A novel preclinical model of the normal human breast.

Improved screening and treatment have decreased breast cancer mortality, although incidence continues to rise. Women at increased risk of breast cancer can be offered risk reducing treatments, such as tamoxifen, but this has not been shown to reduce breast cancer mortality. New, more efficacious, risk-reducing agents are needed. The identification of novel candidates for prevention is hampered by a lack of good preclinical models. Current patient derived in vitro and in vivo models cannot fully recapitulate the complexities of the human tissue, lacking human extracellular matrix, stroma, and immune cells, all of which are known to influence therapy response. Here we describe a normal breast explant model utilising a tuneable hydrogel which maintains epithelial proliferation, hormone receptor expression, and residency of T cells and macrophages over 7 days. Unlike other organotypic tissue cultures which are often limited by hyper-proliferation, loss of hormone signalling, and short treatment windows (< 48h), our model shows that tissue remains viable over 7 days with none of these early changes. This offers a powerful and unique opportunity to model the normal breast and study changes in response to various risk factors, such as breast density and hormone exposure. Further validation of the model, using samples from patients undergoing preventive therapies, will hopefully confirm this to be a valuable tool, allowing us to test novel agents for breast cancer risk reduction preclinically.

State of the Art Modelling of the Breast Cancer Metastatic Microenvironment: Where Are We?

Metastatic spread of tumour cells to tissues and organs around the body is the most frequent cause of death from breast cancer. This has been modelled mainly using mouse models such as syngeneic mammary cancer or human in mouse xenograft models. These have limitations for modelling human disease progression and cannot easily be used for investigation of drug resistance and novel therapy screening. To complement these approaches, advances are being made in ex vivo and 3D in vitro models, which are becoming progressively better at reliably replicating the tumour microenvironment and will in the future facilitate drug development and screening. These approaches include microfluidics, organ-on-a-chip and use of advanced biomaterials. The relevant tissues to be modelled include those that are frequent and clinically important sites of metastasis such as bone, lung, brain, liver for invasive ductal carcinomas and a distinct set of common metastatic sites for lobular breast cancer. These sites all have challenges to model due to their unique cellular compositions, structure and complexity. The models, particularly in vivo, provide key information on the intricate interactions between cancer cells and the native tissue, and will guide us in producing specific therapies that are helpful in different context of metastasis.

Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort.

BACKGROUND: Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort. METHODS: The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions. RESULTS: For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis. CONCLUSIONS: In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.

Systematic review: risk prediction models for metachronous advanced colorectal neoplasia after polypectomy.

BACKGROUND AND AIM: Colorectal cancer (CRC) is the fourth leading cause of cancer death globally. CRC surveillance is a common indication for colonoscopy, representing a considerable burden for endoscopy services. Accurate identification of high-risk patients who would benefit from more intensive surveillance, as well as low-risk patients suitable for less frequent follow-up, could improve the effectiveness of surveillance protocols and resource use. Our aim was to identify and critically appraise published risk models for the occurrence of metachronous advanced colorectal neoplasia (ACN), defined here as CRC or advanced adenomas detected during surveillance colonoscopy. METHODS: We searched PubMed and EMBASE for primary research studies reporting the development and/or validation of multivariable models that predict metachronous ACN risk. Screening of studies for inclusion, data extraction, and risk of bias assessment were conducted by two researchers independently. RESULTS: We identified nine studies describing nine risk models. Six models were internally validated and two were externally validated. No model underwent both internal and external validation. Good model discrimination (concordance index > 0.7) was reported for two models during internal validation and for one model during external validation. Calibration was acceptable when assessed (n = 4). Methodological limitations and a high risk of bias were observed for all studies. CONCLUSIONS: Several published models predicting metachronous ACN risk showed some promise. However, adherence to methodological standards was limited, and only two models were externally validated. Head-to-head comparisons of existing models using populations independent from model development cohorts should be prioritized to identify models suitable for use in clinical practice.

Risk-stratified screening for the early detection of kidney cancer.

Earlier detection and screening for kidney cancer has been identified as a key research priority, however the low prevalence of the disease in unselected populations limits the cost-effectiveness of screening. Risk-stratified screening for kidney cancer may improve early detection by targeting high-risk individuals whilst limiting harms in low-risk individuals, potentially increasing the cost-effectiveness of screening. A number of models have been identified which estimate kidney cancer risk based on both phenotypic and genetic data, and while several of the former have been shown to identify individuals at high-risk of developing kidney cancer with reasonable accuracy, current evidence does not support including a genetic component. Combined screening for lung cancer and kidney cancer has been proposed, as the two malignancies share some common risk factors. A modelling study estimated that using lung cancer risk models (currently used for risk-stratified lung cancer screening) could capture 25% of patients with kidney cancer, which is only slightly lower than using the best performing kidney cancer-specific risk models based on phenotypic data (27%-33%). Additionally, risk-stratified screening for kidney cancer has been shown to be acceptable to the public. The following review summarises existing evidence regarding risk-stratified screening for kidney cancer, highlighting the risks and benefits, as well as exploring the management of potential harms and further research needs.

Patient experience of follow-up after surgery for kidney cancer: a focus group study.

OBJECTIVE: To explore patient experience of follow-up care after kidney cancer surgery and to develop recommendations for best practice. METHODS: We conducted two focus groups, including 14 participants with experience of kidney cancer follow-up after surgery, to elicit patient views on current follow-up care. Thematic analysis was used to identify unifying themes to describe the patient experience of follow-up, and the results were then used to develop a set of recommendations for best practice. RESULTS: We identified six themes (feelings of abandonment; uncertainty about the plan; anxiety about appointments; variation in care; a need for information; and a need for emotional support) that described current patient experience and areas in which current care could be improved. In particular, while most of the participants felt that their physical needs had been met, many had struggled with unmet emotional needs and a lack of information and resources. This was especially noted in the period immediately following surgery, when feelings of abandonment were common, and around follow-up scans and routine appointments, which were a source of anxiety. Our participants also described concerns about the lack of consistency between different hospitals and centres around the United Kingdom, with differences in the content and quality of follow-up care. Based on the results, we developed a list of recommendations to address some of the challenges described through relatively minor changes to the care pathway. CONCLUSIONS: We identified gaps and variability in current follow-up care after kidney cancer surgery, and have developed a set of recommendations that, if implemented, would improve the follow-up care experience for these patients.

Validation and public health modelling of risk prediction models for kidney cancer using the UK Biobank.

OBJECTIVES: To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme. METHODS: We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed-sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6-year risk: 0.1-1.0%). RESULTS: In all, 10 models had reasonable discrimination (area under the receiver-operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age- and sex-based screening. All the models performed less well in women than men. CONCLUSIONS: The present study is the first comprehensive external validation of risk models for kidney cancer. The best-performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme.

The current state of genetic risk models for the development of kidney cancer: a review and validation.

OBJECTIVE: To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models. METHODS: Risk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925). RESULTS: A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers. CONCLUSIONS: Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.

Risk models for recurrence and survival after kidney cancer: a systematic review.

OBJECTIVE: To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent. MATERIALS AND METHODS: We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models. RESULTS: Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival. CONCLUSION: Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates.

A community jury study exploring the public acceptability of using risk stratification to determine eligibility for cancer screening.

INTRODUCTION: Using risk stratification to determine eligibility for cancer screening is likely to improve the efficiency of screening programmes by targeting resources towards those most likely to benefit. We aimed to explore the implications of this approach from a societal perspective by understanding public views on the most acceptable stratification strategies. METHODS: We conducted three online community juries with 9 or 10 participants in each. Participants were purposefully sampled by age (40-79 years), sex, ethnicity, social grade and English region. On the first day, participants were informed of the potential benefits and harms of cancer screening and the implications of different ways of introducing stratification using scenarios based on phenotypic and genetic risk scores. On the second day, participants deliberated to reach a verdict on the research question, 'Which approach(es) to inviting people to screening are acceptable, and under what circumstances?' Deliberations and feedback were recorded and analysed using thematic analysis. RESULTS: Across the juries, the principle of risk stratification was generally considered to be an acceptable approach for determining eligibility for screening. Disregarding increasing capacity, the participants considered it to enable efficient resource allocation to high-risk individuals and could see how it might help to save lives. However, there were concerns regarding fair implementation, particularly how the risk assessment would be performed at scale and how people at low risk would be managed. Some favoured using the most accurate risk prediction model whereas others thought that certain risk factors should be prioritized (particularly factors considered as non-modifiable and relatively stable, such as genetics and family history). Transparently justifying the programme and public education about cancer risk emerged as important contributors to acceptability. CONCLUSION: Using risk stratification to determine eligibility for cancer screening was acceptable to informed members of the public, particularly if it included risk factors they considered fair and when communicated transparently. PATIENT OR PUBLIC CONTRIBUTION: Two patient and public involvement representatives were involved throughout this study. They were not involved in synthesizing the results but contributed to producing study materials, co-facilitated the community juries and commented on the interpretation of the findings and final report.

Acceptability and potential impact on uptake of using different risk stratification approaches to determine eligibility for screening: A population-based survey.

BACKGROUND: Using risk stratification approaches to determine eligibility has the potential to improve efficiency of screening. OBJECTIVES: To compare the public acceptability and potential impact on uptake of using different approaches to determine eligibility for screening. DESIGN: An online population-based survey of 668 adults in the UK aged 45-79 including a series of scenarios in the context of a potential kidney cancer screening programme in which eligibility was determined by age, sex, age and sex combined, a simple risk score (age, sex, body mass index, smoking status), a complex risk score additionally incorporating family history and lifestyle, or a genetic risk score. OUTCOME MEASURES: We used multi-level ordinal logistic regression to compare acceptability and potential uptake within individuals and multivariable ordinal logistic regression differences between individuals. RESULTS: Using sex, age and sex, or the simple risk score were less acceptable than age (P < .0001). All approaches were less acceptable to women than men. Over 70% were comfortable waiting until they were older if the complex risk score or genetics indicated a low risk. If told they were high risk, 85% would be more likely to take up screening. Being told they were low risk had no overall influence on uptake. CONCLUSIONS: Varying the starting age of screening based on estimated risk from models incorporating phenotypic or genetic risk factors would be acceptable to most individuals and may increase uptake. PATIENT OR PUBLIC CONTRIBUTION: Two members of the public contributed to the development of the survey and have commented on this paper.

Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation.

BACKGROUND: Systematic reviews are vital to the pursuit of evidence-based medicine within healthcare. Screening titles and abstracts (T&Ab) for inclusion in a systematic review is an intensive, and often collaborative, step. The use of appropriate tools is therefore important. In this study, we identified and evaluated the usability of software tools that support T&Ab screening for systematic reviews within healthcare research. METHODS: We identified software tools using three search methods: a web-based search; a search of the online "systematic review toolbox"; and screening of references in existing literature. We included tools that were accessible and available for testing at the time of the study (December 2018), do not require specific computing infrastructure and provide basic screening functionality for systematic reviews. Key properties of each software tool were identified using a feature analysis adapted for this purpose. This analysis included a weighting developed by a group of medical researchers, therefore prioritising the most relevant features. The highest scoring tools from the feature analysis were then included in a user survey, in which we further investigated the suitability of the tools for supporting T&Ab screening amongst systematic reviewers working in medical research. RESULTS: Fifteen tools met our inclusion criteria. They vary significantly in relation to cost, scope and intended user community. Six of the identified tools (Abstrackr, Colandr, Covidence, DRAGON, EPPI-Reviewer and Rayyan) scored higher than 75% in the feature analysis and were included in the user survey. Of these, Covidence and Rayyan were the most popular with the survey respondents. Their usability scored highly across a range of metrics, with all surveyed researchers (n = 6) stating that they would be likely (or very likely) to use these tools in the future. CONCLUSIONS: Based on this study, we would recommend Covidence and Rayyan to systematic reviewers looking for suitable and easy to use tools to support T&Ab screening within healthcare research. These two tools consistently demonstrated good alignment with user requirements. We acknowledge, however, the role of some of the other tools we considered in providing more specialist features that may be of great importance to many researchers.

Public attitudes towards screening for kidney cancer: an online survey.

BACKGROUND: Kidney cancer is often asymptomatic, leading to proposals for a screening programme. The views of the public towards introducing a new screening programme for kidney cancer are unknown. The aim of this study was to explore attitudes towards kidney cancer screening and factors influencing intention to attend a future screening programme. METHODS: We conducted an online population-based survey of 1021 adults aged 45-77 years. The main outcome measure was intention to attend four possible screening tests (urine, blood, ultrasound scan, low-dose CT) as well as extended low-dose CT scans within lung cancer screening programmes. We used multivariable regression to examine the association between intention and each screening test. RESULTS: Most participants stated that they would be 'very likely' or 'likely' to undergo each of the screening tests [urine test: n = 961 (94.1%); blood test: n = 922 (90.3%); ultrasound: n = 914 (89.5%); low-dose CT: n = 804 (78.8%); lung CT: n = 962 (95.2%)]. Greater intention to attend was associated with higher general cancer worry and less perceived burden/inconvenience about the screening tests. Less worry about the screening test was also associated with higher intention to attend, but only in those with low general cancer worry (cancer worry scale ≤ 5). Compared with intention to take up screening with a urine test, participants were half as likely to report that they intended to undergo blood [OR 0.56 (0.43-0.73)] or ultrasound [OR 0.50 (0.38-0.67)] testing, and half as likely again to report that they intended to take part in a screening programme featuring a low dose CT scan for kidney cancer screening alone [OR 0.19 (0.14-0.27)]. CONCLUSION: Participants in this study expressed high levels of intention to accept an invitation to screening for kidney cancer, both within a kidney cancer specific screening programme and in conjunction with lung cancer screening. The choice of screening test is likely to influence uptake. Together these findings support on-going research into kidney cancer screening tests and the potential for combining kidney cancer screening with existing or new screening programmes.

Sortilin inhibition limits secretion-induced progranulin-dependent breast cancer progression and cancer stem cell expansion.

BACKGROUND: Cancer progression is influenced by genetic aberrations in the cancer cell population as well as by other factors including the microenvironment present within a tumour. Direct interactions between various cell types as well as cellular signalling via secreted cytokines can drive key tumourigenic properties associated with disease progression and treatment resistance. Also, cancer stem cell functions are influenced by the microenvironment. This challenging subset of cells has been linked to malignant properties. Within a screen, using in vivo like growth conditions, we identified progranulin as a highly secreted cytokine affecting cancer stem cells in breast cancer. This cytokine is known to play a role in numerous biological and tumour-related processes including therapy resistance in a range of cancer types. METHODS: Different in vitro and in vivo relevant conditions were used to validate breast cancer stem cell expansion mediated by progranulin and its receptor sortilin. Small interfering ribonucleic acid (siRNA) and pharmacological inhibition of sortilin were used to elucidate the role of sortilin as a functional receptor during progranulin-induced breast cancer stem cell propagation, both in vitro and in vivo, using breast cancer xenograft models. In addition, single-cell gene expression profiling as well as a Sox2 reporter breast cancer cell line were used to validate the role of dedifferentiation mediated by progranulin. RESULTS: In various in vivo-like screening assays, progranulin was identified as a potent cancer stem cell activator, highly secreted in ERα-negative breast cancer as well as in ERα-positive breast cancer under hypoxic adaptation. Progranulin exposure caused dedifferentiation as well as increased proliferation of the cancer stem cell pool, a process that was shown to be dependent on its receptor sortilin. Subcutaneous injections of progranulin or its active domain (GRN A) induced lung metastases in breast cancer xenograft models, supporting a major role for progranulin in cancer progression. Importantly, an orally bioavailable small molecule (AF38469) targeting sortilin, blocked GRN A-induced lung metastases and prevented cancer cell infiltration of the skin. CONCLUSION: The collective results suggest that sortilin targeting represents a potential novel breast cancer therapy approach inhibiting tumour progression driven by secretion and microenvironmental influences.

HIF1-alpha expressing cells induce a hypoxic-like response in neighbouring cancer cells.

BACKGROUND: Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis. In tumours, oxygen levels vary and hypoxic regions exist within a generally well-oxygenated tumour. However, whilst the heterogeneous environment is known to contribute to metastatic progression, little is known about the mechanism by which heterogeneic hypoxia contributes to cancer progression. This is largely because existing experimental models do not recapitulate the heterogeneous nature of hypoxia. The primary effector of the hypoxic response is the transcription factor Hypoxia inducible factor 1-alpha (HIF1-alpha). HIF1-alpha is stabilised in response to low oxygen levels in the cellular environment and its expression is seen in hypoxic regions throughout the tumour. METHODS: We have developed a model system in which HIF1-alpha can be induced within a sub-population of cancer cells, thus enabling us to mimic the effects of heterogeneic HIF1-alpha expression. RESULTS: We show that induction of HIF1-alpha not only recapitulates elements of the hypoxic response in the induced cells but also results in significant changes in proliferation, gene expression and mammosphere formation within the HIF1-alpha negative population. CONCLUSIONS: These findings suggest that the HIF1-alpha expressing cells found within hypoxic regions are likely to contribute to the subsequent progression of a tumour by modifying the behaviour of cells in the non-hypoxic regions of the local micro-environment.

Predictors of higher-risk chronic opioid prescriptions in an academic primary care setting.

BACKGROUND: Patients with chronic noncancer pain treated with higher doses of opioids or concurrent substance use are at increased risk of adverse events. Although several national guidelines recommend maximum dosing thresholds and urine drug testing, adherence to these guidelines is inconsistent. METHODS: To identify predictors of higher-risk opioid prescriptions in 2 academic primary care clinics, the authors developed a retrospective cohort of 842 patients who were prescribed ≥5 opioid prescriptions for noncancer pain between March 2012 and March 2013. The authors evaluated odds of higher-dose opioid prescriptions and urine drug testing using multivariate logistic models. RESULTS: Among study subjects, 47% received prescriptions for the equivalent of ≥50 mg morphine per day. After adjustment for confounders, patients with a resident primary care provider were less likely to receive higher-dose prescriptions compared with faculty providers (odds ratio = 0.66, 95% confidence interval [CI]: 0.46-0.94), whereas patients with a nonlocal home address were more likely to be prescribed higher doses (odds ratio = 2.1, 95% CI: 1.5-2.9). Hispanic, Asian, and older patients were also less likely to be prescribed higher doses. Urine drug testing was not regularly completed (35% over 2 years), but odds of testing were higher for patients who self-identified as black, had resident primary care providers, lived locally, or were prescribed higher opioid doses. CONCLUSIONS: In this academic clinical setting, patients with a resident primary care provider are less likely to receive higher-risk opioid prescriptions, as are Hispanic, Asian, and older patients. Black patients complete urine drug tests more frequently independent of other patient and provider characteristics. Additional studies are needed to assess why patients who travel larger distances to their primary care clinic are prescribed higher doses of opioids for chronic noncancer pain.

Risk Prediction Models for Oral Cancer: A Systematic Review.

In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (n = 20), alcohol consumption (n = 18) and smoking (n = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (n = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.

Oestrogen increases the activity of oestrogen receptor negative breast cancer stem cells through paracrine EGFR and Notch signalling.

INTRODUCTION: Although oestrogen is essential for the development of the normal breast, adult mammary stem cells are known to be oestrogen receptor alpha (ER) negative and rely on paracrine signals in the mammary epithelium for mediation of developmental cues. However, little is known about how systemic oestrogen regulates breast cancer stem cell (CSC) activity. METHODS: Here, we tested the effects of oestrogen on CSC activity in vitro and in vivo and investigated which paracrine signalling pathways locally mediate oestrogen effects. RESULTS: CSC-enriched populations (ESA+CD44+CD24low) sorted from ER positive patient derived and established cell lines have low or absent ER expression. However, oestrogen stimulated CSC activity demonstrated by increased mammosphere and holoclone formation in vitro and tumour formation in vivo. This effect was abrogated by the anti-oestrogen tamoxifen or ER siRNA. These data suggest that the oestrogen response is mediated through paracrine signalling from non-CSCs to CSCs. We have, therefore, investigated both epidermal growth factor (EGF) and Notch receptor signals downstream of oestrogen. We demonstrate that gefitinib (epidermal growth factor receptor (EGFR) inhibitor) and gamma secretase inhibitors (Notch inhibitor) block oestrogen-induced CSC activity in vitro and in vivo but GSIs more efficiently reduce CSC frequency. CONCLUSIONS: These data establish that EGF and Notch receptor signalling pathways operate downstream of oestrogen in the regulation of ER negative CSCs.

A detailed mammosphere assay protocol for the quantification of breast stem cell activity.

Since the discovery that neural tissue contains a population of stem cells that form neurospheres in vitro, sphere-forming assays have been adapted for use with a number of different tissue types for the quantification of stem cell activity and self-renewal. One tissue type widely used for stem cell investigations is mammary tissue, and the mammosphere assay has been used in both normal tissue and cancer. Although it is a relatively simple assay to learn, it can be difficult to master. There are methodological and analytical aspects to the assay which require careful consideration when interpreting the results. We describe here a detailed mammosphere assay protocol for the assessment of stem cell activity and self-renewal, and discuss how data generated by the assay can be analysed and interpreted.