Biology subjects

Jammula, S.

Publications and source records attributed to Jammula, S..

5 recordsLinked to original sources

bioRxiv2026

Polyclonal and single clonal patient-derived organoid models of Barrett oesophagus and oesophageal adenocarcinoma establish a platform for the analysis of heterogeneity in disease progression and therapy response

SUMMARY/ABSTRACTOesophageal adenocarcinoma (OAC) is a major cause of morbidity and mortality. OAC and its precursor, Barrett oesophagus (BO), are defined by substantial early heterogeneity, complicating prevention and treatment of OAC and remaining poorly recapitulated by current in vitro and animal model systems. We have generated 116 patient- and healthy donor-derived organoids (PDOs) spanning normal oesophagogastric tissue, BO and OAC. These PDOs capture population diversity and recapitulate phenotypic, genomic and transcriptomic features of their respective disease stages. We develop a single cell-derived clonal organoid approach and show that this enables us to capture the heterogeneity and isolate high-risk, subclonal populations that are difficult to discern and maintain in bulk PDO cultures. Using this platform, we demonstrate functional importance of this biobank across the pre-malignant to invasive disease spectrum, including a role for BO in shaping fibroblast phenotype within assembloids, and diverse responses of OAC to chemotherapy, radiotherapy and targeted CDK4/6 inhibition. HIGHLIGHTSO_LIPatient- and healthy donor-derived organoids (PDOs) provide a functional platform of disease progression and heterogeneity across normal gastric, non-dysplastic and dysplastic Barrett oesophagus (BO) and oesophageal adenocarcinoma (OAC). C_LIO_LIWe provide a quantitative phenotypic and molecular framework to assess the provenance and fidelity of each PDO model given the heterogeneity of this disease. C_LIO_LIPDOs recapitulate key features of non-dysplastic and dysplastic BO, as well as invasive OAC. C_LIO_LISingle cell-derived clonal organoids (sc-organoids) isolate and maintain high-risk subclonal populations. C_LIO_LIOAC PDOs mirror known population level variation in response to systemic anti-cancer therapies. C_LI

cancer biology↗

bioRxiv2022

Extrachromosomal DNA in the cancerous transformation of Barrett's esophagus

BACKGROUNDOncogenes are commonly amplified on extrachromosomal DNA (ecDNA) contributing to poor outcomes for patients. Currently, the chronology of ecDNA development is not known. We studied the origination and evolution of ecDNA in patients with Barretts esophagus (BE) who progressed to esophageal adenocarcinoma (EAC). METHODSWe analyzed whole-genome sequencing (WGS) data from a BE surveillance cohort and EAC patients at Cambridge University UK (n=206 patients). We also analyzed WGS data from biopsies taken at two time points from multiple sites in the esophagus from 80 patients enrolled in a case-control study at the Fred Hutchinson Cancer Center (FHCC) - 40 BE patients who progressed to EAC and 40 who did not. RESULTSecDNA was detected in 24% and 43% of BE patients with BE-associated early and late-stage EAC, respectively, in the Cambridge cross-sectional cohort. ecDNA was found in 33% of all FHCC BE patients who developed cancer, either prior to, or at EAC diagnosis. ecDNA was strongly associated with patients who developed cancer, in contrast with FHCC BE patients who did not progress (odds ratio, 18.8, CI - 2.3-152, p=3.3x10-4). ecDNAs were enriched for oncogenes and immunomodulatory genes and could be detected early in the transition from high-grade dysplasia to cancer and increased in copy number and complexity over time. CONCLUSIONSecDNAs can develop before a diagnosis of cancer in BE patients and are strongly selected for during the evolution to EAC. ecDNAs promote diverse oncogene and immunomodulatory gene amplification during EAC development and progression.

genomics↗

bioRxiv2020

Evidence that polyploidy in esophageal adenocarcinoma originates from mitotic slippage caused by defective chromosome attachments

Polyploidy is present in many cancer types and is increasingly recognized as an important factor in promoting chromosomal instability, genome evolution, and heterogeneity in cancer cells. However, the mechanisms that trigger polyploidy in cancer cells are largely unknown. In this study, we investigated the origin of polyploidy in esophageal adenocarcinoma (EAC), a highly heterogenous cancer, using a combination of genomics and cell biology approaches in EAC cell lines, organoids, and tumors. We found the EAC cells and organoids present specific mitotic defects consistent with problems in the attachment of chromosomes to the microtubules of the mitotic spindle. Time-lapse analyses confirmed that EAC cells have problems in congressing and aligning their chromosomes, which can ultimately culminate in mitotic slippage and polyploidy. Furthermore, whole-genome sequencing, RNA-seq, and quantitative immunofluorescence analyses revealed alterations in the copy number, expression, and cellular distribution of several proteins known to be involved in the mechanics and regulation of chromosome dynamics during mitosis. Together, these results provide evidence that an imbalance in the amount of proteins implicated in the attachment of chromosomes to spindle microtubules is the molecular mechanism underlying mitotic slippage in EAC. Our findings that the likely origin of polyploidy in EAC is mitotic failure caused by problems in chromosomal attachments not only improves our understanding of cancer evolution and diversification, but may also aid in the classification and treatment of EAC and possibly other highly heterogeneous cancers.

cancer biology↗

bioRxiv2020

Machine Classification of Methylomes in Cancer

Cancer remains a leading cause of morbidity and mortality worldwide. Its evolutionary nature and resultant complex interactions with the tumour micro-environment and the host immune system engender heterogeneity, make developing interventions difficult. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of this insidious disease. Epigenetic changes such as DNA methylation are some of the early events in carcinogenesis. Here, we report on a machine learning model that can classify 13 types of cancer as well as non-cancer tissue samples using only DNA methylome data, with an accuracy of 98.2%. We utilise the features identified by this model to develop a robust deep neural network that can generalise to independent data sets. We also demonstrate that the methylation associated genomic loci detected by the classifier are associated with genes involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

bioinformatics↗

bioRxiv2018

The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic

Esophageal Adenocarcinoma (EAC) is a poor prognosis cancer type with rapidly rising incidence. Our understanding of genetic events which drive EAC development is limited and there are few molecular biomarkers for prognostication or therapeutics. We have accumulated a cohort of 551 genomically characterised EACs (73% WGS and 27% WES) with clinical annotation and matched RNA-seq. Using a variety of driver gene detection methods, we discover 77 EAC driver genes (73% novel) and 21 non-coding driver elements (95% novel), and describe mutation and CNV types with specific functional impact. We identify a mean of 4.4 driver events per case derived from both copy number events and mutations. We compare driver mutation rates to the exome-wide mutational excess calculated using Non-synonymous vs Synonymous mutation rates (dNdS). We observe mutual exclusivity or co-occurrence of events within and between a number of EAC pathways (GATA factors, Core Cell cycle genes, TP53 regulators and the SWI/SNF complex) suggestive of important functional relationships. These driver variants correlate with tumour differentiation, sex and prognosis. Poor prognostic indicators (SMAD4, GATA4) are verified in independent cohorts with significant predictive value. Over 50% of EACs contain sensitising events for CDK4/6 inhibitors which are highly correlated with clinically relevant sensitivity in a panel EAC cell lines and organoids.

cancer biology↗