Improving biomarker assessment in breast pathology

The accuracy of prognostic and therapy-predictive biomarker assessment in breast tumours is crucial for management and therapy decision in patients with breast cancer. In this thesis, biomarkers used in clinical practice with emphasise on Ki67 and HER2 were studied using several methods including immunocytochemistry, in situ hybridisation, gene expression assays and digital image analysis, with the overall aim to improve routine biomarker evaluation and clarify the prognostic potential in early breast cancer. In paper I, we reported discordances in biomarker status from aspiration cytology and paired surgical specimens from breast tumours. The limited prognostic potential of immunocytochemistry-based Ki67 scoring demonstrated that immunohistochemistry on resected specimens is the superior method for Ki67 evaluation. In addition, neither of the methods were sufficient to predict molecular subtype. Following this in paper II, biomarker agreement between core needle biopsies and subsequent specimens was investigated, both in the adjuvant and neoadjuvant setting. Discordances in Ki67 and HER2 status between core biopsies and paired specimens suggested that these biomarkers should be re-tested on all surgical breast cancer specimens. In paper III, digital image analysis using a virtual double staining software was used to compare methods for assessment of proliferative activity, including mitotic counts, Ki67 and the alternative marker PHH3, in different tumour regions (hot spot, invasive edge and whole section). Digital image analysis using virtual double staining of hot spot Ki67 outperformed the alternative markers of proliferation, especially in discriminating luminal B from luminal A tumours. Replacing mitosis in histological grade with hot spot-scored Ki67 added significant prognostic information. Following these findings, the optimal definition of a hot spot for Ki67 scoring using virtual double staining in relation to molecular subtype and outcome was investigated in paper IV. With the growing evidence of global scoring as a superior method to improve reproducibility of Ki67 scoring, a different digital image analysis software (QuPath) was also used for comparison. Altogether, we found that automated global scoring of Ki67 using QuPath had independent prognostic potential compared to even the best virtual double staining hot spot algorithm, and is also a practical method for routine Ki67 scoring in breast pathology. In paper V, the clinical value of HER2 status was investigated in a unique trastuzumab-treated HER2-positive cohort, on the protein, mRNA and DNA levels. The results demonstrated that low levels of ERBB2 mRNA but neither HER2 copy numbers, HER2 ratio nor ER status, was associated with risk of recurrence among anti-HER2 treated breast cancer patients. In conclusion, we have identified important clinical aspects of Ki67 and HER2 evaluation and provided methods to improve the prognostic potential of Ki67 using digital image analysis. In addition to protein expression of routine biomarkers, mRNA levels by targeted gene expression assays may add further prognostic value in early breast cance

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study

Objective To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches. Design Population based cohort study. Setting QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers. Participants 141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020. Main outcome measures Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility. Results During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model’s random effects meta-analysis pooled estimate for Harrell’s C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell’s C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell’s C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches. Conclusion In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up

Artificial intelligence for breast cancer precision pathology

Breast cancer is the most common cancer type in women globally but is associated with a continuous decline in mortality rates. The improved prognosis can be partially attributed to effective treatments developed for subgroups of patients. However, nowadays, it remains challenging to optimise treatment plans for each individual. To improve disease outcome and to decrease the burden associated with unnecessary treatment and adverse drug effects, the current thesis aimed to develop artificial intelligence based tools to improve individualised medicine for breast cancer patients. In study I, we developed a deep learning based model (DeepGrade) to stratify patients that were associated with intermediate risks. The model was optimised with haematoxylin and eosin (HE) stained whole slide images (WSIs) with grade 1 and 3 tumours and applied to stratify grade 2 tumours into grade 1-like (DG2-low) and grade 3-like (DG2-high) subgroups. The efficacy of the DeepGrade model was validated using recurrence free survival where the dichotomised groups exhibited an adjusted hazard ratio (HR) of 2.94 (95% confidence interval [CI] 1.24-6.97, P = 0.015). The observation was further confirmed in the external test cohort with an adjusted HR of 1.91 (95% CI: 1.11-3.29, P = 0.019). In study II, we investigated whether deep learning models were capable of predicting gene expression levels using the morphological patterns from tumours. We optimised convolutional neural networks (CNNs) to predict mRNA expression for 17,695 genes using HE stained WSIs from the training set. An initial evaluation on the validation set showed that a significant correlation between the RNA-seq measurements and model predictions was observed for 52.75% of the genes. The models were further tested in the internal and external test sets. Besides, we compared the model's efficacy in predicting RNA-seq based proliferation scores. Lastly, the ability of capturing spatial gene expression variations for the optimised CNNs was evaluated and confirmed using spatial transcriptomics profiling. In study III, we investigated the relationship between intra-tumour gene expression heterogeneity and patient survival outcomes. Deep learning models optimised from study II were applied to generate spatial gene expression predictions for the PAM50 gene panel. A set of 11 texture based features and one slide average gene expression feature per gene were extracted as input to train a Cox proportional hazards regression model with elastic net regularisation to predict patient risk of recurrence. Through nested cross-validation, the model dichotomised the training cohort into low and high risk groups with an adjusted HR of 2.1 (95% CI: 1.30-3.30, P = 0.002). The model was further validated on two external cohorts. In study IV, we investigated the agreement between the Stratipath Breast, which is the modified, commercialised DeepGrade model developed in study I, and the Prosigna® test. Both tests sought to stratify patients with distinct prognosis. The outputs from Stratipath Breast comprise a risk score and a two-level risk stratification whereas the outputs from Prosigna® include the risk of recurrence score and a three-tier risk stratification. By comparing the number of patients assigned to ‘low’ or ‘high’ risk groups, we found an overall moderate agreement (76.09%) between the two tests. Besides, the risk scores by two tests also revealed a good correlation (Spearman's rho = 0.59, P = 1.16E-08). In addition, a good correlation was observed between the risk score from each test and the Ki67 index. The comparison was also carried out in the subgroup of patients with grade 2 tumours where similar but slightly dropped correlations were found

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response.

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Specialized Named Entity Recognition For Breast Cancer Subtyping

The amount of data and analysis being published and archived in the biomedical research community is more than can feasibly be sifted through manually, which limits the information an individual or small group can synthesize and integrate into their own research. This presents an opportunity for using automated methods, including Natural Language Processing (NLP), to extract important information from text on various topics. Named Entity Recognition (NER), is one way to automate knowledge extraction of raw text. NER is defined as the task of identifying named entities from text using labels such as people, dates, locations, diseases, and proteins. There are several NLP tools that are designed for entity recognition, but rely on large established corpus for training data. Biomedical research has the potential to guide diagnostic and therapeutic decisions, yet the overwhelming density of publications acts as a barrier to getting these results into a clinical setting. An exceptional example of this is the field of breast cancer biology where over 2 million people are diagnosed worldwide every year and billions of dollars are spent on research. Breast cancer biology literature and research relies on a highly specific domain with unique language and vocabulary, and therefore requires specialized NLP tools which can generate biologically meaningful results. This thesis presents a novel annotation tool, that is optimized for quickly creating training data for spaCy pipelines as well as exploring the viability of said data for analyzing papers with automated processing. Custom pipelines trained on these annotations are shown to be able to recognize custom entities at levels comparable to large corpus based recognition

Locoregional stage assessment in clinically node negative breast cancer: Clinical, imaging, pathologic, and statistical methods

The locoregional staging remains an essential part of prognostication in breast cancer. Tumour size and biology, together with the number of lymph node metastases, guide the planning of appropriate treatments. Accurate clinical, imaging, pathologic, and statistical staging is needed as the surgical staging diminishes. In this study, 743 clinically lymph node negative breast cancer patients treated in 2009‒2017 were evaluated. Clinopathological factors were investigated in association with the number of lymph node metastases, the use of preoperative imaging methods and the surgical treatment method. A nomogram was developed and tested to predict the number of lymph node metastases after sentinel lymph node positivity. Three previously published models were validated to confirm their feasibility in the current population to predict nodal stage pN2a or pN3a. Tumour size, biologic subtype and proliferation associated with higher numbers of lymph node metastases. To predict stage pN2a or pN3a, the machine learning algorithms identified tumour size, invasive ductal histology, multifocality, lymphovascular invasion, oestrogen receptor status and the number of positive sentinel lymph nodes as risk factors. The nomograms performed well with favourable discrimination. Clinopathological factors seemed to guide preoperative magnetic resonance imaging (MRI) prior to more extensive surgery. MRI estimated the increasing tumour size more accurately than mammography or ultrasound. According to this study, clinopathological factors, additional preoperative MRI and modern statistics can be utilized in breast cancer staging without extensive surgical interference. The importance of non-surgical investigations in staging is growing in the planning of surgical, systemic and radiation treatments. Thus, maintaining the impressive survival outcomes of clinically node negative breast cancer patients can be achieved.Kliinisesti imusolmukenegatiivisen rintasyövän paikallislevinneisyyden arvioiminen. Kliiniset, kuvantamisen, patologian alan ja tilastotieteen menetelmät Kasvaimen paikallinen levinneisyys on tärkeä rintasyövän ennustetekijä. Kasvaimen koko ja biologia sekä imusolmukemetastaasien lukumäärä ohjaavat syöpähoitojen suunnittelua. Levinneisyyden selvittelyssä tarvitaan huolellista kliinistä tutkimusta sekä tarkkoja kuvantamisen, patologian alan ja tilastotieteen menetelmiä, kun kirurginen levinneisyysluokittelu vähenee. Tutkimuksessa arvioitiin vuosina 2009‒2017 hoidettujen 743 kliinisesti imusolmukenegatiivisen suomalaisen potilaan tietoja. Työssä selvitettiin kliinispatologisten tekijöiden ja kainaloimusolmukemetastaasien lukumäärän, leikkausta edeltävien kuvantamistutkimusten sekä leikkausmenetelmien yhteyttä. Ennustemalli kehitettiin ja koekäytettiin positiivisen vartijaimusolmuketutkimuksen jälkeisen imusolmukemetastaasien määrän arvioimiseksi. Kolme aiemmin julkaistua mallia validoitiin, jotta niiden käyttökelpoisuus imusolmukeluokan pN2a tai pN3a ennustamisessa varmistuisi tässä aineistossa. Kasvainkoko, biologinen alatyyppi ja jakautumisnopeus olivat yhteydessä suurempaan imusolmukemetastaasien määrään. Koneoppimisalgoritmit määrittivät levinneisyysluokan pN2a tai pN3a ennustamiseksi tarvittaviksi tekijöiksi kasvainkoon, invasiivisen duktaalisen histologian, monipesäkkeisyyden, suoni-invaasion, estrogeenireseptoristatuksen sekä positiivisten vartijaimusolmukkeiden määrän. Ennustemallit toimivat aineistossa hyvin osoittaen suotuisaa erotuskykyä. Kliinispatologiset tekijät näyttivät ohjaavan magneettikuvauspäätöstä ennen laajaa kirurgista hoitoa. Magneettikuvaus oli tarkin kuvantamismenetelmä suurenevan kasvainkoon arvioinnissa. Tämän tutkimuksen perusteella kliinispatologiset tekijät, leikkausta edeltävä täydentävä magneettikuvaus ja nykyaikaiset tilastotieteen menetelmät voivat hyödyttää rintasyövän levinneisyysluokittelua ilman laajoja kirurgisia toimenpiteitä. Kajoamattomien tutkimusten asema levinneisyysluokittelussa on vahvistumassa kirurgisten, lääkkeellisten ja sädehoitojen suunnittelun yhteydessä. Tarkka levinneisyysluokittelu edesauttaa kliinisesti imusolmukenegatiivisten rintasyöpäpotilaiden erinomaista ennustetta

Better prognostic markers for nonmuscle invasive papillary urothelial carcinomas

Bladder cancer is a common type of cancer, especially among men in developed countries. Most cancers in the urinary bladder are papillary urothelial carcinomas. They are characterized by a high recurrence frequency (up to 70 %) after local resection. It is crucial for prognosis to discover these recurrent tumours at an early stage, especially before they become muscle-invasive. Reliable prognostic biomarkers for tumour recurrence and stage progression are lacking. This is why patients diagnosed with a non-muscle invasive bladder cancer follow extensive follow-up regimens with possible serious side effects and with high costs for the healthcare systems. WHO grade and tumour stage are two central biomarkers currently having great impact on both treatment decisions and follow-up regimens. However, there are concerns regarding the reproducibility of WHO grading, and stage classification is challenging in small and fragmented tumour material. In Paper I, we examined the reproducibility and the prognostic value of all the individual microscopic features making up the WHO grading system. Among thirteen extracted features there was considerable variation in both reproducibility and prognostic value. The only feature being both reasonably reproducible and statistically significant prognostic was cell polarity. We concluded that further validation studies are needed on these features, and that future grading systems should be based on well-defined features with true prognostic value. With the implementation of immunotherapy, there is increasing interest in tumour immune response and the tumour microenvironment. In a search for better prognostic biomarkers for tumour recurrence and stage progression, in Paper II, we investigated the prognostic value of tumour infiltrating immune cells (CD4, CD8, CD25 and CD138) and previously investigated cell proliferation markers (Ki-67, PPH3 and MAI). Low Ki 67 and tumour multifocality were associated with increased recurrence risk. Recurrence risk was not affected by the composition of immune cells. For stage progression, the only prognostic immune cell marker was CD25. High values for MAI was also strongly associated with stage progression. However, in a multivariate analysis, the most prognostic feature was a combination of MAI and CD25. BCG-instillations in the bladder are indicated in intermediate and high-risk non-muscle invasive bladder cancer patients. This old-fashion immunotherapy has proved to reduce both recurrence- and progression-risk, although it is frequently followed by unpleasant side-effects. As many as 30-50% of high-risk patients receiving BCG instillations, fail by develop high-grade recurrences. They do not only suffer from unnecessary side-effects, but will also have a delay in further treatment. Together with colleagues at three different Dutch hospitals, in Paper III, we looked at the prognostic and predictive value of T1-substaging. A T1-tumour invades the lamina propria, and we wanted to separate those with micro- from those with extensive invasion. We found that BCG-failure was more common among patients with extensive invasion. Furthermore, T1-substaging was associated with both high-grade recurrence-free and progression-free survival. Finally, in Paper IV, we wanted to investigate the prognostic value of two classical immunohistochemical markers, p53 and CK20, and compare them with previously investigated proliferation markers. p53 is a surrogate marker for mutations in the gene TP53, considered to be a main characteristic for muscle-invasive tumours. CK20 is a surrogate marker for luminal tumours in the molecular classification of bladder cancer, and is frequently used to distinguish reactive urothelial changes from urothelial carcinoma in situ. We found both positivity for p53 and CK20 to be significantly associated with stage progression, although not performing better than WHO grade and stage. The proliferation marker MAI, had the highest prognostic value in our study. Any combination of variables did not perform better in a multivariate analysis than MAI alone

Highlights lecture EANM 2016: "Embracing molecular imaging & multi-modal imaging: a smart move for nuclear medicine towards personalised medicine"

The 2016 EANM Congress took place in Barcelona, Spain, from 15 to 19 October under the leadership of Prof. Wim Oyen, chair of the EANM Scientific Committee. With more than 6,000 participants, this congress was the most important European event in nuclear medicine, bringing together a multidisciplinary community involved in the different fields of nuclear medicine. There were over 600 oral and 1,200 poster or e-Poster presentations with an overwhelming focus on development and application of imaging for personalized care, which is timely for the community. Beyond FDG PET, major highlights included progress in the use of PSMA and SSTR receptor-targeted radiopharmaceuticals and associated theranostics in oncology. Innovations in radiopharmaceuticals for imaging pathologies of the brain and cardiovascular system, as well as infection and inflammation, were also highlighted. In the areas of physics and instrumentation, multimodality imaging and radiomics were highlighted as promising areas of research

Predicting breast cancer risk, recurrence and survivability

This thesis focuses on predicting breast cancer at early stages by using machine learning algorithms based on biological datasets. The accuracy of those algorithms has been improved to enable the physicians to enhance the success of treatment, thus saving lives and avoiding several further medical tests

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 stateof- the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression