Similar genetic profile in early and late stage urothelial tract cancer.

INTRODUCTION: Urothelial tract cancer (UTC) ranks as the tenth most prevalent cancer and holds the seventh position in terms of mortality worldwide. Despite its prevalence and mortality ranking, there are still gaps in the knowledge of the mutational landscape in patients with advanced disease who have limited therapeutic options after multiple lines of prior treatment. This study compares the genomic and transcriptomic landscape, and targeted treatment options between metastatic UTC (mUTC) patients treated with multiple lines of therapy compared to newly diagnosed, untreated Muscle Invasive Bladder Cancer (MIBC). METHODS: We compared genomic and clinical data from two cohorts: mUTC patients who received multiple lines of therapy and were referred to the Copenhagen Prospective Personalized Oncology (CoPPO) project at Rigshospitalet, University of Copenhagen. Data for MIBC UTC patients were acquired from the Cancer Genome Atlas Bladder Cancer (TCGA BLCA) cohort. Biopsies in CoPPO were performed at the time of enrollment. 523 highly important cancer-related genes (TrueSight Oncology-500 targeted sequencing panel) were used from both cohorts for comparative analysis. Analyses included RNA count data to compare predicted molecular subtypes in each cohort separately. RESULTS: Patients from the CoPPO cohort had a lower median age at first-line treatment than the TCGA BLCA cohort, with no significant gender disparity. The predominant histology was urothelial cell carcinoma in both cohorts. Genomic analysis revealed no significant difference between the top mutated genes in the two cohorts, specifically looking into DNA damage repair genes. Molecular subtyping indicated a higher frequency of neuroendocrine differentiation in the CoPPO cohort. 13% of patients in the CoPPO cohort received targeted therapy based on genomic findings, and 16% received non-targeted treatment, totaling 29% receiving CoPPO treatment (9 patients). The remaining 71% received best supportive care. Kaplan-Meier analysis showed a non-significant survival benefit for the intervention group in the CoPPO cohort. CONCLUSION: When focusing on 523 highly relevant cancer genes, the mutational profile of mUTC patients who have undergone numerous treatment lines resembles that of newly diagnosed MIBC. These alterations can be targeted, indicating the potential advantage of early genomic testing for personalized treatment within clinical trials.

ADP-ribosylome analysis reveals homogeneous DNA-damage-induced serine ADP-ribosylation across wild-type and BRCA-mutant breast cancer cell lines.

ADP-ribosylation (ADPr) signaling plays a crucial role in DNA damage response. Inhibitors against the main enzyme catalyzing ADPr after DNA damage, poly(ADP-ribose) polymerase 1 (PARP1), are used to treat patients with breast cancer harboring BRCA1/2 mutations. However, resistance to PARP inhibitors (PARPi) is a major obstacle in treating patients. To understand the role of ADPr in PARPi sensitivity, we use liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze ADPr in six breast cancer cell lines exhibiting different PARPi sensitivities. We identify 1,632 sites on 777 proteins across all cell lines, primarily on serine residues, with site-specific overlap of targeted residues across DNA-damage-related proteins across all cell lines, demonstrating high conservation of serine ADPr-signaling networks upon DNA damage. Furthermore, we observe site-specific differences in ADPr intensities in PARPi-sensitive BRCA mutants and unique ADPr sites in PARPi-resistant BRCA-mutant HCC1937 cells, which have low poly(ADP-ribose) glycohydrolase (PARG) levels and longer ADPr chains on PARP1.

Histopathology and proteomics are synergistic for High-Grade Serous Ovarian Cancer platinum response prediction.

Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E) pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained Whole Slide Images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. This method outperforms the Homologous Recombination Deficiency (HRD) score in predicting platinum response and overall patient survival. The study sets new performance benchmarks and explores the intersection of histology and proteomics, highlighting phenotypes related to treatment response pathways, including homologous recombination, DNA damage response, nucleotide synthesis, apoptosis, and ER stress. This integrative approach has the potential to improve personalized treatment and provide insights into the therapeutic vulnerabilities of HGSOC.

Increased frequency of CHD1 deletions in prostate cancers of African American men is associated with rapid disease progression without inducing homologous recombination deficiency.

We analyzed genomic data derived from the prostate cancer of African and European American men in order to identify differences that may contribute to racial disparity of outcome and that could also define novel therapeutic strategies. In addition to analyzing patient derived next generation sequencing data, we performed FISH based confirmatory studies of Chromodomain helicase DNA-binding protein 1 (CHD1) loss on prostate cancer tissue microarrays. We created CRISPR edited, CHD1 deficient prostate cancer cell lines for genomic, drug sensitivity and functional homologous recombination (HR) activity analysis. We found that subclonal deletion of CHD1 is nearly three times as frequent in prostate tumors of African American men than in men of European ancestry and it associates with rapid disease progression. We further showed that CHD1 deletion is not associated with homologous recombination deficiency associated mutational signatures in prostate cancer. In prostate cancer cell line models CHD1 deletion did not induce HR deficiency as detected by RAD51 foci formation assay or mutational signatures, which was consistent with the moderate increase of olaparib sensitivity. CHD1 deficient prostate cancer cells, however, showed higher sensitivity to talazoparib. CHD1 loss may contribute to worse outcome of prostate cancer in African American men. A deeper understanding of the interaction between CHD1 loss and PARP inhibitor sensitivity will be needed to determine the optimal use of targeted agents such as talazoparib in the context of castration resistant prostate cancer.

Mutational signature-based identification of DNA repair deficient gastroesophageal adenocarcinomas for therapeutic targeting.

Homologous recombination (HR) and nucleotide excision repair (NER) are the two most frequently disabled DNA repair pathways in cancer. HR-deficient breast, ovarian, pancreatic and prostate cancers respond well to platinum chemotherapy and PARP inhibitors. However, the frequency of HR deficiency in gastric and esophageal adenocarcinoma (GEA) still lacks diagnostic and functional validation. Using whole exome and genome sequencing data, we found that a significant subset of GEA, but very few colorectal adenocarcinomas, show evidence of HR deficiency by mutational signature analysis (HRD score). High HRD gastric cancer cell lines demonstrated functional HR deficiency by RAD51 foci assay and increased sensitivity to platinum chemotherapy and PARP inhibitors. Of clinical relevance, analysis of three different GEA patient cohorts demonstrated that platinum treated HR deficient cancers had better outcomes. A gastric cancer cell line with strong sensitivity to cisplatin showed HR proficiency but exhibited NER deficiency by two photoproduct repair assays. Single-cell RNA-sequencing revealed that, in addition to inducing apoptosis, cisplatin treatment triggered ferroptosis in a NER-deficient gastric cancer, validated by intracellular GSH assay. Overall, our study provides preclinical evidence that a subset of GEAs harbor genomic features of HR and NER deficiency and may therefore benefit from platinum chemotherapy and PARP inhibitors.

Are tumor-associated carbohydrates the missing link between the gut microbiome and response to immune checkpoint inhibitor treatment in cancer?

Immune checkpoint inhibitor therapy has dramatically improved survival in a significant subset of patients with several solid tumor types. Increasing the number of patients benefitting from this form of therapy is an important translational research goal. Correlations between the composition of the gut microbiome and response to immune checkpoint inhibitor therapy raised the possibility that direct modulation of the gut microbiome may significantly improve the clinical benefit of this treatment. Several lines of observations suggest that tumor-associated carbohydrates, including those recognized as blood group-related glycolipid antigens, such as the Forssman antigen, may be some of the key factors behind this clinical correlation. Such antigens are expressed in human cancer, humans often produce antibodies against those, and they can induce antibody directed cellular cytotoxicity. Importantly, these antibodies are often induced by antigens present in microbes of the gut. If identified, these antibodies could be boosted by appropriate vaccination techniques and thus enhance anti-tumor immunity with minimal side effects.

Biologically informed deep learning for explainable epigenetic clocks.

Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.