.030; e-adj P=0.420). In 67% of evaluable patients (339/494), a multimarker signature of HGF, VEGF-A, KIT, epigen, and VEGF-C correlated with improved median OS in multivariate analysis (HR, 0.150; P&lt;0.00001). No biomarker predicted efficacy from erlotinib. Conclusions: Baseline plasma HGF, VEGF-A, KIT, and VEGF-C correlated with clinical outcomes in HCC patients treated with sorafenib with or without erlotinib. These biomarkers plus epigen constituted a multimarker signature for improved OS

Prognostic relevance of gene-expression signatures

Cancer prognosis can be regarded as estimating the risk of future outcomes from multiple variables. In prognostic signatures, these variables represent expressions of genes that are summed up to calculate a risk score. However, it is a natural phenomenon in living systems that the whole is more than the sum of its parts. We hypothesize that the prognostic power of signatures is fundamentally limited without incorporating emergent effects. Convergent evidence from a set of unprecedented size (ca. 10,000 signatures) implicates a maximum prognostic power. We show that a signature can correctly discriminate patients' prognoses in no more than 80% of the time. Using a simple simulation, we show that more than 50% of the potentially available information is still missing at this value.Comment: 27 pages, 6 figures, supporting informatio

Bayesian profiling of molecular signatures to predict event times

BACKGROUND: It is of particular interest to identify cancer-specific molecular signatures for early diagnosis, monitoring effects of treatment and predicting patient survival time. Molecular information about patients is usually generated from high throughput technologies such as microarray and mass spectrometry. Statistically, we are challenged by the large number of candidates but only a small number of patients in the study, and the right-censored clinical data further complicate the analysis. RESULTS: We present a two-stage procedure to profile molecular signatures for survival outcomes. Firstly, we group closely-related molecular features into linkage clusters, each portraying either similar or opposite functions and playing similar roles in prognosis; secondly, a Bayesian approach is developed to rank the centroids of these linkage clusters and provide a list of the main molecular features closely related to the outcome of interest. A simulation study showed the superior performance of our approach. When it was applied to data on diffuse large B-cell lymphoma (DLBCL), we were able to identify some new candidate signatures for disease prognosis. CONCLUSION: This multivariate approach provides researchers with a more reliable list of molecular features profiled in terms of their prognostic relationship to the event times, and generates dependable information for subsequent identification of prognostic molecular signatures through either biological procedures or further data analysis