Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types

Telomere maintenance mechanism dysregulation serves as an early predictor of adjuvant therapy response and a potential therapeutic target in human cancers

Telomere maintenance mechanisms (TMMs) rescue cells from telomere crisis, endow cells immortal property, stabilize genomic integrity. However, TMM-associated molecular profiles and their clinical outcomes in cancer remain elusive. Here, we performed a pan-cancer and integrated analysis of TMM gene expression profiles from 10,107 unique samples with clinicopathological, molecular and outcome features across 7 malignancies from the same microarray platform (Affymetrix GPL570 platform). This resource was divided into Case-Control datasets for obtaining dysregulated TMM genes and Survival datasets for evaluating clinical outcomes. Multidimensional data from The Cancer Genome Atlas (TCGA) were used to elucidate associations between TMM dysregulation and survival, genomic instability. Our results demonstrated that TMMs had a consistent dysregulation spectrum across cancers, based on which we developed the TMM-dysregulation signature TMScore that was positively associated with various tumor adverse features. Two opposite prognostic patterns of TMScore independent of clinicopathological and molecular characteristics were identified, which might be explained by genomic instability: breast and lung cancer patients with elevated TMScore had inferior outcomes, suggesting TMScore-related genes as potential therapeutic targets, on the contrary, colon and stomach cancer patients had superior outcomes. Most important, the prognostic value of TMScore was still significant regardless of whether patients had received adjuvant therapy, which was valuable for discriminating non-responders from responders, and could predict the effectiveness of adjuvant therapy. In summary, our resources delineate TMMs dysregulated landscape across cancers, shed light on the impact of TMMs dysregulation on patient outcomes and adjuvant therapy, and provide novel therapeutic opportunities for cancer treatment.

Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.

Prognostic and predictive value of cell cycle progression (CCP) score in ductal carcinoma in situ of the breast

The natural history of ductal carcinoma in situ (DCIS) is highly variable and difficult to predict. Biomarkers are needed to stratify patients with DCIS for adjuvant therapy. We investigated the prognostic and predictive relevance of cell cycle progression (CCP) score in women with DCIS. We measured the expression of 23 genes involved in CCP with quantitative RT-PCR on RNA extracted from formalin-fixed paraffin-embedded tumor samples, and assessed the correlation of a predefined score with histopathologic features and recurrence. The signature was analyzed in a cohort of 909 consecutive DCIS with full histopathological features treated in a single institution. The main outcome measure was ipsilateral breast event (IBE) as first event observed, be it in situ or invasive. Median follow-up time was 8.7 years (IQR 6.5-10.5 years). There were 150 ipsilateral IBEs, 84 (56%) of which were invasive. In the first 5 years of follow-up, the score provided statistically different findings (p = 0.009), with IBE rates of 14.7% (95% CI, 10.4-19.7) for the highest quartile of CCP score (Q4) and 8.7% (95% CI, 6.7-11.0) for the lowest quartiles (Q1-3). The prognostic value for IBEs approached significance also in women treated with mastectomy (adjusted hazard ratio [HR] Q4 vs. Q1-3 = 2.60; 95% CI: 0.96-7.08; P = 0.06). Radiotherapy provided a greater benefit in women with higher CCP score. In addition, Q4 predicted a different risk after tamoxifen depending on menopausal status, with a beneficial trend on IBEs in postmenopausal women (HR 0.30; 95% CI, 0.07-1.39), and an opposite trend in premenopausal women (HR 1.68; 95% CI, 0.38-7.44) (P-interaction = 0.03). The results of this study provide for the first time the evidence that CCP score is a prognostic marker, which, after additional validation, could have an important role in personalizing the management of DCIS.

Biological Determinants of Chemo-Radiotherapy Response in HPV-Negative Head and Neck Cancer: A Multicentric External Validation

Purpose: Tumor markers that are related to hypoxia, proliferation, DNA damage repair and stem cell-ness, have a prognostic value in advanced stage HNSCC patients when assessed individually. Here we aimed to evaluate and validate this in a multifactorial context and assess interrelation and the combined role of these biological factors in determining chemo-radiotherapy response in HPV-negative advanced HNSCC. Methods: RNA sequencing data of pre-treatment biopsy material from 197 HPV-negative advanced stage HNSCC patients treated with definitive chemoradiotherapy was analyzed. Biological parameter scores were assigned to patient samples using previously generated and described gene expression signatures. Locoregional control rates were used to assess the role of these biological parameters in radiation response and compared to distant metastasis data. Biological factors were ranked according to their clinical impact using bootstrapping methods and multivariate Cox regression analyses that included clinical variables. Multivariate Cox regression analyses comprising all biological variables were used to define their relative role among all factors when combined. Results: Only few biomarker scores correlate with each other, underscoring their independence. The different biological factors do not correlate or cluster, except for the two stem cell markers CD44 and SLC3A2 (r = 0.4, p < 0.001) and acute hypoxia prediction scores which correlated with T-cell infiltration score, CD8⁺ T cell abundance and proliferation scores (r = 0.52, 0.56, and 0.6, respectively with p < 0.001). Locoregional control association analyses revealed that chronic (Hazard Ratio (HR) = 3.9) and acute hypoxia (HR = 1.9), followed by stem cell-ness (CD44/SLC3A2; HR = 2.2/2.3), were the strongest and most robust determinants of radiation response. Furthermore, multivariable analysis, considering other biological and clinical factors, reveal a significant role for EGFR expression (HR = 2.9, p < 0.05) and T-cell infiltration (CD8⁺T-cells: HR = 2.2, p < 0.05; CD8⁺T-cells/Treg: HR = 2.6, p < 0.01) signatures in locoregional control of chemoradiotherapy-treated HNSCC. Conclusion: Tumor acute and chronic hypoxia, stem cell-ness, and CD8⁺ T-cell parameters are relevant and largely independent biological factors that together contribute to locoregional control. The combined analyses illustrate the additive value of multifactorial analyses and support a role for EGFR expression analysis and immune cell markers in addition to previously validated biomarkers. This external validation underscores the relevance of biological factors in determining chemoradiotherapy outcome in HNSCC.

Developing a genetic signature to predict drug response in ovarian cancer

There is a lack of personalized treatment options for women with recurrent platinum-resistant ovarian cancer. Outside of bevacizumab and a group of poly ADP-ribose polymerase inhibitors, few options are available to women that relapse. We propose that efficacious drug combinations can be determined via molecular characterization of ovarian tumors along with pre-established pharmacogenomic profiles of repurposed compounds. To that end, we selectively performed multiple two-drug combination treatments in ovarian cancer cell lines that included reactive oxygen species inducers and HSP90 inhibitors. This allowed us to select cell lines that exhibit disparate phenotypes of proliferative inhibition to a specific drug combination of auranofin and AUY922. We profiled altered mechanistic responses from these agents in both reactive oxygen species and HSP90 pathways, as well as investigated PRKCI and lncRNA expression in ovarian cancer cell line models. Generation of dual multi-gene panels implicated in resistance or sensitivity to this drug combination was produced using RNA sequencing data and the validity of the resistant signature was examined using high-density RT-qPCR. Finally, data mining for the prevalence of these signatures in a large-scale clinical study alluded to the prevalence of resistant genes in ovarian tumor biology. Our results demonstrate that high-throughput viability screens paired with reliable in silico data can promote the discovery of effective, personalized therapeutic options for a currently untreatable disease.

Proliferation genes in lung development associated with the prognosis of lung adenocarcinoma but not squamous cell carcinoma

There are many similarities between embryonic development and tumorigenesis, and gene expression profiles show that certain correlations exist between the gene signature during development and the clinical phenotypes of different cancers. Our group previously reported the gene expression profiles of human lung development, and the expression of one group of proliferation-related genes (PTN1 genes) steadily decreased during lung development. Here, we examined the prognostic value of PTN1 genes in 5 independent lung adenocarcinoma (ADC) and 5 lung independent squamous cell carcinoma (SCC) microarray datasets and found that the expression levels of PTN1 genes were associated with survival in lung ADC but not lung SCC. All of the lung ADC datasets contained a set of highly correlated genes from PTN1 genes, but the lung SCC datasets had no similar set of genes. We identified 63 unique core genes from the PTN1 genes in the 5 lung ADC datasets: 17 of these core genes appeared in at least 4 of the lung ADC datasets, and the 17 corresponding proteins clearly interacted more strongly with each other in lung ADC than in lung SCC. Moreover, 16 of the 17 core genes play major roles in the G₂ /M phase of the cell cycle. These data indicate that proliferation-related genes in lung development have a significant prognostic value for lung ADC; the synergistic effects of the 17 core genes play an important role in lung ADC prognosis. These genes may have significant clinical implications for the treatment and prognosis of lung ADC.

Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures

Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA) can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Results. This validation is based on four key concepts. Coherence: elements of a gene signature should be correlated beyond chance. Uniqueness: the general direction of the data being examined can drive most of the observed signal. Robustness: if a gene signature is designed to measure a single biological effect, then this signal should be sufficiently strong and distinct compared to other signals within the signature. Transferability: the derived PCA gene signature score should describe the same biology in the target dataset as it does in the training dataset. Conclusions. The proposed validation procedure ensures that PCA-based gene signatures perform as expected when applied to datasets other than those that the signatures were trained upon. Complex signatures, describing multiple independent biological components, are also easily identified.

Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer

BACKGROUND

High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors.

METHODS

We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA).

RESULTS

High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup.

CONCLUSIONS

We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity.

Commercially available prognostic molecular models in early-stage lung cancer: a review of the Pervenio Lung RS and Myriad myPlan Lung Cancer tests

Lung cancer is the most common cause of cancer death in the USA. Patients with early-stage non-small-cell lung cancer have a 5-year survival of approximately 70%. Effective, accurate and clinically relevant prognostic tests are needed to determine which patients are at high risk of disease-specific mortality after surgical resection. Currently, there are two commercially available prognostic tests based on differential gene expression for this purpose: the Myriad myPlan™ Lung Cancer and Pervenio™ Lung RS tests. One stratifies patients into two risk groups and the other into three risk groups. Both have been validated in independent patient cohorts and neither has yet been demonstrated to improve survival. These tests have the potential to risk-stratify which patients with early-stage lung cancer will have a higher likelihood of disease recurrence after surgical resection and may benefit from adjuvant treatment.

Cost-Utility of a Prognostic Test Guiding Adjuvant Chemotherapy Decisions in Early-Stage Non-Small Cell Lung Cancer

BACKGROUND

A prognostic test was developed to guide adjuvant chemotherapy (ACT) decisions in early-stage non-small cell lung cancer (NSCLC) adenocarcinomas. The objective of this study was to compare the cost-utility of the prognostic test to the current standard of care (SoC) in patients with early-stage NSCLC.

MATERIALS AND METHODS

Lifetime costs (2014 U.S. dollars) and effectiveness (quality-adjusted life-years [QALYs]) of ACT treatment decisions were examined using a Markov microsimulation model from a U.S. third-party payer perspective. Cancer stage distribution and probability of receiving ACT with the SoC were based on data from an academic cancer center. The probability of receiving ACT with the prognostic test was estimated from a physician survey. Risk classification was based on the 5-year predicted NSCLC-related mortality. Treatment benefit with ACT was based on the prognostic score. Discounting at a 3% annual rate was applied to costs and QALYs. Deterministic one-way and probabilistic sensitivity analyses examined parameter uncertainty.

RESULTS

Lifetime costs and effectiveness were $137,403 and 5.45 QALYs with the prognostic test and $127,359 and 5.17 QALYs with the SoC. The resulting incremental cost-effectiveness ratio for the prognostic test versus the SoC was $35,867/QALY gained. One-way sensitivity analyses indicated the model was most sensitive to the utility of patients without recurrence after ACT and the ACT treatment benefit. Probabilistic sensitivity analysis indicated the prognostic test was cost-effective in 65.5% of simulations at a willingness to pay of $50,000/QALY.

CONCLUSION

The study suggests using a prognostic test to guide ACT decisions in early-stage NSCLC is potentially cost-effective compared with using the SoC based on globally accepted willingness-to-pay thresholds.

IMPLICATIONS FOR PRACTICE

Providing prognostic information to decision makers may help some patients with high-risk early stage non-small cell lung cancer receive appropriate adjuvant chemotherapy while avoiding the associated toxicities and costs in patients with low-risk disease. This study used an economic model to assess the effectiveness and costs associated with using a prognostic test to guide adjuvant chemotherapy decisions compared with the current standard of care in patients with non-small cell lung cancer. When compared with current standard care, the prognostic test was potentially cost effective at commonly accepted thresholds in the U.S. This study can be used to help inform decision makers who are considering using prognostic tests.

Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm

Cancer types are commonly classified by histopathology and more recently through molecular characteristics such as gene expression, mutations, copy number variations, and epigenetic alterations. These molecular characterizations have led to the proposal of prognostic biomarkers for many cancer types. Nevertheless, most of these biomarkers have been proposed for a specific cancer type or even specific subtypes. Although more challenging, it is useful to identify biomarkers that can be applied for multiple types of cancer. Here, we have used a network-based exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR1, PRKACA, LRP1, JUN and SMAD2 that can be predictive of clinical outcome in 12 types of cancer from The Cancer Genome Atlas (TCGA) repository. The gene signature of this biomarker is highly supported by cancer literature, biological terms, and prognostic power in other cancer types. Additionally, the signature does not seem to be highly associated with specific mutations or copy number alterations. Comparisons with cancer-type specific and other multi-cancer biomarkers in TCGA and other datasets showed that the performance of the proposed multi-cancer biomarker is superior, making the proposed approach and multi-cancer biomarker potentially useful in research and clinical settings.

Improving the Care of Patients With Stage IB Non-Small-Cell Lung Cancer: Role of Prognostic Signatures and Use of Cell Cycle Progression Biomarkers

Patients with non-small-cell lung cancer have relatively poor survival outcomes after surgery (overall 5-year survival rate < 50%). Adjuvant chemotherapy adds only a small incremental survival benefit (hazard ratio, 0.89) with a 5% improvement in 5-year survival. There is no proven benefit to adjuvant chemotherapy in stage 1A or 1B disease. However, for patients with stage IB disease, outcomes after chemotherapy have been mixed; therefore, additional risk stratification measures are needed to guide decision-making in this patient population. Several significant prognostic indicators have been identified, including the presence of poorly differentiated tumors, tumors > 4 cm, blood vessel invasion, visceral pleural invasion, and incomplete lymph node dissection. A new risk stratification tool based on the expression of cell cycle genes recently has become available. Assessment of cell cycle gene expression may provide useful prognostic and predictive data when considered along with existing prognostic indicators to help identify patients with a poor prognosis and highly proliferative disease who would benefit the most from adjuvant chemotherapy.

Grainyhead-like 2 Promotes Tumor Growth and is Associated with Poor Prognosis in Colorectal Cancer

GRHL2 was implicated in regulating cancer development. Our previous study demonstrated that knockdown GRHL2 in colorectal cancer (CRC) cells inhibited cell proliferation by targeting ZEB1. It is unclear whether GRHL2 expression may have diagnostic or prognostic value in colorectal carcinoma. Additionally, how GRHL2 is associated with the clinical features of colorectal carcinoma is not known. In current study, immunohistochemistry stains were performed to examine GRHL2 in 171 colorectal cancers and paired normal colon mucosa. The prognostic value of GRHL2 was investigated in a retrospective cohort study with a five-year follow-up. The effects of GRHL2 on cell growth in vitro and in vivo were explored by GRHL2 over-expressing in HT29 and SW620 CRC cells. Further, the regulation of cell cycle and proliferation proteins by GRHL2 were assessed by flow cytometry and western blot. We found that GRHL2 was over-expressed in CRC tissues, and played an important role in CRC tumorigenesis. GRHL2 expression positively correlated with tumor size and TNM stage. Kaplan-Meier analysis showed that GRHL2 was an independent prognostic factor for both overall survival and recurrence-free survival. Ectopic over-expression of GRHL2 in CRC cell line HT29 and SW620 induced an increase of cellular proliferation in vitro and promoting tumor growth in vivo. The acquisition of GRHL2 regulated cell cycle and modulates the expression of proliferation proteins p21, p27, cyclin A and cyclin D1. Together, our findings reveal GRHL2 can be used as a novel predictive biomarker and represent a potential therapeutic target against CRC.

The tumour hypoxia marker pimonidazole reflects a transcriptional programme associated with aggressive prostate cancer

Background:

The hypoxia marker pimonidazole is a candidate biomarker of cancer aggressiveness. We investigated the transcriptional programme associated with pimonidazole staining in prostate cancer.

Methods:

Index tumour biopsies were taken by image guidance from an investigation cohort of 52 patients, where 43 patients received pimonidazole before prostatectomy. Biopsy location within the index tumour was verified for 46 (88%) patients, who were included for gene expression profiling and immunohistochemistry. Two independent cohorts of 59 and 281 patients were used for validation.

Results:

Expression of genes in proliferation, DNA repair and hypoxia response was a major part of the transcriptional programme associated with pimonidazole staining. A signature of 32 essential genes was constructed and showed positive correlation to Ki67 staining, confirming the increased proliferation in hypoxic tumours as suggested from the gene data. Positive correlations were also found to tumour stage and lymph node status, but not to blood prostate-specific antigen level, consistent with the findings for pimonidazole staining. The association with aggressiveness was confirmed in validation cohorts, where the signature correlated with Gleason score and had independent prognostic impact, respectively.

Conclusions:

Pimonidazole staining reflects an aggressive hypoxic phenotype of prostate cancer characterised by upregulation of proliferation, DNA repair and hypoxia response genes.

Gene expression profiling in human lung development: an abundant resource for lung adenocarcinoma prognosis

A tumor can be viewed as a special “organ” that undergoes aberrant and poorly regulated organogenesis. Progress in cancer prognosis and therapy might be facilitated by re-examining distinctive processes that operate during normal development, to elucidate the intrinsic features of cancer that are significantly obscured by its heterogeneity. The global gene expression signatures of 44 human lung tissues at four development stages from Asian descent and 69 lung adenocarcinoma (ADC) tissue samples from ethnic Chinese patients were profiled using microarrays. All of the genes were classified into 27 distinct groups based on their expression patterns (named as PTN1 to PTN27) during the developmental process. In lung ADC, genes whose expression levels decreased steadily during lung development (genes in PTN1) generally had their expression reactivated, while those with uniformly increasing expression levels (genes in PTN27) had their expression suppressed. The genes in PTN1 contain many n-gene signatures that are of prognostic value for lung ADC. The prognostic relevance of a 12-gene demonstrator for patient survival was characterized in five cohorts of healthy and ADC patients [ADC_CICAMS (n = 69, p = 0.007), ADC_PNAS (n = 125, p = 0.0063), ADC_GSE13213 (n = 117, p = 0.0027), ADC_GSE8894 (n = 62, p = 0.01), and ADC_NCI (n = 282, p = 0.045)] and in four groups of stage I patients [ADC_CICAMS (n = 22, p = 0.017), ADC_PNAS (n = 76, p = 0.018), ADC_GSE13213 (n = 79, p = 0.02), and ADC_qPCR (n = 62, p = 0.006)]. In conclusion, by comparison of gene expression profiles during human lung developmental process and lung ADC progression, we revealed that the genes with a uniformly decreasing expression pattern during lung development are of enormous prognostic value for lung ADC.

Poly-omic prediction of complex traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

Prognostic value of gene signatures and proliferation in lymph-node-negative breast cancer

Introduction

The overall survival rate is good for lymph-node-negative breast cancer patients, but they still suffer from serious over- and some undertreatments. Prognostic and predictive gene signatures for node-negative breast cancer have a high number of genes related to proliferation. The prognostic value of gene sets from commercial gene-expression assays were compared with proliferation markers.

Methods

Illumina WG6 mRNA microarray analysis was used to examine 94 fresh-frozen tumour samples from node-negative breast cancer patients. The patients were divided into low- and high-risk groups for distant metastasis based on the MammaPrint-related genes, and into low-, intermediate- and high-risk groups based on the recurrence score algorithm with genes included in Oncotype DX. These data were then compared to proliferation status, as measured by the mitotic activity index, the expressions of phosphohistone H3 (PPH3), and Ki67.

Results

Kaplan-Meier survival analysis for distant-metastasis-free survival revealed that patients with weak and strong PPH3 expressions had 14-year survival rates of 87% (n = 45), and 65% (n = 49, p = 0.014), respectively. Analysis of the MammaPrint classification resulted in 14-year survival rates of 80% (n = 45) and 71% (n = 49, p = 0.287) for patients with low and high risks of recurrence, respectively. The Oncotype DX categorization yielded 14-year survival rates of 83% (n = 18), 79% (n = 42) and 68% (n = 34) for those in the low-, intermediate- and high-risk groups, respectively (p = 0.52). Supervised hierarchical cluster analysis for distant-metastasis-free survival in the subgroup of patients with strong PPH3 expression revealed that the genes involved in Notch signalling and cell adhesion were expressed at higher levels in those patients with distant metastasis.

Conclusion

This pilot study indicates that proliferation has greater prognostic value than the expressions of either MammaPrint- or Oncotype-DX-related genes. Furthermore, in the subgroup of patients with high proliferation, Notch signalling pathway genes appear to be expressed at higher levels in patients who develop distant metastasis.

PhysioSpace: relating gene expression experiments from heterogeneous sources using shared physiological processes

Relating expression signatures from different sources such as cell lines, in vitro cultures from primary cells and biopsy material is an important task in drug development and translational medicine as well as for tracking of cell fate and disease progression. Especially the comparison of large scale gene expression changes to tissue or cell type specific signatures is of high interest for the tracking of cell fate in (trans-) differentiation experiments and for cancer research, which increasingly focuses on shared processes and the involvement of the microenvironment. These signature relation approaches require robust statistical methods to account for the high biological heterogeneity in clinical data and must cope with small sample sizes in lab experiments and common patterns of co-expression in ubiquitous cellular processes. We describe a novel method, called PhysioSpace, to position dynamics of time series data derived from cellular differentiation and disease progression in a genome-wide expression space. The PhysioSpace is defined by a compendium of publicly available gene expression signatures representing a large set of biological phenotypes. The mapping of gene expression changes onto the PhysioSpace leads to a robust ranking of physiologically relevant signatures, as rigorously evaluated via sample-label permutations. A spherical transformation of the data improves the performance, leading to stable results even in case of small sample sizes. Using PhysioSpace with clinical cancer datasets reveals that such data exhibits large heterogeneity in the number of significant signature associations. This behavior was closely associated with the classification endpoint and cancer type under consideration, indicating shared biological functionalities in disease associated processes. Even though the time series data of cell line differentiation exhibited responses in larger clusters covering several biologically related patterns, top scoring patterns were highly consistent with a priory known biological information and separated from the rest of response patterns.

Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma

PURPOSE

New prognostic markers to guide treatment decisions in early stage non-small cell lung cancer are necessary to improve patient outcomes. In this report, we assess the utility of a predefined mRNA expression signature of cell-cycle progression genes (CCP score) to define 5-year risk of lung cancer-related death in patients with early stage lung adenocarcinoma.

EXPERIMENTAL DESIGN

A CCP score was calculated from the mRNA expression levels of 31 proliferation genes in stage I and stage II tumor samples from two public microarray datasets [Director's Consortium (DC) and GSE31210]. The same gene set was tested by quantitative PCR in 381 formalin-fixed paraffin-embedded (FFPE) primary tumors. Association of the CCP score with outcome was assessed by Cox proportional hazards analysis.

RESULTS

In univariate analysis, the CCP score was a strong predictor of cancer-specific survival in both the Director's Consortium cohort (P = 0.00014; HR = 2.08; 95% CI, 1.43-3.02) and GSE31210 (P = 0.0010; HR = 2.25; 95% CI, 1.42-3.56). In multivariate analysis, the CCP score remained the dominant prognostic marker in the presence of clinical variables (P = 0.0022; HR = 2.02; 95% CI, 1.29-3.17 in Director's Consortium, P = 0.0026; HR = 2.16; 95% CI, 1.32-3.53 in GSE31210). On a quantitative PCR platform, the CCP score maintained highly significant prognostic value in FFPE-derived mRNA from clinical samples in both univariate (P = 0.00033; HR = 2.10; 95% CI, 1.39-3.17) and multivariate analyses (P = 0.0071; HR = 1.92; 95% CI, 1.18-3.10).

CONCLUSIONS

The CCP score is a significant predictor of lung cancer death in early stage lung adenocarcinoma treated with surgery and may be a valuable tool in selecting patients for adjuvant treatment.

Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules

Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for using such signatures in the analysis of gene expression data produced by complex transcriptional regulatory programs. Our framework integrates ChIP-seq data and appropriately matched gene expression profiles to identify True REGulatory (TREG) TF-gene interactions. It provides genome-wide quantification of the likelihood of regulatory TF-gene interaction that can be used to either identify regulated genes, or as genome-wide signature of TF activity. To effectively use ChIP-seq data, we introduce a novel statistical model that integrates information from all binding "peaks" within 2 Mb window around a gene's transcription start site (TSS), and provides gene-level binding scores and probabilities of regulatory interaction. In the second step we integrate these binding scores and regulatory probabilities with gene expression data to assess the likelihood of True REGulatory (TREG) TF-gene interactions. We demonstrate the advantages of TREG framework in identifying genes regulated by two TFs with widely different distribution of functional binding events (ERα and E2f1). We also show that TREG signatures of TF activity vastly improve our ability to detect involvement of ERα in producing complex diseases-related transcriptional profiles. Through a large study of disease-related transcriptional signatures and transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures makes the crucial difference in identifying key targets for treatment, and drugs to use for treatment. All methods are implemented in an open-source R package treg. The package also contains all data used in the analysis including 494 TREG binding profiles based on ENCODE ChIP-seq data. The treg package can be downloaded at http://GenomicsPortals.org.

Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes

Background

The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data. It is, therefore, demanding to systematically compare independent validation of gene lists or classifiers based on metagene or individual gene (SG) features.

Methods

In this study we compared the performance of either metagene-or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

Results

MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms, while classifiers had a poorer performance when validated in strictly independent datasets. The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms.

Conclusion

Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

Predicting outcomes in radiation oncology--multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Proliferation and tissue remodeling in cancer: the hallmarks revisited

Although cancers are highly heterogeneous at the genomic level, they can manifest common patterns of gene expression. Here, we use gene expression signatures to interrogate two major processes in cancer, proliferation and tissue remodeling. We demonstrate that proliferation and remodeling signatures are partially independent and result in four distinctive cancer subtypes. Cancers with the proliferation signature are characterized by signatures of p53 and PTEN inactivation and concomitant Myc activation. In contrast, remodeling correlates with RAS, HIF-1α and NFκB activation. From the metabolic point of view, proliferation is associated with upregulation of glycolysis and serine/glycine metabolism, whereas remodeling is characterized by a downregulation of oxidative phosphorylation. Notably, the proliferation signature correlates with poor outcome in lung, prostate, breast and brain cancer, whereas remodeling increases mortality rates in colorectal and ovarian cancer.

Clinically relevant biomarkers in targeted radiotherapy

Three classic parameters have been recognized as predictors or biomarkers of radiation response: intrinsic radiosensitivity, degree of hypoxia and repopulation capacity of clonogenic cells during a course of fractionated radiation therapy. Although good functional assays exist to measure these tumor parameters, and their use has led to the understanding of factors affecting outcome after radiotherapy, their application in clinical practice is hampered by technical difficulties, the length of time needed to obtain results and the lack of prospective randomized clinical trials. Recently, with the progress in molecular biology, genome-wide screening methods have been used to look for genetic signatures that can distinguish between good and bad outcome after radiotherapy. One of the most promising candidates is the epidermal growth factor receptor which is overexpressed or mutated in a variety of malignancies, such lung and head and neck cancer. Inhibition of this receptor has led to radio-sensitization with the prolongation of median survival in several cancers. Since there is significant variability in the response of patients with the same disease to radiotherapy, it would be very valuable to be able to predict which patients would benefit from a molecularly targeted therapy administered with concomitant radiation in order to increase the response rate (and cure rate) of those patients with radioresistant tumors. Optimally, this assay should be able to provide results in an efficient and reproducible manner and detect tumor genetic mutations that would provide specificity to the intervention. One approach currently in clinical practice to overcome intrinsic radioresistance and repopulation is stereotactic body radiotherapy coupled with image-guided radiation, a highly precise and powerful form of radiation, allowing radiation oncologist to treat tumors with more aggressive biological doses of radiation without causing serious normal tissues injury.

Predicting recurrence after radiotherapy in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Radiotherapy is a mainstay of treatment, either alone for early stage tumors or combined with chemotherapy for late stage tumors. An overall 5-year survival rate of around 50% for HNSCC demonstrates that treatment is often unsuccessful. Prediction of outcome is, therefore, aimed at sparing patients from ineffective and toxic treatments on the one hand, and indicating more successful treatment modalities on the other. Both functional and genetic assays have been developed to predict intrinsic radiosensitivity, hypoxia, and repopulation rate. Few, however, have shown consistent correlations with outcome across multiple studies. Messenger RNA and microRNA profiling show promise for predicting hypoxia, whereas epidermal growth factor receptor expression combined with other measures of tumor differentiation grade shows promise for predicting repopulation rate. Intrinsic radiosensitivity assays have not proven useful to date, although development of repair protein foci assays indicates promise from preclinical studies. Assays for cancer stem cell content have shown promise in several clinical studies. In addition, 2 assays showing robustness as predictors for outcome in HNSCC are human papilloma virus status and epidermal growth factor receptor expression. Neither these nor stem cell assays, however, can as yet reliably indicate alternative and better treatments for poor prognosis patients. It would be of great value to have assays that predict the benefit for an individual from combining new molecularly targeted agents with radiotherapy to increase response, in particular those that exploit tumor mutations to provide tumor specificity. Predictive assays are being developed for detecting defects in repair pathways for single- and double-strand DNA breaks, which should allow selection of drugs targeting the appropriate backup pathway, thus exploiting the concept of synthetic lethality. This is one of the most promising areas for prediction, both currently and in the future.

Independent and functional validation of a multi-tumour-type proliferation signature

Background:

Previously we demonstrated that an mRNA signature reflecting cellular proliferation had strong prognostic value. As clinical applicability of signatures can be controversial, we sought to improve our marker’s clinical utility by validating its biological relevance, reproducibility in independent data sets and applicability using an independent technique.

Methods:

To facilitate signature evaluation with quantitative PCR (qPCR) a novel computational procedure was used to reduce the number of signature genes without significant information loss. These genes were validated in different human cancer cell lines upon serum starvation and in a 168 xenografts panel. Analyses were then extended to breast cancer and non-small-cell lung cancer (NSCLC) patient cohorts.

Results:

Expression of the qPCR-based signature was dramatically decreased under starvation conditions and inversely correlated with tumour volume doubling time in xenografts. The signature validated in breast cancer (hazard ratio (HR)=1.63, P<0.001, n=1820) and NSCLC adenocarcinoma (HR=1.64, P<0.001, n=639) microarray data sets. Lastly, qPCR in a node-negative, non-adjuvantly treated breast cancer cohort (n=129) showed that patients assigned to the high-proliferation group had worse disease-free survival (HR=2.25, P<0.05).

Conclusion:

We have developed and validated a qPCR-based proliferation signature. This test might be used in the clinic to select (early-stage) patients for specific treatments that target proliferation.

A comprehensive analysis of human gene expression profiles identifies stromal immunoglobulin κ C as a compatible prognostic marker in human solid tumors

PURPOSE

Although the central role of the immune system for tumor prognosis is generally accepted, a single robust marker is not yet available.

EXPERIMENTAL DESIGN

On the basis of receiver operating characteristic analyses, robust markers were identified from a 60-gene B cell-derived metagene and analyzed in gene expression profiles of 1,810 breast cancer; 1,056 non-small cell lung carcinoma (NSCLC); 513 colorectal; and 426 ovarian cancer patients. Protein and RNA levels were examined in paraffin-embedded tissue of 330 breast cancer patients. The cell types were identified with immunohistochemical costaining and confocal fluorescence microscopy.

RESULTS

We identified immunoglobulin κ C (IGKC) which as a single marker is similarly predictive and prognostic as the entire B-cell metagene. IGKC was consistently associated with metastasis-free survival across different molecular subtypes in node-negative breast cancer (n = 965) and predicted response to anthracycline-based neoadjuvant chemotherapy (n = 845; P < 0.001). In addition, IGKC gene expression was prognostic in NSCLC and colorectal cancer. No association was observed in ovarian cancer. IGKC protein expression was significantly associated with survival in paraffin-embedded tissues of 330 breast cancer patients. Tumor-infiltrating plasma cells were identified as the source of IGKC expression.

CONCLUSION

Our findings provide IGKC as a novel diagnostic marker for risk stratification in human cancer and support concepts to exploit the humoral immune response for anticancer therapy. It could be validated in several independent cohorts and carried out similarly well in RNA from fresh frozen as well as from paraffin tissue and on protein level by immunostaining.

Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer

The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.

Cell-based models provide a convenient system to conduct studies that would be impossible to apply to human subjects, but the phenotypes measured on these models can be marred with biological noise. We propose a method (MEM) to address this issue by statistically combining data from various sources, and we apply it to the proliferation rates of cell lines collected as part of the International HapMap project. We show that the proliferation rate computed using our method is a better measure of the true proliferation rate of the cells and produces a much stronger association with gene expression phenotypes on the same cell lines: more than 30% of the genes tested were significantly associated with proliferation rate. We also demonstrate that genetic variants have an effect on growth rate. Finally, we make these intrinsic proliferation rates and the strength of the association with gene expression phenotypes public, which should allow other researchers to explore the mediating effects of proliferation on other phenotypes.

A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients

Introduction

The classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures.

Methods

We thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes).

Results

The DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis.

Conclusion

This finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.

Development and external validation of a predictive model for pathological complete response of rectal cancer patients including sequential PET-CT imaging

PURPOSE

To develop and validate an accurate predictive model and a nomogram for pathologic complete response (pCR) after chemoradiotherapy (CRT) for rectal cancer based on clinical and sequential PET-CT data. Accurate prediction could enable more individualised surgical approaches, including less extensive resection or even a wait-and-see policy.

METHODS AND MATERIALS

Population based databases from 953 patients were collected from four different institutes and divided into three groups: clinical factors (training: 677 patients, validation: 85 patients), pre-CRT PET-CT (training: 114 patients, validation: 37 patients) and post-CRT PET-CT (training: 107 patients, validation: 55 patients). A pCR was defined as ypT0N0 reported by pathology after surgery. The data were analysed using a linear multivariate classification model (support vector machine), and the model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

RESULTS

The occurrence rate of pCR in the datasets was between 15% and 31%. The model based on clinical variables (AUC(train)=0.61±0.03, AUC(validation)=0.69±0.08) resulted in the following predictors: cT- and cN-stage and tumour length. Addition of pre-CRT PET data did not result in a significantly higher performance (AUC(train)=0.68±0.08, AUC(validation)=0.68±0.10) and revealed maximal radioactive isotope uptake (SUV(max)) and tumour location as extra predictors. The best model achieved was based on the addition of post-CRT PET-data (AUC(train)=0.83±0.05, AUC(validation)=0.86±0.05) and included the following predictors: tumour length, post-CRT SUV(max) and relative change of SUV(max). This model performed significantly better than the clinical model (p(train)<0.001, p(validation)=0.056).

CONCLUSIONS

The model and the nomogram developed based on clinical and sequential PET-CT data can accurately predict pCR, and can be used as a decision support tool for surgery after prospective validation.

Mature results of an individualized radiation dose prescription study based on normal tissue constraints in stages I to III non-small-cell lung cancer

PURPOSE

We previously showed that individualized radiation dose escalation based on normal tissue constraints would allow safe administration of high radiation doses with low complication rate. Here, we report the mature results of a prospective, single-arm study that used this individualized tolerable dose approach.

PATIENTS AND METHODS

In total, 166 patients with stage III or medically inoperable stage I to II non-small-cell lung cancer, WHO performance status 0 to 2, a forced expiratory volume at 1 second and diffusing capacity of lungs for carbon monoxide >or= 30% were included. Patients were irradiated using an individualized prescribed total tumor dose (TTD) based on normal tissue dose constraints (mean lung dose, 19 Gy; maximal spinal cord dose, 54 Gy) up to a maximal TTD of 79.2 Gy in 1.8 Gy fractions twice daily. Only sequential chemoradiation was administered. The primary end point was overall survival (OS), and the secondary end point was toxicity according to Common Terminology Criteria of Adverse Events (CTCAE) v3.0.

RESULTS

The median prescribed TTD was 64.8 Gy (standard deviation, +/- 11.4 Gy) delivered in 25 +/- 5.8 days. With a median follow-up of 31.6 months, the median OS was 21.0 months with a 1-year OS of 68.7% and a 2-year OS of 45.0%. Multivariable analysis showed that only a large gross tumor volume significantly decreased OS (P < .001). Both acute (grade 3, 21.1%; grade 4, 2.4%) and late toxicity (grade 3, 4.2%; grade 4, 1.8%) were acceptable.

CONCLUSION

Individualized prescribed radical radiotherapy based on normal tissue constraints with sequential chemoradiation shows survival rates that come close to results of concurrent chemoradiation schedules, with acceptable acute and late toxicity. A prospective randomized study is warranted to further investigate its efficacy.

The use of a comprehensive tumour xenograft dataset to validate gene signatures relevant for radiation response

PURPOSE

To investigate the use of xenograft models in a novel gene signature validation method using gene expression microarrays.

MATERIALS AND METHODS

Gene expression profiles of ten human Head and Neck squamous cell carcinomas (HNSCCs) were obtained. Several published prognostic gene expression signatures were evaluated within this set. These consisted of different radiotherapy relevant signatures (i.e. for hypoxia, proliferation and 'stemness'). Signatures were correlated with various endpoints that have been determined in the ten different xenograft models. These include immunohistochemical measures for hypoxia and proliferation, volume doubling time (VDT) and local tumour control after fractionated irradiation or after single dose irradiation under clamp hypoxia.

RESULTS

We found several significant correlations between the published gene expression signatures and tumour parameters. Several signatures, like the proliferation and wound signature correlated with BrdU labelling index. Further a 'stemness'-related gene signature showed a strong negative correlation with hypoxic fraction.

CONCLUSIONS

Simultaneous assessment of immunohistochemistry, in vivo tumour properties and gene expression profiling in a comprehensive set of xenograft models can be used to validate and potentially infer biological information about prognostic gene signatures.