丝氨酸蛋白酶抑制剂(SERPIN)家族相关基因在结肠腺癌中预后模型的建立及应用

应用生物信息学方法,构建结肠腺癌(COAD)丝氨酸蛋白酶抑制剂(SERPIN)家族相关基因预后模型。从TCGA数据库和GEO数据库下载结肠腺癌(COAD)转录组和临床数据,根据数据中SERPINs家族基因的表达量对COAD患者进行一致性聚类分析；将数据随机均分为训练集(Train)组和验证集(Test)组,基于两个亚型的差异基因,利用Train组进行COX回归和Lasso回归构建预后模型,根据模型风险评分中位值将样本分为高、低风险两组,绘制高低风险组患者生存曲线；通过ROC曲线评价模型预测能力；利用Test组数据验证模型；构建列线图,评估患者生存率模型预测值与实际值的一致性；并利用利用ESTIMATE算法和CIBERSORT算法评估风险评分和肿瘤微环境(TME)以及免疫浸润的相关性。通过34个SERPIN基因确定了两个亚型,基于2个亚型筛选出了436个预后相关分型差异基因,通过Lasso回归确定出了11个预后相关基因参与风险模型的构建,根据模型评分区分的高低风险组具有明显的生存差异,列线图可以准确预测1、3和5年生存率。肿瘤微环境分析和免疫浸润分析显示高风险评分组患者免疫活性差。SERPIN家族相关基因构建的风险评分模型能够预测COAD的预后,有利于进一步指导临床对COAD的诊治,提高患者生存率。     

癌症DNA甲基化调控位点的识别

DNA甲基化是一种重要的表观遗传学修饰,在基因的转录调控方面具有重要的作用。异常的DNA甲基化可以导致癌症等复杂疾病发生,癌基因相关的DNA甲基化调控位点的识别对于解析癌症的发生发展机制及识别新的癌症标记具有重要意义。本研究通过整合The Cancer Genome Atlas(TCGA)的泛癌症基因组的高通量甲基化谱和基因表达谱,识别癌基因相关的DNA甲基化调控位点。对于每种癌症分批次计算Cp G位点甲基化与相关基因表达之间的相关性,并筛选调控下游基因的Cp G位点(包括强调控位点、弱调控位点和不调控位点),结果表明仅有一半的Cp G位点对下游基因具有调控作用;对癌症间共享的调控位点的分析发现不同癌症间共享的调控位点不尽相同,表明癌症特异的甲基化调控位点的存在。进一步地,对差异甲基化和差异表达基因的功能富集分析揭示了受甲基化调控的基因确实参与了癌症发生发展相关的功能。本研究的结果是对当前甲基化调控位点集的重要补充,也是识别癌症新型分子标记特征的重要资源。     

外周全血中与老化相关的DNA甲基化标记的来源

机体老化与癌症、神经退行性疾病等许多复杂疾病相关。目前,研究者已在外周全血中识别了大量的与老化相关的DNA甲基化标记,这些标记可能反映外周血白细胞在机体老化过程中发生的变化,也可能反映外周血中与年龄相关的细胞构成比例的变化。文章利用3组正常个体外周全血DNA甲基化谱,采用Spearman秩相关分析识别了与老化相关的CpG甲基化位点(age-related DNA methylation CpG sites, arCpGs)并评价了其可重复性;利用去卷积算法估计了各外周血样本中髓性和淋巴性细胞的比例并分析了其与年龄的相关性;比较了在外周全血、CD4+T细胞和CD14+单核细胞中识别的arCpGs的一致性。结果显示,在独立外周全血数据中识别的arCpGs具有显著的可重复性(超几何检验,P=1.65×10^-11)。外周血髓性和淋巴性细胞的比例分别与年龄显著正、负相关(Spearman秩相关检验,P<0.05,r≤0.22),它们间DNA甲基化水平差异较大的CpG位点倾向于在外周全血中被识别为arCpGs。在CD4+T细胞中识别的arCpGs与在外周全血中识别的arCpGs显著交叠(超几何检验,P=6.14×10^-12),且99.1%的交叠位点在CD4+T细胞及外周全血中的DNA甲基化水平与年龄的正、负相关性一致。尽管在CD14+单核细胞中识别的arCpGs与在外周全血中识别的arCpGs并不显著交叠,但是在交叠的51个arCpGs中,有90.1%的位点在CD14+单核细胞、外周全血以及CD4+T细胞中的DNA甲基化水平与年龄的正、负相关性一致,提示它们可能主要反映细胞间共同的改变。在外周全血中识别的arCpGs主要反映某些白细胞共同或特异的DNA甲基化改变,但是也有一部分反映外周血细胞比例构成的变化。     

结合自噬相关基因表达谱和临床因素的肺腺癌预后预测模型

对肺腺癌自噬相关基因进行生物信息学分析,结合多基因预后标志和临床参数构建能够预测肺腺癌患者预后的模型。首先,对TCGA肺腺癌数据中的938个自噬相关基因进行差异分析,获得了82个差异自噬相关基因,使用单因素Cox比例风险回归模型从差异自噬相关基因中筛选出候选基因,通过 lasso回归进一步筛选出预后相关基因,分别是ARNTL2、NAPSA、ATG9B、CAPN12、MAP1LC3C和KRT81。通过多因素Cox回归分析以构建风险评分模型,根据最优cutoff值将患者分为高低风险组,生存曲线显示高低风险组之间生存差异显著,ROC曲线显示风险评分的预测能力良好,并在内、外验证集中得到验证。同时对传统的临床因素进行单因素和多因素Cox回归分析,结果显示Stage、复发和风险评分能够独立预测预后,结合这三个独立的预后参数以构建列线图模型,使用一致性指数、校准曲线评估列线图的预测能力,结果显示预测结果与实际结果之间具有良好的一致性。通过与Stage和风险评分的比较发现,列线图的预测能力表现最佳。基于肺腺癌相关的自噬基因和临床参数构建了一个列线图模型来预测肺腺癌患者的预后生存,这可能为临床医生提供了一种可靠的预后评估工具。     

肺腺癌预后相关miRNA生物信息学筛选及其临床意义

目的：探讨肺腺癌预后相关miRNA组学特征及其临床意义。方法：应用癌症基因组图谱（TCGA）数据库,检索人肺腺癌miRNA表达谱数据,进行差异分析,再利用Cox风险回归模型筛选预后相关miRNA;利用mirwalk分析平台,对筛选出的miRNA进行靶向调控基因预测、KEGG功能富集分析,最后,预测出预后相关miRNA的功能。结果：共筛选肺腺癌差异miRNA46个,其中,上调19个、下调27个;通过Cox生存分析筛选到预后相关miRNA有6个,即hsa-mir-21、hsa-mir-142、hsa-mir-200a高表达,hsa-mir-101、hsa-let-7c、hsa-mir-378e低表达,其中hsa-mir-21、hsa-mir-378e与肺腺癌患者不良预后有关,生存期显著缩短（P<0.05,AUC=0.618）。KEGG分析上述预后相关miRNA靶向调控基因与免疫反应通路、miRNA与癌症通路、代谢通路等有关。结论：hsa-mir-21、hsa-mir-378e与肺腺癌预后不良有关,未来经进一步临床验证有可能作为肺腺癌预后相关的分子标记物。     

HNRNPC可能成为肺腺癌独立的预后因子

为探讨RNA m⁶A甲基化调节因子在肺腺癌中的作用,从TCGA数据库下载肺腺癌患者的RNA表达数据和临床数据。通过limma软件包分析12种m⁶A调节剂的表达情况。使用Pheatmap、vioplot和corrplot软件包生成热图、小提琴图和表达相关图。采用Kaplan-Meier方法分别计算肺腺癌中12种RNA m⁶A调节因子的生存曲线。使用Cox回归和Kaplan-Meier方法分析TCGA肺腺癌患者的总体存活相关的临床病理学特征。最后用Kruskal(KS)检验和logistic回归分析临床病理学特征与HNRNPC表达的关系。 在肺腺癌的TCGA队列中,发现HNRNPC、WTAP、YTHDF3、FTO、ZC3H13、METTL14、METTL3、YTHDF1、YTHDF2这些基因是差异表达的。Kaplan-Meier生存分析显示,在这些差异表达的基因中仅仅HNRNPC和YTHDF2的表达与生存显著相关。然后,通过多因素Cox回归结果表明HNRNPC的表达在肺腺癌TCGA队列中是个独立危险因素。最后,HNRNPC在肺腺癌中的表达与临床分期(IV vs I, OR=3.692 308)和组织浸润(T2 vs T1, OR=1.776 471;T4 vs T1, OR=6.303 03)显著相关(所有p<0.05)。 结论认为HNRNPC可能作为肺腺癌的独立的预后因子。     

全基因组甲基化差异分析发现OSBPL5甲基化异常可能在Silver-Russell综合征中有致病作用

Silver-Russell综合征(SRS)是一组临床和遗传异质性疾病.主要临床表现为低出生体重、严重矮小、极低BMI、三角脸、肢体不对称等. SRS是表观遗传疾病的典型代表. 38%~62%患者有染色体11p15 IGF2-H19基因簇印记控制区1 (ICR1)低甲基化, 7%~10%患者有第7号染色体母源单亲二倍体(UPD7(mat)).另有约40%病因不清.本研究通过Illumina Methylation 450K芯片检测全基因组甲基化差异分析,旨在了解是否存在与SRS致病性相关的未知基因或印记基因,以及能否通过全基因组甲基化检测精细定位SRS低甲基化位置.选取临床确诊的7例SRS患儿,其中MLPA方法发现甲基化异常2例、未发现异常5例.年龄匹配的正常儿童5例作对照,通过Illumina 450K Infinium Methylation BeadChip芯片检测全基因组甲基化位点,并用经典的焦磷酸盐测序及数字PCR定量2种方法进行验证.甲基化位点探针筛选差异位点的标准同时满足:Adjust Pval≤0.05,如果Adjust Pval≥0.05,则参考校正之前的Pval,该数值需要≤0.05; case vs. control Beta-Difference不小于0.2.即|Beta-Difference|≥0.2.在484821个探针位点中共筛选出116个差异性甲基化位点.通过GO Pathway功能分析,本芯片研究再次证实经典的H19/IGF2低甲基化特征是SRS主要的表观遗传改变.并且发现1个位于染色体11p14印记基因OSBPL5的cg25963939位点,在实验组与正常对照组有最显著的甲基化差异(P=0.023,β值-0.243).经用经典焦磷酸盐测序及数字PCR定量分析两种方法进行验证,证实实验组与对照组OSBPL5的cg25963939位点的甲基化存在差异,提示与SRS致病性相关.本研究还发现, TGFβ3, HSF1, GAP43, NOTCH4, MYH14上述5个基因在实验组与对照组存在明显的甲基化差异,通过GO pathway功能分析,有可能与SRS致病相关.本研究通过全基因组甲基化芯片检测,焦磷酸盐测序及数字PCR定量两种方法验证,发现11p14的印记基因OSBPL5 5′UTR区cg25963939位点,有可能为SRS发病机制之一.本研究通过Illumina 450K Infinium高密度微阵列甲基化水平检测,再次证实SRS最主要的表观遗传甲基化改变最主要定位于11p1区.     

DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值

肝细胞癌(hepatocellular carcinoma,简称肝癌)是最常见的恶性肿瘤之一。DNA甲基化的异常是恶性肿瘤的特征之一,并被发现在肝癌等肿瘤的发生发展中发挥重要作用。为了能为肝癌患者提供新的临床预后预测标志物,本研究首先采用整合组学分析策略在全基因组范围内鉴定与肝癌患者预后相关的DNA甲基化驱动的差异表达基因;然后,采用LASSO (least absolute shrinkage and selection operator)分析建立了10个最优基因组合的预后预测模型。Cox比例风险回归分析显示,在校正临床特征参数后,此预测模型高风险评分与患者不良预后显著相关,表明该模型具有潜在的独立预后价值。受试者工作特征(receiver operating characteristic,ROC)曲线分析显示该风险评分模型在预测患者短期和长期预后方面优于其他已被报道的肝癌预后预测模型。基因集富集分析(gene set enrichment analysis, GSEA)表明,高风险评分与细胞周期和DNA损伤修复通路相关。以上结果表明,本研究构建了一个基于10个DNA甲基化驱动基因的预后风险评分模型,该模型可作为肝癌患者的潜在预后生物标志物,有助于肝癌患者的生存预后评估和治疗策略的指导。     

不同生理年龄毛竹DNA甲基化的MSAP分析

为分析竹子年龄变化与基因组DNA甲基化之间的相关性,以5年、31年和>60年起源(从种子萌发年龄算起)的毛竹当年生叶片为材料,采用35对引物对其进行MSAP检测。结果表明:3个年龄段的总甲基化率和全甲基化率分别为24.44%、28.21%、32.12%和16.57%、19.41%、21.23%;发生DNA甲基化的变异位点为52.3%,去甲基化变异位点为10.3%。可以看出,随着年龄的增加,毛竹基因组DNA甲基化敏感多态性呈上升趋势。总甲基化率单因素方差分析的结果表明相同年龄的毛竹个体间没有差异(P=0.307>0.05),而不同年龄间的差异达极显著水平(P<0.001)。同时,对所用引物组合进行分析后发现有6对引物(E3/HM2、E3/HM6、E3/HM7、E4/HM5、E4/HM6和E5/HM5)扩增出的位点与总趋势显著相关,为进一步开展深入研究奠定基础。     

Prognostic significance of immune landscape in tumour microenvironment of endometrial cancer

Tumour microenvironment (TME) is crucial to tumorigenesis. This study aimed to uncover the differences in immune phenotypes of TME in endometrial cancer (EC) using Uterine Corpus Endometrial Carcinoma (UCEC) cohort and explore the prognostic significance. We employed GVSA enrichment analysis to cluster The Cancer Genome Atlas (TCGA) EC samples into immune signature cluster modelling, evaluated immune cell profiling in UCEC cohort (n = 538) and defined four immune subtypes of EC. Next, we analysed the correlation between immune subtypes and clinical data including patient prognosis. Furthermore, we analysed the expression of immunomodulators and DNA methylation modification. The profiles of immune infiltration in TCGA UCEC cohort showed significant difference among four immune subtypes of EC. Among each immune subtype, natural killer T cells (NKT), dendritic cells (DCs) and CD8⁺T cells were significantly associated with EC patients survival. Each immune subtype exhibited specific molecular classification, immune cell characterization and immunomodulators expression. Moreover, the expression immunomodulators were significantly related to DNA methylation level. In conclusion, the identification of immune subtypes in EC tissues could reveal unique immune microenvironments in EC and predict the prognosis of EC patients.     

Multi-Omics Analysis Elucidates The Immune And Intratumor Microbes Characteristics Of Ubiquitination Subtypes In Lung Adenocarcinoma

Ubiquitination modification is closely related to cancer and participates in the regulation of tumor microenvironment. However, the role of ubiquitination modification in the immune response and prognosis of lung adenocarcinoma has not been elucidated. This study aims to establish a disease classification associated with ubiquitination and reveal the landscape of intratumor microbes in patients with lung adenocarcinoma for the first time. A total of 1314 patients with lung adenocarcinoma in the GEO and TCGA databases were included in our study. We constructed a ubiquitination scoring model using WGCNA and constructed ubiquitination subtypes using unsupervised clustering, analyzed the clinical characteristics, immune characteristics, and intratumor microbes characteristics, and screened out the relevant gene signatures, which were verified by RT-qPCR in human cancer cells. The results showed that the high ubiquitination subtype had poor prognosis, low degree of immune infiltration, high index of tumor stemness, and poor effect of immunotherapy. The subtypes with lower ubiquitination scores have better prognosis, higher tumor microenvironment score and better immunotherapy effect. The C2 subtype has high level of immune infiltration, lower intratumor microbes diversity and abundance, and good prognosis. The C3 subtype has low level of immune infiltration, higher intratumor microbes diversity and abundance, and poor prognosis. The C1 subtype has characteristics between C2 and C3. In summary, this paper constructs a scoring system and several subtypes based on ubiquitination genes, and analyzed the characteristics, which can help provide new methods for clinical treatment.     

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.     

Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK–STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.     

A Simplified Approach for the Molecular Classification of Glioblastomas

Glioblastoma (GBM) is the most common malignant primary brain tumors in adults and exhibit striking aggressiveness. Although GBM constitute a single histological entity, they exhibit considerable variability in biological behavior, resulting in significant differences in terms of prognosis and response to treatment. In an attempt to better understand the biology of GBM, many groups have performed high-scale profiling studies based on gene or protein expression. These studies have revealed the existence of several GBM subtypes. Although there remains to be a clear consensus, two to four major subtypes have been identified. Interestingly, these different subtypes are associated with both differential prognoses and responses to therapy. In the present study, we investigated an alternative immunohistochemistry (IHC)-based approach to achieve a molecular classification for GBM. For this purpose, a cohort of 100 surgical GBM samples was retrospectively evaluated by immunohistochemical analysis of EGFR, PDGFRA and p53. The quantitative analysis of these immunostainings allowed us to identify the following two GBM subtypes: the “Classical-like” (CL) subtype, characterized by EGFR-positive and p53- and PDGFRA-negative staining and the “Proneural-like” (PNL) subtype, characterized by p53- and/or PDGFRA-positive staining. This classification represents an independent prognostic factor in terms of overall survival compared to age, extent of resection and adjuvant treatment, with a significantly longer survival associated with the PNL subtype. Moreover, these two GBM subtypes exhibited different responses to chemotherapy. The addition of temozolomide to conventional radiotherapy significantly improved the survival of patients belonging to the CL subtype, but it did not affect the survival of patients belonging to the PNL subtype. We have thus shown that it is possible to differentiate between different clinically relevant subtypes of GBM by using IHC-based profiling, a method that is advantageous in its ease of daily implementation and in large-scale clinical application.     

Genome wide analysis of acute myeloid leukemia reveal leukemia specific methylome and subtype specific hypomethylation of repeats

Methylated DNA immunoprecipitation followed by high-throughput sequencing (MeDIP-seq) has the potential to identify changes in DNA methylation important in cancer development. In order to understand the role of epigenetic modulation in the development of acute myeloid leukemia (AML) we have applied MeDIP-seq to the DNA of 12 AML patients and 4 normal bone marrows. This analysis revealed leukemia-associated differentially methylated regions that included gene promoters, gene bodies, CpG islands and CpG island shores. Two genes (SPHKAP and DPP6) with significantly methylated promoters were of interest and further analysis of their expression showed them to be repressed in AML. We also demonstrated considerable cytogenetic subtype specificity in the methylomes affecting different genomic features. Significantly distinct patterns of hypomethylation of certain interspersed repeat elements were associated with cytogenetic subtypes. The methylation patterns of members of the SINE family tightly clustered all leukemic patients with an enrichment of Alu repeats with a high CpG density (P<0.0001). We were able to demonstrate significant inverse correlation between intragenic interspersed repeat sequence methylation and gene expression with SINEs showing the strongest inverse correlation (R(2) = 0.7). We conclude that the alterations in DNA methylation that accompany the development of AML affect not only the promoters, but also the non-promoter genomic features, with significant demethylation of certain interspersed repeat DNA elements being associated with AML cytogenetic subtypes. MeDIP-seq data were validated using bisulfite pyrosequencing and the Infinium array.     

Molecular Diagnostic and Prognostic Subtyping of Gliomas in Tunisian Population

It has become increasingly evident that morphologically similar gliomas may have distinct clinical phenotypes arising from diverse genetic signatures. To date, glial tumours from the Tunisian population have not been investigated. To address this, we correlated the clinico-pathology with molecular data of 110 gliomas by a combination of HM450K array, MLPA and TMA-IHC. PTEN loss and EGFR amplification were distributed in different glioma histological groups. However, 1p19q co-deletion and KIAA1549:BRAF fusion were, respectively, restricted to Oligodendroglioma and Pilocytic Astrocytoma. CDKN2A loss and EGFR overexpression were more common within high-grade gliomas. Furthermore, survival statistical correlations led us to identify Glioblastoma (GB) prognosis subtypes. In fact, significant lower overall survival (OS) was detected within GB that overexpressed EGFR and Cox2. In addition, IDH1R132H mutation seemed to provide a markedly survival advantage. Interestingly, the association of IDHR132H mutation and EGFR normal status, as well as the association of differentiation markers, defined GB subtypes with good prognosis. By contrast, poor survival GB subtypes were defined by the combination of PTEN loss with PDGFRa expression and/or EGFR amplification. Additionally, GB presenting p53-negative staining associated with CDKN2A loss or p21 positivity represented a subtype with short survival. Thus, distinct molecular subtypes with individualised prognosis were identified. Interestingly, we found a unique histone mutation in a poor survival young adult GB case. This tumour exceptionally associated the H3F3A G34R mutation and MYCN amplification as well as 1p36 loss and 10q loss. Furthermore, by exhibiting a remarkable methylation profile, it emphasised the oncogenic power of G34R mutation connecting gliomagenesis and chromatin regulation.     

Borrowing strength with nonexchangeable priors over subpopulations

We introduce a nonparametric Bayesian model for a phase II clinical trial with patients presenting different subtypes of the disease under study. The objective is to estimate the success probability of an experimental therapy for each subtype. We consider the case when small sample sizes require extensive borrowing of information across subtypes, but the subtypes are not a priori exchangeable. The lack of a priori exchangeability hinders the straightforward use of traditional hierarchical models to implement borrowing of strength across disease subtypes. We introduce instead a random partition model for the set of disease subtypes. This is a variation of the product partition model that allows us to model a nonexchangeable prior structure. Like a hierarchical model, the proposed clustering approach considers all observations, across all disease subtypes, to estimate individual success probabilities. But in contrast to standard hierarchical models, the model considers disease subtypes a priori nonexchangeable. This implies that when assessing the success probability for a particular type our model borrows more information from the outcome of the patients sharing the same prognosis than from the others. Our data arise from a phase II clinical trial of patients with sarcoma, a rare type of cancer affecting connective or supportive tissues and soft tissue (e.g., cartilage and fat). Each patient presents one subtype of the disease and subtypes are grouped by good, intermediate, and poor prognosis. The prior model should respect the varying prognosis across disease subtypes. The practical motivation for the proposed approach is that the number of accrued patients within each disease subtype is small. Thus it is not possible to carry out a clinical study of possible new therapies for rare conditions, because it would be impossible to plan for sufficiently large sample size to achieve the desired power. We carry out a simulation study to compare the proposed model with a model that assumes similar success probabilities for all subtypes with the same prognosis, i.e., a fixed partition of subtypes by prognosis. When the assumption is satisfied the two models perform comparably. But the proposed model outperforms the competing model when the assumption is incorrect.