Gene Risk Signature Research Articles

Development and verification of a nomogram for prediction of recurrence-free survival in clear cell renal cell carcinoma.

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.

Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk

BackgroundBreast cancer (BC) risk assessment models are statistical estimates based on patient characteristics. We developed a gene expression assay to assess BC risk using benign breast biopsy tissue. MethodsA NanoString-based malignancy risk (MR) gene signature was validated for formalin-fixed paraffin-embedded (FFPE) tissue. It was applied to FFPE benign and BC specimens obtained from women who underwent breast biopsy, some of whom developed BC during follow-up to evaluate diagnostic capability of the MR signature. BC risk was calculated with MR score, Gail risk score, and both tests combined. Logistic regression and receiver operating characteristic curves were used to evaluate these 3 models. ResultsNanoString MR demonstrated concordance between fresh frozen and FFPE malignant samples (r = 0.99). Within the validation set, 563 women with benign breast biopsies from 2007 to 2011 were identified and followed for at least 5 y; 50 women developed BC (affected) within 5 y from biopsy. Three groups were compared: benign tissue from unaffected and affected patients and malignant tissue from affected patients. Kruskal–Wallis test suggested difference between the groups (P = 0.09) with trend in higher predicted MR score for benign tissue from affected patients before development of BC. Neither the MR signature nor Gail risk score were statistically different between affected and unaffected patients; combining both tests demonstrated best predictive value (AUC = 0.71). ConclusionsFFPE gene expression assays can be used to develop a predictive test for BC. Further investigation of the combined MR signature and Gail Model is required. Our assay was limited by scant cellularity of archived breast tissue.

Clear-cell Renal Cell Carcinoma: Molecular Characterization of IMDC Risk Groups and Sarcomatoid Tumors.

Recent trials have suggested predictive biomarkers in advanced clear-cell renal cell carcinoma (accRCC): International Metastatic RCC Database Consortium (IMDC) good risk or angiogenic gene signature for sunitinib and IMDC intermediate/poor risk for ipilimumab-nivolumab and T-effector cell signature or sarcomatoid dedifferentiation for atezolizumab-bevacizumab. We hypothesized that earlier described molecular subtypes, ccrcc1 to ccrcc4, could provide similar information as a single generic biomarker and molecularly characterize the heterogeneous intermediate-risk group. Patients with accRCC treated with systemic therapies were included. We assessed associations between the 5 biomarkers and their impact on progression-free survival (PFS) and response rate (RR) on first-line sunitinib or pazopanib. The cutoff percentage of sarcomatoid dedifferentiation with optimal discriminative value was determined. In total, 430 patients were included (163 with molecular data). The molecular ccrcc2 subtype identified tumors with higher angiogenic gene expression across IMDC risk groups: prevalence was high in IMDC good risk and low in IMDC poor risk (P< .001). Molecular subtype, IMDC, and angiogenic gene expression had comparable C-indices to predict PFS and RR (range, 60%-66%). The ccrcc2 subtype and angiogenic gene expression were positive predictors of PFS in IMDC intermediate-risk patients (P= .006; P=.04). Immune signature did not differ between IMDC groups, but was strongly correlated with molecular subtype (P= .8 and P= .0007). A cutoff value of 25% sarcomatoid differentiation discriminated tumors with distinct molecular characteristics and therapeutic sensitivity. In accRCC, molecular subtypes can explain differences in IMDC risk group, expression of angiogenesis and immune response genes, and sarcomatoid dedifferentiation. They can identify molecularly different patient populations within the heterogeneous IMDC intermediate group and select patients for systemic therapies.

1p19q Gene Transcription Profiles Closely Correlated to Malignancy and Prognosis of 1p/19q Non-Codeletion Gliomas

Background: Co-deletion of chromosome arms 1p and 19q in oligodendroglial tumors is associated with chemosensitivity and favorable prognosis. Here, we explored the prognostic value of the expression of a panel of 1p19q genes in 1p/19q non-codeletion gliomas. Methods: We analyzed expression of 1p19q genes in 668 1p/19q non-codel gliomas obtained from The Cancer Genome Atlas (TCGA) (n = 447) and the Chinese Glioma Genome Atlas (CGGA) (n = 221) for training and validation, respectively. Least Absolute Shrinkage and Selection Operator Cox regression analysis was applied to build a gene risk signature. Results: Expression of 1p19q genes was significantly associated with the overall survival of patients with 1p/19q non-codel gliomas. We derived a risk signature of 25 selected 1p19q genes that successfully stratified the overall survival of 1p/19q non-codel glioma patients in both the TCGA and CGGA datasets into high- and low-risk groups (both P < 0.0001). Bioinformatics analysis revealed that the signature genes were significantly enriched in cell division, extracellular matrix, angiogenesis, DNA damage repair, and immune or inflammatory response processes, and these were supported by the immunohistochemistry. The risk signature not only predicted the chemoradiotherapeutic outcomes of glioblastoma and WHO grade III glioma patients but was also an independent prognostic marker for patients with 2016 WHO integrated diagnosis glioma subtypes. Conclusion: A risk signature consisting of 25 1p19q genes identified high-risk and low-risk categories of 1p/19q non-codel gliomas. This signature may be helpful for developing potential treatments strategies for these patients. Funding Statement: This work was supported by the National Natural Science Foundation of China (81773208, 81402052); the Beijing Nova Program (Z16110004916082); the National Key Research and Development Plan (2016YFC0902500). Declaration of Interests: The authors declare: none. Ethics Approval Statement: This study was approved by the institutional review board of Beijing Tiantan Hospital.

Molecular Liver Cancer Prevention in Cirrhosis by Organ Transcriptome Analysis and Lysophosphatidic Acid Pathway Inhibition

Cirrhosis is a milieu that develops hepatocellular carcinoma (HCC), the second most lethal cancer worldwide. HCC prediction and prevention in cirrhosis are key unmet medical needs. Here we have established an HCC risk gene signature applicable to all major HCC etiologies: hepatitis B/C, alcohol, and non-alcoholic steatohepatitis. A transcriptome meta-analysis of >500 human cirrhotics revealed global regulatory gene modules driving HCC risk and the lysophosphatidic acid pathway as a central chemoprevention target. Pharmacological inhibition of the pathway invivo reduced tumors and reversed the gene signature, which was verified in organotypic exvivo culture of patient-derived fibrotic liver tissues. These results demonstrate the utility of clinical organ transcriptome to enable a strategy, namely, reverse-engineering precision cancer prevention.

Genetic Risk Profiling and Gene Signature Modeling to Predict Risk of Complications After IPAA

Severe pouchitis and Crohn's disease-like complications are 2 adverse postoperative complications that confound the success of the IPAA in patients with ulcerative colitis. To date, approximately 83 single nucleotide polymorphisms within 55 genes have been associated with IBD. The aim of this study was to identify single-nucleotide polymorphisms that correlate with complications after IPAA that could be utilized in a gene signature fashion to predict postoperative complications and aid in preoperative surgical decision making. One hundred forty-two IPAA patients were retrospectively classified as "asymptomatic" (n = 104, defined as no Crohn's disease-like complications or severe pouchitis for at least 2 years after IPAA) and compared with a "severe pouchitis" group (n = 12, ≥ 4 episodes pouchitis per year for 2 years including the need for long-term therapy to maintain remission) and a "Crohn's disease-like" group (n = 26, presence of fistulae, pouch inlet stricture, proximal small-bowel disease, or pouch granulomata, occurring at least 6 months after surgery). Genotyping for 83 single-nucleotide polymorphisms previously associated with Crohn's disease and/or ulcerative colitis was performed on a customized Illumina genotyping platform. The top 2 single-nucleotide polymorphisms statistically identified as being independently associated with each of Crohn's disease-like and severe pouchitis were used in a multivariate logistic regression model. These single-nucleotide polymorphisms were then used to create probability equations to predict overall chance of a positive or negative outcome for that complication. The top 2 single-nucleotide polymorphisms for Crohn's disease-like complications were in the 10q21 locus and the gene for PTGER4 (p = 0.006 and 0.007), whereas for severe pouchitis it was NOD2 and TNFSF15 (p = 0.003 and 0.011). Probability equations suggested that the risk of these 2 complications greatly increased with increasing number of risk alleles, going as high as 92% for severe pouchitis and 65% for Crohn's disease-like complications. In this IPAA patient cohort, mutations in the 10q21 locus and the PTGER4 gene were associated with Crohn's disease-like complications, whereas mutations in NOD2 and TNFSF15 correlated with severe pouchitis. Preoperative genetic analysis and use of such gene signatures hold promise for improved preoperative surgical patient selection to minimize these IPAA complications.

Prognostic Impact of Genetic Subgroups and Development of Gene Classifiers for Response, PFS and OS In Multiple Myeloma Patients Treated with Bortezomib or Conventional Agents In HOVON65/GMMG-HD4 Trial

AbstractAbstract 445 Background.In newly diagnosed myeloma patients, bortezomib treatment induces high rates of complete response (CR) and very good partial response (VGPR). Recently, we published the clustering of gene expression profiles in 320 MM patients, who were included in a large prospective, randomized, phase III transplantation trial with bortezomib (PAD) versus conventional vincristine (VAD) based induction treatment (HOVON65/GMMG-HD4). We identified 12 distinct subgroups CD-1, CD-2, MF, MS, PR, HY, LB, Myeloid, including three novel defined subgroups NFκB, CTA, and PRL3 and a subgroup with no clear gene expression profile (NP). Aim.To look at the prognostic impact of these 12 clusters in the trial and group clusters together into a high risk (HR) and low risk (LR) group in the different treatment arms. Furthermore, to define a high risk signature to identify the patients at increased risk of disease progression. Methods.Gene expression profiles of myeloma cells obtained at diagnosis of 320 HOVON65/GMMG-HD4 patients were available. Response, progression free survival (PFS) and overall survival (OS) data were available for the first 628 patients, resulting in analysis of gene expression in relation to prognosis in 229 patients. The prognostic impact of the genetic subgroups separately and grouped into high and low risk were evaluated using Kaplan Meier and Cox regression analysis using exhaustive search (R). For the high risk gene signature the HOVON65 gene expression data was used as training set with PFS as outcome measure. Two independent myeloma datasets with survival data were used as an external validation, UAMS (GSE2658) and APEX (GSE9782)). The signature was generated by a Cox proportional hazard model in combination with LASSO (Least Absolute Shrinkages and Selection Operator) for simultaneous parameter estimation and variable selection using the R package glmnet. ISS stage was implemented by adjusting the individual covariant penalization factors of the LASSO. Results.The highest CR+nCR rates were found in the PRL3 and NP clusters, i.e. 78% and 86%, respectively (VAD), and 100% (PAD). The lowest CR+nCR rate was 17% in the CD1 cluster (PAD) and 0% in the CD2, MF and PR clusters (VAD). Based on the impact of clusters on PFS and OS in the VAD arm, the MS, MF, PR and CTA clusters were included into a High Risk (HR) group. This HR group showed a median PFS of 13 months and OS of 21 months vs. the Low Risk (LR) group consisting of the remainder of clusters with a median PFS of 31 months and a median OS not reached (P<.001).In contrast, in the PAD arm, only the PR cluster conferred a poor prognosis and exclusively formed the HR group, showing a median PFS of 12 months and OS of 13 months vs. the LR groups consisting of the remainder of clusters with a median PFS of 32 months and a median OS not reached (P=.004). In the poor prognostic subgroup MF, a striking difference in outcome on PAD vs. VAD was observed, i.e. 24 vs. 3 months (PFS) and 39 vs. 4 months (OS), respectively. In the multivariate analysis, including the covariates recurrent translocations, 17p deletion, 13q loss, LDH and ISS stage, the HR group definition remained independent poor prognostic indicators for both treatment arms. A LASSO-based high-risk signature was generated (48 probe sets). External validation was performed in the data sets UAMS TT2 and APEX. In both studies, the high-risk signature identified a high-risk population of 13% with highly significant log rank p-values (7.7*10−8 and 4.2*10−9, respectively). Combination of all validation data, n=615, corrected for type of treatment and study, resulted in an overall Cox proportional hazard model's hazard ratio of 3.8 with p <2*10−16. Conclusion.Distinctive gene expression clusters affect prognosis, which differ depending on treatment. In the conventional treatment arm (VAD), MS, MF, PR and CTA clusters confer a worse prognosis while with bortezomib based treatment, only the PR cluster affects prognosis negatively. These HR groups remain independent poor prognostic indicators. Based on the HOVON65/GMMG-HD4 study population, a high-risk signature was generated with strong and highly significant predicting ability in two independent data sets. Disclosures:Mulligan:Millenium: Employment. Goldschmidt:Celgene: Membership on an entity's Board of Directors or advisory committees; Ortho Biotech: Membership on entity's Board of Directors or advisory committees; Ortho Biotech: Research Funding; Celgene: Research Funding; Chugai Pharma: Research Funding; Amgen: Research Funding. Sonneveld:Ortho Biotech: Research Funding; International Myeloma Foundation: Research Funding; Ortho Biotech: Consultancy.

Prediction of Metastasis in Node-Negative, Hormone Receptor Positive, Tamoxifen Adjuvant Treated Primary Breast Cancer Patients Using Ki-67 (IHC) and RT-PCR Based 14-Gene Prognostic Signature.

Abstract Background: Risk estimation based on the recommendations of St. Gallen Consensus commonly decides for additional cytostatic therapy in node-negative (N-), hormone receptor positive (HR+) primary breast cancer patients. High proliferative activity in the HR+ subtype confers a 19-fold relative risk of relapse compared with HR+ tumors of low proliferative activity. Furthermore immunohistochemically determined Ki-67 is characterized as a Luminal B marker that identifies a high risk subgroup in HR+, N- breast cancer patients.Aim of this investigation was to compare risk estimation using Ki-67 (%) with the results of a RT-PCR based multi-gene prognostic signature. Methods: Tumor tissues of totally 321 unselected primary breast cancer patients were formalin fixed and routinely processed for immunohistochemical determination and scored for protein expression of ER, PgR and Ki-67. HER2 was determined using dd-PCR. Content of both hormone receptors and proliferation activity were evaluated counting positively coloured nuclei from at least 100 tumor cells. The median follow up was 61 months. To calculate the cut off for high proliferation Ki-67 values were subjected to a log rank CART analysis. High proliferation was defined by Ki-67 of 19% and more. With this cut off it was possible to distinguish significantly various patient cohorts (N-, pT1, postmenopausal) into two different risk groups regarding metastasis free survival. The definition of low risk was low proliferation and HER2 negativity, of high risk high proliferation or HER2 positivity. The risk for metastasis of 16 new primary breast cancer patients was estimated based on two surrogate risk groups and the multi-gene prognostic signature. Results: Tamoxifen adjuvant treated HR+, HER2-, low proliferating (Ki-67 &amp;lt;19%), N- low risk patients (N= 68) and HR+, HER2-, high proliferating/HR+, HER2+, N- high risk patients (N= 84) showed a recurrence rate of 2.94% and 27.4%, respectively (P&amp;lt; .000). In the ongoing observation study all 5 low proliferating HER2- tumors had a low risk gene prognostic signature. From 11 high proliferating HER2- patients one patient had a low risk gene signature (9%), 3 patients a moderate risk gene signature (27%) and 7 patients a high risk gene signature (64%). Conclusion: Despite the small number of patients investigated up to now (N= 16) the preliminary results appear to show that low proliferative activity is associated with a low risk gene prognostic signature, whereas high proliferation means only in part a high risk gene prognostic signature. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6025.

Early determination of metastatic potential in breast cancer: The 70-gene signature in small tumors

518 Background: Small breast cancers are often considered as low risk tumors in clinico-pathologic risk assessment and treatment guidelines. However, since the ability to metastasize is an early and inherent genetic property of breast cancer and consequently even small tumors can metastasize, identification of prognostic subgroups for patients with small tumors would help to optimize treatment strategies. In the present study the 70-gene prognosis signature (MammaPrint), validated as independent prognostic indicator for node-negative and positive patients, was used to assess prognosis for pT1 breast cancer. Methods: 965 tumor samples of women with pT1 breast cancer from 7 previously reported studies were analyzed and classified by MammaPrint as good or poor prognosis. 10-year distant disease-free survival (DDFS) and breast cancer-specific survival (BCSS) was analyzed according to the 70-gene prognosis signature separately for pT1a/b (1–10mm) and pT1c (11–20mm) tumors. Results: Median follow-up was 7.1 years (range 0.2–25.2). 526 of 965 patients (55%) were classified as good prognosis and 439 (45%) as poor prognosis by the 70-gene signature. 562 patients (59%) received no adjuvant treatment, 19% received hormonal treatment only, 10% chemotherapy only and 12% both respectively. The 70-gene signature accurately predicted differences in 10-year DDFS (HR 2.7 [1.9–3.9], p&lt;0.01) and BCSS (HR 4.0 [2.6–6.3], p&lt;0.01) for all T1 tumors. Similar results were obtained by multivariate analysis for all patients, adjusted for known prognostic factors and adjuvant therapy, as well as for adjuvant untreated patients. At 10 years, for small pT1a/b tumors (n=140), DDFS was 93% vs. 78% for the good and poor prognosis groups (HR 3.9 [1.0–15.2], p=0.048), while in the T1c group (n=825) DDFS was 86% vs. 72% (HR 2.6 [1.8–4.0], p&lt;0.01). BCSS was 87% vs. 73% for the good and poor prognosis groups in the T1a/b group (HR 2.4 [0.8–7.7], p=0.128) and 92% vs. 72% (HR 4.4 [2.7–7.1], p&lt;0.01) in T1c tumors, respectively. Conclusions: The 70-gene signature is a strong and independent prognostic indicator also in small breast tumors. Patients with T1a/b or T1c tumors and a genomic high risk gene signature for developing distant metastases may be selected for additional adjuvant hormonal and chemotherapy. [Table: see text]

Evaluation of malignancy-risk gene signature in breast cancer patients

We recently developed a malignancy-risk gene signature that was shown to identify histologically-normal tissues with a cancer-like profile. Because the signature was rich with proliferative genes, we postulated it might also be prognostic for existing breast cancers. We evaluated the malignancy risk gene signature to see its clinical association with cancer relapse/progression, and cancer prognosis using six independent external datasets. Six independent external breast cancer datasets were collected and analyzed using the malignancy risk gene signature designed to assess normal breast tissues. Evaluation of the signature in external datasets suggested a strong clinical association with cancer relapse/progression, and prognosis with minimal overlap of signature gene sets. These results suggest a prognostic role for the malignancy risk gene signature in the assessment of existing cancer. Proliferative biology dominates not only the earliest stages of tumor development but also later stages of tumor progression and metastasis.