Recurrent exon-deleting activating mutations in AHR act as drivers of urinary tract cancer

Bladder cancer has a high recurrence rate and low survival of advanced stage patients. Few genetic drivers of bladder cancer have thus far been identified. We performed in-depth structural variant analysis on whole-genome sequencing data of 206 metastasized urinary tract cancers. In ~ 10% of the patients, we identified recurrent in-frame deletions of exons 8 and 9 in the aryl hydrocarbon receptor gene (AHR Δe8-9), which codes for a ligand-activated transcription factor. Pan-cancer analyses show that AHR Δe8-9 is highly specific to urinary tract cancer and mutually exclusive with other bladder cancer drivers. The ligand-binding domain of the AHR Δe8-9 protein is disrupted and we show that this results in ligand-independent AHR-pathway activation. In bladder organoids, AHR Δe8-9 induces a transformed phenotype that is characterized by upregulation of AHR target genes, downregulation of differentiation markers and upregulation of genes associated with stemness and urothelial cancer. Furthermore, AHR Δe8-9 expression results in anchorage independent growth of bladder organoids, indicating tumorigenic potential. DNA-binding deficient AHR Δe8-9 fails to induce transformation, suggesting a role for AHR target genes in the acquisition of the oncogenic phenotype. In conclusion, we show that AHR Δe8-9 is a novel driver of urinary tract cancer and that the AHR pathway could be an interesting therapeutic target

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Abstract: In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here,as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Oncogenic Addiction to ERBB2 Signaling Predicts Response to Trastuzumab in Urothelial Cancer

Urothelial carcinoma (UC) is a common and frequently lethal cancer. Despite the presence of genomic alterations creating dependency on particular signaling pathways, the use of targeted therapies in advanced and metastatic UC has been limited. We performed an integrated analysis of whole-exome and RNA sequencing of primary and metastatic tumors in a patient with platinum-resistant UC. We found a strikingly high ERBB2 mRNA expression and enrichment of downstream oncogenic ERBB2 signaling in this patient\u27s tumors compared with tumors from an unselected group of patients with UC (N=17). This patient had an exceptional sustained response to trastuzumab. Our findings show that oncogenic addiction to ERBB2 signaling potentially predicts response to ERBB2-directed therapy of UC

Serial Circulating Tumor DNA (ctDNA) Measurement to Predict Progression in patients (pts) with Advanced Urothelial Carcinoma (aUC)

Background: Next-generation sequencing (NGS) of plasma ctDNA is a promising non-invasive method to detect somatic genomic alterations (GAs). We investigated whether serial ctDNA measurements can predict disease progression and map the molecular evolution of advanced UC (aUC) through successive treatments. Methods: We analyzed cohorts from 2 academic centers, WCM and UNLV, with serial (≥2) ctDNA targeted NGS using the Guardant360 panel of 73 genes, ctDNA was collected within four weeks of restaging scans. Non-progressive disease (non-PD) was defined as stable disease or radiological response. Results: Our cohort included 176 individual ctDNA samples (median 3/patient) from 52 patients (38 men), the median age was 69 (range 38-88), 38 pts (73%) had metastatic disease, 20 pts (38.5%) had visceral metastases and 35 pts (67%) received first-line platinum-based combination chemotherapy for aUC. The median number of lines of therapy was 2. 49 pts (94%) had detectable GAs in at least one ctDNA sample. Most commonly identified GAs involved TP53 (67%), PIK3CA, EGFR, and ERBB2 (each in 19%). FGFR3 GAs were identified in 9 pts (17%, 2 FGFR3-TACC3 fusions, and 7 SNVs, including two novel mutations P250R and K650M). The mean variant allele fraction (VAF) significantly decreased in pts with non-PD (80 non-PD events) compared to pts with PD (45 PD events) with a mean VAF decrease of 2.06 % (p\u3c 0.0001). This effect was consistent independent of the number of prior lines of therapy. The mean number of GAs per patient at each time point was significantly decreased in non-PD vs. PD group (2.8 vs. 4.4, p=0.004). Pts with detectable VAF in somatic alterations at any time point during the disease course had shorter overall survival (HR 1.18, 95% CI: 1.08 – 1.28, p\u3c0.0001). Pts with sustained partial or complete responses were more likely to have VAF ≤0.2% suggesting that molecular remission was associated with clinical remission. Conclusions: Serial ctDNA testing is a promising dynamic tool to access clonal evolution and predict progression in aUC pts

Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution.

Purpose To understand the clinical context of tumor mutational burden (TMB) when comparing a pan-cancer threshold and a cancer-specific threshold. Materials and Methods Using whole exome sequencing (WES) data from primary tumors in The Cancer Genome Atlas (TCGA) (n=3,534) and advanced/metastatic tumors from Weill Cornell Medicine (WCM Advanced) (n=696), TMB status was determined using a pan-cancer and cancer-specific threshold. Survival curves, number of samples classified as TMB high, and predicted neoantigens were used to evaluate the differences between thresholds. Results The distribution of TMB varied dramatically between cancer types. A cancer-specific threshold was able to adjust for the different TMB distributions, while the pan-cancer threshold was often too stringent. The dynamic nature of the cancer-specific threshold resulted in more tumors being classified as TMB high compared to the static pan-cancer threshold. Additionally, no significant difference in survival outcomes was found with the cancer-specific threshold compared to the pan-cancer one. Further, the cancer-specific threshold maintains higher predicted neoantigen load for the TMB high samples compared to the TMB low samples, even when the threshold is lower than the pan-cancer threshold. Conclusion TMB is relative to the context of cancer type, metastatic state, and disease stage. Compared to a pan-cancer threshold, a cancer-specific threshold classifies more patients as TMB high while maintaining clinical outcomes that were not significantly different. Furthermore, the cancer-specific threshold identifies patients with a high number of predicted neoantigens. Due to the potential impact in cancer patient care, TMB status should be determined in a cancer-specific manner