Deep Learning in Cancer Biology

Deep learning methods have significantly advanced the state of computer vision and natural language processing. Their ability to discover intricate patterns in ever-expanding datasets is critical in solving cancer biology problems. However, cancer biology poses unique challenges. Typical input data, such as tumor images and DNA sequences, have significantly different semantic contexts than the traditional datasets used to train the deep learning methods. Thus, it is infeasible to leverage large pre-trained networks and requires training from scratch. Moreover, these data types are not human readable, making it difficult to annotate the data and interpret what the model has learned. This thesis aims to resolve these challenges and solve three urgent cancer biology problems using deep learning methods.Cancer is mediated through various mechanisms. One such mechanism is circular extrachromosomal DNA (ecDNA), one of the primary drivers of oncogene amplification. EcDNA is prevalent across a wide variety of cancer types and leads to worse patient survival. Thus, there is a critical need for tools to study these genomic lesions. However, it is difficult to understand various facets of ecDNA just through sequence-based methods and requires image-based reconstructions. I first present ecSeg, a deep learning tool to reconstruct ecDNA in images of tumor cells in metaphase. EcSeg uses a fully convolutional network and traditional computer vision techniques to semantically segment ecDNA. EcSeg correlates these segmentations with amplification profiles to reveal ecDNA mechanics and their resistance to drug therapy. To translate ecSeg to clinical practice, I present ecSeg-i to resolve the ecDNA status of interphase cells in cancer patient tissue. Tissue images primarily contain interphase cells in which the DNA is loosely wound, making it extremely challenging to distinguish ecDNA. EcSeg-i uses a DenseNet to determine the ecDNA status and amplification profiles of cancer patient tissue. Lastly, I present DeepViFi to identify oncoviral infections in cancer genomes. Rapidly mutating oncoviruses, such as HPV, can infect the host and disrupt various biological pathways, sometimes causing hybrid human-viral ecDNA to appear. DeepViFi is a transformer-based tool which uses an openset framework to embed DNA reads and detect oncoviral infections in next-generation sequencing data

EcSeg: Semantic Segmentation of Metaphase Images Containing Extrachromosomal DNA.

Oncogene amplification is one of the most common drivers of genetic events in cancer, potently promoting tumor development, growth, and progression. The recent discovery that oncogene amplification commonly occurs on extrachromosomal DNA, driving intratumoral genetic heterogeneity and high copy number owing to its non-chromosomal mechanism of inheritance, raises important questions about how the subnuclear location of amplified oncogenes mediates tumor pathogenesis. Next-generation sequencing is powerful but does not provide spatial resolution for amplified oncogenes, and new approaches are needed for accurately quantifying oncogenes located on ecDNA. Here, we introduce ecSeg, an image analysis tool that integrates conventional microscopy with deep neural networks to accurately resolve ecDNA and oncogene amplification at the single cell level

NAD metabolic dependency in cancer is shaped by gene amplification and enhancer remodelling.

Precision oncology hinges on linking tumour genotype with molecularly targeted drugs1; however, targeting the frequently dysregulated metabolic landscape of cancer has proven to be a major challenge2. Here we show that tissue context is the major determinant of dependence on the nicotinamide adenine dinucleotide (NAD) metabolic pathway in cancer. By analysing more than 7,000 tumours and 2,600 matched normal samples of 19 tissue types, coupled with mathematical modelling and extensive in vitro and in vivo analyses, we identify a simple and actionable set of rules. If the rate-limiting enzyme of de novo NAD synthesis, NAPRT, is highly expressed in a normal tissue type, cancers that arise from that tissue will have a high frequency of NAPRT amplification and be completely and irreversibly dependent on NAPRT for survival. By contrast, tumours that arise from normal tissues that do not express NAPRT highly are entirely dependent on the NAD salvage pathway for survival. We identify the previously unknown enhancer that underlies this dependence. Amplification of NAPRT is shown to generate a pharmacologically actionable tumour cell dependence for survival. Dependence on another rate-limiting enzyme of the NAD synthesis pathway, NAMPT, as a result of enhancer remodelling is subject to resistance by NMRK1-dependent synthesis of NAD. These results identify a central role for tissue context in determining the choice of NAD biosynthetic pathway, explain the failure of NAMPT inhibitors, and pave the way for more effective treatments

Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers.

Extrachromosomal DNA (ecDNA) amplification promotes intratumoral genetic heterogeneity and accelerated tumor evolution1-3; however, its frequency and clinical impact are unclear. Using computational analysis of whole-genome sequencing data from 3,212 cancer patients, we show that ecDNA amplification frequently occurs in most cancer types but not in blood or normal tissue. Oncogenes were highly enriched on amplified ecDNA, and the most common recurrent oncogene amplifications arose on ecDNA. EcDNA amplifications resulted in higher levels of oncogene transcription compared to copy number-matched linear DNA, coupled with enhanced chromatin accessibility, and more frequently resulted in transcript fusions. Patients whose cancers carried ecDNA had significantly shorter survival, even when controlled for tissue type, than patients whose cancers were not driven by ecDNA-based oncogene amplification. The results presented here demonstrate that ecDNA-based oncogene amplification is common in cancer, is different from chromosomal amplification and drives poor outcome for patients across many cancer types

Circular ecDNA promotes accessible chromatin and high oncogene expression.

Oncogenes are commonly amplified on particles of extrachromosomal DNA (ecDNA) in cancer1,2, but our understanding of the structure of ecDNA and its effect on gene regulation is limited. Here, by integrating ultrastructural imaging, long-range optical mapping and computational analysis of whole-genome sequencing, we demonstrate the structure of circular ecDNA. Pan-cancer analyses reveal that oncogenes encoded on ecDNA are among the most highly expressed genes in the transcriptome of the tumours, linking increased copy number with high transcription levels. Quantitative assessment of the chromatin state reveals that although ecDNA is packaged into chromatin with intact domain structure, it lacks higher-order compaction that is typical of chromosomes and displays significantly enhanced chromatin accessibility. Furthermore, ecDNA is shown to have a significantly greater number of ultra-long-range interactions with active chromatin, which provides insight into how the structure of circular ecDNA affects oncogene function, and connects ecDNA biology with modern cancer genomics and epigenetics

Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastoma

Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative 'enhancer rewiring' events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA