Meta-Analysis of the Luminal and Basal Subtypes of Bladder Cancer and the Identification of Signature Immunohistochemical Markers for Clinical Use

AbstractBackgroundIt has been suggested that bladder cancer can be divided into two molecular subtypes referred to as luminal and basal with distinct clinical behaviors and sensitivities to chemotherapy. We aimed to validate these subtypes in several clinical cohorts and identify signature immunohistochemical markers that would permit simple and cost-effective classification of the disease in primary care centers.MethodsWe analyzed genomic expression profiles of bladder cancer in three cohorts of fresh frozen tumor samples: MD Anderson (n=132), Lund (n=308), and The Cancer Genome Atlas (TCGA) (n=408) to validate the expression signatures of luminal and basal subtypes and relate them to clinical follow-up data. We also used an MD Anderson cohort of archival bladder tumor samples (n=89) and a parallel tissue microarray to identify immunohistochemical markers that permitted the molecular classification of bladder cancer.FindingsBladder cancers could be assigned to two candidate intrinsic molecular subtypes referred to here as luminal and basal in all of the datasets analyzed. Luminal tumors were characterized by the expression signature similar to the intermediate/superficial layers of normal urothelium. They showed the upregulation of PPARγ target genes and the enrichment for FGFR3, ELF3, CDKN1A, and TSC1 mutations. In addition, luminal tumors were characterized by the overexpression of E-Cadherin, HER2/3, Rab-25, and Src. Basal tumors showed the expression signature similar to the basal layer of normal urothelium. They showed the upregulation of p63 target genes, the enrichment for TP53 and RB1 mutations, and overexpression of CD49, Cyclin B1, and EGFR. Survival analyses showed that the muscle-invasive basal bladder cancers were more aggressive when compared to luminal cancers. The immunohistochemical expressions of only two markers, luminal (GATA3) and basal (KRT5/6), were sufficient to identify the molecular subtypes of bladder cancer with over 90% accuracy.InterpretationThe molecular subtypes of bladder cancer have distinct clinical behaviors and sensitivities to chemotherapy, and a simple two-marker immunohistochemical classifier can be used for prognostic and therapeutic stratification.FundingU.S. National Cancer Institute and National Institute of Health

Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer

Abstract Background TMPRSS2-ERG gene rearrangement, the most common E26 transformation specific (ETS) gene fusion within prostate cancer, is known to contribute to the pathogenesis of this disease and carries diagnostic annotations for prostate cancer patients clinically. The ERG rearrangement status in prostatic adenocarcinoma currently cannot be reliably identified from histologic features on H&E-stained slides alone and hence requires ancillary studies such as immunohistochemistry (IHC), fluorescent in situ hybridization (FISH) or next generation sequencing (NGS) for identification. Methods Objective We accordingly sought to develop a deep learning-based algorithm to identify ERG rearrangement status in prostatic adenocarcinoma based on digitized slides of H&E morphology alone. Design Setting, and Participants: Whole slide images from 392 in-house and TCGA cases were employed and annotated using QuPath. Image patches of 224 × 224 pixel were exported at 10 ×, 20 ×, and 40 × for input into a deep learning model based on MobileNetV2 convolutional neural network architecture pre-trained on ImageNet. A separate model was trained for each magnification. Training and test datasets consisted of 261 cases and 131 cases, respectively. The output of the model included a prediction of ERG-positive (ERG rearranged) or ERG-negative (ERG not rearranged) status for each input patch. Outcome measurements and statistical analysis: Various accuracy measurements including area under the curve (AUC) of the receiver operating characteristic (ROC) curves were used to evaluate the deep learning model. Results and Limitations All models showed similar ROC curves with AUC results ranging between 0.82 and 0.85. The sensitivity and specificity of these models were 75.0% and 83.1% (20 × model), respectively. Conclusions A deep learning-based model can successfully predict ERG rearrangement status in the majority of prostatic adenocarcinomas utilizing only H&E-stained digital slides. Such an artificial intelligence-based model can eliminate the need for using extra tumor tissue to perform ancillary studies in order to assess for ERG gene rearrangement in prostatic adenocarcinoma.http://deepblue.lib.umich.edu/bitstream/2027.42/173544/1/12885_2022_Article_9559.pd

Dysregulation of EMT Drives the Progression to Clinically Aggressive Sarcomatoid Bladder Cancer

Summary: Sarcomatoid urothelial bladder cancer (SARC) displays a high propensity for distant metastasis and is associated with short survival. We report a comprehensive genomic analysis of 28 cases of SARC and 84 cases of conventional urothelial carcinoma (UC), with the TCGA cohort of 408 muscle-invasive bladder cancers serving as the reference. SARCs show a distinct mutational landscape, with enrichment of TP53, RB1, and PIK3CA mutations. They are related to the basal molecular subtype of conventional UCs and could be divided into epithelial-basal and more clinically aggressive mesenchymal subsets on the basis of TP63 and its target gene expression levels. Other analyses reveal that SARCs are driven by downregulation of homotypic adherence genes and dysregulation of the EMT network, and nearly half exhibit a heavily infiltrated immune phenotype. Our observations have important implications for prognostication and the development of more effective therapies for this highly lethal variant of bladder cancer. : Guo et al. report that sarcomatoid carcinoma of the bladder evolves by the progression of the basal subtype of conventional urothelial carcinoma with the enrichment of mutagenesis signature 1 and mutations of TP53, RB1, and PIK3CA. It is driven by the dysregulation of the EMT network and shows increased immune infiltrate with overexpression of PD-L1. Keywords: bladder cancer, sarcomatoid carcinoma, urothelial carcinoma, epithelial-mesenchymal transformation, genomic expression, chromatin remodeling, microRNA expression, molecular classification, basal subtype, immune phenotyp