Using machine learning to support better and intelligent visualisation for genomic data

Massive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Great technological advances in methods of characterising the human diseases, including genetic and environmental factors, make it a great opportunity to understand the diseases and to find new diagnoses and treatments. Translating medical data becomes more and more rich and challenging. Visualisation can greatly aid the processing and integration of complex data. Genomic data visual analytics is rapidly evolving alongside with advances in high-throughput technologies such as Artificial Intelligence (AI), and Virtual Reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data effectively and speed up expert decisions about the best treatment of an individual patient’s needs. However, meaningful visual analysis of such large genomic data remains a serious challenge. Visualising these complex genomic data requires not only simply plotting of data but should also lead to better decisions. Machine learning has the ability to make prediction and aid in decision-making. Machine learning and visualisation are both effective ways to deal with big data, but they focus on different purposes. Machine learning applies statistical learning techniques to automatically identify patterns in data to make highly accurate prediction, while visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. Clinicians, experts and researchers intend to use both visualisation and machine learning to analyse their complex genomic data, but it is a serious challenge for them to understand and trust machine learning models in the serious medical industry. The main goal of this thesis is to study the feasibility of intelligent and interactive visualisation which combined with machine learning algorithms for medical data analysis. A prototype has also been developed to illustrate the concept that visualising genomics data from childhood cancers in meaningful and dynamic ways could lead to better decisions. Machine learning algorithms are used and illustrated during visualising the cancer genomic data in order to provide highly accurate predictions. This research could open a new and exciting path to discovery for disease diagnostics and therapies

Immersive analytics for oncology patient cohorts

This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness

Comparison of visualization methods of genome-wide SNP profiles in childhood acute lymphoblastic leukaemia

Data mining and knowledge discovery have been applied to datasets in various industries including biomedical data. Modelling, data mining and visualization in biomedical data address the problem of extracting knowledge from large and complex biomedical data. The current challenge of dealing with such data is to develop statistical-based and data mining methods that search and browse the underlying patterns within the data. In this paper, we employ several data reduction methods for visualizing genome- wide Single Nucleotide Polymorphism (SNP) datasets based on state-of-art data reduction techniques. Visualization approach has been selected based on the trustworthiness of the resultant visualizations. To deal with large amounts of genetic variation data, we have chosen to apply different data reduction methods to deal with the problem induced by high dimensionality. Based on the trustworthiness metric we found that neighbour Retrieval Visualizer (NeRV) outperformed other methods. This method optimizes the retrieval quality of Stochastic neighbour Embedding. The quality measure of the visualization (i.e. NeRV) showed excellent results, even though the dataset was reduced from 13917 to 2 dimensions. The visualization results will assist clinicians and biomedical researchers in understanding the systems biology of patients and how to compare different groups of clusters in visualizations. © 2008, Australian Computer Society, Inc

Biological characterization of Philadelphia chromosome-positive acute lymphoblastic leukemia

The prognosis of Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) has significantly improved with the introduction of tyrosine kinase inhibitors (TKIs). As the incidence of Ph-positivity increases with age, a substantial number of elderly Ph+ ALL patients are ineligible for intensive treatment modalities. Currently, a proportion of patients experience prolonged survival with TKI-based therapies only, and many succumb eventually to non leukemia-related causes. The aim of this thesis was to identify potential predictive biomarkers for more personalized risk stratification in Ph+ ALL, including characterization of the immune microenvironment in ALL bone marrow (BM). We also wanted to assess the drug sensitivity of primary patient samples to identify potential novel or repurposed drugs, with especially non-fit patients in mind, and to study the prevalence of copy number alterations and other secondary mutations. In study I, we collected archived formalin-fixed and paraffin-embedded BM biopsies from Ph+ (n = 31) and Philadelphia chromosome-negative (Ph−; n = 21) ALL patients and non-leukemic controls (n = 14). The samples were constructed to tissue microarrays and analyzed with multiplex immunohistochemistry and automated image analysis. The immune contexture of Ph+ and Ph− ALL BM did not differ significantly. Instead, ALL BM was characterized by an increased amount of immune cells associated with immunosuppression when compared to healthy controls. Further, the higher proportion of CD4+PD1+TIM3+ T cells, older age, and lower platelet count at diagnosis segregated a group with poor survival. In study II, we analyzed the drug sensitivity of 18 primary B-ALL BM samples (Ph+ n=10, Ph− n=8) to a selection of 64 drugs by using a well-established drug sensitivity and resistance testing assay. The results were combined with whole transcriptome sequencing and publicly available gene expression data. Apoptosis-modulating BCL2 inhibitors and MDM2 inhibitors were widely effective. BCL2-selective venetoclax was more effective in Ph− samples, whereas BCL2, BCL-W, and BCL-XL targeting navitoclax showed uniform potency. BCL2 expression was significantly higher in Ph− ALL, whereas BCL-W and BCL-XL were overexpressed in Ph+ ALL, explaining the differential drug responses. In addition, the sequencing strategies recognized three previously undiagnosed Ph-like patients with a sensitivity to TKIs. In study III, we investigated the frequency and significance of copy number alterations (CNAs) and other secondary mutations in Ph+ ALL by applying targeted next-generation sequencing (NGS) gene panel and multiplex ligation-dependent probe amplification to diagnostic (n=40) and relapse-phase (n=11) BM samples. We also assessed the prevalence of subclonal T315I kinase domain mutations. The results were combined with clinical registry data. Deletions of IKZF1 together with deletions in CDKN2A/B and/or PAX5 were common, and they stratified a group with dismal outcome. Other secondary mutations at diagnosis were rare. In conclusion, this thesis shows Ph+ ALL BM immune contexture did not differ from Ph− ALL. Instead, ALL BM immune microenvironment differs from healthy controls, and immune profiling can serve as a tool in identifying novel prognostic biomarkers. Copy number alterations (CNA) defined a subset in Ph+ ALL with dismal outcome, and we recommend incorporating CNA analysis to routine diagnostic procedures. In addition, with ex vivo drug testing, we identified several potential compounds to be further tested in clinical trials.Tyrosiinikinaasiestäjät (TKE) ovat parantaneet merkittävästi Philadelphia-kromosomipositiivisen (Ph+) akuutin lymfaattisen leukemian (ALL) ennustetta. Koska Ph+ ALL :n yleisyys kasvaa iän myötä, merkittävää osaa näistä iäkkäämmistä tai heikkokuntoisemmista potilaista ei voida kuitenkaan hoitaa tavanomaisilla intensiivisillä hoito-ohjelmilla hoitoon liittyvien haittojen vuoksi. Toisaalta osa potilaista saa hyvän vasteen pelkälle TKE-pohjaiselle kevennetylle hoidolle, ja monet menehtyvät lopulta leukemiaan liittymättömiin syihin. Tämän väitöskirjatyön tavoitteena oli selvittää potentiaalisia biomarkkereita Ph+ ALL :n yksilöllisemmän riskinarvioinnin kehittämiseksi, sekä kuvata immuunijärjestelmän koostumusta ALL :n luuytimen mikroympäristössä. Analysoimme myös potilasnäytteiden herkkyyttä lupaaville lääkeaineille ajatellen erityisesti hauraampien potilaiden ilmeistä tarvetta tehokkaille ja samalla turvallisille lääkehoidoille. Arvioimme myös kopiolukumuutosten ja muiden sekundaaristen mutaatioiden esiintyvyyttä Ph+ ALL:ssa. Ensimmäisessä osatyössä keräsimme vanhoja luuydinbiopsioita Ph+ (n=31) ja Philadelphia-kromosominegatiivista (Ph−; n=21) ALL:ia sairastavilta potilailta sekä terveiltä kontrolleilta (n=14). Näytteistä koostetut kudosblokit värjättiin multipleksatulla immunohistokemialla ja analysoitiin käyttäen apuna automatisoitua kuva-analyysia. Ph+ ja Ph− ALL -potilaiden luuytimen immunologinen mikroympäristö ei eronnut merkittävästi toisistaan. Sen sijaan ALL-potilailla immuunivasteen heikentämiseen liittyvien solutyyppien osuus oli korostunut verrattuna terveisiin kontrolleihin. Lisäksi CD4+PD1+TIM3+ T-solujen suurempi osuus, korkeampi ikä sekä matalampi verihiutaleiden määrä diagnoosihetkellä erottelivat monimuuttujamallissa ALL-potilaista huonoennusteisen ryhmän. Toisessa osatyössä analysoimme 18 potilasnäytteen (Ph+ n=10, Ph− n=8) herkkyyttä 64 eri lääkeaineelle käyttämällä vakiintunutta lääkeherkkyystestausmenetelmää. Näytteistä tehtiin myös RNA-sekvensointi, sekä tulokset yhdistettiin julkisista tietokannoista saatavilla olevaan geenien ilmentymistä kuvaavaan dataan. Ohjelmoitua solukuolemaa edistävät BCL2:n ja MDM2:n estäjät olivat tehokkaita valtaosassa näytteitä. Valikoivasti BCL2:een kohdistuva venetoklaksi oli tehokkaampi Ph− näytteissä, kun taas laajemmin BCL2:een, BCL-W:een sekä BCL-XL:ään kohdistuva navitoklaksi oli tehokas lähes kaikissa näytteissä. BCL2-geenin ilmentyminen oli lisääntynyt Ph− ALL-potilailla, kun taas BCL-W- ja BCL-XL-geenien ilmentymistasot olivat korkeampia Ph+ ALL:ssa tarjoten samalla mekanistisen selityksen eroille lääkevasteissa. Sekvensointi tunnisti lisäksi kolmen Ph− potilaan näytteessä geneettisiä muutoksia, jotka aiheuttivat herkkyyttä TKE-lääkkeille. Kolmannessa osatyössä selvitimme kopiolukumuutosten ja muiden sekundaaristen geneettisen muutosten yleisyyttä ja merkitystä Ph+ ALL:ssa hyödyntämällä kohdennettua syväsekvensointia sekä MLPA-menetelmää (MLPA, multiplex ligation-dependent probe amplification) diagnoosi- (n=40) ja relapsivaiheen (n=11) luuydinnäytteissä. Arvioimme myös subklonaalisten T315I kinaasialueen mutaatioiden esiintyvyyttä. Tulokset analysoitiin yhdessä kliinisen rekisteridatan kanssa. IKZF1-geenin deleetiot yhdessä CDKN2A/B ja/tai PAX5-geenin deleetioiden kanssa olivat yleisiä ja erottelivat erityisen huonon ennusteen ryhmän. Muita sekundaarisia geneettisiä muutoksia esiintyi lähinnä relapsivaiheen näytteissä. Tässä väitöskirjatyössä osoitimme, että Ph+ ALL:ia ja Ph− ALL:ia sairastavien potilaiden luuytimen immunologinen mikroympäristö ei eronnut merkittävästi toisistaan. Sen sijaan ALL:n luuytimen immunologinen mikroympäristö erosi terveistä kontrolleista, ja immuunijärjestelmän profilointia voidaan hyödyntää etsittäessä uusia ennusteeseen vaikuttavia biomarkkereita. Yhdistelmä epäsuotuisia kopiolukumuutoksia erotteli huonon ennusteen alaryhmän Ph+ ALL:ssa, ja suosittelemme kopiolukumuutosten rutiininomaista määrittämistä diagnoosivaiheessa. Lisäksi tunnistimme ex vivo -lääkeherkkyystestauksella useita ALL:n kliinisiin lääketutkimuksiin soveltuvia, lupaavia lääkeaineita

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

Introduction The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction). Area covered In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions. Expert opinion The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.Peer reviewe

Integrative Molecular Pathological Epidemiology of Congenital and Infant Acute Leukemia

Congenital and infant acute leukemia remain one of the most puzzling clinical issues in pediatric hematology-oncology. There is a paucity of studies focused on these rare, aggressive, acute leukemias; specifically, there is little study on the differences in disease in the youngest of infants less than 1 year of age unlike the numerous studies of the disease in older children. The United States National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) cancer population registry program has been integral for a plethora of clinical population and pathology research studies for numerous diseases in the last 40 years and has an excellent resource for investigation of the infant population. Laboratory medicine and pathology professionals must use pathology results not only to diagnose individuals after the disease has been discovered, but the information must be applied retrospectively to develop new testing strategies. By classifying the intense heterogeneity within these cancers, the distinct changes of the diseases within individuals can be established, ultimately reshaping diagnostic methodologies. Through the application of Integrative Molecular Pathological Epidemiology to a 325-infant case series from the SEER program from 2008 to 2014, this dissertation study was used to evolve the classification of these pediatric cancers with the application of scientific nosology. This dissertation study has documented characteristics of this population for application in further precision medicine investigations to influence laboratory medicine algorithms for diagnosis and management of patients guiding health policy that are aimed at improving outcomes in the youngest of children

An automated classification system for leukocyte morphology in acute myeloid Leukemia

Diagnosis of hematological malignancies and of acute myeloid leukemia in particular have undergone wide-ranging advances in recent years, driven by an increasingly detailed knowledge of its underlying biological and genetic mechanisms. Nevertheless, cytomorphologic evaluation of samples of peripheral blood and bone marrow is still an integral part of the routine diagnostic workup. Microscopic analysis of these samples has so far defied automation and is still mainly performed by human cytologists manually classifying and counting relevant cell populations. Access to this diagnostic modality is therefore limited by the number and availability of educated cytologists. Furthermore, its results rest on judgments of examiners, which may vary according to their education and experience, rendering rigorous quantification and standardization of the method difficult. In this thesis, an approach to cytomorphologic classification is presented that aims to harness recent advances in computational image classification for leukocyte differentiation using Deep Learning techniques that derive from the domain of Artificial Intelligence. In a first stage of the project, peripheral blood smear samples from both AML patients and controls were scanned using techniques from digital pathology. Experienced cytologists from the Laboratory of Leukemia Diagnostics at the LMU Klinikum annotated the digitized samples according to a scheme of 15 morphological categories derived from standard routine diagnostics. The resulting set of over 18,000 annotated single-cell images is the largest public database of leukocyte morphologies in leukemia available today. In a second step, the compiled dataset was used to develop a neural network that is able to classify leukocytes into the standard morphological scheme. Evaluation of network predictions show that the network performs well at the classification task for most clinically relevant categories, with an error pattern similar to that of human examiners. The network can also be employed to answer two questions of immediate clinical relevance, namely if a given single-cell image shows a blast-like cell, or if it belongs to the set of atypical cells which are not present in peripheral blood smears under physiological conditions. At these questions, the network is found to show similar and slightly better performance compared to the human examiner. These results show the potential of Deep Learning techniques in the field of hematological diagnostics and suggest avenues for their further development as a helpful tool of leukemia diagnostics.In der Diagnostik hämatologischer Erkrankungen wie der akuten myeloischen Leukämie haben sich in den vergangenen Jahren bedeutende Fortschritte ergeben, die vor allem auf einem vertieften Verständnis ihrer biologischen und genetischen Ursachen beruhen. Trotzdem spielt die zytomorphologische Untersuchung von Blut- und Knochenmarkspräparaten nach wie vor eine zentrale Rolle in der diagnostischen Aufarbeitung. Die mikroskopische Begutachtung dieser Präparate konnte bisher nicht automatisiert werden und erfolgt nach wie vor durch menschliche Befunder, die eine manuelle Differentierung und Auszählung relevanter Zelltypen vornehmen. Daher ist der Zugang zu zytomorphologischen Untersuchungen durch die Zahl verfügbarer zytologischer Befunder begrenzt. Darüber hinaus beruht die Beurteilung der Präparate auf der individuellen Einschätzung der Befunder und ist somit von deren Ausbildung und Erfahrung abhängig, was eine standardisierte und quantitative Auswertung der Morphologie zusätzlich erschwert. Ziel der vorliegenden Arbeit ist es, ein computerbasiertes System zu entwickeln, die die morphologische Differenzierung von Leukozyten unterstützt. Zu diesem Zweck wird auf in den letzten Jahren entwickelte leistungsfähige Algorithmen aus dem Bereich der Künstlichen Intelligenz, insbesondere des sogenannten Tiefen Lernens zurückgegriffen. In einem ersten Schritt des Projekts wurden periphere Blutausstriche von AML-Patienten und Kontrollen mit Methoden der digitalen Pathologie erfasst. Erfahrene Befunder aus dem Labor für Leukämiediagnostik am LMU-Klinikum München annotierten die digitalisierten Präparate und differenzierten sie in ein 15-klassiges, aus der Routinediagnostik stammendes Standardschema. Auf diese Weise wurde mit über 18,000 morphologisch annotierten Leukozyten der aktuell größte öffentlich verfügbare Datensatz relevanter Einzelzellbilder zusammengestellt. In einer zweiten Phase des Projekts wurde dieser Datensatz verwendet, um Algorithmen vom Typ neuronaler Faltungsnetze zur Klassifikation von Einzelzellbilden zu trainieren. Eine Analyse ihrer Vorhersagen zeigt dass diese Netzwerke Einzelzellbilder der meisten Zellklassen sehr erfolgreich differenzieren können. Für falsch klassifizierte Bilder ähnelt ihr Fehlermuster dem menschlicher Befunder. Neben der Klassifikation einzelner Zellen erlauben die Netzwerke auch die Beantwortung gröberer, binärer Fragestellungen, etwa ob eine bestimmte Zelle blastären Charakter hat oder zu den morphologischen Klassen gehört die in einem peripheren Blutausstrich nicht unter physiologischen Bedingungen vorkommen. Bei diesen Fragen zeigen die Netzwerke eine ähnliche und leicht bessere Leistung als der menschliche Befunder. Die Ergebnisse dieser Arbeit illustrieren das Potential von Methoden der künstlichen Intelligenz auf dem Gebiet der Hämatologie und eröffnen Möglichkeiten zu ihrer Weiterentwicklung zu einem praktischen Hilfsmittel der Leukämiediagnostik

KNOWLEDGE DISCOVERY FROM GENE EXPRESSION DATA: NOVEL METHODS FOR SIMILARITY SEARCH, SIGNATURE DETECTION, AND CONFOUNDER CORRECTION

Gene expression microarray data is used to answer a variety of scientific questions. For example, it can be used for gaining a better understanding of a drug, segmenting a disease, and predicting an optimal therapeutic response. The amount of gene expression data publicly available is extremely large and continues to grow at an increasing rate. However, this rapid growth of gene expression data from laboratories across the world has not fully achieved its potential impact on the scientific community. This shortcoming is due to the fact that the majority of the data has been gathered under varying conditions, and there is no principled way for combining and fully utilizing related data. Even within a closely controlled gene expression experiment, there are confounding factors that may mask the true signatures when analyzed with current methods. Therefore, we are interested in three core tasks that we believe are important for improving the utilization of gene array data: similarity search, signature detection, and confounder correction. We have developed novel methods that address each of these tasks. In this work, we first address the similarity search problem. More specifically, we propose methods which overcome experimental barriers in pariwise gene expression similarity calculations. We introduce a method, which we refer to as indirect similarity, which, unlike previous approaches, uses all of the information in a database to better inform the similarity calculation of a pair of gene expression profiles. We demonstrate that our method is more robust and better able to cope with experimental barriers such as vehicle and batch effects. We evaluate the ability of our method to retrieve compounds with similar therapeutic effects in two independent datasets. We evaluate the recall ability of our approach and show that our method results in an improvement of 97.03% and 49.44% respectively over existing state of the art approaches. The second problem we focus on is signature detection. Gene expression experiments are performed to test a specific hypothesis. Generally, this hypothesis is that there is some genetic signature common in a group of samples. Current methods try to find the differentially expressed genes within a group of samples using a variety of methods, however, they all are parametric. We introduce a nonparametric approach to group profile creation which we refer to as the Weighted Influence Model - Rank of Ranks method. For every probe on the microarray, the average rank is calculated across all members of a group. These average ranks are then re-ranked to form the group profile. We demonstrate the ability of our group profile method to better understand a disease and the underlying mechanism common to its treatments. Additionally, we demonstrate the predictive power of this group profile to detect novel drugs that could treat a particular disease. This method leads the detection of robust group signatures even with unknown confounding effects. The final problem that we address is the challenge of removing known (annotated) confounding effects from gene expression profiles. We propose an extension to our non-parametric gene expression profile method to correct for observed confounding effects. This correction is performed on ranked lists directly, and it provides a robust alternative to parametric batch profile correction methods. We evaluate our novel profile subtraction method on two real world datasets, comparing against several state-of-the-art parametric methods. We demonstrate an improvement in group signature detection using our method to remove confounding effects. Additionally, we show that in a dataset with the true group assignments removed and only the confounding effects labelled, our profile subtraction method allows for the discovery of the true groups. We evaluate the robustness of our methods using a gene expression profile generator that we developed

The Long Term Mental Health of Survivors of Childhood and Young Adult Cancers

Since the 1970s, cancer in children and young people has become both increasingly common and more survivable. Whilst physical late effects of cancer are well documented, less is known about long-term mental health. A systematic review highlighted increased mental ill health amongst childhood and young adult cancer survivors. However, few studies included clinician-diagnosed mental health problems, and no population-based studies were found. The Yorkshire Specialist Register of Cancer in Children and Young People was used to identify 7253 long-term survivors of early-life cancer. Records from routinely collected mental health data sets were used to identify individuals who had had contact with specialist mental health services, or who had a recorded mental health condition during an inpatient hospital stay. These were compared with population rates of specialist mental health services use and recorded mental health conditions, and standardised incidence ratios were calculated. Logistic regression was used to identify sub-groups at increased risk of mental health difficulties. Cancer survivors were 73.7% more likely than the general population to have a recorded contact with specialist mental health services, but no more likely to have a recorded mental health diagnosis during an inpatient stay. Teenagers and young adults treated on specialist teenage and young adult units had more specialist mental health services contacts than those treated on standard wards. The increased risk of mental health services use amongst cancer survivors should prompt clinicians to routinely enquire about mental health during contacts with this cohort. The increased risk amongst teenagers and young adults treated on specialist units was surprising, and it is unclear whether this represents a true increase in prevalence of mental ill health, or simply improved access to specialist services. Further work to understand the reasons behind increased mental health services use is essential, and should include analysis of primary care records