Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.

QPOLE: A Quick, Simple, and Cheap Alternative for POLE Sequencing in Endometrial Cancer by Multiplex Genotyping Quantitative Polymerase Chain Reaction.

PURPOSE: Detection of 11 pathogenic variants in the POLE gene in endometrial cancer (EC) is critically important to identify women with a good prognosis and reduce overtreatment. Currently, POLE status is determined by DNA sequencing, which can be expensive, relatively time-consuming, and unavailable in hospitals without specialized equipment and personnel. This may hamper the implementation of POLE-testing in clinical practice. To overcome this, we developed and validated a rapid, low-cost POLE hotspot test by a quantitative polymerase chain reaction (qPCR) assay, QPOLE. MATERIALS AND METHODS: Primer and fluorescence-labeled 5'-nuclease probe sequences of the 11 established pathogenic POLE mutations were designed. Three assays, QPOLE-frequent for the most common mutations and QPOLE-rare-1 and QPOLE-rare-2 for the rare variants, were developed and optimized using DNA extracted from formalin-fixed paraffin-embedded tumor tissues. The simplicity of the design enables POLE status assessment within 4-6 hours after DNA isolation. An interlaboratory external validation study was performed to determine the practical feasibility of this assay. RESULTS: Cutoffs for POLE wild-type, POLE-mutant, equivocal, and failed results were predefined on the basis of a subset of POLE mutants and POLE wild-types for the internal and external validation. For equivocal cases, additional DNA sequencing is recommended. Performance in 282 EC cases, of which 99 were POLE-mutated, demonstrated an overall accuracy of 98.6% (95% CI, 97.2 to 99.9), a sensitivity of 95.2% (95% CI, 90.7 to 99.8), and a specificity of 100%. After DNA sequencing of 8.8% equivocal cases, the final sensitivity and specificity were 96.0% (95% CI, 92.1 to 99.8) and 100%. External validation confirmed feasibility and accuracy. CONCLUSION: QPOLE is a qPCR assay that is a quick, simple, and reliable alternative for DNA sequencing. QPOLE detects all pathogenic variants in the exonuclease domain of the POLE gene. QPOLE will make low-cost POLE-testing available for all women with EC around the globe.

Prognostic refinement of NSMP high-risk endometrial cancers using oestrogen receptor immunohistochemistry.

BACKGROUND: Risk-assessment of endometrial cancer (EC) is based on clinicopathological factors and molecular subgroup. It is unclear whether adding hormone receptor expression, L1CAM expression or CTNNB1 status yields prognostic refinement. METHODS: Paraffin-embedded tumour samples of women with high-risk EC (HR-EC) from the PORTEC-3 trial (n = 424), and a Dutch prospective clinical cohort called MST (n = 256), were used. All cases were molecularly classified. Expression of L1CAM, ER and PR were analysed by whole-slide immunohistochemistry and CTNNB1 mutations were assessed with a next-generation sequencing. Kaplan-Meier method, log-rank tests and Cox's proportional hazard models were used for survival analysis. RESULTS: In total, 648 HR-EC were included. No independent prognostic value of ER, PR, L1CAM, and CTNNB1 was found, while age, stage, and adjuvant chemotherapy had an independent impact on risk of recurrence. Subgroup-analysis showed that only in NSMP HR-EC, ER-positivity was independently associated with a reduced risk of recurrence (HR 0.33, 95%CI 0.15-0.75). CONCLUSIONS: We confirmed the prognostic impact of the molecular classification, age, stage, and adjuvant CTRT in a large cohort of high-risk EC. ER-positivity is a strong favourable prognostic factor in NSMP HR-EC and identifies a homogeneous subgroup of NSMP tumours. Assessment of ER status in high-risk NSMP EC is feasible in clinical practice and could improve risk stratification and treatment.

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.

BACKGROUND: Endometrial cancer can be molecularly classified into POLEmut, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication. METHODS: This combined analysis included diagnostic haematoxylin and eosin-stained slides and molecular and clinicopathological data from 2028 patients with intermediate-to-high-risk endometrial cancer from the PORTEC-1 (n=466), PORTEC-2 (n=375), and PORTEC-3 (n=393) randomised trials and the TransPORTEC pilot study (n=110), the Medisch Spectrum Twente cohort (n=242), a case series of patients with POLEmut endometrial cancer in the Leiden Endometrial Cancer Repository (n=47), and The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma cohort (n=395). PORTEC-3 was held out as an independent test set and a four-fold cross validation was performed. Performance was measured with the macro and class-wise area under the receiver operating characteristic curve (AUROC). Whole-slide images were segmented into tiles of 360 µm resized to 224 × 224 pixels. im4MEC was trained to learn tile-level morphological features with self-supervised learning and to molecularly classify whole-slide images with an attention mechanism. The top 20 tiles with the highest attention scores were reviewed to identify morpho-molecular correlates. Predictions of a nuclear classification deep learning model serve to derive interpretable morphological features. We analysed 5-year recurrence-free survival and explored prognostic refinement by molecular class using the Kaplan-Meier method. FINDINGS: im4MEC attained macro-average AUROCs of 0·874 (95% CI 0·856-0·893) on four-fold cross-validation and 0·876 on the independent test set. The class-wise AUROCs were 0·849 for POLEmut (n=51), 0·844 for MMRd (n=134), 0·883 for NSMP (n=120), and 0·928 for p53abn (n=88). POLEmut and MMRd tiles had a high density of lymphocytes, p53abn tiles had strong nuclear atypia, and the morphology of POLEmut and MMRd endometrial cancer overlapped. im4MEC highlighted a low tumour-to-stroma ratio as a potentially novel characteristic feature of the NSMP class. 5-year recurrence-free survival was significantly different between im4MEC predicted molecular classes in PORTEC-3 (log-rank p<0·0001). The ten patients with aggressive p53abn endometrial cancer that was predicted as MMRd showed inflammatory morphology and appeared to have a better prognosis than patients with correctly predicted p53abn endometrial cancer (p=0·30). The four patients with NSMP endometrial cancer that was predicted as p53abn showed higher nuclear atypia and appeared to have a worse prognosis than patients with correctly predicted NSMP (p=0·13). Patients with MMRd endometrial cancer predicted as POLEmut had an excellent prognosis, as do those with true POLEmut endometrial cancer. INTERPRETATION: We present the first interpretable deep learning model, im4MEC, for haematoxylin and eosin-based prediction of molecular endometrial cancer classification. im4MEC robustly identified morpho-molecular correlates and could enable further prognostic refinement of patients with endometrial cancer. FUNDING: The Hanarth Foundation, the Promedica Foundation, and the Swiss Federal Institutes of Technology.

Diagnostics in Patients Suspect for Breast Cancer in The Netherlands.

The goal of this study was to describe the variation in hospital-based diagnostic care activities for patients with symptomatology suspect for breast cancer in The Netherlands. Two cohorts were included: the 'benign' cohort (30,334 women suspected of, but without breast cancer) and the 'malignant' cohort (2236 breast cancer patients). Hospital-based financial data was combined with tumor data (malignant cohort) from The Netherlands Cancer Registry. Patterns within diagnostic pathways were analyzed. Factors influencing the number of visits and number of diagnostic care activities until diagnosis were identified in the malignant cohort with multivariable Cox and Poisson regression models. Compared to patients with benign diagnosis, patients with malignant disease received their diagnosis less frequently in one day, after an equal average number of hospital visits and higher average number of diagnostic activities. Factors increasing the number of diagnostic care activities were the following: lower age and higher cM-and cN-stages. Factors increasing the number of days until (malignant) diagnosis were as follows: higher BIRADS-score, screen-detected and higher cN-and cT-stages. Hospital of diagnosis influenced both number of activities and days to diagnosis. The diagnostic care pathway of patients with malignant disease required more time and diagnostic activities than benign disease and depends on hospital, tumor and patient characteristics.

Interobserver Agreement of PD-L1/SP142 Immunohistochemistry and Tumor-Infiltrating Lymphocytes (TILs) in Distant Metastases of Triple-Negative Breast Cancer: A Proof-of-Concept Study. A Report on Behalf of the International Immuno-Oncology Biomarker Working Group.

Patients with advanced triple-negative breast cancer (TNBC) benefit from treatment with atezolizumab, provided that the tumor contains ≥1% of PD-L1/SP142-positive immune cells. Numbers of tumor-infiltrating lymphocytes (TILs) vary strongly according to the anatomic localization of TNBC metastases. We investigated inter-pathologist agreement in the assessment of PD-L1/SP142 immunohistochemistry and TILs. Ten pathologists evaluated PD-L1/SP142 expression in a proficiency test comprising 28 primary TNBCs, as well as PD-L1/SP142 expression and levels of TILs in 49 distant TNBC metastases with various localizations. Interobserver agreement for PD-L1 status (positive vs. negative) was high in the proficiency test: the corresponding scores as percentages showed good agreement with the consensus diagnosis. In TNBC metastases, there was substantial variability in PD-L1 status at the individual patient level. For one in five patients, the chance of treatment was essentially random, with half of the pathologists designating them as positive and half negative. Assessment of PD-L1/SP142 and TILs as percentages in TNBC metastases showed poor and moderate agreement, respectively. Additional training for metastatic TNBC is required to enhance interobserver agreement. Such training, focusing on metastatic specimens, seems worthwhile, since the same pathologists obtained high percentages of concordance (ranging from 93% to 100%) on the PD-L1 status of primary TNBCs.

Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts.

BACKGROUND: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). METHODS: We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. RESULTS: The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. CONCLUSIONS: Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

Prediction and clinical utility of a contralateral breast cancer risk model.

BACKGROUND: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. METHODS: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. RESULTS: In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS: We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

Breast Cancer Survival of BRCA1/BRCA2 Mutation Carriers in a Hospital-Based Cohort of Young Women.

Background: The primary aim of the study was to investigate prognosis and long-term survival in young breast cancer patients with a BRCA1 or BRCA2 germline mutation compared with noncarriers. The secondary aim was to investigate whether differences in survival originate from associations with tumor characteristics, second cancers, and/or treatment response. Methods: We established a cohort of invasive breast cancer patients diagnosed younger than age 50 years in 10 Dutch hospitals between 1970 and 2003. BRCA1/2 testing of most prevalent mutations was mainly done using DNA isolate from formalin-fixed paraffin-embedded nontumor tissue. Survival estimates were derived using Cox regression and competing risk models. Results: In 6478 breast cancer patients, we identified 3.2% BRCA1 and 1.2% BRCA2 mutation carriers. BRCA1 mutation carriers had a worse overall survival independent of clinico-pathological/treatment characteristics, compared with noncarriers (adjusted hazard ratio [HR] = 1.20, 95% confidence interval [CI] = 0.97 to 1.47), though only statistically significant in the first five years of follow-up (adjusted HR = 1.40, 95% CI = 1.07 to 1.84). A large part of the worse survival was explained by incidence of ovarian cancers. Breast cancer-specific, disease-free, and metastasis-free survival results were less pronounced and mostly statistically nonsignificant but in the same direction with those of overall survival. Overall survival was worse, although not statistically significantly, within the ER-negative or ER-positive, grade 3, and small tumor subgroups. The worse survival was most pronounced in non-chemotherapy-treated patients (adjusted HR = 1.54, 95% CI = 1.08 to 2.19). Power for BRCA2 mutation carriers was limited; only after five years' follow-up overall survival was worse (adjusted HR = 1.47, 95% CI = 1.00 to 2.17). Conclusions: BRCA1/2 mutation carriers diagnosed with breast cancer before age 50 years are prone to a worse survival, which is partly explained by differences in tumor characteristics, treatment response, and second ovarian cancers.

Accuracy of the online prognostication tools PREDICT and Adjuvant! for early-stage breast cancer patients younger than 50 years.

IMPORTANCE: Online prognostication tools such as PREDICT and Adjuvant! are increasingly used in clinical practice by oncologists to inform patients and guide treatment decisions about adjuvant systemic therapy. However, their validity for young breast cancer patients is debated. OBJECTIVE: To assess first, the prognostic accuracy of PREDICT's and Adjuvant! 10-year all-cause mortality, and second, its breast cancer-specific mortality estimates, in a large cohort of breast cancer patients diagnosed <50 years. DESIGN: Hospital-based cohort. SETTING: General and cancer hospitals. PARTICIPANTS: A consecutive series of 2710 patients without a prior history of cancer, diagnosed between 1990 and 2000 with unilateral stage I-III breast cancer aged <50 years. MAIN OUTCOME MEASURES: Calibration and discriminatory accuracy, measured with C-statistics, of estimated 10-year all-cause and breast cancer-specific mortality. RESULTS: Overall, PREDICT's calibration for all-cause mortality was good (predicted versus observed) meandifference: -1.1% (95%CI: -3.2%-0.9%; P = 0.28). PREDICT tended to underestimate all-cause mortality in good prognosis subgroups (range meandifference: -2.9% to -4.8%), overestimated all-cause mortality in poor prognosis subgroups (range meandifference: 2.6%-9.4%) and underestimated survival in patients < 35 by -6.6%. Overall, PREDICT overestimated breast cancer-specific mortality by 3.2% (95%CI: 0.8%-5.6%; P = 0.007); and also overestimated it seemingly indiscriminately in numerous subgroups (range meandifference: 3.2%-14.1%). Calibration was poor in the cohort of patients with the lowest and those with the highest mortality probabilities. Discriminatory accuracy was moderate-to-good for all-cause mortality in PREDICT (0.71 [95%CI: 0.68 to 0.73]), and the results were similar for breast cancer-specific mortality. Adjuvant!'s calibration and discriminatory accuracy for both all-cause and breast cancer-specific mortality were in line with PREDICT's findings. CONCLUSIONS: Although imprecise at the extremes, PREDICT's estimates of 10-year all-cause mortality seem reasonably sound for breast cancer patients <50 years; Adjuvant! findings were similar. Prognostication tools should be used with caution due to the intrinsic variability of their estimates, and because the threshold to discuss adjuvant systemic treatment is low. Thus, seemingly insignificant mortality overestimations or underestimations of a few percentages can significantly impact treatment decision-making.

Potential Targets' Analysis Reveals Dual PI3K/mTOR Pathway Inhibition as a Promising Therapeutic Strategy for Uterine Leiomyosarcomas-an ENITEC Group Initiative.

Purpose: Uterine sarcomas are rare and heterogeneous tumors characterized by an aggressive clinical behavior. Their high rates of recurrence and mortality point to the urgent need for novel targeted therapies and alternative treatment strategies. However, no molecular prognostic or predictive biomarkers are available so far to guide choice and modality of treatment.Experimental Design: We investigated the expression of several druggable targets (phospho-S6S240 ribosomal protein, PTEN, PDGFR-α, ERBB2, and EGFR) in a large cohort of human uterine sarcoma samples (288), including leiomyosarcomas, low-grade and high-grade endometrial stromal sarcomas, undifferentiated uterine sarcomas, and adenosarcomas, together with 15 smooth muscle tumors of uncertain malignant potential (STUMP), 52 benign uterine stromal tumors, and 41 normal uterine tissues. The potential therapeutic value of the most promising target, p-S6S240, was tested in patient-derived xenograft (PDX) leiomyosarcoma models.Results: In uterine sarcomas and STUMPs, S6S240 phosphorylation (reflecting mTOR pathway activation) was associated with higher grade (P = 0.001) and recurrence (P = 0.019), as shown by logistic regression. In addition, p-S6S240 correlated with shorter progression-free survival (P = 0.034). Treatment with a dual PI3K/mTOR inhibitor significantly reduced tumor growth in 4 of 5 leiomyosarcoma PDX models (with tumor shrinkage in 2 models). Remarkably, the 4 responding models showed basal p-S6S240 expression, whereas the nonresponding model was scored as negative, suggesting a role for p-S6S240 in response prediction to PI3K/mTOR inhibition.Conclusions: Dual PI3K/mTOR inhibition represents an effective therapeutic strategy in uterine leiomyosarcoma, and p-S6S240 expression is a potential predictive biomarker for response to treatment. Clin Cancer Res; 23(5); 1274-85. ©2017 AACR.

Long-term effects of first degree family history of breast cancer in young women: Recurrences and bilateral breast cancer.

BACKGROUND: The aim of this study is to analyze the impact of first degree relative (FDR) of young breast cancer patients. METHODS: Data were used from our prospective population-based cohort study which started in 1983. The family history (FH) was registered with regard to FDR: the presence or absence of invasive breast cancer in none vs. one or more FDRs at any age. RESULTS: A total of 1109 women, ≤50 years with 1128 breast conserving treatments was seen. The incidence of FDR was 17.0% for one FDR and 3.2% ≥2 FDR. The three groups, none, 1 or ≥2 FDR, were comparable. The local failure rate is comparable for all three groups. Women with a positive FH and metachronous bilateral breast cancer (MBBC) showed a lower local failure (HR 0.2; 95% CI 0.05-0.8). A positive FH was an independent predictor for a better disease-specific survival (HR 0.6; 95% CI 0.4-0.9). CONCLUSION: A positive FH, based on FDR implies a better prognosis in relation to survival for young women treated with BCT. In contrast to no FH for FDR, MBBC in women with a positive FH was not associated with an increased risk of local recurrence.

The prognostic relevance of the mitotic activity index in axillary lymph node-negative breast cancer.

The aim of the present study is to look at the mitotic activity index (MAI) as a prognostic factor in a prospective population-based cohort of lymph node-negative invasive breast cancer patients. Analyses were based on 2,048 breast-conserving therapies in 1,971 patients, node-negative, and without any form of adjuvant systemic therapy with long-term follow-up. The 15-year distant metastases-free survival (DMFS) for women ≤55 years was 88.3 % for low MAI values (≤12) versus 73.4 % for high MAI values (>12); (HR 2.8; 95 % CI 1.8-4.4; p < 0.001). Multivariate analyses for DMFS showed significance for MAI. For MAI and Bloom-Richardson grading, by performing a likelihood ratio test, we showed the statistical significance for both. For women >55-years, the MAI was not an independent significant factor. We also confirmed the above findings for disease-specific survival. When multi-gene assays are not available, the MAI remains a robust prognostic marker in women younger than 55 years of age with early node-negative breast cancer.

Sequence of radiotherapy and chemotherapy in breast cancer after breast-conserving surgery.

PURPOSE: The optimal sequence of radiotherapy and chemotherapy in breast-conserving therapy is unknown. METHODS AND MATERIALS: From 1983 through 2007, a total of 641 patients with 653 instances of breast-conserving therapy (BCT), received both chemotherapy and radiotherapy and are the basis of this analysis. Patients were divided into three groups. Groups A and B comprised patients treated before 2005, Group A radiotherapy first and Group B chemotherapy first. Group C consisted of patients treated from 2005 onward, when we had a fixed sequence of radiotherapy first, followed by chemotherapy. RESULTS: Local control did not show any differences among the three groups. For distant metastasis, no difference was shown between Groups A and B. Group C, when compared with Group A, showed, on univariate and multivariate analyses, a significantly better distant metastasis-free survival. The same was noted for disease-free survival. With respect to disease-specific survival, no differences were shown on multivariate analysis among the three groups. CONCLUSION: Radiotherapy, as an integral part of the primary treatment of BCT, should be administered first, followed by adjuvant chemotherapy.

Future Prospects in Breast Cancer Research - Cancer Stem Cells.

Breast cancer is one of the leading causes of cancer deaths among women. Although significant advances in the prevention, diagnosis and management are made, still every year half a million women die of breast cancer. Personalised treatment has the potential to increase treatment efficacy, and hence decrease mortality rates. Moreover, understanding cancer biology and translating this knowledge to the clinic, will improve the breast cancer therapy regime tremendously. Recently, it has been proposed that cancer stem cells (CSC) play an important role in tumour biology. CSC have the ability for self-renewal and are pivotal in setting the heterogeneous character of a tumour. Additionally, CSC possess several characteristics that make them resistant and more aggressive to the conventional chemo- and radiotherapy. Nowadays, breast cancer therapy is focused on killing the differentiated tumour cells, leaving the CSC unharmed, potentially causing recurrence of the disease and metastasis. Specific targeting of the CSC will improve the disease-free survival of breast cancer patients. In this article, two methods are described, aiming at specifically attacking the differentiated tumour cells ('Apoptosis chip') and the cancer stem cell. For this, microfluidics is used.

Expression of multidrug resistance-associated markers, their relation to quantitative pathologic tumour characteristics and prognosis in advanced ovarian cancer.

Mean nuclear area has been consistently shown by different researchers to be a strong and independent prognostic factor in advanced ovarian carcinoma. However, the biological background of the prognostic value of nuclear area remains unclear. Others have found that the multidrug-resistance (MDR) related protein LRP has strong prognostic value. In the present study we have analysed whether the mean nuclear area and LRP are related in tumour tissue of the ovary obtained at the debulking operation before the administration of chemotherapy in 40 patients. The mitotic activity index, volume percentage epithelium, standard deviation of nuclear area and the other MDR-related proteins P-glycoprotein (JSB-1, MRK-16) and MRP have been investigated additionally for correlations and prognostic value. No correlations were found between the morphometrical features and MDR-related proteins. Mean nuclear area tended to be larger in LRP positive tumours, but the correlation was not significant. In multivariate analysis LRP-protein expression and mean nuclear area had independent prognostic value. Further studies are required to elucidate the biological background of the strong prognostic value of mean nuclear area in advanced ovarian cancer.