Exploring the molecular landscape of cancer of unknown primary: A comparative analysis with other metastatic cancers.

Cancer of unknown primary (CUP) tumors are biologically very heterogeneous, which complicates stratification of patients for treatment. Consequently, these patients face limited treatment options and a poor prognosis. With this study, we aim to expand on the current knowledge of CUP biology by analyzing two cohorts: a well-characterized cohort of 44 CUP patients, and 213 metastatic patients with known primary. These cohorts were treated at the same institution and characterized by identical molecular assessments. Through comparative analysis of genomic and transcriptomic data, we found that CUP tumors were characterized by high expression of immune-related genes and pathways compared to other metastatic tumors. Moreover, CUP tumors uniformly demonstrated high levels of tumor-infiltrating leukocytes and circulating T cells, indicating a strong immune response. Finally, the genetic landscape of CUP tumors resembled that of other metastatic cancers and demonstrated mutations in established cancer genes. In conclusion, CUP tumors possess a distinct immunophenotype that distinguishes them from other metastatic cancers. These results may suggest an immune response in CUP that facilitates metastatic tumor growth while limiting growth of the primary tumor.

Spatial transformation of multi-omics data unlocks novel insights into cancer biology.

The application of next-generation sequencing (NGS) has transformed cancer research. As costs have decreased, NGS has increasingly been applied to generate multiple layers of molecular data from the same samples, covering genomics, transcriptomics, and methylomics. Integrating these types of multi-omics data in a combined analysis is now becoming a common issue with no obvious solution, often handled on an ad hoc basis, with multi-omics data arriving in a tabular format and analyzed using computationally intensive statistical methods. These methods particularly ignore the spatial orientation of the genome and often apply stringent p-value corrections that likely result in the loss of true positive associations. Here, we present GENIUS (GEnome traNsformatIon and spatial representation of mUltiomicS data), a framework for integrating multi-omics data using deep learning models developed for advanced image analysis. The GENIUS framework is able to transform multi-omics data into images with genes displayed as spatially connected pixels and successfully extract relevant information with respect to the desired output. We demonstrate the utility of GENIUS by applying the framework to multi-omics datasets from the Cancer Genome Atlas. Our results are focused on predicting the development of metastatic cancer from primary tumors, and demonstrate how through model inference, we are able to extract the genes which are driving the model prediction and are likely associated with metastatic disease progression. We anticipate our framework to be a starting point and strong proof of concept for multi-omics data transformation and analysis without the need for statistical correction.

Elevated T-cell Exhaustion and Urinary Tumor DNA Levels Are Associated with Bacillus Calmette-Guérin Failure in Patients with Non-muscle-invasive Bladder Cancer.

BACKGROUND: The functional status of immune cells in the tumor microenvironment and tumor characteristics may explain bacillus Calmette-Guérin (BCG) failure in high-risk non-muscle-invasive bladder cancer (NMIBC). OBJECTIVE: To characterize molecular correlates of post-BCG high-grade (HG) recurrence using multiomics analysis. DESIGN, SETTING, AND PARTICIPANTS: Patients with BCG-treated NMIBC (n = 156) were included in the study. Metachronous tumors were analyzed using RNA sequencing (n = 170) and whole-exome sequencing (n = 195). Urine samples were analyzed for immuno-oncology-related proteins (n = 190) and tumor-derived DNA (tdDNA; n = 187). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was post-BCG HG recurrence. Cox regression and Wilcoxon rank-sum, t, and Fisher's exact tests were used for analyses. RESULTS AND LIMITATIONS: BCG induced activation of the immune system regardless of clinical response; however, immunoinhibitory proteins were observed in the urine of patients with post-BCG HG recurrence (CD70, PD1, CD5). Post-BCG HG recurrence was associated with post-BCG T-cell exhaustion (p = 0.002). Pre-BCG tumors from patients with post-BCG T-cell exhaustion had high expression of genes related to cell division and immune function. A high predicted post-BCG exhaustion score for pre-BCG tumors was associated with worse post-BCG HG recurrence-free survival (HGRFS; p = 0.002). This was validated in independent cohorts. Pre-BCG class 2a and 2b tumors (UROMOL2021 scheme) were associated with worse post-BCG HGRFS (p = 0.015). Post-BCG exhaustion was observed in patients with high pre-BCG neoantigen load (p = 0.017) and MUC4 mutations (p = 0.002). Finally, the absence of post-BCG tdDNA clearance identified patients at high risk of recurrence (p = 0.018). The retrospective design and partial overlap for analyses are study limitations. CONCLUSIONS: Post-BCG HG recurrence may be caused by T-cell exhaustion. Tumor subtype and pre-BCG tumor characteristics may identify patients at high risk of post-BCG HG recurrence. Urinary measurements have potential for real-time assessment of treatment response. PATIENT SUMMARY: A dysfunctional immune response to bacillus Calmette-Guérin (BCG) therapy may explain high-grade recurrences of bladder cancer.