Optimizing identification of consensus molecular subtypes in muscle-invasive bladder cancer: a comparison of two sequencing methods and gene sets using FFPE specimens

Predictive Signatures for Responses to Checkpoint Blockade in Small-Cell Lung Cancer in Second-Line Therapy Do Not Predict Responses in First-Line Patients

Although immune checkpoint blockade (ICB) is currently approved for the treatment of extensive-stage small-cell lung cancer (SCLC) in combination with chemotherapy, relatively few patients have demonstrated durable clinical benefit (DCB) to these therapies. Biomarkers predicting responses are needed. Biopsies from 35 SCLC patients treated with ICB were subjected to transcriptomic analysis; gene signatures were assessed for associations with responses. Twenty-one patients were treated with ICB in the first-line setting in combination with platinum-based chemotherapy; fourteen patients were treated in the second-line setting with ICB alone. DCB after ICB in SCLC in the second-line setting (3 of 14 patients) was associated with statistically higher transcriptomic levels of genes associated with inflammation (p = 0.003), antigen presentation machinery (p = 0.03), interferon responses (p < 0.05), and increased CD8 T cells (p = 0.02). In contrast, these gene signatures were not significantly different in the first-line setting. Our data suggest that responses to ICB in SCLC in the second-line setting can be predicted by the baseline inflammatory state of the tumor; however, this strong association with inflammation was not seen in the first-line setting. We postulate that chemotherapy alters the immune milieu allowing a response to ICB. Other biomarkers will be needed to predict responses in first-line therapy patients.

[Histomolecular classification of urothelial carcinoma of the urinary bladder : From histological phenotype to genotype and back]

BACKGROUND

Of all urothelial carcinomas (UCs), 25% are muscle invasive and associated with a 5-year overall survival rate of 50%. Findings regarding the molecular classification of muscle-invasive urothelial carcinomas (MIUCs) have not yet found their way into clinical practice.

OBJECTIVES

Prediction of molecular consensus subtypes in MIUCs with artificial intelligence (AI) based on histologic hematoxylin-eosin (HE) sections.

METHODS

Pathologic review and annotation of The Cancer Genome Atlas (TCGA) Bladder Cancer (BLCA) Cohort (N = 412) and the Dr. Senckenberg Institute of Pathology (SIP) BLCA Cohort (N = 181). An AI model for the prediction of molecular subtypes based on annotated histomorphology was trained.

RESULTS

For a five-fold cross-validation with TCGA cases (N = 274), an internal TCGA test set (N = 18) and an external SIP test set (N = 27), we reached mean area under the receiver operating characteristic curve (AUROC) scores of 0.73, 0.8 and 0.75 for the classification of the used molecular subtypes "luminal", "basal/squamous" and "stroma-rich". By training on correlations to individual molecular subtypes, rather than training on one subtype assignment per case, the AI prediction of subtypes could be significantly improved.

DISCUSSION

Follow-up studies with RNA extraction from various areas of AI-predicted molecular heterogeneity may improve molecular classifications and thereby AI algorithms trained on these classifications.

Impact of consensus molecular subtypes on survival with and without adjuvant chemotherapy in muscle-invasive urothelial bladder cancer

AIMS

Adjuvant chemotherapy after radical cystectomy can reduce the risk of recurrence and death in advanced muscle-invasive urothelial bladder cancer (MIBC). Molecular subtypes have been shown to be associated with survival. However, their predictive value to guide treatment decisions is controversial and data to use subtypes as guidance for adjuvant chemotherapy is sparse. We aimed to assess survival rates based on MIBC consensus molecular subtypes with and without adjuvant chemotherapy.

METHODS

Gene expression profiles of 143 patients with MIBC undergoing radical cystectomy were determined from formalin-fixed, paraffin-embedded specimen to assign consensus molecular subtypes. Expression of programmed cell death ligand-1 (PD-L1) and immune cell infiltration were determined using multiplex immunofluorescence. Matched-pair analysis was performed to evaluate the effect of adjuvant chemotherapy on overall survival (OS) for molecular subtypes applying Kaplan-Meier and Cox regression survival analyses.

RESULTS

Samples were luminal papillary: 9.1% (n=13), luminal non-specified: 6.3% (n=9), luminal unstable: 4.9% (n=7), stroma-rich: 27.9% (n=40), basal/squamous (Ba/Sq): 48.9% (n=70) and neuroendocrine-like (NE-like): 2.8% (n=4). Ba/Sq tumours had the highest concentration of PD-L1+ tumour and immune cells. Patients with luminal subtypes had better OS than those with NE-like (HR 0.2, 95% CI 0.1 to 0.7, p<0.05) and Ba/Sq (HR 0.5, 95% CI 0.2 to 0.9, p<0.05). No survival benefit with adjuvant chemotherapy was observed for luminal tumours, whereas Ba/Sq had significantly improved survival rates with adjuvant chemotherapy. Retrospective design and sample size are the main limitations.

CONCLUSION

Consensus molecular subtypes can be used to stratify patients with MIBC. Luminal tumours have the best prognosis and less benefit when receiving adjuvant chemotherapy compared with Ba/Sq tumours.